Supervised Learning : Exploring Boosting, Bagging and Stacking Algorithms for Ensemble Learning
John Pauline Pineda
August 22, 2023
- 1.
Table of Contents
- 1.1 Sample Data
- 1.2 Data Quality Assessment
- 1.3 Data Preprocessing
- 1.4 Data Exploration
- 1.5 Model Boosting
- 1.6 Model Bagging
- 1.7 Model
Stacking
- 1.7.1 Base Learner Model Development using Linear Discriminant Analysis (BAL_LDA)
- 1.7.2 Base Learner Model Development using Classification and Regression Trees (BAL_CART)
- 1.7.3 Base Learner Model Development using Support Vector Machine - Radial Basis Function Kernel (BAL_SVM_R)
- 1.7.4 Base Learner Model Development using K-Nearest Neighbors (BAL_KNN)
- 1.7.5 Base Learner Model Development using Naive Bayes (BAL_NB)
- 1.7.6 Base Learner Ensemble
- 1.7.7 Meta-Learner Model Development using Logistic Regression (MEL_LR)
- 1.7.8 Meta-Learner Model Development using Random Forest (MEL_RF)
- 1.8 Consolidated Findings
- 2. Summary
- 3. References
1. Table of Contents
This project explores different ensemble learning approaches which combine the predictions from multiple models in an effort to achieve better predictive performance using various helpful packages in R. The ensemble frameworks applied in the analysis were grouped into three classes including boosting models which add ensemble members sequentially that correct the predictions made by prior models and outputs a weighted average of the predictions; bagging models which fit many decision trees on different samples of the same dataset and averaging the predictions; and stacking which consolidate many different models types on the same data and using another model to learn how to best combine the predictions. Boosting models included the Adaptive Boosting, Stochastic Gradient Boosting and Extreme Gradient Boosting algorithms. Bagging models applied were the Random Forest and Bagged Classification and Regression Trees algorithms. Individual base learners including the Linear Discriminant Analysis, Classification and Regression Trees, Support Vector Machine (Radial Basis Function Kernel), K-Nearest Neighbors and Naive Bayes algorithms were evaluated for correlation and stacked together as contributors to the Logistic Regression and Random Forest meta-models. The resulting predictions derived from all ensemble learning models were evaluated based on their discrimination power using the area under the receiver operating characteristic curve (AUROC) metric. All results were consolidated in a Summary presented at the end of the document.
Ensemble learning aims to improve predictive performance by reaching a consensus in predictions through the fusion of the salient properties of two or more models. The final ensemble learning framework is more robust than the individual members that constitute the ensemble because of the variance reduction in the prediction errors. The algorithms applied in this study (mostly contained in the caret and caretEnsemble packages) attempt to capture complementary information from similar or diverse learners, as applicable, to further advance prediction accuracy over that of any of the contributing models.
1.1 Sample Data
The Wisconsin Breast Cancer dataset shared from the Kaggle website as obtained from the UCI Machine Learning Repository was used for this illustrated example.
Preliminary dataset assessment:
[A] 1138 rows (observations)
[B] 32 columns (variables)
[B.1] 1/32 metadata (unique identifiers) = id (numeric)
[B.2] 1/32 response = diagnosis (factor)
[B.3] 30/32 predictors = 30/30 numeric
[B.3.1] radius_mean (numeric)
[B.3.2] texture_mean (numeric)
[B.3.3] perimeter_mean (numeric)
[B.3.4] area_mean (numeric)
[B.3.5] smoothness_mean (numeric)
[B.3.6] compactness_mean (numeric)
[B.3.7] concavity_mean (numeric)
[B.3.8] concave.points_mean (numeric)
[B.3.9] symmetry_mean (numeric)
[B.3.10] fractal_dimension_mean (numeric)
[B.3.11] radius_se (numeric)
[B.3.12] texture_se (numeric)
[B.3.13] perimeter_se (numeric)
[B.3.14] area_se (numeric)
[B.3.15] smoothness_se (numeric)
[B.3.16] compactness_se (numeric)
[B.3.17] concavity_se (numeric)
[B.3.18] concave.points_se (numeric)
[B.3.19] symmetry_se (numeric)
[B.3.20] fractal_dimension_se (numeric)
[B.3.21] radius_worst (numeric)
[B.3.22] texture_worst (numeric)
[B.3.23] perimeter_worst (numeric)
[B.3.24] area_worst (numeric)
[B.3.25] smoothness_worst (numeric)
[B.3.26] compactness_worst (numeric)
[B.3.27] concavity_worst (numeric)
[B.3.28] concave.points_worst (numeric)
[B.3.29] symmetry_worst (numeric)
[B.3.30] fractal_dimension_worst (numeric)
Code Chunk | Output
##################################
# Loading R libraries
##################################
library(AppliedPredictiveModeling)
library(tidyr)
library(caret)
library(lattice)
library(dplyr)
library(moments)
library(skimr)
library(RANN)
library(pls)
library(corrplot)
library(lares)
library(DMwR2)
library(gridExtra)
library(rattle)
library(RColorBrewer)
library(stats)
library(caretEnsemble)
library(pROC)
library(adabag)
library(gbm)
library(xgboost)
library(randomForest)
library(kernlab)
library(klaR)
library(rpart)
library(rpart.plot)
##################################
# Defining file paths
##################################
DATASETS_ORIGINAL_PATH <- file.path("datasets","original")
##################################
# Loading source and
# formulating the analysis set
##################################
BreastCancer <- read.csv(file.path("..", DATASETS_ORIGINAL_PATH, "WisconsinBreastCancer.csv"),
na.strings=c("NA","NaN"," ",""),
stringsAsFactors = FALSE)
BreastCancer <- as.data.frame(BreastCancer)
##################################
# Performing a general exploration of the data set
##################################
dim(BreastCancer)## [1] 1138 32str(BreastCancer)## 'data.frame': 1138 obs. of 32 variables:
## $ id : int 842302 842517 84300903 84348301 84358402 843786 844359 84458202 844981 84501001 ...
## $ diagnosis : chr "M" "M" "M" "M" ...
## $ radius_mean : num 18 20.6 19.7 11.4 20.3 ...
## $ texture_mean : num 10.4 17.8 21.2 20.4 14.3 ...
## $ perimeter_mean : num 122.8 132.9 130 77.6 135.1 ...
## $ area_mean : num 1001 1326 1203 386 1297 ...
## $ smoothness_mean : num 0.1184 0.0847 0.1096 0.1425 0.1003 ...
## $ compactness_mean : num 0.2776 0.0786 0.1599 0.2839 0.1328 ...
## $ concavity_mean : num 0.3001 0.0869 0.1974 0.2414 0.198 ...
## $ concave.points_mean : num 0.1471 0.0702 0.1279 0.1052 0.1043 ...
## $ symmetry_mean : num 0.242 0.181 0.207 0.26 0.181 ...
## $ fractal_dimension_mean : num 0.0787 0.0567 0.06 0.0974 0.0588 ...
## $ radius_se : num 1.095 0.543 0.746 0.496 0.757 ...
## $ texture_se : num 0.905 0.734 0.787 1.156 0.781 ...
## $ perimeter_se : num 8.59 3.4 4.58 3.44 5.44 ...
## $ area_se : num 153.4 74.1 94 27.2 94.4 ...
## $ smoothness_se : num 0.0064 0.00522 0.00615 0.00911 0.01149 ...
## $ compactness_se : num 0.049 0.0131 0.0401 0.0746 0.0246 ...
## $ concavity_se : num 0.0537 0.0186 0.0383 0.0566 0.0569 ...
## $ concave.points_se : num 0.0159 0.0134 0.0206 0.0187 0.0188 ...
## $ symmetry_se : num 0.03 0.0139 0.0225 0.0596 0.0176 ...
## $ fractal_dimension_se : num 0.00619 0.00353 0.00457 0.00921 0.00511 ...
## $ radius_worst : num 25.4 25 23.6 14.9 22.5 ...
## $ texture_worst : num 17.3 23.4 25.5 26.5 16.7 ...
## $ perimeter_worst : num 184.6 158.8 152.5 98.9 152.2 ...
## $ area_worst : num 2019 1956 1709 568 1575 ...
## $ smoothness_worst : num 0.162 0.124 0.144 0.21 0.137 ...
## $ compactness_worst : num 0.666 0.187 0.424 0.866 0.205 ...
## $ concavity_worst : num 0.712 0.242 0.45 0.687 0.4 ...
## $ concave.points_worst : num 0.265 0.186 0.243 0.258 0.163 ...
## $ symmetry_worst : num 0.46 0.275 0.361 0.664 0.236 ...
## $ fractal_dimension_worst: num 0.1189 0.089 0.0876 0.173 0.0768 ...summary(BreastCancer)## id diagnosis radius_mean texture_mean
## Min. : 8670 Length:1138 Min. : 6.981 Min. : 9.71
## 1st Qu.: 869218 Class :character 1st Qu.:11.700 1st Qu.:16.17
## Median : 906024 Mode :character Median :13.370 Median :18.84
## Mean : 30371831 Mean :14.127 Mean :19.29
## 3rd Qu.: 8813129 3rd Qu.:15.780 3rd Qu.:21.80
## Max. :911320502 Max. :28.110 Max. :39.28
## perimeter_mean area_mean smoothness_mean compactness_mean
## Min. : 43.79 Min. : 143.5 Min. :0.05263 Min. :0.01938
## 1st Qu.: 75.17 1st Qu.: 420.3 1st Qu.:0.08637 1st Qu.:0.06492
## Median : 86.24 Median : 551.1 Median :0.09587 Median :0.09263
## Mean : 91.97 Mean : 654.9 Mean :0.09636 Mean :0.10434
## 3rd Qu.:104.10 3rd Qu.: 782.7 3rd Qu.:0.10530 3rd Qu.:0.13040
## Max. :188.50 Max. :2501.0 Max. :0.16340 Max. :0.34540
## concavity_mean concave.points_mean symmetry_mean fractal_dimension_mean
## Min. :0.00000 Min. :0.00000 Min. :0.1060 Min. :0.04996
## 1st Qu.:0.02956 1st Qu.:0.02031 1st Qu.:0.1619 1st Qu.:0.05770
## Median :0.06154 Median :0.03350 Median :0.1792 Median :0.06154
## Mean :0.08880 Mean :0.04892 Mean :0.1812 Mean :0.06280
## 3rd Qu.:0.13070 3rd Qu.:0.07400 3rd Qu.:0.1957 3rd Qu.:0.06612
## Max. :0.42680 Max. :0.20120 Max. :0.3040 Max. :0.09744
## radius_se texture_se perimeter_se area_se
## Min. :0.1115 Min. :0.3602 Min. : 0.757 Min. : 6.802
## 1st Qu.:0.2324 1st Qu.:0.8339 1st Qu.: 1.606 1st Qu.: 17.850
## Median :0.3242 Median :1.1080 Median : 2.287 Median : 24.530
## Mean :0.4052 Mean :1.2169 Mean : 2.866 Mean : 40.337
## 3rd Qu.:0.4789 3rd Qu.:1.4740 3rd Qu.: 3.357 3rd Qu.: 45.190
## Max. :2.8730 Max. :4.8850 Max. :21.980 Max. :542.200
## smoothness_se compactness_se concavity_se concave.points_se
## Min. :0.001713 Min. :0.002252 Min. :0.00000 Min. :0.000000
## 1st Qu.:0.005169 1st Qu.:0.013080 1st Qu.:0.01509 1st Qu.:0.007638
## Median :0.006380 Median :0.020450 Median :0.02589 Median :0.010930
## Mean :0.007041 Mean :0.025478 Mean :0.03189 Mean :0.011796
## 3rd Qu.:0.008146 3rd Qu.:0.032450 3rd Qu.:0.04205 3rd Qu.:0.014710
## Max. :0.031130 Max. :0.135400 Max. :0.39600 Max. :0.052790
## symmetry_se fractal_dimension_se radius_worst texture_worst
## Min. :0.007882 Min. :0.0008948 Min. : 7.93 Min. :12.02
## 1st Qu.:0.015160 1st Qu.:0.0022480 1st Qu.:13.01 1st Qu.:21.08
## Median :0.018730 Median :0.0031870 Median :14.97 Median :25.41
## Mean :0.020542 Mean :0.0037949 Mean :16.27 Mean :25.68
## 3rd Qu.:0.023480 3rd Qu.:0.0045580 3rd Qu.:18.79 3rd Qu.:29.72
## Max. :0.078950 Max. :0.0298400 Max. :36.04 Max. :49.54
## perimeter_worst area_worst smoothness_worst compactness_worst
## Min. : 50.41 Min. : 185.2 Min. :0.07117 Min. :0.02729
## 1st Qu.: 84.11 1st Qu.: 515.3 1st Qu.:0.11660 1st Qu.:0.14720
## Median : 97.66 Median : 686.5 Median :0.13130 Median :0.21190
## Mean :107.26 Mean : 880.6 Mean :0.13237 Mean :0.25427
## 3rd Qu.:125.40 3rd Qu.:1084.0 3rd Qu.:0.14600 3rd Qu.:0.33910
## Max. :251.20 Max. :4254.0 Max. :0.22260 Max. :1.05800
## concavity_worst concave.points_worst symmetry_worst fractal_dimension_worst
## Min. :0.0000 Min. :0.00000 Min. :0.1565 Min. :0.05504
## 1st Qu.:0.1145 1st Qu.:0.06493 1st Qu.:0.2504 1st Qu.:0.07146
## Median :0.2267 Median :0.09993 Median :0.2822 Median :0.08004
## Mean :0.2722 Mean :0.11461 Mean :0.2901 Mean :0.08395
## 3rd Qu.:0.3829 3rd Qu.:0.16140 3rd Qu.:0.3179 3rd Qu.:0.09208
## Max. :1.2520 Max. :0.29100 Max. :0.6638 Max. :0.20750##################################
# Setting the data type
# for the response variable
##################################
BreastCancer$diagnosis <- factor(BreastCancer$diagnosis,
levels = c("B","M"))
##################################
# Formulating a data type assessment summary
##################################
PDA <- BreastCancer
(PDA.Summary <- data.frame(
Column.Index=c(1:length(names(PDA))),
Column.Name= names(PDA),
Column.Type=sapply(PDA, function(x) class(x)),
row.names=NULL)
)## Column.Index Column.Name Column.Type
## 1 1 id integer
## 2 2 diagnosis factor
## 3 3 radius_mean numeric
## 4 4 texture_mean numeric
## 5 5 perimeter_mean numeric
## 6 6 area_mean numeric
## 7 7 smoothness_mean numeric
## 8 8 compactness_mean numeric
## 9 9 concavity_mean numeric
## 10 10 concave.points_mean numeric
## 11 11 symmetry_mean numeric
## 12 12 fractal_dimension_mean numeric
## 13 13 radius_se numeric
## 14 14 texture_se numeric
## 15 15 perimeter_se numeric
## 16 16 area_se numeric
## 17 17 smoothness_se numeric
## 18 18 compactness_se numeric
## 19 19 concavity_se numeric
## 20 20 concave.points_se numeric
## 21 21 symmetry_se numeric
## 22 22 fractal_dimension_se numeric
## 23 23 radius_worst numeric
## 24 24 texture_worst numeric
## 25 25 perimeter_worst numeric
## 26 26 area_worst numeric
## 27 27 smoothness_worst numeric
## 28 28 compactness_worst numeric
## 29 29 concavity_worst numeric
## 30 30 concave.points_worst numeric
## 31 31 symmetry_worst numeric
## 32 32 fractal_dimension_worst numeric1.2 Data Quality Assessment
[A] No missing observations noted for any predictor.
[B] Low variance observed for 1 predictor with First.Second.Mode.Ratio>5.
[B.1] concavity_se = 6.50
[C] No low variance observed for any predictor with Unique.Count.Ratio<0.01.
[D] High skewness observed for 5 predictors with Skewness>3 or Skewness<(-3).
[D.1] radius_se = +3.08
[D.2] perimeter_se = +3.43
[D.3] area_se = +5.43
[D.4] concavity_se = +5.10
[D.5] fractal_dimension_se = +3.91
Code Chunk | Output
##################################
# Loading dataset
##################################
DQA <- BreastCancer
##################################
# Formulating an overall data quality assessment summary
##################################
(DQA.Summary <- data.frame(
Column.Name= names(DQA),
Column.Type=sapply(DQA, function(x) class(x)),
Row.Count=sapply(DQA, function(x) nrow(DQA)),
NA.Count=sapply(DQA,function(x)sum(is.na(x))),
Fill.Rate=sapply(DQA,function(x)format(round((sum(!is.na(x))/nrow(DQA)),3),nsmall=3)),
row.names=NULL)
)## Column.Name Column.Type Row.Count NA.Count Fill.Rate
## 1 id integer 1138 0 1.000
## 2 diagnosis factor 1138 0 1.000
## 3 radius_mean numeric 1138 0 1.000
## 4 texture_mean numeric 1138 0 1.000
## 5 perimeter_mean numeric 1138 0 1.000
## 6 area_mean numeric 1138 0 1.000
## 7 smoothness_mean numeric 1138 0 1.000
## 8 compactness_mean numeric 1138 0 1.000
## 9 concavity_mean numeric 1138 0 1.000
## 10 concave.points_mean numeric 1138 0 1.000
## 11 symmetry_mean numeric 1138 0 1.000
## 12 fractal_dimension_mean numeric 1138 0 1.000
## 13 radius_se numeric 1138 0 1.000
## 14 texture_se numeric 1138 0 1.000
## 15 perimeter_se numeric 1138 0 1.000
## 16 area_se numeric 1138 0 1.000
## 17 smoothness_se numeric 1138 0 1.000
## 18 compactness_se numeric 1138 0 1.000
## 19 concavity_se numeric 1138 0 1.000
## 20 concave.points_se numeric 1138 0 1.000
## 21 symmetry_se numeric 1138 0 1.000
## 22 fractal_dimension_se numeric 1138 0 1.000
## 23 radius_worst numeric 1138 0 1.000
## 24 texture_worst numeric 1138 0 1.000
## 25 perimeter_worst numeric 1138 0 1.000
## 26 area_worst numeric 1138 0 1.000
## 27 smoothness_worst numeric 1138 0 1.000
## 28 compactness_worst numeric 1138 0 1.000
## 29 concavity_worst numeric 1138 0 1.000
## 30 concave.points_worst numeric 1138 0 1.000
## 31 symmetry_worst numeric 1138 0 1.000
## 32 fractal_dimension_worst numeric 1138 0 1.000##################################
# Listing all Predictors
##################################
DQA.Predictors <- DQA[,!names(DQA) %in% c("id","diagnosis")]
##################################
# Listing all numeric Predictors
##################################
DQA.Predictors.Numeric <- DQA.Predictors[,sapply(DQA.Predictors, is.numeric)]
if (length(names(DQA.Predictors.Numeric))>0) {
print(paste0("There are ",
(length(names(DQA.Predictors.Numeric))),
" numeric predictor variable(s)."))
} else {
print("There are no numeric predictor variables.")
}## [1] "There are 30 numeric predictor variable(s)."##################################
# Listing all factor Predictors
##################################
DQA.Predictors.Factor <- DQA.Predictors[,sapply(DQA.Predictors, is.factor)]
if (length(names(DQA.Predictors.Factor))>0) {
print(paste0("There are ",
(length(names(DQA.Predictors.Factor))),
" factor predictor variable(s)."))
} else {
print("There are no factor predictor variables.")
}## [1] "There are no factor predictor variables."##################################
# Formulating a data quality assessment summary for factor Predictors
##################################
if (length(names(DQA.Predictors.Factor))>0) {
##################################
# Formulating a function to determine the first mode
##################################
FirstModes <- function(x) {
ux <- unique(na.omit(x))
tab <- tabulate(match(x, ux))
ux[tab == max(tab)]
}
##################################
# Formulating a function to determine the second mode
##################################
SecondModes <- function(x) {
ux <- unique(na.omit(x))
tab <- tabulate(match(x, ux))
fm = ux[tab == max(tab)]
sm = x[!(x %in% fm)]
usm <- unique(sm)
tabsm <- tabulate(match(sm, usm))
ifelse(is.na(usm[tabsm == max(tabsm)])==TRUE,
return("x"),
return(usm[tabsm == max(tabsm)]))
}
(DQA.Predictors.Factor.Summary <- data.frame(
Column.Name= names(DQA.Predictors.Factor),
Column.Type=sapply(DQA.Predictors.Factor, function(x) class(x)),
Unique.Count=sapply(DQA.Predictors.Factor, function(x) length(unique(x))),
First.Mode.Value=sapply(DQA.Predictors.Factor, function(x) as.character(FirstModes(x)[1])),
Second.Mode.Value=sapply(DQA.Predictors.Factor, function(x) as.character(SecondModes(x)[1])),
First.Mode.Count=sapply(DQA.Predictors.Factor, function(x) sum(na.omit(x) == FirstModes(x)[1])),
Second.Mode.Count=sapply(DQA.Predictors.Factor, function(x) sum(na.omit(x) == SecondModes(x)[1])),
Unique.Count.Ratio=sapply(DQA.Predictors.Factor, function(x) format(round((length(unique(x))/nrow(DQA.Predictors.Factor)),3), nsmall=3)),
First.Second.Mode.Ratio=sapply(DQA.Predictors.Factor, function(x) format(round((sum(na.omit(x) == FirstModes(x)[1])/sum(na.omit(x) == SecondModes(x)[1])),3), nsmall=3)),
row.names=NULL)
)
}
##################################
# Formulating a data quality assessment summary for numeric Predictors
##################################
if (length(names(DQA.Predictors.Numeric))>0) {
##################################
# Formulating a function to determine the first mode
##################################
FirstModes <- function(x) {
ux <- unique(na.omit(x))
tab <- tabulate(match(x, ux))
ux[tab == max(tab)]
}
##################################
# Formulating a function to determine the second mode
##################################
SecondModes <- function(x) {
ux <- unique(na.omit(x))
tab <- tabulate(match(x, ux))
fm = ux[tab == max(tab)]
sm = na.omit(x)[!(na.omit(x) %in% fm)]
usm <- unique(sm)
tabsm <- tabulate(match(sm, usm))
ifelse(is.na(usm[tabsm == max(tabsm)])==TRUE,
return(0.00001),
return(usm[tabsm == max(tabsm)]))
}
(DQA.Predictors.Numeric.Summary <- data.frame(
Column.Name= names(DQA.Predictors.Numeric),
Column.Type=sapply(DQA.Predictors.Numeric, function(x) class(x)),
Unique.Count=sapply(DQA.Predictors.Numeric, function(x) length(unique(x))),
Unique.Count.Ratio=sapply(DQA.Predictors.Numeric, function(x) format(round((length(unique(x))/nrow(DQA.Predictors.Numeric)),3), nsmall=3)),
First.Mode.Value=sapply(DQA.Predictors.Numeric, function(x) format(round((FirstModes(x)[1]),3),nsmall=3)),
Second.Mode.Value=sapply(DQA.Predictors.Numeric, function(x) format(round((SecondModes(x)[1]),3),nsmall=3)),
First.Mode.Count=sapply(DQA.Predictors.Numeric, function(x) sum(na.omit(x) == FirstModes(x)[1])),
Second.Mode.Count=sapply(DQA.Predictors.Numeric, function(x) sum(na.omit(x) == SecondModes(x)[1])),
First.Second.Mode.Ratio=sapply(DQA.Predictors.Numeric, function(x) format(round((sum(na.omit(x) == FirstModes(x)[1])/sum(na.omit(x) == SecondModes(x)[1])),3), nsmall=3)),
Minimum=sapply(DQA.Predictors.Numeric, function(x) format(round(min(x,na.rm = TRUE),3), nsmall=3)),
Mean=sapply(DQA.Predictors.Numeric, function(x) format(round(mean(x,na.rm = TRUE),3), nsmall=3)),
Median=sapply(DQA.Predictors.Numeric, function(x) format(round(median(x,na.rm = TRUE),3), nsmall=3)),
Maximum=sapply(DQA.Predictors.Numeric, function(x) format(round(max(x,na.rm = TRUE),3), nsmall=3)),
Skewness=sapply(DQA.Predictors.Numeric, function(x) format(round(skewness(x,na.rm = TRUE),3), nsmall=3)),
Kurtosis=sapply(DQA.Predictors.Numeric, function(x) format(round(kurtosis(x,na.rm = TRUE),3), nsmall=3)),
Percentile25th=sapply(DQA.Predictors.Numeric, function(x) format(round(quantile(x,probs=0.25,na.rm = TRUE),3), nsmall=3)),
Percentile75th=sapply(DQA.Predictors.Numeric, function(x) format(round(quantile(x,probs=0.75,na.rm = TRUE),3), nsmall=3)),
row.names=NULL)
)
}## Column.Name Column.Type Unique.Count Unique.Count.Ratio
## 1 radius_mean numeric 456 0.401
## 2 texture_mean numeric 479 0.421
## 3 perimeter_mean numeric 522 0.459
## 4 area_mean numeric 539 0.474
## 5 smoothness_mean numeric 474 0.417
## 6 compactness_mean numeric 537 0.472
## 7 concavity_mean numeric 537 0.472
## 8 concave.points_mean numeric 542 0.476
## 9 symmetry_mean numeric 432 0.380
## 10 fractal_dimension_mean numeric 499 0.438
## 11 radius_se numeric 540 0.475
## 12 texture_se numeric 519 0.456
## 13 perimeter_se numeric 533 0.468
## 14 area_se numeric 528 0.464
## 15 smoothness_se numeric 547 0.481
## 16 compactness_se numeric 541 0.475
## 17 concavity_se numeric 533 0.468
## 18 concave.points_se numeric 507 0.446
## 19 symmetry_se numeric 498 0.438
## 20 fractal_dimension_se numeric 545 0.479
## 21 radius_worst numeric 457 0.402
## 22 texture_worst numeric 511 0.449
## 23 perimeter_worst numeric 514 0.452
## 24 area_worst numeric 544 0.478
## 25 smoothness_worst numeric 411 0.361
## 26 compactness_worst numeric 529 0.465
## 27 concavity_worst numeric 539 0.474
## 28 concave.points_worst numeric 492 0.432
## 29 symmetry_worst numeric 500 0.439
## 30 fractal_dimension_worst numeric 535 0.470
## First.Mode.Value Second.Mode.Value First.Mode.Count Second.Mode.Count
## 1 12.340 13.000 8 6
## 2 15.700 21.250 6 4
## 3 82.610 132.900 6 4
## 4 512.200 658.800 6 4
## 5 0.101 0.108 10 8
## 6 0.121 0.160 6 4
## 7 0.000 0.120 26 6
## 8 0.000 0.029 26 6
## 9 0.177 0.181 8 6
## 10 0.057 0.059 6 4
## 11 0.286 0.298 6 4
## 12 1.150 0.734 6 4
## 13 1.778 2.406 8 4
## 14 16.970 74.080 6 4
## 15 0.006 0.005 4 2
## 16 0.023 0.014 6 4
## 17 0.000 0.017 26 4
## 18 0.000 0.012 26 6
## 19 0.013 0.015 8 6
## 20 0.003 0.006 4 2
## 21 12.360 13.340 10 8
## 22 27.260 27.660 6 4
## 23 117.700 184.600 6 4
## 24 1269.000 2019.000 4 2
## 25 0.131 0.149 8 6
## 26 0.342 0.177 6 4
## 27 0.000 0.450 26 6
## 28 0.000 0.026 26 6
## 29 0.320 0.361 6 4
## 30 0.074 0.084 6 4
## First.Second.Mode.Ratio Minimum Mean Median Maximum Skewness Kurtosis
## 1 1.333 6.981 14.127 13.370 28.110 0.940 3.828
## 2 1.500 9.710 19.290 18.840 39.280 0.649 3.741
## 3 1.500 43.790 91.969 86.240 188.500 0.988 3.953
## 4 1.500 143.500 654.889 551.100 2501.000 1.641 6.610
## 5 1.250 0.053 0.096 0.096 0.163 0.455 3.838
## 6 1.500 0.019 0.104 0.093 0.345 1.187 4.625
## 7 4.333 0.000 0.089 0.062 0.427 1.397 4.971
## 8 4.333 0.000 0.049 0.034 0.201 1.168 4.047
## 9 1.333 0.106 0.181 0.179 0.304 0.724 4.266
## 10 1.500 0.050 0.063 0.062 0.097 1.301 5.969
## 11 1.500 0.112 0.405 0.324 2.873 3.080 20.521
## 12 1.500 0.360 1.217 1.108 4.885 1.642 8.292
## 13 2.000 0.757 2.866 2.287 21.980 3.435 24.204
## 14 1.500 6.802 40.337 24.530 542.200 5.433 51.767
## 15 2.000 0.002 0.007 0.006 0.031 2.308 13.368
## 16 1.500 0.002 0.025 0.020 0.135 1.897 8.051
## 17 6.500 0.000 0.032 0.026 0.396 5.097 51.423
## 18 4.333 0.000 0.012 0.011 0.053 1.441 8.071
## 19 1.333 0.008 0.021 0.019 0.079 2.189 10.816
## 20 2.000 0.001 0.004 0.003 0.030 3.914 29.040
## 21 1.250 7.930 16.269 14.970 36.040 1.100 3.925
## 22 1.500 12.020 25.677 25.410 49.540 0.497 3.212
## 23 1.500 50.410 107.261 97.660 251.200 1.125 4.050
## 24 2.000 185.200 880.583 686.500 4254.000 1.854 7.347
## 25 1.333 0.071 0.132 0.131 0.223 0.414 3.503
## 26 1.500 0.027 0.254 0.212 1.058 1.470 6.002
## 27 4.333 0.000 0.272 0.227 1.252 1.147 4.591
## 28 4.333 0.000 0.115 0.100 0.291 0.491 2.459
## 29 1.500 0.156 0.290 0.282 0.664 1.430 7.395
## 30 1.500 0.055 0.084 0.080 0.208 1.658 8.188
## Percentile25th Percentile75th
## 1 11.700 15.780
## 2 16.170 21.800
## 3 75.170 104.100
## 4 420.300 782.700
## 5 0.086 0.105
## 6 0.065 0.130
## 7 0.030 0.131
## 8 0.020 0.074
## 9 0.162 0.196
## 10 0.058 0.066
## 11 0.232 0.479
## 12 0.834 1.474
## 13 1.606 3.357
## 14 17.850 45.190
## 15 0.005 0.008
## 16 0.013 0.032
## 17 0.015 0.042
## 18 0.008 0.015
## 19 0.015 0.023
## 20 0.002 0.005
## 21 13.010 18.790
## 22 21.080 29.720
## 23 84.110 125.400
## 24 515.300 1084.000
## 25 0.117 0.146
## 26 0.147 0.339
## 27 0.114 0.383
## 28 0.065 0.161
## 29 0.250 0.318
## 30 0.071 0.092##################################
# Identifying potential data quality issues
##################################
##################################
# Checking for missing observations
##################################
if ((nrow(DQA.Summary[DQA.Summary$NA.Count>0,]))>0){
print(paste0("Missing observations noted for ",
(nrow(DQA.Summary[DQA.Summary$NA.Count>0,])),
" variable(s) with NA.Count>0 and Fill.Rate<1.0."))
DQA.Summary[DQA.Summary$NA.Count>0,]
} else {
print("No missing observations noted.")
}## [1] "No missing observations noted."##################################
# Checking for zero or near-zero variance Predictors
##################################
if (length(names(DQA.Predictors.Factor))==0) {
print("No factor predictors noted.")
} else if (nrow(DQA.Predictors.Factor.Summary[as.numeric(as.character(DQA.Predictors.Factor.Summary$First.Second.Mode.Ratio))>5,])>0){
print(paste0("Low variance observed for ",
(nrow(DQA.Predictors.Factor.Summary[as.numeric(as.character(DQA.Predictors.Factor.Summary$First.Second.Mode.Ratio))>5,])),
" factor variable(s) with First.Second.Mode.Ratio>5."))
DQA.Predictors.Factor.Summary[as.numeric(as.character(DQA.Predictors.Factor.Summary$First.Second.Mode.Ratio))>5,]
} else {
print("No low variance factor predictors due to high first-second mode ratio noted.")
}## [1] "No factor predictors noted."if (length(names(DQA.Predictors.Numeric))==0) {
print("No numeric predictors noted.")
} else if (nrow(DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$First.Second.Mode.Ratio))>5,])>0){
print(paste0("Low variance observed for ",
(nrow(DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$First.Second.Mode.Ratio))>5,])),
" numeric variable(s) with First.Second.Mode.Ratio>5."))
DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$First.Second.Mode.Ratio))>5,]
} else {
print("No low variance numeric predictors due to high first-second mode ratio noted.")
}## [1] "Low variance observed for 1 numeric variable(s) with First.Second.Mode.Ratio>5."## Column.Name Column.Type Unique.Count Unique.Count.Ratio First.Mode.Value
## 17 concavity_se numeric 533 0.468 0.000
## Second.Mode.Value First.Mode.Count Second.Mode.Count First.Second.Mode.Ratio
## 17 0.017 26 4 6.500
## Minimum Mean Median Maximum Skewness Kurtosis Percentile25th Percentile75th
## 17 0.000 0.032 0.026 0.396 5.097 51.423 0.015 0.042if (length(names(DQA.Predictors.Numeric))==0) {
print("No numeric predictors noted.")
} else if (nrow(DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$Unique.Count.Ratio))<0.01,])>0){
print(paste0("Low variance observed for ",
(nrow(DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$Unique.Count.Ratio))<0.01,])),
" numeric variable(s) with Unique.Count.Ratio<0.01."))
DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$Unique.Count.Ratio))<0.01,]
} else {
print("No low variance numeric predictors due to low unique count ratio noted.")
}## [1] "No low variance numeric predictors due to low unique count ratio noted."##################################
# Checking for skewed Predictors
##################################
if (length(names(DQA.Predictors.Numeric))==0) {
print("No numeric predictors noted.")
} else if (nrow(DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$Skewness))>3 |
as.numeric(as.character(DQA.Predictors.Numeric.Summary$Skewness))<(-3),])>0){
print(paste0("High skewness observed for ",
(nrow(DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$Skewness))>3 |
as.numeric(as.character(DQA.Predictors.Numeric.Summary$Skewness))<(-3),])),
" numeric variable(s) with Skewness>3 or Skewness<(-3)."))
DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$Skewness))>3 |
as.numeric(as.character(DQA.Predictors.Numeric.Summary$Skewness))<(-3),]
} else {
print("No skewed numeric predictors noted.")
}## [1] "High skewness observed for 5 numeric variable(s) with Skewness>3 or Skewness<(-3)."## Column.Name Column.Type Unique.Count Unique.Count.Ratio
## 11 radius_se numeric 540 0.475
## 13 perimeter_se numeric 533 0.468
## 14 area_se numeric 528 0.464
## 17 concavity_se numeric 533 0.468
## 20 fractal_dimension_se numeric 545 0.479
## First.Mode.Value Second.Mode.Value First.Mode.Count Second.Mode.Count
## 11 0.286 0.298 6 4
## 13 1.778 2.406 8 4
## 14 16.970 74.080 6 4
## 17 0.000 0.017 26 4
## 20 0.003 0.006 4 2
## First.Second.Mode.Ratio Minimum Mean Median Maximum Skewness Kurtosis
## 11 1.500 0.112 0.405 0.324 2.873 3.080 20.521
## 13 2.000 0.757 2.866 2.287 21.980 3.435 24.204
## 14 1.500 6.802 40.337 24.530 542.200 5.433 51.767
## 17 6.500 0.000 0.032 0.026 0.396 5.097 51.423
## 20 2.000 0.001 0.004 0.003 0.030 3.914 29.040
## Percentile25th Percentile75th
## 11 0.232 0.479
## 13 1.606 3.357
## 14 17.850 45.190
## 17 0.015 0.042
## 20 0.002 0.0051.3 Data Preprocessing
1.3.1 Outlier Detection
[A] Outliers noted for 29 out of the 30 predictors. Predictor values were visualized through a boxplot including observations classified as suspected outliers using the IQR criterion. The IQR criterion means that all observations above the (75th percentile + 1.5 x IQR) or below the (25th percentile - 1.5 x IQR) are suspected outliers, where IQR is the difference between the third quartile (75th percentile) and first quartile (25th percentile).
[A.1] radius_mean = 28
[A.2] texture_mean = 14
[A.3] perimeter_mean = 26
[A.4] area_mean = 50
[A.5] smoothness_mean = 12
[A.6] compactness_mean = 32
[A.7] concavity_mean = 36
[A.8] concave.points_mean = 20
[A.9] symmetry_mean = 30
[A.10] fractal_dimension_mean = 30
[A.11] radius_se = 76
[A.12] texture_se = 40
[A.13] perimeter_se = 76
[A.14] area_se = 130
[A.15] smoothness_se = 60
[A.16] compactness_se = 56
[A.17] concavity_se = 44
[A.18] concave.points_se = 38
[A.19] symmetry_se = 54
[A.20] fractal_dimension_se = 56
[A.21] radius_worst = 34
[A.22] texture_worst = 10
[A.23] perimeter_worst = 30
[A.24] area_worst = 70
[A.25] smoothness_worst = 14
[A.26] compactness_worst = 32
[A.27] concavity_worst = 24
[A.28] symmetry_worst = 46
[A.29] fractal_dimension_worst = 48
Code Chunk | Output
##################################
# Loading dataset
##################################
DPA <- DQA[,!names(DQA) %in% c("id")]
##################################
# Gathering descriptive statistics
##################################
(DPA_Skimmed <- skim(DPA)) | Name | DPA |
| Number of rows | 1138 |
| Number of columns | 31 |
| _______________________ | |
| Column type frequency: | |
| factor | 1 |
| numeric | 30 |
| ________________________ | |
| Group variables | None |
Variable type: factor
| skim_variable | n_missing | complete_rate | ordered | n_unique | top_counts |
|---|---|---|---|---|---|
| diagnosis | 0 | 1 | FALSE | 2 | B: 714, M: 424 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| radius_mean | 0 | 1 | 14.13 | 3.52 | 6.98 | 11.70 | 13.37 | 15.78 | 28.11 | ▂▇▃▁▁ |
| texture_mean | 0 | 1 | 19.29 | 4.30 | 9.71 | 16.17 | 18.84 | 21.80 | 39.28 | ▃▇▃▁▁ |
| perimeter_mean | 0 | 1 | 91.97 | 24.29 | 43.79 | 75.17 | 86.24 | 104.10 | 188.50 | ▃▇▃▁▁ |
| area_mean | 0 | 1 | 654.89 | 351.76 | 143.50 | 420.30 | 551.10 | 782.70 | 2501.00 | ▇▃▂▁▁ |
| smoothness_mean | 0 | 1 | 0.10 | 0.01 | 0.05 | 0.09 | 0.10 | 0.11 | 0.16 | ▁▇▇▁▁ |
| compactness_mean | 0 | 1 | 0.10 | 0.05 | 0.02 | 0.06 | 0.09 | 0.13 | 0.35 | ▇▇▂▁▁ |
| concavity_mean | 0 | 1 | 0.09 | 0.08 | 0.00 | 0.03 | 0.06 | 0.13 | 0.43 | ▇▃▂▁▁ |
| concave.points_mean | 0 | 1 | 0.05 | 0.04 | 0.00 | 0.02 | 0.03 | 0.07 | 0.20 | ▇▃▂▁▁ |
| symmetry_mean | 0 | 1 | 0.18 | 0.03 | 0.11 | 0.16 | 0.18 | 0.20 | 0.30 | ▁▇▅▁▁ |
| fractal_dimension_mean | 0 | 1 | 0.06 | 0.01 | 0.05 | 0.06 | 0.06 | 0.07 | 0.10 | ▆▇▂▁▁ |
| radius_se | 0 | 1 | 0.41 | 0.28 | 0.11 | 0.23 | 0.32 | 0.48 | 2.87 | ▇▁▁▁▁ |
| texture_se | 0 | 1 | 1.22 | 0.55 | 0.36 | 0.83 | 1.11 | 1.47 | 4.88 | ▇▅▁▁▁ |
| perimeter_se | 0 | 1 | 2.87 | 2.02 | 0.76 | 1.61 | 2.29 | 3.36 | 21.98 | ▇▁▁▁▁ |
| area_se | 0 | 1 | 40.34 | 45.47 | 6.80 | 17.85 | 24.53 | 45.19 | 542.20 | ▇▁▁▁▁ |
| smoothness_se | 0 | 1 | 0.01 | 0.00 | 0.00 | 0.01 | 0.01 | 0.01 | 0.03 | ▇▃▁▁▁ |
| compactness_se | 0 | 1 | 0.03 | 0.02 | 0.00 | 0.01 | 0.02 | 0.03 | 0.14 | ▇▃▁▁▁ |
| concavity_se | 0 | 1 | 0.03 | 0.03 | 0.00 | 0.02 | 0.03 | 0.04 | 0.40 | ▇▁▁▁▁ |
| concave.points_se | 0 | 1 | 0.01 | 0.01 | 0.00 | 0.01 | 0.01 | 0.01 | 0.05 | ▇▇▁▁▁ |
| symmetry_se | 0 | 1 | 0.02 | 0.01 | 0.01 | 0.02 | 0.02 | 0.02 | 0.08 | ▇▃▁▁▁ |
| fractal_dimension_se | 0 | 1 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.03 | ▇▁▁▁▁ |
| radius_worst | 0 | 1 | 16.27 | 4.83 | 7.93 | 13.01 | 14.97 | 18.79 | 36.04 | ▆▇▃▁▁ |
| texture_worst | 0 | 1 | 25.68 | 6.14 | 12.02 | 21.08 | 25.41 | 29.72 | 49.54 | ▃▇▆▁▁ |
| perimeter_worst | 0 | 1 | 107.26 | 33.59 | 50.41 | 84.11 | 97.66 | 125.40 | 251.20 | ▇▇▃▁▁ |
| area_worst | 0 | 1 | 880.58 | 569.11 | 185.20 | 515.30 | 686.50 | 1084.00 | 4254.00 | ▇▂▁▁▁ |
| smoothness_worst | 0 | 1 | 0.13 | 0.02 | 0.07 | 0.12 | 0.13 | 0.15 | 0.22 | ▂▇▇▂▁ |
| compactness_worst | 0 | 1 | 0.25 | 0.16 | 0.03 | 0.15 | 0.21 | 0.34 | 1.06 | ▇▅▁▁▁ |
| concavity_worst | 0 | 1 | 0.27 | 0.21 | 0.00 | 0.11 | 0.23 | 0.38 | 1.25 | ▇▅▂▁▁ |
| concave.points_worst | 0 | 1 | 0.11 | 0.07 | 0.00 | 0.06 | 0.10 | 0.16 | 0.29 | ▅▇▅▃▁ |
| symmetry_worst | 0 | 1 | 0.29 | 0.06 | 0.16 | 0.25 | 0.28 | 0.32 | 0.66 | ▅▇▁▁▁ |
| fractal_dimension_worst | 0 | 1 | 0.08 | 0.02 | 0.06 | 0.07 | 0.08 | 0.09 | 0.21 | ▇▃▁▁▁ |
##################################
# Outlier Detection
##################################
##################################
# Listing all Predictors
##################################
DPA.Predictors <- DPA[,!names(DPA) %in% c("diagnosis")]
##################################
# Listing all numeric Predictors
##################################
DPA.Predictors.Numeric <- DPA.Predictors[,sapply(DPA.Predictors, is.numeric)]
##################################
# Identifying outliers for the numeric Predictors
##################################
OutlierCountList <- c()
for (i in 1:ncol(DPA.Predictors.Numeric)) {
Outliers <- boxplot.stats(DPA.Predictors.Numeric[,i])$out
OutlierCount <- length(Outliers)
OutlierCountList <- append(OutlierCountList,OutlierCount)
OutlierIndices <- which(DPA.Predictors.Numeric[,i] %in% c(Outliers))
print(
ggplot(DPA.Predictors.Numeric, aes(x=DPA.Predictors.Numeric[,i])) +
geom_boxplot() +
theme_bw() +
theme(axis.text.y=element_blank(),
axis.ticks.y=element_blank()) +
xlab(names(DPA.Predictors.Numeric)[i]) +
labs(title=names(DPA.Predictors.Numeric)[i],
subtitle=paste0(OutlierCount, " Outlier(s) Detected")))
}
1.3.2 Zero and Near-Zero Variance
[A] No low variance observed for any predictor using a preprocessing summary from the caret package. The nearZeroVar method using both the freqCut and uniqueCut criteria set at 95/5 and 10, respectively, were applied on the dataset.
Code Chunk | Output
##################################
# Zero and Near-Zero Variance
##################################
##################################
# Identifying columns with low variance
###################################
DPA_LowVariance <- nearZeroVar(DPA,
freqCut = 80/20,
uniqueCut = 10,
saveMetrics= TRUE)
(DPA_LowVariance[DPA_LowVariance$nzv,])## [1] freqRatio percentUnique zeroVar nzv
## <0 rows> (or 0-length row.names)if ((nrow(DPA_LowVariance[DPA_LowVariance$nzv,]))==0){
print("No low variance descriptors noted.")
} else {
print(paste0("Low variance observed for ",
(nrow(DPA_LowVariance[DPA_LowVariance$nzv,])),
" numeric variable(s) with First.Second.Mode.Ratio>4 and Unique.Count.Ratio<0.10."))
DPA_LowVarianceForRemoval <- (nrow(DPA_LowVariance[DPA_LowVariance$nzv,]))
print(paste0("Low variance can be resolved by removing ",
(nrow(DPA_LowVariance[DPA_LowVariance$nzv,])),
" numeric variable(s)."))
for (j in 1:DPA_LowVarianceForRemoval) {
DPA_LowVarianceRemovedVariable <- rownames(DPA_LowVariance[DPA_LowVariance$nzv,])[j]
print(paste0("Variable ",
j,
" for removal: ",
DPA_LowVarianceRemovedVariable))
}
DPA %>%
skim() %>%
dplyr::filter(skim_variable %in% rownames(DPA_LowVariance[DPA_LowVariance$nzv,]))
}## [1] "No low variance descriptors noted."1.3.3 Collinearity
[A] High correlation values were noted for 15 pairs of numeric predictors with Pearson correlation coefficients >80% as confirmed using the preprocessing summaries from the caret package.
[A.1] radius_mean and perimeter_mean = +100%
[A.2] radius_worst and perimeter_worst = +99%
[A.3] radius_mean and area_mean = +99%
[A.4] perimeter_mean and area_mean = +99%
[A.5] radius_worst and area_worst = +98%
[A.6] perimeter_worst and area_worst = +98%
[A.7] radius_se and perimeter_se = +97%
[A.8] perimeter_mean and perimeter_worst = +97%
[A.9] radius_mean and radius_worst = +97%
[A.10] perimeter_mean and radius_worst = +97%%
[A.11] radius_mean and perimeter_worst = +96%
[A.12] area_mean and radius_worst = +96%
[A.13] area_mean and area_worst = +96%
[A.14] area_mean and perimeter_worst = +96%
[A.15] radius_se and area_se = +95%
[B] 7 predictors driving high pairwise correlation were recommended for removal using the findCorrelation preprocessing method from the caret package. The function looks at the mean absolute correlation of each predictor and removes that with the largest mean absolute correlation.
[B.1] perimeter_worst
[B.2] radius_worst
[B.3] perimeter_mean
[B.4] area_worst
[B.5] radius_mean
[B.6] perimeter_se
[B.7] area_se
Code Chunk | Output
##################################
# Visualizing pairwise correlation between Predictor
##################################
(DPA_Correlation <- cor(DPA.Predictors.Numeric,
method = "pearson",
use="pairwise.complete.obs"))## radius_mean texture_mean perimeter_mean area_mean
## radius_mean 1.000000000 0.323781891 0.997855281 0.987357170
## texture_mean 0.323781891 1.000000000 0.329533059 0.321085696
## perimeter_mean 0.997855281 0.329533059 1.000000000 0.986506804
## area_mean 0.987357170 0.321085696 0.986506804 1.000000000
## smoothness_mean 0.170581187 -0.023388516 0.207278164 0.177028377
## compactness_mean 0.506123578 0.236702222 0.556936211 0.498501682
## concavity_mean 0.676763550 0.302417828 0.716135650 0.685982829
## concave.points_mean 0.822528522 0.293464051 0.850977041 0.823268869
## symmetry_mean 0.147741242 0.071400980 0.183027212 0.151293079
## fractal_dimension_mean -0.311630826 -0.076437183 -0.261476908 -0.283109812
## radius_se 0.679090388 0.275868676 0.691765014 0.732562227
## texture_se -0.097317443 0.386357623 -0.086761078 -0.066280214
## perimeter_se 0.674171616 0.281673115 0.693134890 0.726628328
## area_se 0.735863663 0.259844987 0.744982694 0.800085921
## smoothness_se -0.222600125 0.006613777 -0.202694026 -0.166776667
## compactness_se 0.205999980 0.191974611 0.250743681 0.212582551
## concavity_se 0.194203623 0.143293077 0.228082345 0.207660060
## concave.points_se 0.376168956 0.163851025 0.407216916 0.372320282
## symmetry_se -0.104320881 0.009127168 -0.081629327 -0.072496588
## fractal_dimension_se -0.042641269 0.054457520 -0.005523391 -0.019886963
## radius_worst 0.969538973 0.352572947 0.969476363 0.962746086
## texture_worst 0.297007644 0.912044589 0.303038372 0.287488627
## perimeter_worst 0.965136514 0.358039575 0.970386887 0.959119574
## area_worst 0.941082460 0.343545947 0.941549808 0.959213326
## smoothness_worst 0.119616140 0.077503359 0.150549404 0.123522939
## compactness_worst 0.413462823 0.277829592 0.455774228 0.390410309
## concavity_worst 0.526911462 0.301025224 0.563879263 0.512605920
## concave.points_worst 0.744214198 0.295315843 0.771240789 0.722016626
## symmetry_worst 0.163953335 0.105007910 0.189115040 0.143569914
## fractal_dimension_worst 0.007065886 0.119205351 0.051018530 0.003737597
## smoothness_mean compactness_mean concavity_mean
## radius_mean 0.17058119 0.50612358 0.67676355
## texture_mean -0.02338852 0.23670222 0.30241783
## perimeter_mean 0.20727816 0.55693621 0.71613565
## area_mean 0.17702838 0.49850168 0.68598283
## smoothness_mean 1.00000000 0.65912322 0.52198377
## compactness_mean 0.65912322 1.00000000 0.88312067
## concavity_mean 0.52198377 0.88312067 1.00000000
## concave.points_mean 0.55369517 0.83113504 0.92139103
## symmetry_mean 0.55777479 0.60264105 0.50066662
## fractal_dimension_mean 0.58479200 0.56536866 0.33678336
## radius_se 0.30146710 0.49747345 0.63192482
## texture_se 0.06840645 0.04620483 0.07621835
## perimeter_se 0.29609193 0.54890526 0.66039079
## area_se 0.24655243 0.45565285 0.61742681
## smoothness_se 0.33237544 0.13529927 0.09856375
## compactness_se 0.31894330 0.73872179 0.67027882
## concavity_se 0.24839568 0.57051687 0.69127021
## concave.points_se 0.38067569 0.64226185 0.68325992
## symmetry_se 0.20077438 0.22997659 0.17800921
## fractal_dimension_se 0.28360670 0.50731813 0.44930075
## radius_worst 0.21312014 0.53531540 0.68823641
## texture_worst 0.03607180 0.24813283 0.29987889
## perimeter_worst 0.23885263 0.59021043 0.72956492
## area_worst 0.20671836 0.50960381 0.67598723
## smoothness_worst 0.80532420 0.56554117 0.44882204
## compactness_worst 0.47246844 0.86580904 0.75496802
## concavity_worst 0.43492571 0.81627525 0.88410264
## concave.points_worst 0.50305335 0.81557322 0.86132303
## symmetry_worst 0.39430948 0.51022343 0.40946413
## fractal_dimension_worst 0.49931637 0.68738232 0.51492989
## concave.points_mean symmetry_mean
## radius_mean 0.82252852 0.14774124
## texture_mean 0.29346405 0.07140098
## perimeter_mean 0.85097704 0.18302721
## area_mean 0.82326887 0.15129308
## smoothness_mean 0.55369517 0.55777479
## compactness_mean 0.83113504 0.60264105
## concavity_mean 0.92139103 0.50066662
## concave.points_mean 1.00000000 0.46249739
## symmetry_mean 0.46249739 1.00000000
## fractal_dimension_mean 0.16691738 0.47992133
## radius_se 0.69804983 0.30337926
## texture_se 0.02147958 0.12805293
## perimeter_se 0.71064987 0.31389276
## area_se 0.69029854 0.22397022
## smoothness_se 0.02765331 0.18732117
## compactness_se 0.49042425 0.42165915
## concavity_se 0.43916707 0.34262702
## concave.points_se 0.61563413 0.39329787
## symmetry_se 0.09535079 0.44913654
## fractal_dimension_se 0.25758375 0.33178615
## radius_worst 0.83031763 0.18572775
## texture_worst 0.29275171 0.09065069
## perimeter_worst 0.85592313 0.21916856
## area_worst 0.80962962 0.17719338
## smoothness_worst 0.45275305 0.42667503
## compactness_worst 0.66745368 0.47320001
## concavity_worst 0.75239950 0.43372101
## concave.points_worst 0.91015531 0.43029661
## symmetry_worst 0.37574415 0.69982580
## fractal_dimension_worst 0.36866113 0.43841350
## fractal_dimension_mean radius_se texture_se
## radius_mean -0.3116308263 0.6790903880 -0.09731744
## texture_mean -0.0764371834 0.2758686762 0.38635762
## perimeter_mean -0.2614769081 0.6917650135 -0.08676108
## area_mean -0.2831098117 0.7325622270 -0.06628021
## smoothness_mean 0.5847920019 0.3014670983 0.06840645
## compactness_mean 0.5653686634 0.4974734461 0.04620483
## concavity_mean 0.3367833594 0.6319248221 0.07621835
## concave.points_mean 0.1669173832 0.6980498336 0.02147958
## symmetry_mean 0.4799213301 0.3033792632 0.12805293
## fractal_dimension_mean 1.0000000000 0.0001109951 0.16417397
## radius_se 0.0001109951 1.0000000000 0.21324734
## texture_se 0.1641739659 0.2132473373 1.00000000
## perimeter_se 0.0398299316 0.9727936770 0.22317073
## area_se -0.0901702475 0.9518301121 0.11156725
## smoothness_se 0.4019644254 0.1645142198 0.39724285
## compactness_se 0.5598366906 0.3560645755 0.23169970
## concavity_se 0.4466303217 0.3323575376 0.19499846
## concave.points_se 0.3411980444 0.5133464414 0.23028340
## symmetry_se 0.3450073971 0.2405673625 0.41162068
## fractal_dimension_se 0.6881315775 0.2277535327 0.27972275
## radius_worst -0.2536914949 0.7150651951 -0.11169031
## texture_worst -0.0512692020 0.1947985568 0.40900277
## perimeter_worst -0.2051512113 0.7196838037 -0.10224192
## area_worst -0.2318544512 0.7515484761 -0.08319499
## smoothness_worst 0.5049420754 0.1419185529 -0.07365766
## compactness_worst 0.4587981567 0.2871031656 -0.09243935
## concavity_worst 0.3462338763 0.3805846346 -0.06895622
## concave.points_worst 0.1753254492 0.5310623278 -0.11963752
## symmetry_worst 0.3340186839 0.0945428304 -0.12821476
## fractal_dimension_worst 0.7672967792 0.0495594325 -0.04565457
## perimeter_se area_se smoothness_se compactness_se
## radius_mean 0.67417162 0.73586366 -0.222600125 0.2060000
## texture_mean 0.28167311 0.25984499 0.006613777 0.1919746
## perimeter_mean 0.69313489 0.74498269 -0.202694026 0.2507437
## area_mean 0.72662833 0.80008592 -0.166776667 0.2125826
## smoothness_mean 0.29609193 0.24655243 0.332375443 0.3189433
## compactness_mean 0.54890526 0.45565285 0.135299268 0.7387218
## concavity_mean 0.66039079 0.61742681 0.098563746 0.6702788
## concave.points_mean 0.71064987 0.69029854 0.027653308 0.4904242
## symmetry_mean 0.31389276 0.22397022 0.187321165 0.4216591
## fractal_dimension_mean 0.03982993 -0.09017025 0.401964425 0.5598367
## radius_se 0.97279368 0.95183011 0.164514220 0.3560646
## texture_se 0.22317073 0.11156725 0.397242853 0.2316997
## perimeter_se 1.00000000 0.93765541 0.151075331 0.4163224
## area_se 0.93765541 1.00000000 0.075150338 0.2848401
## smoothness_se 0.15107533 0.07515034 1.000000000 0.3366961
## compactness_se 0.41632237 0.28484006 0.336696081 1.0000000
## concavity_se 0.36248158 0.27089473 0.268684760 0.8012683
## concave.points_se 0.55626408 0.41572957 0.328429499 0.7440827
## symmetry_se 0.26648709 0.13410898 0.413506125 0.3947128
## fractal_dimension_se 0.24414277 0.12707090 0.427374207 0.8032688
## radius_worst 0.69720059 0.75737319 -0.230690710 0.2046072
## texture_worst 0.20037085 0.19649665 -0.074742965 0.1430026
## perimeter_worst 0.72103131 0.76121264 -0.217303755 0.2605158
## area_worst 0.73071297 0.81140796 -0.182195478 0.1993713
## smoothness_worst 0.13005439 0.12538943 0.314457456 0.2273942
## compactness_worst 0.34191945 0.28325654 -0.055558139 0.6787804
## concavity_worst 0.41889882 0.38510014 -0.058298387 0.6391467
## concave.points_worst 0.55489723 0.53816631 -0.102006796 0.4832083
## symmetry_worst 0.10993043 0.07412629 -0.107342098 0.2778784
## fractal_dimension_worst 0.08543257 0.01753930 0.101480315 0.5909728
## concavity_se concave.points_se symmetry_se
## radius_mean 0.1942036 0.37616896 -0.104320881
## texture_mean 0.1432931 0.16385103 0.009127168
## perimeter_mean 0.2280823 0.40721692 -0.081629327
## area_mean 0.2076601 0.37232028 -0.072496588
## smoothness_mean 0.2483957 0.38067569 0.200774376
## compactness_mean 0.5705169 0.64226185 0.229976591
## concavity_mean 0.6912702 0.68325992 0.178009208
## concave.points_mean 0.4391671 0.61563413 0.095350787
## symmetry_mean 0.3426270 0.39329787 0.449136542
## fractal_dimension_mean 0.4466303 0.34119804 0.345007397
## radius_se 0.3323575 0.51334644 0.240567362
## texture_se 0.1949985 0.23028340 0.411620680
## perimeter_se 0.3624816 0.55626408 0.266487092
## area_se 0.2708947 0.41572957 0.134108980
## smoothness_se 0.2686848 0.32842950 0.413506125
## compactness_se 0.8012683 0.74408267 0.394712835
## concavity_se 1.0000000 0.77180399 0.309428578
## concave.points_se 0.7718040 1.00000000 0.312780223
## symmetry_se 0.3094286 0.31278022 1.000000000
## fractal_dimension_se 0.7273722 0.61104414 0.369078083
## radius_worst 0.1869035 0.35812667 -0.128120769
## texture_worst 0.1002410 0.08674121 -0.077473420
## perimeter_worst 0.2266804 0.39499925 -0.103753044
## area_worst 0.1883527 0.34227116 -0.110342743
## smoothness_worst 0.1684813 0.21535060 -0.012661800
## compactness_worst 0.4848578 0.45288838 0.060254879
## concavity_worst 0.6625641 0.54959238 0.037119049
## concave.points_worst 0.4404723 0.60244961 -0.030413396
## symmetry_worst 0.1977878 0.14311567 0.389402485
## fractal_dimension_worst 0.4393293 0.31065455 0.078079476
## fractal_dimension_se radius_worst texture_worst
## radius_mean -0.042641269 0.96953897 0.297007644
## texture_mean 0.054457520 0.35257295 0.912044589
## perimeter_mean -0.005523391 0.96947636 0.303038372
## area_mean -0.019886963 0.96274609 0.287488627
## smoothness_mean 0.283606699 0.21312014 0.036071799
## compactness_mean 0.507318127 0.53531540 0.248132833
## concavity_mean 0.449300749 0.68823641 0.299878889
## concave.points_mean 0.257583746 0.83031763 0.292751713
## symmetry_mean 0.331786146 0.18572775 0.090650688
## fractal_dimension_mean 0.688131577 -0.25369149 -0.051269202
## radius_se 0.227753533 0.71506520 0.194798557
## texture_se 0.279722748 -0.11169031 0.409002766
## perimeter_se 0.244142773 0.69720059 0.200370854
## area_se 0.127070903 0.75737319 0.196496649
## smoothness_se 0.427374207 -0.23069071 -0.074742965
## compactness_se 0.803268818 0.20460717 0.143002583
## concavity_se 0.727372184 0.18690352 0.100240984
## concave.points_se 0.611044139 0.35812667 0.086741210
## symmetry_se 0.369078083 -0.12812077 -0.077473420
## fractal_dimension_se 1.000000000 -0.03748762 -0.003195029
## radius_worst -0.037487618 1.00000000 0.359920754
## texture_worst -0.003195029 0.35992075 1.000000000
## perimeter_worst -0.001000398 0.99370792 0.365098245
## area_worst -0.022736147 0.98401456 0.345842283
## smoothness_worst 0.170568316 0.21657443 0.225429415
## compactness_worst 0.390158842 0.47582004 0.360832339
## concavity_worst 0.379974661 0.57397471 0.368365607
## concave.points_worst 0.215204013 0.78742385 0.359754610
## symmetry_worst 0.111093956 0.24352920 0.233027461
## fractal_dimension_worst 0.591328066 0.09349198 0.219122425
## perimeter_worst area_worst smoothness_worst
## radius_mean 0.965136514 0.94108246 0.11961614
## texture_mean 0.358039575 0.34354595 0.07750336
## perimeter_mean 0.970386887 0.94154981 0.15054940
## area_mean 0.959119574 0.95921333 0.12352294
## smoothness_mean 0.238852626 0.20671836 0.80532420
## compactness_mean 0.590210428 0.50960381 0.56554117
## concavity_mean 0.729564917 0.67598723 0.44882204
## concave.points_mean 0.855923128 0.80962962 0.45275305
## symmetry_mean 0.219168559 0.17719338 0.42667503
## fractal_dimension_mean -0.205151211 -0.23185445 0.50494208
## radius_se 0.719683804 0.75154848 0.14191855
## texture_se -0.102241922 -0.08319499 -0.07365766
## perimeter_se 0.721031310 0.73071297 0.13005439
## area_se 0.761212636 0.81140796 0.12538943
## smoothness_se -0.217303755 -0.18219548 0.31445746
## compactness_se 0.260515840 0.19937133 0.22739423
## concavity_se 0.226680426 0.18835265 0.16848132
## concave.points_se 0.394999252 0.34227116 0.21535060
## symmetry_se -0.103753044 -0.11034274 -0.01266180
## fractal_dimension_se -0.001000398 -0.02273615 0.17056832
## radius_worst 0.993707916 0.98401456 0.21657443
## texture_worst 0.365098245 0.34584228 0.22542941
## perimeter_worst 1.000000000 0.97757809 0.23677460
## area_worst 0.977578091 1.00000000 0.20914533
## smoothness_worst 0.236774604 0.20914533 1.00000000
## compactness_worst 0.529407690 0.43829628 0.56818652
## concavity_worst 0.618344080 0.54333053 0.51852329
## concave.points_worst 0.816322102 0.74741880 0.54769090
## symmetry_worst 0.269492769 0.20914551 0.49383833
## fractal_dimension_worst 0.138956862 0.07964703 0.61762419
## compactness_worst concavity_worst concave.points_worst
## radius_mean 0.41346282 0.52691146 0.7442142
## texture_mean 0.27782959 0.30102522 0.2953158
## perimeter_mean 0.45577423 0.56387926 0.7712408
## area_mean 0.39041031 0.51260592 0.7220166
## smoothness_mean 0.47246844 0.43492571 0.5030534
## compactness_mean 0.86580904 0.81627525 0.8155732
## concavity_mean 0.75496802 0.88410264 0.8613230
## concave.points_mean 0.66745368 0.75239950 0.9101553
## symmetry_mean 0.47320001 0.43372101 0.4302966
## fractal_dimension_mean 0.45879816 0.34623388 0.1753254
## radius_se 0.28710317 0.38058463 0.5310623
## texture_se -0.09243935 -0.06895622 -0.1196375
## perimeter_se 0.34191945 0.41889882 0.5548972
## area_se 0.28325654 0.38510014 0.5381663
## smoothness_se -0.05555814 -0.05829839 -0.1020068
## compactness_se 0.67878035 0.63914670 0.4832083
## concavity_se 0.48485780 0.66256413 0.4404723
## concave.points_se 0.45288838 0.54959238 0.6024496
## symmetry_se 0.06025488 0.03711905 -0.0304134
## fractal_dimension_se 0.39015884 0.37997466 0.2152040
## radius_worst 0.47582004 0.57397471 0.7874239
## texture_worst 0.36083234 0.36836561 0.3597546
## perimeter_worst 0.52940769 0.61834408 0.8163221
## area_worst 0.43829628 0.54333053 0.7474188
## smoothness_worst 0.56818652 0.51852329 0.5476909
## compactness_worst 1.00000000 0.89226090 0.8010804
## concavity_worst 0.89226090 1.00000000 0.8554339
## concave.points_worst 0.80108036 0.85543386 1.0000000
## symmetry_worst 0.61444050 0.53251973 0.5025285
## fractal_dimension_worst 0.81045486 0.68651092 0.5111141
## symmetry_worst fractal_dimension_worst
## radius_mean 0.16395333 0.007065886
## texture_mean 0.10500791 0.119205351
## perimeter_mean 0.18911504 0.051018530
## area_mean 0.14356991 0.003737597
## smoothness_mean 0.39430948 0.499316369
## compactness_mean 0.51022343 0.687382323
## concavity_mean 0.40946413 0.514929891
## concave.points_mean 0.37574415 0.368661134
## symmetry_mean 0.69982580 0.438413498
## fractal_dimension_mean 0.33401868 0.767296779
## radius_se 0.09454283 0.049559432
## texture_se -0.12821476 -0.045654569
## perimeter_se 0.10993043 0.085432572
## area_se 0.07412629 0.017539295
## smoothness_se -0.10734210 0.101480315
## compactness_se 0.27787843 0.590972763
## concavity_se 0.19778782 0.439329269
## concave.points_se 0.14311567 0.310654551
## symmetry_se 0.38940248 0.078079476
## fractal_dimension_se 0.11109396 0.591328066
## radius_worst 0.24352920 0.093491979
## texture_worst 0.23302746 0.219122425
## perimeter_worst 0.26949277 0.138956862
## area_worst 0.20914551 0.079647034
## smoothness_worst 0.49383833 0.617624192
## compactness_worst 0.61444050 0.810454856
## concavity_worst 0.53251973 0.686510921
## concave.points_worst 0.50252849 0.511114146
## symmetry_worst 1.00000000 0.537848206
## fractal_dimension_worst 0.53784821 1.000000000DPA_CorrelationTest <- cor.mtest(DPA.Predictors.Numeric,
method = "pearson",
conf.level = 0.95)
corrplot(cor(DPA.Predictors.Numeric,
method = "pearson",
use="pairwise.complete.obs"),
method = "circle",
type = "upper",
order = "original",
tl.col = "black",
tl.cex = 0.75,
tl.srt = 90,
sig.level = 0.05,
p.mat = DPA_CorrelationTest$p,
insig = "blank")
corrplot(cor(DPA.Predictors.Numeric,
method = "pearson",
use="pairwise.complete.obs"),
method = "number",
type = "upper",
order = "original",
tl.col = "black",
tl.cex = 0.75,
tl.srt = 90,
sig.level = 0.05,
number.cex = 0.65,
p.mat = DPA_CorrelationTest$p,
insig = "blank")
##################################
# Identifying the highly correlated variables
##################################
(DPA_HighlyCorrelatedCount <- sum(abs(DPA_Correlation[upper.tri(DPA_Correlation)])>0.95))## [1] 15if (DPA_HighlyCorrelatedCount == 0) {
print("No highly correlated predictors noted.")
} else {
print(paste0("High correlation observed for ",
(DPA_HighlyCorrelatedCount),
" pairs of numeric variable(s) with Correlation.Coefficient>0.95."))
(DPA_HighlyCorrelatedPairs <- corr_cross(DPA.Predictors.Numeric,
max_pvalue = 0.05,
top = DPA_HighlyCorrelatedCount,
rm.na = TRUE,
grid = FALSE
))
}## [1] "High correlation observed for 15 pairs of numeric variable(s) with Correlation.Coefficient>0.95."
if (DPA_HighlyCorrelatedCount > 0) {
DPA_HighlyCorrelated <- findCorrelation(DPA_Correlation, cutoff = 0.95)
(DPA_HighlyCorrelatedForRemoval <- length(DPA_HighlyCorrelated))
print(paste0("High correlation can be resolved by removing ",
(DPA_HighlyCorrelatedForRemoval),
" numeric variable(s)."))
for (j in 1:DPA_HighlyCorrelatedForRemoval) {
DPA_HighlyCorrelatedRemovedVariable <- colnames(DPA.Predictors.Numeric)[DPA_HighlyCorrelated[j]]
print(paste0("Variable ",
j,
" for removal: ",
DPA_HighlyCorrelatedRemovedVariable))
}
}## [1] "High correlation can be resolved by removing 7 numeric variable(s)."
## [1] "Variable 1 for removal: perimeter_worst"
## [1] "Variable 2 for removal: radius_worst"
## [1] "Variable 3 for removal: perimeter_mean"
## [1] "Variable 4 for removal: area_worst"
## [1] "Variable 5 for removal: radius_mean"
## [1] "Variable 6 for removal: perimeter_se"
## [1] "Variable 7 for removal: area_se"1.3.4 Linear Dependency
[A] No linear dependencies noted for any subset of numeric variables using the preprocessing summary from the caret package applying the findLinearCombos method which utilizes the QR decomposition of a matrix to enumerate sets of linear combinations (if they exist).
Code Chunk | Output
##################################
# Linear Dependencies
##################################
##################################
# Finding linear dependencies
##################################
DPA_LinearlyDependent <- findLinearCombos(DPA.Predictors.Numeric)
##################################
# Identifying the linearly dependent variables
##################################
DPA_LinearlyDependent <- findLinearCombos(DPA.Predictors.Numeric)
(DPA_LinearlyDependentCount <- length(DPA_LinearlyDependent$linearCombos))## [1] 0if (DPA_LinearlyDependentCount == 0) {
print("No linearly dependent predictors noted.")
} else {
print(paste0("Linear dependency observed for ",
(DPA_LinearlyDependentCount),
" subset(s) of numeric variable(s)."))
for (i in 1:DPA_LinearlyDependentCount) {
DPA_LinearlyDependentSubset <- colnames(DPA.Predictors.Numeric)[DPA_LinearlyDependent$linearCombos[[i]]]
print(paste0("Linear dependent variable(s) for subset ",
i,
" include: ",
DPA_LinearlyDependentSubset))
}
}## [1] "No linearly dependent predictors noted."##################################
# Identifying the linearly dependent variables for removal
##################################
if (DPA_LinearlyDependentCount > 0) {
DPA_LinearlyDependent <- findLinearCombos(DPA.Predictors.Numeric)
DPA_LinearlyDependentForRemoval <- length(DPA_LinearlyDependent$remove)
print(paste0("Linear dependency can be resolved by removing ",
(DPA_LinearlyDependentForRemoval),
" numeric variable(s)."))
for (j in 1:DPA_LinearlyDependentForRemoval) {
DPA_LinearlyDependentRemovedVariable <- colnames(DPA.Predictors.Numeric)[DPA_LinearlyDependent$remove[j]]
print(paste0("Variable ",
j,
" for removal: ",
DPA_LinearlyDependentRemovedVariable))
}
}1.3.5 Distributional Shape
[A] Shape transformation was applied to improve against skewness and minimize outliers for data distribution stability using the BoxCox method from the caret package which transforms the distributional shape for predictors with strictly positive values.
[B] Skewness measurements were improved for most except for 1 predictor with Skewness>3.
[B.1] concavity_se = +5.10
[C] Outliers were minimized for most except for 5 predictors which did not show any improvement even after shape transformation as noted using the IQR criterion.
[C.1] concavity_mean = 36
[C.2] concave.points_mean = 20
[C.3] concavity_se = 44
[C.4] concave.points_se = 38
[C.5] concavity_worst = 24
Code Chunk | Output
##################################
# Shape Transformation
##################################
##################################
# Applying a Box-Cox transformation
##################################
DPA_BoxCox <- preProcess(DPA.Predictors.Numeric, method = c("BoxCox"))
DPA_BoxCoxTransformed <- predict(DPA_BoxCox, DPA.Predictors.Numeric)
for (i in 1:ncol(DPA_BoxCoxTransformed)) {
Median <- format(round(median(DPA_BoxCoxTransformed[,i],na.rm = TRUE),2), nsmall=2)
Mean <- format(round(mean(DPA_BoxCoxTransformed[,i],na.rm = TRUE),2), nsmall=2)
Skewness <- format(round(skewness(DPA_BoxCoxTransformed[,i],na.rm = TRUE),2), nsmall=2)
print(
ggplot(DPA_BoxCoxTransformed, aes(x=DPA_BoxCoxTransformed[,i])) +
geom_histogram(binwidth=1,color="black", fill="white") +
geom_vline(aes(xintercept=mean(DPA_BoxCoxTransformed[,i])),
color="blue", size=1) +
geom_vline(aes(xintercept=median(DPA_BoxCoxTransformed[,i])),
color="red", size=1) +
theme_bw() +
ylab("Count") +
xlab(names(DPA_BoxCoxTransformed)[i]) +
labs(title=names(DPA_BoxCoxTransformed)[i],
subtitle=paste0("Median = ", Median,
", Mean = ", Mean,
", Skewness = ", Skewness)))
}
##################################
# Identifying outliers for the numeric predictors
##################################
OutlierCountList <- c()
for (i in 1:ncol(DPA_BoxCoxTransformed)) {
Outliers <- boxplot.stats(DPA_BoxCoxTransformed[,i])$out
OutlierCount <- length(Outliers)
OutlierCountList <- append(OutlierCountList,OutlierCount)
OutlierIndices <- which(DPA_BoxCoxTransformed[,i] %in% c(Outliers))
print(
ggplot(DPA_BoxCoxTransformed, aes(x=DPA_BoxCoxTransformed[,i])) +
geom_boxplot() +
theme_bw() +
theme(axis.text.y=element_blank(),
axis.ticks.y=element_blank()) +
xlab(names(DPA_BoxCoxTransformed)[i]) +
labs(title=names(DPA_BoxCoxTransformed)[i],
subtitle=paste0(OutlierCount, " Outlier(s) Detected")))
}
DPA_BoxCoxTransformed$diagnosis <- DPA[,c("diagnosis")]1.3.6 Pre-Processed Dataset
[A] A total of 12 predictors were removed prior to data exploration and modelling due to issues identified during data preprocessing.
[A.1] concavity_se = Low variance and high skewness
[A.2] perimeter_worst = High correlation with radius_worst, area_worst, perimeter_mean, radius_mean and area_mean
[A.3] radius_worst = High correlation with perimeter_worst, area_worst, radius_mean, perimeter_mean and area_mean
[A.4] perimeter_mean = High correlation with radius_mean, area_mean, perimeter_worst and radius_worst
[A.5] area_worst = High correlation with radius_worst, perimeter_worst and area_mean
[A.6] radius_mean = High correlation with perimeter_mean, area_mean, radius_worst and perimeter_worst .
[A.7] perimeter_se = High correlation with radius_se
[A.8] area_se = High correlation with radius_se
[A.9] concavity_mean = High outlier count even after shape transformation
[A.10] concave.points_mean = High outlier count even after shape transformation
[A.11] concave.points_se = High outlier count even after shape transformation
[A.12] concavity_worst = High outlier count even after shape transformation
[B] The preprocessed tabular dataset was comprised of 1138 observations and 19 variables (including 1 response and 18 predictors).
[B.1] 1138 rows (observations)
[B.2] 19 columns (variables)
[B.2.1] 1/19 response = diagnosis (factor)
[B.2.2] 18/19 predictors = 18/18 numeric
[B.2.2.1] texture_mean (numeric)
[B.2.2.2] area_mean (numeric)
[B.2.2.3] smoothness_mean (numeric)
[B.2.2.4] compactness_mean (numeric)
[B.2.2.5] symmetry_mean (numeric)
[B.2.2.6] fractal_dimension_mean (numeric)
[B.2.2.7] radius_se (numeric)
[B.2.2.8] texture_se (numeric)
[B.2.2.9] smoothness_se (numeric)
[B.2.2.10] compactness_se (numeric)
[B.2.2.11] symmetry_se (numeric)
[B.2.2.12] fractal_dimension_se (numeric)
[B.2.2.13] texture_worst (numeric)
[B.2.2.14] smoothness_worst (numeric)
[B.2.2.15] compactness_worst (numeric)
[B.2.2.16] concave.points_worst (numeric)
[B.2.2.17] symmetry_worst (numeric)
[B.2.2.18] fractal_dimension_worst (numeric)
Code Chunk | Output
##################################
# Creating the pre-modelling
# train set
##################################
PMA <- DPA_BoxCoxTransformed[,!names(DPA_BoxCoxTransformed) %in% c("concavity_se",
"perimeter_worst",
"radius_worst",
"perimeter_mean",
"area_worst",
"radius_mean",
"perimeter_se",
"area_se",
"concavity_mean",
"concave.points_mean",
"concave.points_se",
"concavity_worst")]
##################################
# Gathering descriptive statistics
##################################
(PMA_Skimmed <- skim(PMA))| Name | PMA |
| Number of rows | 1138 |
| Number of columns | 19 |
| _______________________ | |
| Column type frequency: | |
| factor | 1 |
| numeric | 18 |
| ________________________ | |
| Group variables | None |
Variable type: factor
| skim_variable | n_missing | complete_rate | ordered | n_unique | top_counts |
|---|---|---|---|---|---|
| diagnosis | 0 | 1 | FALSE | 2 | B: 714, M: 424 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| texture_mean | 0 | 1 | 2.94 | 0.22 | 2.27 | 2.78 | 2.94 | 3.08 | 3.67 | ▁▅▇▃▁ |
| area_mean | 0 | 1 | 6.36 | 0.48 | 4.97 | 6.04 | 6.31 | 6.66 | 7.82 | ▁▅▇▃▁ |
| smoothness_mean | 0 | 1 | -2.35 | 0.15 | -2.94 | -2.45 | -2.34 | -2.25 | -1.81 | ▁▂▇▃▁ |
| compactness_mean | 0 | 1 | -2.38 | 0.49 | -3.94 | -2.73 | -2.38 | -2.04 | -1.06 | ▁▅▇▇▂ |
| symmetry_mean | 0 | 1 | -2.26 | 0.25 | -3.20 | -2.42 | -2.25 | -2.10 | -1.43 | ▁▂▇▅▁ |
| fractal_dimension_mean | 0 | 1 | -130.58 | 26.03 | -199.82 | -149.68 | -131.52 | -113.87 | -52.16 | ▁▆▇▃▁ |
| radius_se | 0 | 1 | -1.42 | 0.81 | -3.51 | -1.98 | -1.42 | -0.86 | 0.86 | ▁▆▇▅▁ |
| texture_se | 0 | 1 | 0.10 | 0.43 | -1.02 | -0.18 | 0.10 | 0.39 | 1.59 | ▂▆▇▂▁ |
| smoothness_se | 0 | 1 | -11.83 | 1.66 | -19.20 | -12.84 | -11.85 | -10.78 | -6.11 | ▁▂▇▅▁ |
| compactness_se | 0 | 1 | -3.88 | 0.65 | -6.10 | -4.34 | -3.89 | -3.43 | -2.00 | ▁▃▇▆▁ |
| symmetry_se | 0 | 1 | -16.51 | 3.52 | -28.80 | -18.91 | -16.46 | -14.16 | -5.98 | ▁▃▇▅▁ |
| fractal_dimension_se | 0 | 1 | -15.48 | 2.88 | -24.04 | -17.43 | -15.37 | -13.46 | -6.23 | ▁▅▇▃▁ |
| texture_worst | 0 | 1 | 4.53 | 0.46 | 3.22 | 4.20 | 4.55 | 4.85 | 5.91 | ▁▅▇▅▁ |
| smoothness_worst | 0 | 1 | -1.52 | 0.09 | -1.82 | -1.58 | -1.52 | -1.46 | -1.21 | ▁▃▇▃▁ |
| compactness_worst | 0 | 1 | -1.55 | 0.62 | -3.60 | -1.92 | -1.55 | -1.08 | 0.06 | ▁▃▇▆▁ |
| concave.points_worst | 0 | 1 | 0.11 | 0.07 | 0.00 | 0.06 | 0.10 | 0.16 | 0.29 | ▅▇▅▃▁ |
| symmetry_worst | 0 | 1 | -1.77 | 0.37 | -3.06 | -2.00 | -1.76 | -1.55 | -0.45 | ▁▃▇▂▁ |
| fractal_dimension_worst | 0 | 1 | -19.62 | 4.79 | -32.59 | -22.99 | -19.73 | -16.32 | -5.17 | ▁▅▇▃▁ |
1.4 Data Exploration
[A] Individual predictors which demonstrated excellent discrimination between diagnosis=M and diagnosis=B in terms of the area under the receiver operating characteristics curve (AUROC>0.80) are as follows:
[A.1] concave.points_worst = 0.97
[A.2] area_mean = 0.94
[A.3] radius_se = 0.87
[A.4] compactness_mean = 0.86
[A.5] compactness_worst = 0.86
[B] To allow a better comparison of the ensemble learning methods, only predictors which demonstrated fair discrimination between diagnosis=M and diagnosis=B in terms of the area under the receiver operating characteristics curve (0.70<AUROC<0.80) were selected to proceed with the modelling process, enumerated as follows:
[B.1] texture_worst = 0.78
[B.2] texture_mean = 0.77
[B.3] smoothness_worst = 0.75
[B.4] symmetry_worst = 0.74
[B.5] compactness_se = 0.73
[B.6] smoothness_mean = 0.72
[C] Using an 80-20 ratio, train and test sets were created from the preliminary dataset for the subsequent analysis and modelling steps:
[C.1] Training Set = 912 observations
[C.2] Test Set = 226 observations
Code Chunk | Output
##################################
# Loading dataset
##################################
DPA <- PMA
##################################
# Listing all predictors
##################################
DPA.Predictors <- DPA[,!names(DPA) %in% c("diagnosis")]
##################################
# Listing all numeric predictors
##################################
DPA.Predictors.Numeric <- DPA.Predictors[,sapply(DPA.Predictors, is.numeric)]
ncol(DPA.Predictors.Numeric)## [1] 18##################################
# Converting response variable data type to factor
##################################
DPA$diagnosis <- as.factor(DPA$diagnosis)
length(levels(DPA$diagnosis))## [1] 2##################################
# Formulating the box plots
##################################
featurePlot(x = DPA.Predictors.Numeric,
y = DPA$diagnosis,
plot = "box",
scales = list(x = list(relation="free", rot = 90),
y = list(relation="free")),
adjust = 1.5,
pch = "|",
layout = c(6, 3))
##################################
# Obtaining the AUROC
##################################
AUROC <- filterVarImp(x = DPA.Predictors.Numeric,
y = DPA$diagnosis)
##################################
# Formulating the summary table
##################################
AUROC_Summary <- AUROC
AUROC_Summary$Predictor <- rownames(AUROC)
names(AUROC_Summary)[1] <- "AUROC"
AUROC_Summary$Metric <- rep("AUROC",nrow(AUROC))
AUROC_Summary[order(AUROC_Summary$AUROC, decreasing=TRUE),] ## AUROC M Predictor Metric
## concave.points_worst 0.9667037 0.9667037 concave.points_worst AUROC
## area_mean 0.9383159 0.9383159 area_mean AUROC
## radius_se 0.8683341 0.8683341 radius_se AUROC
## compactness_mean 0.8637823 0.8637823 compactness_mean AUROC
## compactness_worst 0.8623025 0.8623025 compactness_worst AUROC
## texture_worst 0.7846308 0.7846308 texture_worst AUROC
## texture_mean 0.7758245 0.7758245 texture_mean AUROC
## smoothness_worst 0.7540563 0.7540563 smoothness_worst AUROC
## symmetry_worst 0.7369391 0.7369391 symmetry_worst AUROC
## compactness_se 0.7272805 0.7272805 compactness_se AUROC
## smoothness_mean 0.7220416 0.7220416 smoothness_mean AUROC
## symmetry_mean 0.6985624 0.6985624 symmetry_mean AUROC
## fractal_dimension_worst 0.6859706 0.6859706 fractal_dimension_worst AUROC
## fractal_dimension_se 0.6203028 0.6203028 fractal_dimension_se AUROC
## symmetry_se 0.5551107 0.5551107 symmetry_se AUROC
## smoothness_se 0.5311625 0.5311625 smoothness_se AUROC
## fractal_dimension_mean 0.5154656 0.5154656 fractal_dimension_mean AUROC
## texture_se 0.5115943 0.5115943 texture_se AUROC##################################
# Exploring predictor performance
##################################
dotplot(Predictor ~ AUROC | Metric,
AUROC_Summary,
origin = 0,
type = c("p", "h"),
pch = 16,
cex = 2,
alpha = 0.45,
prepanel = function(x, y) {
list(ylim = levels(reorder(y, x)))
},
panel = function(x, y, ...) {
panel.dotplot(x, reorder(y, x), ...)
})
##################################
# Creating the pre-modelling dataset
# into the train and test sets
##################################
DPA <- DPA[,colnames(DPA) %in% c("diagnosis",
"texture_worst",
"texture_mean",
"smoothness_worst",
"symmetry_worst",
"compactness_se",
"smoothness_mean")]
set.seed(12345678)
MA_Train_Index <- createDataPartition(DPA$diagnosis,p=0.8)[[1]]
MA_Train <- DPA[ MA_Train_Index, ]
MA_Test <- DPA[-MA_Train_Index, ]
dim(MA_Train)## [1] 912 7dim(MA_Test)## [1] 226 71.5 Model Boosting
Model Boosting is an ensemble approach which applies a sequential algorithm that makes predictions for a defined set of rounds on the entire training sample and iteratively improves the performance using the information from the prior round’s prediction accuracy. Boosted models build a weak learner that has low predictive accuracy - possessing low variance and high bias. As boosted models go through the process of sequentially improving previous learners, the overall model is able to slowly reduce the bias at each step with minimal variance increase. The final model tends to have sufficiently low bias and variance.
1.5.1 Adaptive Boosting (MBS_AB)
Adaptive Boosting applies an adaptive reweighting and combining approach which is initiated using a learner based on the overall mean or the log-odds of the target variable. For each boosting iteration, a weak learner (using a decision stump - a single level decision tree) is fitted to the training data applying equal sample weights. With the predictions obtained, the weighted error of the base learner is determined and used to calculate the base learner’s weight in the ensemble. The sample weights are recomputed, putting more weight on difficult to classify instances and less on those already handled well. The process is repeated for a defined number of cycles where new weak learners are added sequentially that focus their training on the more difficult patterns. The final ensemble prediction is determined as the weighted sum of the individual predictions from all boosting iterations.
[A] The adaptive boosting model was implemented through the adabag package.
[B] The model contains 3 hyperparameters:
[B.1] mfinal = the number of iterations for which boosting is run or the number of trees to use made to vary across a range of values equal to 50 to 100
[B.2] maxdepth = maximum depth of the trees made to vary across a range of values equal to 4 to 6
[B.3] coeflearn = coefficient learning method held constant using the setting equal to Breiman
[C] The 5-cycle repeated 5-fold cross-validated model performance of the final model is summarized as follows:
[C.1] Final model configuration involves mfinal=100, maxdepth=6 and coeflearn=Breiman
[C.2] AUROC = 0.97109
[D] The model allows for ranking of predictors in terms of variable importance. The top-performing predictors in the model are as follows:
[D.1] symmetry_worst (numeric)
[D.2] compactness_se (numeric)
[D.3] smoothness_worst (numeric)
[D.4] texture_mean (numeric)
[D.5] smoothness_mean (numeric)
[E] The independent test model performance of the final model is summarized as follows:
[E.1] AUROC = 0.99564
Code Chunk | Output
##################################
# Setting the cross validation process
# using the Repeated K-Fold
##################################
set.seed(12345678)
RKFold_Control <- trainControl(method="repeatedcv",
summaryFunction = twoClassSummary,
number=5,
repeats=5,
classProbs = TRUE)
##################################
# Setting the conditions
# for hyperparameter tuning
##################################
AB_Grid = expand.grid(mfinal = c(50,100,100),
maxdepth = c(4,5,6),
coeflearn = "Breiman")
##################################
# Running the adaptive boosting model
# by setting the caret method to 'AdaBoost.M1'
##################################
set.seed(12345678)
MBS_AB_Tune <- train(x = MA_Train[,!names(MA_Train) %in% c("diagnosis")],
y = MA_Train$diagnosis,
method = "AdaBoost.M1",
tuneGrid = AB_Grid,
metric = "ROC",
trControl = RKFold_Control)
##################################
# Reporting the cross-validation results
# for the train set
##################################
MBS_AB_Tune## AdaBoost.M1
##
## 912 samples
## 6 predictor
## 2 classes: 'B', 'M'
##
## No pre-processing
## Resampling: Cross-Validated (5 fold, repeated 5 times)
## Summary of sample sizes: 729, 729, 730, 730, 730, 730, ...
## Resampling results across tuning parameters:
##
## maxdepth mfinal ROC Sens Spec
## 4 50 0.9539212 0.9454523 0.8682353
## 4 100 0.9600671 0.9506972 0.9017647
## 5 50 0.9624131 0.9531137 0.9011765
## 5 100 0.9671294 0.9506636 0.9000000
## 6 50 0.9666688 0.9496323 0.8982353
## 6 100 0.9710985 0.9527750 0.8964706
##
## Tuning parameter 'coeflearn' was held constant at a value of Breiman
## ROC was used to select the optimal model using the largest value.
## The final values used for the model were mfinal = 100, maxdepth = 6
## and coeflearn = Breiman.MBS_AB_Tune$finalModel## $formula
## .outcome ~ .
## <environment: 0x0000024d60f9b4c0>
##
## $trees
## $trees[[1]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 331 B (0.637061404 0.362938596)
## 2) smoothness_worst< -1.499656 536 100 B (0.813432836 0.186567164)
## 4) texture_mean< 2.963467 314 23 B (0.926751592 0.073248408)
## 8) symmetry_worst>=-2.923662 311 20 B (0.935691318 0.064308682)
## 16) compactness_se< -3.711798 242 7 B (0.971074380 0.028925620)
## 32) texture_mean< 2.874407 167 1 B (0.994011976 0.005988024)
## 64) compactness_se< -4.173143 126 0 B (1.000000000 0.000000000) *
## 65) compactness_se>=-4.173143 41 1 B (0.975609756 0.024390244) *
## 33) texture_mean>=2.874407 75 6 B (0.920000000 0.080000000)
## 66) texture_mean>=2.879477 73 4 B (0.945205479 0.054794521) *
## 67) texture_mean< 2.879477 2 0 M (0.000000000 1.000000000) *
## 17) compactness_se>=-3.711798 69 13 B (0.811594203 0.188405797)
## 34) compactness_se>=-3.48221 50 0 B (1.000000000 0.000000000) *
## 35) compactness_se< -3.48221 19 6 M (0.315789474 0.684210526)
## 70) texture_worst< 3.888609 3 0 B (1.000000000 0.000000000) *
## 71) texture_worst>=3.888609 16 3 M (0.187500000 0.812500000) *
## 9) symmetry_worst< -2.923662 3 0 M (0.000000000 1.000000000) *
## 5) texture_mean>=2.963467 222 77 B (0.653153153 0.346846847)
## 10) smoothness_mean< -2.488676 72 8 B (0.888888889 0.111111111)
## 20) texture_mean>=2.971159 68 4 B (0.941176471 0.058823529)
## 40) symmetry_worst< -1.667161 62 1 B (0.983870968 0.016129032)
## 80) symmetry_worst< -1.695215 56 0 B (1.000000000 0.000000000) *
## 81) symmetry_worst>=-1.695215 6 1 B (0.833333333 0.166666667) *
## 41) symmetry_worst>=-1.667161 6 3 B (0.500000000 0.500000000)
## 82) texture_mean>=3.135016 3 0 B (1.000000000 0.000000000) *
## 83) texture_mean< 3.135016 3 0 M (0.000000000 1.000000000) *
## 21) texture_mean< 2.971159 4 0 M (0.000000000 1.000000000) *
## 11) smoothness_mean>=-2.488676 150 69 B (0.540000000 0.460000000)
## 22) symmetry_worst< -1.527595 128 50 B (0.609375000 0.390625000)
## 44) texture_mean< 3.096482 64 13 B (0.796875000 0.203125000)
## 88) smoothness_mean< -2.411294 23 0 B (1.000000000 0.000000000) *
## 89) smoothness_mean>=-2.411294 41 13 B (0.682926829 0.317073171) *
## 45) texture_mean>=3.096482 64 27 M (0.421875000 0.578125000)
## 90) smoothness_mean< -2.471478 6 0 B (1.000000000 0.000000000) *
## 91) smoothness_mean>=-2.471478 58 21 M (0.362068966 0.637931034) *
## 23) symmetry_worst>=-1.527595 22 3 M (0.136363636 0.863636364)
## 46) smoothness_worst>=-1.506135 3 0 B (1.000000000 0.000000000) *
## 47) smoothness_worst< -1.506135 19 0 M (0.000000000 1.000000000) *
## 3) smoothness_worst>=-1.499656 376 145 M (0.385638298 0.614361702)
## 6) texture_mean< 2.927988 168 55 B (0.672619048 0.327380952)
## 12) symmetry_worst< -1.611674 88 4 B (0.954545455 0.045454545)
## 24) smoothness_worst< -1.427424 78 1 B (0.987179487 0.012820513)
## 48) smoothness_worst>=-1.480531 54 0 B (1.000000000 0.000000000) *
## 49) smoothness_worst< -1.480531 24 1 B (0.958333333 0.041666667)
## 98) smoothness_worst< -1.482701 23 0 B (1.000000000 0.000000000) *
## 99) smoothness_worst>=-1.482701 1 0 M (0.000000000 1.000000000) *
## 25) smoothness_worst>=-1.427424 10 3 B (0.700000000 0.300000000)
## 50) texture_mean< 2.84692 7 0 B (1.000000000 0.000000000) *
## 51) texture_mean>=2.84692 3 0 M (0.000000000 1.000000000) *
## 13) symmetry_worst>=-1.611674 80 29 M (0.362500000 0.637500000)
## 26) compactness_se< -3.646366 36 12 B (0.666666667 0.333333333)
## 52) texture_worst< 4.517878 26 3 B (0.884615385 0.115384615)
## 104) smoothness_mean>=-2.402211 23 0 B (1.000000000 0.000000000) *
## 105) smoothness_mean< -2.402211 3 0 M (0.000000000 1.000000000) *
## 53) texture_worst>=4.517878 10 1 M (0.100000000 0.900000000)
## 106) smoothness_mean< -2.397771 1 0 B (1.000000000 0.000000000) *
## 107) smoothness_mean>=-2.397771 9 0 M (0.000000000 1.000000000) *
## 27) compactness_se>=-3.646366 44 5 M (0.113636364 0.886363636)
## 54) compactness_se>=-2.646661 4 0 B (1.000000000 0.000000000) *
## 55) compactness_se< -2.646661 40 1 M (0.025000000 0.975000000)
## 110) texture_mean< 2.77286 10 1 M (0.100000000 0.900000000) *
## 111) texture_mean>=2.77286 30 0 M (0.000000000 1.000000000) *
## 7) texture_mean>=2.927988 208 32 M (0.153846154 0.846153846)
## 14) compactness_se< -4.025757 40 15 M (0.375000000 0.625000000)
## 28) smoothness_mean>=-2.30109 17 4 B (0.764705882 0.235294118)
## 56) smoothness_mean< -2.222419 13 0 B (1.000000000 0.000000000) *
## 57) smoothness_mean>=-2.222419 4 0 M (0.000000000 1.000000000) *
## 29) smoothness_mean< -2.30109 23 2 M (0.086956522 0.913043478)
## 58) compactness_se>=-4.064037 1 0 B (1.000000000 0.000000000) *
## 59) compactness_se< -4.064037 22 1 M (0.045454545 0.954545455)
## 118) texture_mean>=3.182435 2 1 B (0.500000000 0.500000000) *
## 119) texture_mean< 3.182435 20 0 M (0.000000000 1.000000000) *
## 15) compactness_se>=-4.025757 168 17 M (0.101190476 0.898809524)
## 30) symmetry_worst< -2.179978 12 5 B (0.583333333 0.416666667)
## 60) smoothness_mean< -2.272702 8 1 B (0.875000000 0.125000000)
## 120) texture_mean>=3.061196 7 0 B (1.000000000 0.000000000) *
## 121) texture_mean< 3.061196 1 0 M (0.000000000 1.000000000) *
## 61) smoothness_mean>=-2.272702 4 0 M (0.000000000 1.000000000) *
## 31) symmetry_worst>=-2.179978 156 10 M (0.064102564 0.935897436)
## 62) texture_worst< 4.414433 1 0 B (1.000000000 0.000000000) *
## 63) texture_worst>=4.414433 155 9 M (0.058064516 0.941935484)
## 126) smoothness_mean>=-2.099273 18 4 M (0.222222222 0.777777778) *
## 127) smoothness_mean< -2.099273 137 5 M (0.036496350 0.963503650) *
##
## $trees[[2]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 338 B (0.629385965 0.370614035)
## 2) texture_worst< 4.577679 482 91 B (0.811203320 0.188796680)
## 4) texture_mean< 2.96681 410 58 B (0.858536585 0.141463415)
## 8) symmetry_worst< -1.294443 393 46 B (0.882951654 0.117048346)
## 16) texture_worst< 4.262771 222 9 B (0.959459459 0.040540541)
## 32) texture_mean< 2.909882 221 8 B (0.963800905 0.036199095)
## 64) compactness_se< -3.957552 133 0 B (1.000000000 0.000000000) *
## 65) compactness_se>=-3.957552 88 8 B (0.909090909 0.090909091) *
## 33) texture_mean>=2.909882 1 0 M (0.000000000 1.000000000) *
## 17) texture_worst>=4.262771 171 37 B (0.783625731 0.216374269)
## 34) symmetry_worst>=-2.49184 164 31 B (0.810975610 0.189024390)
## 68) smoothness_worst< -1.55307 59 2 B (0.966101695 0.033898305) *
## 69) smoothness_worst>=-1.55307 105 29 B (0.723809524 0.276190476) *
## 35) symmetry_worst< -2.49184 7 1 M (0.142857143 0.857142857)
## 70) texture_mean< 2.855865 1 0 B (1.000000000 0.000000000) *
## 71) texture_mean>=2.855865 6 0 M (0.000000000 1.000000000) *
## 9) symmetry_worst>=-1.294443 17 5 M (0.294117647 0.705882353)
## 18) compactness_se>=-2.588521 4 0 B (1.000000000 0.000000000) *
## 19) compactness_se< -2.588521 13 1 M (0.076923077 0.923076923)
## 38) smoothness_mean< -2.302887 1 0 B (1.000000000 0.000000000) *
## 39) smoothness_mean>=-2.302887 12 0 M (0.000000000 1.000000000) *
## 5) texture_mean>=2.96681 72 33 B (0.541666667 0.458333333)
## 10) smoothness_worst< -1.481325 56 18 B (0.678571429 0.321428571)
## 20) texture_mean>=2.996231 31 4 B (0.870967742 0.129032258)
## 40) smoothness_worst>=-1.678162 29 2 B (0.931034483 0.068965517)
## 80) symmetry_worst< -1.516281 28 1 B (0.964285714 0.035714286) *
## 81) symmetry_worst>=-1.516281 1 0 M (0.000000000 1.000000000) *
## 41) smoothness_worst< -1.678162 2 0 M (0.000000000 1.000000000) *
## 21) texture_mean< 2.996231 25 11 M (0.440000000 0.560000000)
## 42) texture_worst< 4.357182 7 0 B (1.000000000 0.000000000) *
## 43) texture_worst>=4.357182 18 4 M (0.222222222 0.777777778)
## 86) texture_worst>=4.467472 2 0 B (1.000000000 0.000000000) *
## 87) texture_worst< 4.467472 16 2 M (0.125000000 0.875000000) *
## 11) smoothness_worst>=-1.481325 16 1 M (0.062500000 0.937500000)
## 22) compactness_se< -4.16079 1 0 B (1.000000000 0.000000000) *
## 23) compactness_se>=-4.16079 15 0 M (0.000000000 1.000000000) *
## 3) texture_worst>=4.577679 430 183 M (0.425581395 0.574418605)
## 6) smoothness_mean< -2.362601 195 64 B (0.671794872 0.328205128)
## 12) smoothness_worst< -1.60101 65 4 B (0.938461538 0.061538462)
## 24) symmetry_worst< -1.180749 63 2 B (0.968253968 0.031746032)
## 48) smoothness_mean< -2.4008 62 1 B (0.983870968 0.016129032)
## 96) symmetry_worst< -1.681012 57 0 B (1.000000000 0.000000000) *
## 97) symmetry_worst>=-1.681012 5 1 B (0.800000000 0.200000000) *
## 49) smoothness_mean>=-2.4008 1 0 M (0.000000000 1.000000000) *
## 25) symmetry_worst>=-1.180749 2 0 M (0.000000000 1.000000000) *
## 13) smoothness_worst>=-1.60101 130 60 B (0.538461538 0.461538462)
## 26) texture_worst>=4.646117 105 36 B (0.657142857 0.342857143)
## 52) symmetry_worst< -1.39888 96 27 B (0.718750000 0.281250000)
## 104) compactness_se>=-4.567426 89 20 B (0.775280899 0.224719101) *
## 105) compactness_se< -4.567426 7 0 M (0.000000000 1.000000000) *
## 53) symmetry_worst>=-1.39888 9 0 M (0.000000000 1.000000000) *
## 27) texture_worst< 4.646117 25 1 M (0.040000000 0.960000000)
## 54) compactness_se< -4.694501 1 0 B (1.000000000 0.000000000) *
## 55) compactness_se>=-4.694501 24 0 M (0.000000000 1.000000000) *
## 7) smoothness_mean>=-2.362601 235 52 M (0.221276596 0.778723404)
## 14) symmetry_worst< -1.659152 88 38 M (0.431818182 0.568181818)
## 28) texture_mean< 3.081899 42 12 B (0.714285714 0.285714286)
## 56) compactness_se< -3.644943 32 5 B (0.843750000 0.156250000)
## 112) smoothness_mean< -2.22335 25 1 B (0.960000000 0.040000000) *
## 113) smoothness_mean>=-2.22335 7 3 M (0.428571429 0.571428571) *
## 57) compactness_se>=-3.644943 10 3 M (0.300000000 0.700000000)
## 114) smoothness_mean>=-2.12394 3 0 B (1.000000000 0.000000000) *
## 115) smoothness_mean< -2.12394 7 0 M (0.000000000 1.000000000) *
## 29) texture_mean>=3.081899 46 8 M (0.173913043 0.826086957)
## 58) smoothness_mean>=-2.099273 4 0 B (1.000000000 0.000000000) *
## 59) smoothness_mean< -2.099273 42 4 M (0.095238095 0.904761905)
## 118) smoothness_worst< -1.550482 3 1 B (0.666666667 0.333333333) *
## 119) smoothness_worst>=-1.550482 39 2 M (0.051282051 0.948717949) *
## 15) symmetry_worst>=-1.659152 147 14 M (0.095238095 0.904761905)
## 30) smoothness_worst< -1.500466 34 11 M (0.323529412 0.676470588)
## 60) smoothness_worst>=-1.506135 7 0 B (1.000000000 0.000000000) *
## 61) smoothness_worst< -1.506135 27 4 M (0.148148148 0.851851852)
## 122) smoothness_mean< -2.336585 9 4 M (0.444444444 0.555555556) *
## 123) smoothness_mean>=-2.336585 18 0 M (0.000000000 1.000000000) *
## 31) smoothness_worst>=-1.500466 113 3 M (0.026548673 0.973451327)
## 62) texture_worst< 4.599763 2 0 B (1.000000000 0.000000000) *
## 63) texture_worst>=4.599763 111 1 M (0.009009009 0.990990991)
## 126) texture_worst< 4.682677 7 1 M (0.142857143 0.857142857) *
## 127) texture_worst>=4.682677 104 0 M (0.000000000 1.000000000) *
##
## $trees[[3]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 392 B (0.570175439 0.429824561)
## 2) smoothness_worst< -1.603315 161 10 B (0.937888199 0.062111801)
## 4) symmetry_worst< -1.165944 160 9 B (0.943750000 0.056250000)
## 8) texture_worst< 4.850312 133 4 B (0.969924812 0.030075188)
## 16) smoothness_worst>=-1.723213 119 1 B (0.991596639 0.008403361)
## 32) symmetry_worst< -1.804928 83 0 B (1.000000000 0.000000000) *
## 33) symmetry_worst>=-1.804928 36 1 B (0.972222222 0.027777778)
## 66) symmetry_worst>=-1.748651 35 0 B (1.000000000 0.000000000) *
## 67) symmetry_worst< -1.748651 1 0 M (0.000000000 1.000000000) *
## 17) smoothness_worst< -1.723213 14 3 B (0.785714286 0.214285714)
## 34) compactness_se< -3.013033 11 0 B (1.000000000 0.000000000) *
## 35) compactness_se>=-3.013033 3 0 M (0.000000000 1.000000000) *
## 9) texture_worst>=4.850312 27 5 B (0.814814815 0.185185185)
## 18) texture_mean>=2.992821 24 2 B (0.916666667 0.083333333)
## 36) texture_worst>=4.929933 22 0 B (1.000000000 0.000000000) *
## 37) texture_worst< 4.929933 2 0 M (0.000000000 1.000000000) *
## 19) texture_mean< 2.992821 3 0 M (0.000000000 1.000000000) *
## 5) symmetry_worst>=-1.165944 1 0 M (0.000000000 1.000000000) *
## 3) smoothness_worst>=-1.603315 751 369 M (0.491344874 0.508655126)
## 6) texture_worst< 4.275472 155 32 B (0.793548387 0.206451613)
## 12) symmetry_worst< -1.42974 132 15 B (0.886363636 0.113636364)
## 24) smoothness_mean< -2.074653 126 10 B (0.920634921 0.079365079)
## 48) smoothness_worst>=-1.602623 124 8 B (0.935483871 0.064516129)
## 96) compactness_se< -3.892047 80 0 B (1.000000000 0.000000000) *
## 97) compactness_se>=-3.892047 44 8 B (0.818181818 0.181818182) *
## 49) smoothness_worst< -1.602623 2 0 M (0.000000000 1.000000000) *
## 25) smoothness_mean>=-2.074653 6 1 M (0.166666667 0.833333333)
## 50) texture_mean>=2.515298 1 0 B (1.000000000 0.000000000) *
## 51) texture_mean< 2.515298 5 0 M (0.000000000 1.000000000) *
## 13) symmetry_worst>=-1.42974 23 6 M (0.260869565 0.739130435)
## 26) smoothness_worst< -1.505076 5 0 B (1.000000000 0.000000000) *
## 27) smoothness_worst>=-1.505076 18 1 M (0.055555556 0.944444444)
## 54) texture_mean< 2.622235 3 1 M (0.333333333 0.666666667)
## 108) texture_mean>=2.463446 1 0 B (1.000000000 0.000000000) *
## 109) texture_mean< 2.463446 2 0 M (0.000000000 1.000000000) *
## 55) texture_mean>=2.622235 15 0 M (0.000000000 1.000000000) *
## 7) texture_worst>=4.275472 596 246 M (0.412751678 0.587248322)
## 14) smoothness_mean< -2.416986 117 39 B (0.666666667 0.333333333)
## 28) smoothness_mean>=-2.467991 71 12 B (0.830985915 0.169014085)
## 56) symmetry_worst>=-1.984547 58 3 B (0.948275862 0.051724138)
## 112) compactness_se>=-4.650552 54 1 B (0.981481481 0.018518519) *
## 113) compactness_se< -4.650552 4 2 B (0.500000000 0.500000000) *
## 57) symmetry_worst< -1.984547 13 4 M (0.307692308 0.692307692)
## 114) texture_mean< 2.991795 4 0 B (1.000000000 0.000000000) *
## 115) texture_mean>=2.991795 9 0 M (0.000000000 1.000000000) *
## 29) smoothness_mean< -2.467991 46 19 M (0.413043478 0.586956522)
## 58) compactness_se< -4.356557 18 4 B (0.777777778 0.222222222)
## 116) texture_worst< 5.05366 14 0 B (1.000000000 0.000000000) *
## 117) texture_worst>=5.05366 4 0 M (0.000000000 1.000000000) *
## 59) compactness_se>=-4.356557 28 5 M (0.178571429 0.821428571)
## 118) texture_mean< 2.868712 2 0 B (1.000000000 0.000000000) *
## 119) texture_mean>=2.868712 26 3 M (0.115384615 0.884615385) *
## 15) smoothness_mean>=-2.416986 479 168 M (0.350730689 0.649269311)
## 30) symmetry_worst< -1.652093 263 126 M (0.479087452 0.520912548)
## 60) smoothness_mean>=-2.355934 155 56 B (0.638709677 0.361290323)
## 120) texture_mean< 3.214868 142 44 B (0.690140845 0.309859155) *
## 121) texture_mean>=3.214868 13 1 M (0.076923077 0.923076923) *
## 61) smoothness_mean< -2.355934 108 27 M (0.250000000 0.750000000)
## 122) smoothness_worst< -1.579002 11 0 B (1.000000000 0.000000000) *
## 123) smoothness_worst>=-1.579002 97 16 M (0.164948454 0.835051546) *
## 31) symmetry_worst>=-1.652093 216 42 M (0.194444444 0.805555556)
## 62) compactness_se< -4.002529 43 21 B (0.511627907 0.488372093)
## 124) symmetry_worst>=-1.539708 23 4 B (0.826086957 0.173913043) *
## 125) symmetry_worst< -1.539708 20 3 M (0.150000000 0.850000000) *
## 63) compactness_se>=-4.002529 173 20 M (0.115606936 0.884393064)
## 126) smoothness_mean< -2.216408 92 20 M (0.217391304 0.782608696) *
## 127) smoothness_mean>=-2.216408 81 0 M (0.000000000 1.000000000) *
##
## $trees[[4]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 414 B (0.546052632 0.453947368)
## 2) smoothness_worst< -1.556752 261 57 B (0.781609195 0.218390805)
## 4) compactness_se< -3.489046 203 31 B (0.847290640 0.152709360)
## 8) symmetry_worst>=-2.896033 198 26 B (0.868686869 0.131313131)
## 16) symmetry_worst< -1.863339 120 6 B (0.950000000 0.050000000)
## 32) smoothness_mean< -2.332092 108 1 B (0.990740741 0.009259259)
## 64) smoothness_worst< -1.558926 107 0 B (1.000000000 0.000000000) *
## 65) smoothness_worst>=-1.558926 1 0 M (0.000000000 1.000000000) *
## 33) smoothness_mean>=-2.332092 12 5 B (0.583333333 0.416666667)
## 66) texture_mean< 2.884144 5 0 B (1.000000000 0.000000000) *
## 67) texture_mean>=2.884144 7 2 M (0.285714286 0.714285714) *
## 17) symmetry_worst>=-1.863339 78 20 B (0.743589744 0.256410256)
## 34) symmetry_worst>=-1.859739 73 15 B (0.794520548 0.205479452)
## 68) texture_worst< 5.110945 65 10 B (0.846153846 0.153846154) *
## 69) texture_worst>=5.110945 8 3 M (0.375000000 0.625000000) *
## 35) symmetry_worst< -1.859739 5 0 M (0.000000000 1.000000000) *
## 9) symmetry_worst< -2.896033 5 0 M (0.000000000 1.000000000) *
## 5) compactness_se>=-3.489046 58 26 B (0.551724138 0.448275862)
## 10) texture_worst< 4.425081 25 3 B (0.880000000 0.120000000)
## 20) compactness_se>=-3.439472 22 0 B (1.000000000 0.000000000) *
## 21) compactness_se< -3.439472 3 0 M (0.000000000 1.000000000) *
## 11) texture_worst>=4.425081 33 10 M (0.303030303 0.696969697)
## 22) smoothness_worst< -1.647098 13 4 B (0.692307692 0.307692308)
## 44) compactness_se< -2.979429 8 0 B (1.000000000 0.000000000) *
## 45) compactness_se>=-2.979429 5 1 M (0.200000000 0.800000000)
## 90) texture_mean< 3.076827 1 0 B (1.000000000 0.000000000) *
## 91) texture_mean>=3.076827 4 0 M (0.000000000 1.000000000) *
## 23) smoothness_worst>=-1.647098 20 1 M (0.050000000 0.950000000)
## 46) smoothness_worst< -1.603323 3 1 M (0.333333333 0.666666667)
## 92) smoothness_mean< -2.421794 1 0 B (1.000000000 0.000000000) *
## 93) smoothness_mean>=-2.421794 2 0 M (0.000000000 1.000000000) *
## 47) smoothness_worst>=-1.603323 17 0 M (0.000000000 1.000000000) *
## 3) smoothness_worst>=-1.556752 651 294 M (0.451612903 0.548387097)
## 6) texture_mean< 2.810904 136 37 B (0.727941176 0.272058824)
## 12) smoothness_mean< -2.074653 119 23 B (0.806722689 0.193277311)
## 24) symmetry_worst< -1.180109 113 17 B (0.849557522 0.150442478)
## 48) smoothness_worst>=-1.54469 106 12 B (0.886792453 0.113207547)
## 96) texture_mean< 2.739547 61 0 B (1.000000000 0.000000000) *
## 97) texture_mean>=2.739547 45 12 B (0.733333333 0.266666667) *
## 49) smoothness_worst< -1.54469 7 2 M (0.285714286 0.714285714)
## 98) texture_mean< 2.679131 2 0 B (1.000000000 0.000000000) *
## 99) texture_mean>=2.679131 5 0 M (0.000000000 1.000000000) *
## 25) symmetry_worst>=-1.180109 6 0 M (0.000000000 1.000000000) *
## 13) smoothness_mean>=-2.074653 17 3 M (0.176470588 0.823529412)
## 26) symmetry_worst>=-1.411591 6 3 B (0.500000000 0.500000000)
## 52) texture_mean< 2.692775 3 0 B (1.000000000 0.000000000) *
## 53) texture_mean>=2.692775 3 0 M (0.000000000 1.000000000) *
## 27) symmetry_worst< -1.411591 11 0 M (0.000000000 1.000000000) *
## 7) texture_mean>=2.810904 515 195 M (0.378640777 0.621359223)
## 14) smoothness_mean< -2.486388 17 0 B (1.000000000 0.000000000) *
## 15) smoothness_mean>=-2.486388 498 178 M (0.357429719 0.642570281)
## 30) symmetry_worst< -2.202388 31 7 B (0.774193548 0.225806452)
## 60) symmetry_worst>=-2.379234 27 3 B (0.888888889 0.111111111)
## 120) smoothness_mean>=-2.443464 25 1 B (0.960000000 0.040000000) *
## 121) smoothness_mean< -2.443464 2 0 M (0.000000000 1.000000000) *
## 61) symmetry_worst< -2.379234 4 0 M (0.000000000 1.000000000) *
## 31) symmetry_worst>=-2.202388 467 154 M (0.329764454 0.670235546)
## 62) symmetry_worst< -1.424186 391 146 M (0.373401535 0.626598465)
## 124) smoothness_worst>=-1.536189 342 142 M (0.415204678 0.584795322) *
## 125) smoothness_worst< -1.536189 49 4 M (0.081632653 0.918367347) *
## 63) symmetry_worst>=-1.424186 76 8 M (0.105263158 0.894736842)
## 126) smoothness_worst< -1.49649 6 2 B (0.666666667 0.333333333) *
## 127) smoothness_worst>=-1.49649 70 4 M (0.057142857 0.942857143) *
##
## $trees[[5]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 374 B (0.58991228 0.41008772)
## 2) symmetry_worst< -1.294443 861 327 B (0.62020906 0.37979094)
## 4) compactness_se< -4.05215 321 79 B (0.75389408 0.24610592)
## 8) smoothness_mean>=-2.294121 82 1 B (0.98780488 0.01219512)
## 16) smoothness_worst< -1.433156 61 0 B (1.00000000 0.00000000) *
## 17) smoothness_worst>=-1.433156 21 1 B (0.95238095 0.04761905)
## 34) smoothness_worst>=-1.428706 20 0 B (1.00000000 0.00000000) *
## 35) smoothness_worst< -1.428706 1 0 M (0.00000000 1.00000000) *
## 9) smoothness_mean< -2.294121 239 78 B (0.67364017 0.32635983)
## 18) texture_mean< 2.897016 115 15 B (0.86956522 0.13043478)
## 36) smoothness_mean< -2.295113 112 12 B (0.89285714 0.10714286)
## 72) compactness_se< -4.17052 92 4 B (0.95652174 0.04347826) *
## 73) compactness_se>=-4.17052 20 8 B (0.60000000 0.40000000) *
## 37) smoothness_mean>=-2.295113 3 0 M (0.00000000 1.00000000) *
## 19) texture_mean>=2.897016 124 61 M (0.49193548 0.50806452)
## 38) compactness_se< -4.706178 21 0 B (1.00000000 0.00000000) *
## 39) compactness_se>=-4.706178 103 40 M (0.38834951 0.61165049)
## 78) smoothness_worst>=-1.452317 15 1 B (0.93333333 0.06666667) *
## 79) smoothness_worst< -1.452317 88 26 M (0.29545455 0.70454545) *
## 5) compactness_se>=-4.05215 540 248 B (0.54074074 0.45925926)
## 10) smoothness_mean< -2.332015 250 74 B (0.70400000 0.29600000)
## 20) smoothness_worst< -1.602165 61 6 B (0.90163934 0.09836066)
## 40) smoothness_worst>=-1.723213 58 4 B (0.93103448 0.06896552)
## 80) symmetry_worst< -1.806005 37 0 B (1.00000000 0.00000000) *
## 81) symmetry_worst>=-1.806005 21 4 B (0.80952381 0.19047619) *
## 41) smoothness_worst< -1.723213 3 1 M (0.33333333 0.66666667)
## 82) texture_mean< 3.026052 1 0 B (1.00000000 0.00000000) *
## 83) texture_mean>=3.026052 2 0 M (0.00000000 1.00000000) *
## 21) smoothness_worst>=-1.602165 189 68 B (0.64021164 0.35978836)
## 42) texture_mean< 3.038055 116 31 B (0.73275862 0.26724138)
## 84) texture_worst>=4.450993 72 8 B (0.88888889 0.11111111) *
## 85) texture_worst< 4.450993 44 21 M (0.47727273 0.52272727) *
## 43) texture_mean>=3.038055 73 36 M (0.49315068 0.50684932)
## 86) texture_worst>=4.803681 52 16 B (0.69230769 0.30769231) *
## 87) texture_worst< 4.803681 21 0 M (0.00000000 1.00000000) *
## 11) smoothness_mean>=-2.332015 290 116 M (0.40000000 0.60000000)
## 22) smoothness_worst>=-1.479941 152 73 B (0.51973684 0.48026316)
## 44) compactness_se< -3.294139 124 46 B (0.62903226 0.37096774)
## 88) compactness_se>=-3.492992 49 4 B (0.91836735 0.08163265) *
## 89) compactness_se< -3.492992 75 33 M (0.44000000 0.56000000) *
## 45) compactness_se>=-3.294139 28 1 M (0.03571429 0.96428571)
## 90) texture_mean< 2.701935 1 0 B (1.00000000 0.00000000) *
## 91) texture_mean>=2.701935 27 0 M (0.00000000 1.00000000) *
## 23) smoothness_worst< -1.479941 138 37 M (0.26811594 0.73188406)
## 46) symmetry_worst< -2.182761 13 3 B (0.76923077 0.23076923)
## 92) smoothness_mean< -2.27667 10 0 B (1.00000000 0.00000000) *
## 93) smoothness_mean>=-2.27667 3 0 M (0.00000000 1.00000000) *
## 47) symmetry_worst>=-2.182761 125 27 M (0.21600000 0.78400000)
## 94) smoothness_mean>=-2.274972 53 18 M (0.33962264 0.66037736) *
## 95) smoothness_mean< -2.274972 72 9 M (0.12500000 0.87500000) *
## 3) symmetry_worst>=-1.294443 51 4 M (0.07843137 0.92156863)
## 6) smoothness_mean< -2.28924 13 4 M (0.30769231 0.69230769)
## 12) compactness_se>=-4.00428 6 2 B (0.66666667 0.33333333)
## 24) compactness_se< -3.441917 4 0 B (1.00000000 0.00000000) *
## 25) compactness_se>=-3.441917 2 0 M (0.00000000 1.00000000) *
## 13) compactness_se< -4.00428 7 0 M (0.00000000 1.00000000) *
## 7) smoothness_mean>=-2.28924 38 0 M (0.00000000 1.00000000) *
##
## $trees[[6]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 378 B (0.58552632 0.41447368)
## 2) texture_worst< 4.178472 132 14 B (0.89393939 0.10606061)
## 4) symmetry_worst< -1.086115 127 9 B (0.92913386 0.07086614)
## 8) texture_mean< 2.767575 100 2 B (0.98000000 0.02000000)
## 16) texture_mean>=2.479051 85 0 B (1.00000000 0.00000000) *
## 17) texture_mean< 2.479051 15 2 B (0.86666667 0.13333333)
## 34) texture_mean< 2.449364 13 0 B (1.00000000 0.00000000) *
## 35) texture_mean>=2.449364 2 0 M (0.00000000 1.00000000) *
## 9) texture_mean>=2.767575 27 7 B (0.74074074 0.25925926)
## 18) symmetry_worst< -1.431268 23 3 B (0.86956522 0.13043478)
## 36) texture_mean>=2.771335 20 0 B (1.00000000 0.00000000) *
## 37) texture_mean< 2.771335 3 0 M (0.00000000 1.00000000) *
## 19) symmetry_worst>=-1.431268 4 0 M (0.00000000 1.00000000) *
## 5) symmetry_worst>=-1.086115 5 0 M (0.00000000 1.00000000) *
## 3) texture_worst>=4.178472 780 364 B (0.53333333 0.46666667)
## 6) smoothness_worst< -1.520292 366 122 B (0.66666667 0.33333333)
## 12) symmetry_worst< -1.861897 175 30 B (0.82857143 0.17142857)
## 24) symmetry_worst>=-3.054794 172 27 B (0.84302326 0.15697674)
## 48) smoothness_worst>=-1.543427 50 1 B (0.98000000 0.02000000)
## 96) smoothness_worst< -1.52112 49 0 B (1.00000000 0.00000000) *
## 97) smoothness_worst>=-1.52112 1 0 M (0.00000000 1.00000000) *
## 49) smoothness_worst< -1.543427 122 26 B (0.78688525 0.21311475)
## 98) smoothness_worst< -1.550482 115 19 B (0.83478261 0.16521739) *
## 99) smoothness_worst>=-1.550482 7 0 M (0.00000000 1.00000000) *
## 25) symmetry_worst< -3.054794 3 0 M (0.00000000 1.00000000) *
## 13) symmetry_worst>=-1.861897 191 92 B (0.51832461 0.48167539)
## 26) symmetry_worst>=-1.750623 128 48 B (0.62500000 0.37500000)
## 52) texture_mean< 2.984348 62 9 B (0.85483871 0.14516129)
## 104) compactness_se>=-4.62493 53 1 B (0.98113208 0.01886792) *
## 105) compactness_se< -4.62493 9 1 M (0.11111111 0.88888889) *
## 53) texture_mean>=2.984348 66 27 M (0.40909091 0.59090909)
## 106) symmetry_worst< -1.720387 12 0 B (1.00000000 0.00000000) *
## 107) symmetry_worst>=-1.720387 54 15 M (0.27777778 0.72222222) *
## 27) symmetry_worst< -1.750623 63 19 M (0.30158730 0.69841270)
## 54) compactness_se< -3.737913 33 16 M (0.48484848 0.51515152)
## 108) compactness_se>=-4.157608 10 0 B (1.00000000 0.00000000) *
## 109) compactness_se< -4.157608 23 6 M (0.26086957 0.73913043) *
## 55) compactness_se>=-3.737913 30 3 M (0.10000000 0.90000000)
## 110) symmetry_worst< -1.843767 2 0 B (1.00000000 0.00000000) *
## 111) symmetry_worst>=-1.843767 28 1 M (0.03571429 0.96428571) *
## 7) smoothness_worst>=-1.520292 414 172 M (0.41545894 0.58454106)
## 14) symmetry_worst< -1.424186 346 162 M (0.46820809 0.53179191)
## 28) smoothness_worst>=-1.51308 325 162 M (0.49846154 0.50153846)
## 56) texture_worst< 4.858219 230 99 B (0.56956522 0.43043478)
## 112) texture_worst>=4.352293 186 65 B (0.65053763 0.34946237) *
## 113) texture_worst< 4.352293 44 10 M (0.22727273 0.77272727) *
## 57) texture_worst>=4.858219 95 31 M (0.32631579 0.67368421)
## 114) smoothness_mean< -2.336091 26 8 B (0.69230769 0.30769231) *
## 115) smoothness_mean>=-2.336091 69 13 M (0.18840580 0.81159420) *
## 29) smoothness_worst< -1.51308 21 0 M (0.00000000 1.00000000) *
## 15) symmetry_worst>=-1.424186 68 10 M (0.14705882 0.85294118)
## 30) texture_worst< 4.391935 10 4 B (0.60000000 0.40000000)
## 60) smoothness_mean< -2.187813 6 0 B (1.00000000 0.00000000) *
## 61) smoothness_mean>=-2.187813 4 0 M (0.00000000 1.00000000) *
## 31) texture_worst>=4.391935 58 4 M (0.06896552 0.93103448)
## 62) smoothness_worst< -1.501886 3 0 B (1.00000000 0.00000000) *
## 63) smoothness_worst>=-1.501886 55 1 M (0.01818182 0.98181818)
## 126) texture_worst< 4.575764 4 1 M (0.25000000 0.75000000) *
## 127) texture_worst>=4.575764 51 0 M (0.00000000 1.00000000) *
##
## $trees[[7]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 413 B (0.54714912 0.45285088)
## 2) smoothness_worst< -1.603315 113 14 B (0.87610619 0.12389381)
## 4) smoothness_worst>=-1.723213 110 11 B (0.90000000 0.10000000)
## 8) symmetry_worst< -1.777195 69 0 B (1.00000000 0.00000000) *
## 9) symmetry_worst>=-1.777195 41 11 B (0.73170732 0.26829268)
## 18) symmetry_worst>=-1.748651 37 7 B (0.81081081 0.18918919)
## 36) compactness_se>=-4.279133 26 2 B (0.92307692 0.07692308)
## 72) texture_mean< 3.160844 25 1 B (0.96000000 0.04000000) *
## 73) texture_mean>=3.160844 1 0 M (0.00000000 1.00000000) *
## 37) compactness_se< -4.279133 11 5 B (0.54545455 0.45454545)
## 74) smoothness_mean< -2.529127 5 0 B (1.00000000 0.00000000) *
## 75) smoothness_mean>=-2.529127 6 1 M (0.16666667 0.83333333) *
## 19) symmetry_worst< -1.748651 4 0 M (0.00000000 1.00000000) *
## 5) smoothness_worst< -1.723213 3 0 M (0.00000000 1.00000000) *
## 3) smoothness_worst>=-1.603315 799 399 B (0.50062578 0.49937422)
## 6) texture_mean< 2.927988 339 121 B (0.64306785 0.35693215)
## 12) texture_mean>=2.89867 71 5 B (0.92957746 0.07042254)
## 24) compactness_se>=-4.62493 65 1 B (0.98461538 0.01538462)
## 48) texture_worst< 4.739939 64 0 B (1.00000000 0.00000000) *
## 49) texture_worst>=4.739939 1 0 M (0.00000000 1.00000000) *
## 25) compactness_se< -4.62493 6 2 M (0.33333333 0.66666667)
## 50) texture_mean< 2.912851 2 0 B (1.00000000 0.00000000) *
## 51) texture_mean>=2.912851 4 0 M (0.00000000 1.00000000) *
## 13) texture_mean< 2.89867 268 116 B (0.56716418 0.43283582)
## 26) texture_mean< 2.892591 239 88 B (0.63179916 0.36820084)
## 52) texture_mean< 2.708379 62 6 B (0.90322581 0.09677419)
## 104) texture_mean>=2.496294 51 0 B (1.00000000 0.00000000) *
## 105) texture_mean< 2.496294 11 5 M (0.45454545 0.54545455) *
## 53) texture_mean>=2.708379 177 82 B (0.53672316 0.46327684)
## 106) compactness_se< -4.50262 20 0 B (1.00000000 0.00000000) *
## 107) compactness_se>=-4.50262 157 75 M (0.47770701 0.52229299) *
## 27) texture_mean>=2.892591 29 1 M (0.03448276 0.96551724)
## 54) smoothness_mean< -2.409236 1 0 B (1.00000000 0.00000000) *
## 55) smoothness_mean>=-2.409236 28 0 M (0.00000000 1.00000000) *
## 7) texture_mean>=2.927988 460 182 M (0.39565217 0.60434783)
## 14) compactness_se< -3.721197 212 98 B (0.53773585 0.46226415)
## 28) compactness_se>=-3.865662 44 7 B (0.84090909 0.15909091)
## 56) smoothness_worst< -1.48132 33 0 B (1.00000000 0.00000000) *
## 57) smoothness_worst>=-1.48132 11 4 M (0.36363636 0.63636364)
## 114) texture_mean< 2.971675 4 0 B (1.00000000 0.00000000) *
## 115) texture_mean>=2.971675 7 0 M (0.00000000 1.00000000) *
## 29) compactness_se< -3.865662 168 77 M (0.45833333 0.54166667)
## 58) symmetry_worst< -2.218846 17 0 B (1.00000000 0.00000000) *
## 59) symmetry_worst>=-2.218846 151 60 M (0.39735099 0.60264901)
## 118) compactness_se< -4.557422 19 3 B (0.84210526 0.15789474) *
## 119) compactness_se>=-4.557422 132 44 M (0.33333333 0.66666667) *
## 15) compactness_se>=-3.721197 248 68 M (0.27419355 0.72580645)
## 30) texture_worst< 4.411908 18 3 B (0.83333333 0.16666667)
## 60) smoothness_worst< -1.510166 15 0 B (1.00000000 0.00000000) *
## 61) smoothness_worst>=-1.510166 3 0 M (0.00000000 1.00000000) *
## 31) texture_worst>=4.411908 230 53 M (0.23043478 0.76956522)
## 62) smoothness_mean< -2.289177 124 47 M (0.37903226 0.62096774)
## 124) smoothness_worst>=-1.476409 37 11 B (0.70270270 0.29729730) *
## 125) smoothness_worst< -1.476409 87 21 M (0.24137931 0.75862069) *
## 63) smoothness_mean>=-2.289177 106 6 M (0.05660377 0.94339623)
## 126) smoothness_mean>=-2.093138 15 6 M (0.40000000 0.60000000) *
## 127) smoothness_mean< -2.093138 91 0 M (0.00000000 1.00000000) *
##
## $trees[[8]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 327 B (0.64144737 0.35855263)
## 2) compactness_se< -3.678758 549 147 B (0.73224044 0.26775956)
## 4) symmetry_worst< -1.329407 535 134 B (0.74953271 0.25046729)
## 8) texture_worst< 4.389172 160 17 B (0.89375000 0.10625000)
## 16) compactness_se< -4.166611 77 0 B (1.00000000 0.00000000) *
## 17) compactness_se>=-4.166611 83 17 B (0.79518072 0.20481928)
## 34) compactness_se>=-4.160164 76 10 B (0.86842105 0.13157895)
## 68) smoothness_mean< -2.099567 74 8 B (0.89189189 0.10810811) *
## 69) smoothness_mean>=-2.099567 2 0 M (0.00000000 1.00000000) *
## 35) compactness_se< -4.160164 7 0 M (0.00000000 1.00000000) *
## 9) texture_worst>=4.389172 375 117 B (0.68800000 0.31200000)
## 18) symmetry_worst>=-2.164014 342 95 B (0.72222222 0.27777778)
## 36) texture_worst>=4.642157 187 31 B (0.83422460 0.16577540)
## 72) smoothness_mean< -2.203647 183 27 B (0.85245902 0.14754098) *
## 73) smoothness_mean>=-2.203647 4 0 M (0.00000000 1.00000000) *
## 37) texture_worst< 4.642157 155 64 B (0.58709677 0.41290323)
## 74) compactness_se< -4.198706 61 12 B (0.80327869 0.19672131) *
## 75) compactness_se>=-4.198706 94 42 M (0.44680851 0.55319149) *
## 19) symmetry_worst< -2.164014 33 11 M (0.33333333 0.66666667)
## 38) smoothness_mean< -2.392268 5 0 B (1.00000000 0.00000000) *
## 39) smoothness_mean>=-2.392268 28 6 M (0.21428571 0.78571429)
## 78) smoothness_mean>=-2.302192 5 1 B (0.80000000 0.20000000) *
## 79) smoothness_mean< -2.302192 23 2 M (0.08695652 0.91304348) *
## 5) symmetry_worst>=-1.329407 14 1 M (0.07142857 0.92857143)
## 10) texture_mean< 2.814567 1 0 B (1.00000000 0.00000000) *
## 11) texture_mean>=2.814567 13 0 M (0.00000000 1.00000000) *
## 3) compactness_se>=-3.678758 363 180 B (0.50413223 0.49586777)
## 6) smoothness_mean< -2.296611 228 86 B (0.62280702 0.37719298)
## 12) texture_mean< 3.058688 146 39 B (0.73287671 0.26712329)
## 24) compactness_se>=-3.593774 129 24 B (0.81395349 0.18604651)
## 48) texture_mean>=2.782752 96 8 B (0.91666667 0.08333333)
## 96) texture_worst< 4.674843 72 1 B (0.98611111 0.01388889) *
## 97) texture_worst>=4.674843 24 7 B (0.70833333 0.29166667) *
## 49) texture_mean< 2.782752 33 16 B (0.51515152 0.48484848)
## 98) smoothness_mean< -2.461467 15 0 B (1.00000000 0.00000000) *
## 99) smoothness_mean>=-2.461467 18 2 M (0.11111111 0.88888889) *
## 25) compactness_se< -3.593774 17 2 M (0.11764706 0.88235294)
## 50) texture_worst< 4.254671 2 0 B (1.00000000 0.00000000) *
## 51) texture_worst>=4.254671 15 0 M (0.00000000 1.00000000) *
## 13) texture_mean>=3.058688 82 35 M (0.42682927 0.57317073)
## 26) compactness_se< -3.477558 42 11 B (0.73809524 0.26190476)
## 52) texture_mean< 3.410351 36 5 B (0.86111111 0.13888889)
## 104) texture_mean>=3.101852 30 0 B (1.00000000 0.00000000) *
## 105) texture_mean< 3.101852 6 1 M (0.16666667 0.83333333) *
## 53) texture_mean>=3.410351 6 0 M (0.00000000 1.00000000) *
## 27) compactness_se>=-3.477558 40 4 M (0.10000000 0.90000000)
## 54) smoothness_mean< -2.638103 3 0 B (1.00000000 0.00000000) *
## 55) smoothness_mean>=-2.638103 37 1 M (0.02702703 0.97297297)
## 110) smoothness_worst>=-1.417917 1 0 B (1.00000000 0.00000000) *
## 111) smoothness_worst< -1.417917 36 0 M (0.00000000 1.00000000) *
## 7) smoothness_mean>=-2.296611 135 41 M (0.30370370 0.69629630)
## 14) texture_worst< 4.391935 49 19 B (0.61224490 0.38775510)
## 28) compactness_se>=-3.57366 43 13 B (0.69767442 0.30232558)
## 56) texture_mean< 2.857891 30 5 B (0.83333333 0.16666667)
## 112) symmetry_worst< -1.001713 28 3 B (0.89285714 0.10714286) *
## 113) symmetry_worst>=-1.001713 2 0 M (0.00000000 1.00000000) *
## 57) texture_mean>=2.857891 13 5 M (0.38461538 0.61538462)
## 114) texture_mean>=2.870166 5 0 B (1.00000000 0.00000000) *
## 115) texture_mean< 2.870166 8 0 M (0.00000000 1.00000000) *
## 29) compactness_se< -3.57366 6 0 M (0.00000000 1.00000000) *
## 15) texture_worst>=4.391935 86 11 M (0.12790698 0.87209302)
## 30) smoothness_mean>=-2.093138 12 3 B (0.75000000 0.25000000)
## 60) compactness_se< -3.039084 9 0 B (1.00000000 0.00000000) *
## 61) compactness_se>=-3.039084 3 0 M (0.00000000 1.00000000) *
## 31) smoothness_mean< -2.093138 74 2 M (0.02702703 0.97297297)
## 62) texture_worst< 4.541747 10 2 M (0.20000000 0.80000000)
## 124) texture_worst>=4.530419 1 0 B (1.00000000 0.00000000) *
## 125) texture_worst< 4.530419 9 1 M (0.11111111 0.88888889) *
## 63) texture_worst>=4.541747 64 0 M (0.00000000 1.00000000) *
##
## $trees[[9]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 396 B (0.56578947 0.43421053)
## 2) texture_mean< 2.960623 438 139 B (0.68264840 0.31735160)
## 4) smoothness_worst< -1.500665 225 45 B (0.80000000 0.20000000)
## 8) symmetry_worst>=-1.748321 81 5 B (0.93827160 0.06172840)
## 16) smoothness_mean< -2.171581 78 2 B (0.97435897 0.02564103)
## 32) compactness_se>=-4.602061 70 0 B (1.00000000 0.00000000) *
## 33) compactness_se< -4.602061 8 2 B (0.75000000 0.25000000)
## 66) compactness_se< -4.691273 6 0 B (1.00000000 0.00000000) *
## 67) compactness_se>=-4.691273 2 0 M (0.00000000 1.00000000) *
## 17) smoothness_mean>=-2.171581 3 0 M (0.00000000 1.00000000) *
## 9) symmetry_worst< -1.748321 144 40 B (0.72222222 0.27777778)
## 18) symmetry_worst< -1.815934 97 9 B (0.90721649 0.09278351)
## 36) symmetry_worst>=-2.179403 78 3 B (0.96153846 0.03846154)
## 72) compactness_se< -3.49316 69 1 B (0.98550725 0.01449275) *
## 73) compactness_se>=-3.49316 9 2 B (0.77777778 0.22222222) *
## 37) symmetry_worst< -2.179403 19 6 B (0.68421053 0.31578947)
## 74) texture_mean< 2.876144 10 0 B (1.00000000 0.00000000) *
## 75) texture_mean>=2.876144 9 3 M (0.33333333 0.66666667) *
## 19) symmetry_worst>=-1.815934 47 16 M (0.34042553 0.65957447)
## 38) compactness_se< -4.354176 6 0 B (1.00000000 0.00000000) *
## 39) compactness_se>=-4.354176 41 10 M (0.24390244 0.75609756)
## 78) smoothness_mean>=-2.313605 4 0 B (1.00000000 0.00000000) *
## 79) smoothness_mean< -2.313605 37 6 M (0.16216216 0.83783784) *
## 5) smoothness_worst>=-1.500665 213 94 B (0.55868545 0.44131455)
## 10) smoothness_mean>=-2.290163 107 27 B (0.74766355 0.25233645)
## 20) symmetry_worst< -1.511499 76 7 B (0.90789474 0.09210526)
## 40) smoothness_worst>=-1.498447 74 5 B (0.93243243 0.06756757)
## 80) smoothness_mean< -2.007355 73 4 B (0.94520548 0.05479452) *
## 81) smoothness_mean>=-2.007355 1 0 M (0.00000000 1.00000000) *
## 41) smoothness_worst< -1.498447 2 0 M (0.00000000 1.00000000) *
## 21) symmetry_worst>=-1.511499 31 11 M (0.35483871 0.64516129)
## 42) smoothness_mean< -2.222401 13 4 B (0.69230769 0.30769231)
## 84) texture_worst>=4.174841 10 1 B (0.90000000 0.10000000) *
## 85) texture_worst< 4.174841 3 0 M (0.00000000 1.00000000) *
## 43) smoothness_mean>=-2.222401 18 2 M (0.11111111 0.88888889)
## 86) smoothness_mean>=-2.000349 1 0 B (1.00000000 0.00000000) *
## 87) smoothness_mean< -2.000349 17 1 M (0.05882353 0.94117647) *
## 11) smoothness_mean< -2.290163 106 39 M (0.36792453 0.63207547)
## 22) symmetry_worst>=-1.759286 47 16 B (0.65957447 0.34042553)
## 44) smoothness_mean< -2.296107 42 11 B (0.73809524 0.26190476)
## 88) texture_worst>=4.382736 24 2 B (0.91666667 0.08333333) *
## 89) texture_worst< 4.382736 18 9 B (0.50000000 0.50000000) *
## 45) smoothness_mean>=-2.296107 5 0 M (0.00000000 1.00000000) *
## 23) symmetry_worst< -1.759286 59 8 M (0.13559322 0.86440678)
## 46) smoothness_mean< -2.3918 6 0 B (1.00000000 0.00000000) *
## 47) smoothness_mean>=-2.3918 53 2 M (0.03773585 0.96226415)
## 94) texture_mean< 2.755881 2 0 B (1.00000000 0.00000000) *
## 95) texture_mean>=2.755881 51 0 M (0.00000000 1.00000000) *
## 3) texture_mean>=2.960623 474 217 M (0.45780591 0.54219409)
## 6) symmetry_worst< -2.01934 124 44 B (0.64516129 0.35483871)
## 12) compactness_se< -3.037823 112 33 B (0.70535714 0.29464286)
## 24) texture_mean>=3.336125 21 0 B (1.00000000 0.00000000) *
## 25) texture_mean< 3.336125 91 33 B (0.63736264 0.36263736)
## 50) smoothness_mean>=-2.330377 36 4 B (0.88888889 0.11111111)
## 100) smoothness_worst< -1.44137 34 2 B (0.94117647 0.05882353) *
## 101) smoothness_worst>=-1.44137 2 0 M (0.00000000 1.00000000) *
## 51) smoothness_mean< -2.330377 55 26 M (0.47272727 0.52727273)
## 102) smoothness_worst< -1.604518 21 2 B (0.90476190 0.09523810) *
## 103) smoothness_worst>=-1.604518 34 7 M (0.20588235 0.79411765) *
## 13) compactness_se>=-3.037823 12 1 M (0.08333333 0.91666667)
## 26) texture_mean< 3.005071 1 0 B (1.00000000 0.00000000) *
## 27) texture_mean>=3.005071 11 0 M (0.00000000 1.00000000) *
## 7) symmetry_worst>=-2.01934 350 137 M (0.39142857 0.60857143)
## 14) texture_worst< 5.034396 276 131 M (0.47463768 0.52536232)
## 28) texture_mean>=3.192731 36 3 B (0.91666667 0.08333333)
## 56) symmetry_worst< -1.345645 34 1 B (0.97058824 0.02941176)
## 112) texture_worst>=4.863973 33 0 B (1.00000000 0.00000000) *
## 113) texture_worst< 4.863973 1 0 M (0.00000000 1.00000000) *
## 57) symmetry_worst>=-1.345645 2 0 M (0.00000000 1.00000000) *
## 29) texture_mean< 3.192731 240 98 M (0.40833333 0.59166667)
## 58) smoothness_worst>=-1.443422 76 30 B (0.60526316 0.39473684)
## 116) texture_worst>=4.641775 58 12 B (0.79310345 0.20689655) *
## 117) texture_worst< 4.641775 18 0 M (0.00000000 1.00000000) *
## 59) smoothness_worst< -1.443422 164 52 M (0.31707317 0.68292683)
## 118) smoothness_worst< -1.556752 52 25 B (0.51923077 0.48076923) *
## 119) smoothness_worst>=-1.556752 112 25 M (0.22321429 0.77678571) *
## 15) texture_worst>=5.034396 74 6 M (0.08108108 0.91891892)
## 30) smoothness_mean< -2.526959 4 0 B (1.00000000 0.00000000) *
## 31) smoothness_mean>=-2.526959 70 2 M (0.02857143 0.97142857)
## 62) texture_mean>=3.35917 21 2 M (0.09523810 0.90476190)
## 124) texture_mean< 3.386045 2 0 B (1.00000000 0.00000000) *
## 125) texture_mean>=3.386045 19 0 M (0.00000000 1.00000000) *
## 63) texture_mean< 3.35917 49 0 M (0.00000000 1.00000000) *
##
## $trees[[10]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 403 B (0.55811404 0.44188596)
## 2) texture_worst< 4.260219 133 21 B (0.84210526 0.15789474)
## 4) symmetry_worst< -1.429489 121 13 B (0.89256198 0.10743802)
## 8) texture_mean< 2.909334 118 10 B (0.91525424 0.08474576)
## 16) compactness_se< -3.894783 62 0 B (1.00000000 0.00000000) *
## 17) compactness_se>=-3.894783 56 10 B (0.82142857 0.17857143)
## 34) compactness_se>=-3.878107 50 4 B (0.92000000 0.08000000)
## 68) texture_mean>=2.534356 46 2 B (0.95652174 0.04347826) *
## 69) texture_mean< 2.534356 4 2 B (0.50000000 0.50000000) *
## 35) compactness_se< -3.878107 6 0 M (0.00000000 1.00000000) *
## 9) texture_mean>=2.909334 3 0 M (0.00000000 1.00000000) *
## 5) symmetry_worst>=-1.429489 12 4 M (0.33333333 0.66666667)
## 10) texture_mean< 2.706904 4 0 B (1.00000000 0.00000000) *
## 11) texture_mean>=2.706904 8 0 M (0.00000000 1.00000000) *
## 3) texture_worst>=4.260219 779 382 B (0.50962773 0.49037227)
## 6) symmetry_worst< -1.52865 651 291 B (0.55299539 0.44700461)
## 12) compactness_se< -3.671151 404 151 B (0.62623762 0.37376238)
## 24) smoothness_mean>=-2.283768 83 13 B (0.84337349 0.15662651)
## 48) smoothness_worst< -1.419369 79 9 B (0.88607595 0.11392405)
## 96) texture_mean< 3.133277 67 3 B (0.95522388 0.04477612) *
## 97) texture_mean>=3.133277 12 6 B (0.50000000 0.50000000) *
## 49) smoothness_worst>=-1.419369 4 0 M (0.00000000 1.00000000) *
## 25) smoothness_mean< -2.283768 321 138 B (0.57009346 0.42990654)
## 50) texture_worst>=4.3976 278 107 B (0.61510791 0.38489209)
## 100) texture_worst< 4.517889 42 2 B (0.95238095 0.04761905) *
## 101) texture_worst>=4.517889 236 105 B (0.55508475 0.44491525) *
## 51) texture_worst< 4.3976 43 12 M (0.27906977 0.72093023)
## 102) texture_mean< 2.808677 8 0 B (1.00000000 0.00000000) *
## 103) texture_mean>=2.808677 35 4 M (0.11428571 0.88571429) *
## 13) compactness_se>=-3.671151 247 107 M (0.43319838 0.56680162)
## 26) symmetry_worst>=-1.608735 35 6 B (0.82857143 0.17142857)
## 52) smoothness_mean< -2.3007 29 1 B (0.96551724 0.03448276)
## 104) texture_worst< 4.993407 28 0 B (1.00000000 0.00000000) *
## 105) texture_worst>=4.993407 1 0 M (0.00000000 1.00000000) *
## 53) smoothness_mean>=-2.3007 6 1 M (0.16666667 0.83333333)
## 106) compactness_se>=-3.239083 1 0 B (1.00000000 0.00000000) *
## 107) compactness_se< -3.239083 5 0 M (0.00000000 1.00000000) *
## 27) symmetry_worst< -1.608735 212 78 M (0.36792453 0.63207547)
## 54) symmetry_worst< -1.762226 137 68 B (0.50364964 0.49635036)
## 108) smoothness_worst>=-1.468425 20 2 B (0.90000000 0.10000000) *
## 109) smoothness_worst< -1.468425 117 51 M (0.43589744 0.56410256) *
## 55) symmetry_worst>=-1.762226 75 9 M (0.12000000 0.88000000)
## 110) smoothness_mean>=-2.109794 4 0 B (1.00000000 0.00000000) *
## 111) smoothness_mean< -2.109794 71 5 M (0.07042254 0.92957746) *
## 7) symmetry_worst>=-1.52865 128 37 M (0.28906250 0.71093750)
## 14) texture_worst< 4.433296 26 3 B (0.88461538 0.11538462)
## 28) smoothness_mean< -2.195585 23 0 B (1.00000000 0.00000000) *
## 29) smoothness_mean>=-2.195585 3 0 M (0.00000000 1.00000000) *
## 15) texture_worst>=4.433296 102 14 M (0.13725490 0.86274510)
## 30) texture_mean< 2.947292 21 10 M (0.47619048 0.52380952)
## 60) symmetry_worst< -1.367423 10 0 B (1.00000000 0.00000000) *
## 61) symmetry_worst>=-1.367423 11 0 M (0.00000000 1.00000000) *
## 31) texture_mean>=2.947292 81 4 M (0.04938272 0.95061728)
## 62) compactness_se< -4.507761 3 1 B (0.66666667 0.33333333)
## 124) texture_mean>=3.111935 2 0 B (1.00000000 0.00000000) *
## 125) texture_mean< 3.111935 1 0 M (0.00000000 1.00000000) *
## 63) compactness_se>=-4.507761 78 2 M (0.02564103 0.97435897)
## 126) compactness_se>=-2.983317 9 1 M (0.11111111 0.88888889) *
## 127) compactness_se< -2.983317 69 1 M (0.01449275 0.98550725) *
##
## $trees[[11]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 410 B (0.55043860 0.44956140)
## 2) texture_mean< 2.931727 329 105 B (0.68085106 0.31914894)
## 4) smoothness_mean< -2.469112 36 0 B (1.00000000 0.00000000) *
## 5) smoothness_mean>=-2.469112 293 105 B (0.64163823 0.35836177)
## 10) compactness_se>=-3.427747 56 8 B (0.85714286 0.14285714)
## 20) smoothness_worst< -1.431481 48 2 B (0.95833333 0.04166667)
## 40) symmetry_worst< -1.330042 41 0 B (1.00000000 0.00000000) *
## 41) symmetry_worst>=-1.330042 7 2 B (0.71428571 0.28571429)
## 82) compactness_se>=-2.646661 5 0 B (1.00000000 0.00000000) *
## 83) compactness_se< -2.646661 2 0 M (0.00000000 1.00000000) *
## 21) smoothness_worst>=-1.431481 8 2 M (0.25000000 0.75000000)
## 42) texture_mean< 2.67609 2 0 B (1.00000000 0.00000000) *
## 43) texture_mean>=2.67609 6 0 M (0.00000000 1.00000000) *
## 11) compactness_se< -3.427747 237 97 B (0.59071730 0.40928270)
## 22) compactness_se< -3.4389 221 81 B (0.63348416 0.36651584)
## 44) texture_worst< 4.522453 152 44 B (0.71052632 0.28947368)
## 88) smoothness_mean< -2.081877 140 34 B (0.75714286 0.24285714) *
## 89) smoothness_mean>=-2.081877 12 2 M (0.16666667 0.83333333) *
## 45) texture_worst>=4.522453 69 32 M (0.46376812 0.53623188)
## 90) texture_worst>=4.543638 51 20 B (0.60784314 0.39215686) *
## 91) texture_worst< 4.543638 18 1 M (0.05555556 0.94444444) *
## 23) compactness_se>=-3.4389 16 0 M (0.00000000 1.00000000) *
## 3) texture_mean>=2.931727 583 278 M (0.47684391 0.52315609)
## 6) symmetry_worst< -2.20425 53 12 B (0.77358491 0.22641509)
## 12) texture_worst>=4.653864 43 4 B (0.90697674 0.09302326)
## 24) smoothness_mean< -2.282229 40 1 B (0.97500000 0.02500000)
## 48) texture_mean< 3.330945 35 0 B (1.00000000 0.00000000) *
## 49) texture_mean>=3.330945 5 1 B (0.80000000 0.20000000)
## 98) texture_mean>=3.357516 4 0 B (1.00000000 0.00000000) *
## 99) texture_mean< 3.357516 1 0 M (0.00000000 1.00000000) *
## 25) smoothness_mean>=-2.282229 3 0 M (0.00000000 1.00000000) *
## 13) texture_worst< 4.653864 10 2 M (0.20000000 0.80000000)
## 26) smoothness_mean>=-2.337576 3 1 B (0.66666667 0.33333333)
## 52) smoothness_mean< -2.242961 2 0 B (1.00000000 0.00000000) *
## 53) smoothness_mean>=-2.242961 1 0 M (0.00000000 1.00000000) *
## 27) smoothness_mean< -2.337576 7 0 M (0.00000000 1.00000000) *
## 7) symmetry_worst>=-2.20425 530 237 M (0.44716981 0.55283019)
## 14) texture_worst< 5.030101 444 215 M (0.48423423 0.51576577)
## 28) compactness_se>=-4.094455 315 139 B (0.55873016 0.44126984)
## 56) compactness_se< -4.05446 20 0 B (1.00000000 0.00000000) *
## 57) compactness_se>=-4.05446 295 139 B (0.52881356 0.47118644)
## 114) smoothness_worst< -1.462821 208 81 B (0.61057692 0.38942308) *
## 115) smoothness_worst>=-1.462821 87 29 M (0.33333333 0.66666667) *
## 29) compactness_se< -4.094455 129 39 M (0.30232558 0.69767442)
## 58) texture_mean>=3.181081 7 0 B (1.00000000 0.00000000) *
## 59) texture_mean< 3.181081 122 32 M (0.26229508 0.73770492)
## 118) texture_worst< 4.849569 81 32 M (0.39506173 0.60493827) *
## 119) texture_worst>=4.849569 41 0 M (0.00000000 1.00000000) *
## 15) texture_worst>=5.030101 86 22 M (0.25581395 0.74418605)
## 30) smoothness_mean< -2.505388 9 0 B (1.00000000 0.00000000) *
## 31) smoothness_mean>=-2.505388 77 13 M (0.16883117 0.83116883)
## 62) compactness_se< -4.509895 12 5 B (0.58333333 0.41666667)
## 124) texture_mean>=3.186756 7 0 B (1.00000000 0.00000000) *
## 125) texture_mean< 3.186756 5 0 M (0.00000000 1.00000000) *
## 63) compactness_se>=-4.509895 65 6 M (0.09230769 0.90769231)
## 126) smoothness_worst< -1.563077 4 1 B (0.75000000 0.25000000) *
## 127) smoothness_worst>=-1.563077 61 3 M (0.04918033 0.95081967) *
##
## $trees[[12]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 453 B (0.50328947 0.49671053)
## 2) symmetry_worst< -2.048468 145 29 B (0.80000000 0.20000000)
## 4) symmetry_worst>=-2.923662 139 23 B (0.83453237 0.16546763)
## 8) compactness_se< -3.351361 114 13 B (0.88596491 0.11403509)
## 16) symmetry_worst>=-2.379234 102 7 B (0.93137255 0.06862745)
## 32) texture_worst< 5.309594 94 3 B (0.96808511 0.03191489)
## 64) texture_mean>=3.067819 59 0 B (1.00000000 0.00000000) *
## 65) texture_mean< 3.067819 35 3 B (0.91428571 0.08571429) *
## 33) texture_worst>=5.309594 8 4 B (0.50000000 0.50000000)
## 66) texture_mean>=3.375155 4 0 B (1.00000000 0.00000000) *
## 67) texture_mean< 3.375155 4 0 M (0.00000000 1.00000000) *
## 17) symmetry_worst< -2.379234 12 6 B (0.50000000 0.50000000)
## 34) texture_mean< 2.865666 4 0 B (1.00000000 0.00000000) *
## 35) texture_mean>=2.865666 8 2 M (0.25000000 0.75000000)
## 70) symmetry_worst< -2.522371 2 0 B (1.00000000 0.00000000) *
## 71) symmetry_worst>=-2.522371 6 0 M (0.00000000 1.00000000) *
## 9) compactness_se>=-3.351361 25 10 B (0.60000000 0.40000000)
## 18) texture_mean< 3.076827 14 0 B (1.00000000 0.00000000) *
## 19) texture_mean>=3.076827 11 1 M (0.09090909 0.90909091)
## 38) smoothness_mean< -2.638103 1 0 B (1.00000000 0.00000000) *
## 39) smoothness_mean>=-2.638103 10 0 M (0.00000000 1.00000000) *
## 5) symmetry_worst< -2.923662 6 0 M (0.00000000 1.00000000) *
## 3) symmetry_worst>=-2.048468 767 343 M (0.44719687 0.55280313)
## 6) texture_worst< 4.592857 350 132 B (0.62285714 0.37714286)
## 12) texture_mean< 2.708379 36 1 B (0.97222222 0.02777778)
## 24) texture_mean>=2.496294 30 0 B (1.00000000 0.00000000) *
## 25) texture_mean< 2.496294 6 1 B (0.83333333 0.16666667)
## 50) texture_mean< 2.434062 5 0 B (1.00000000 0.00000000) *
## 51) texture_mean>=2.434062 1 0 M (0.00000000 1.00000000) *
## 13) texture_mean>=2.708379 314 131 B (0.58280255 0.41719745)
## 26) texture_mean>=2.771335 256 93 B (0.63671875 0.36328125)
## 52) texture_mean< 2.927988 146 34 B (0.76712329 0.23287671)
## 104) texture_mean>=2.893423 40 0 B (1.00000000 0.00000000) *
## 105) texture_mean< 2.893423 106 34 B (0.67924528 0.32075472) *
## 53) texture_mean>=2.927988 110 51 M (0.46363636 0.53636364)
## 106) texture_worst>=4.528527 38 9 B (0.76315789 0.23684211) *
## 107) texture_worst< 4.528527 72 22 M (0.30555556 0.69444444) *
## 27) texture_mean< 2.771335 58 20 M (0.34482759 0.65517241)
## 54) smoothness_mean< -2.443516 7 0 B (1.00000000 0.00000000) *
## 55) smoothness_mean>=-2.443516 51 13 M (0.25490196 0.74509804)
## 110) compactness_se>=-3.364454 5 0 B (1.00000000 0.00000000) *
## 111) compactness_se< -3.364454 46 8 M (0.17391304 0.82608696) *
## 7) texture_worst>=4.592857 417 125 M (0.29976019 0.70023981)
## 14) smoothness_mean>=-2.093138 19 3 B (0.84210526 0.15789474)
## 28) smoothness_mean< -2.073133 16 0 B (1.00000000 0.00000000) *
## 29) smoothness_mean>=-2.073133 3 0 M (0.00000000 1.00000000) *
## 15) smoothness_mean< -2.093138 398 109 M (0.27386935 0.72613065)
## 30) smoothness_mean< -2.549773 8 0 B (1.00000000 0.00000000) *
## 31) smoothness_mean>=-2.549773 390 101 M (0.25897436 0.74102564)
## 62) compactness_se>=-4.096569 233 77 M (0.33047210 0.66952790)
## 124) compactness_se< -3.721197 85 42 M (0.49411765 0.50588235) *
## 125) compactness_se>=-3.721197 148 35 M (0.23648649 0.76351351) *
## 63) compactness_se< -4.096569 157 24 M (0.15286624 0.84713376)
## 126) smoothness_worst>=-1.399898 4 0 B (1.00000000 0.00000000) *
## 127) smoothness_worst< -1.399898 153 20 M (0.13071895 0.86928105) *
##
## $trees[[13]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 435 B (0.52302632 0.47697368)
## 2) texture_mean< 2.711046 40 7 B (0.82500000 0.17500000)
## 4) compactness_se< -3.039458 37 4 B (0.89189189 0.10810811)
## 8) texture_mean>=2.479051 26 0 B (1.00000000 0.00000000) *
## 9) texture_mean< 2.479051 11 4 B (0.63636364 0.36363636)
## 18) texture_mean< 2.471475 7 0 B (1.00000000 0.00000000) *
## 19) texture_mean>=2.471475 4 0 M (0.00000000 1.00000000) *
## 5) compactness_se>=-3.039458 3 0 M (0.00000000 1.00000000) *
## 3) texture_mean>=2.711046 872 428 B (0.50917431 0.49082569)
## 6) texture_mean>=2.856753 728 334 B (0.54120879 0.45879121)
## 12) symmetry_worst>=-2.491275 713 319 B (0.55259467 0.44740533)
## 24) texture_worst< 4.458511 92 22 B (0.76086957 0.23913043)
## 48) symmetry_worst< -1.735506 51 2 B (0.96078431 0.03921569)
## 96) compactness_se>=-4.327955 45 0 B (1.00000000 0.00000000) *
## 97) compactness_se< -4.327955 6 2 B (0.66666667 0.33333333) *
## 49) symmetry_worst>=-1.735506 41 20 B (0.51219512 0.48780488)
## 98) smoothness_worst>=-1.566151 30 9 B (0.70000000 0.30000000) *
## 99) smoothness_worst< -1.566151 11 0 M (0.00000000 1.00000000) *
## 25) texture_worst>=4.458511 621 297 B (0.52173913 0.47826087)
## 50) compactness_se>=-4.671834 576 263 B (0.54340278 0.45659722)
## 100) smoothness_mean< -2.335108 312 114 B (0.63461538 0.36538462) *
## 101) smoothness_mean>=-2.335108 264 115 M (0.43560606 0.56439394) *
## 51) compactness_se< -4.671834 45 11 M (0.24444444 0.75555556)
## 102) compactness_se< -4.803674 8 0 B (1.00000000 0.00000000) *
## 103) compactness_se>=-4.803674 37 3 M (0.08108108 0.91891892) *
## 13) symmetry_worst< -2.491275 15 0 M (0.00000000 1.00000000) *
## 7) texture_mean< 2.856753 144 50 M (0.34722222 0.65277778)
## 14) smoothness_mean>=-2.271585 33 12 B (0.63636364 0.36363636)
## 28) symmetry_worst< -1.524164 22 2 B (0.90909091 0.09090909)
## 56) smoothness_mean< -2.061717 21 1 B (0.95238095 0.04761905)
## 112) texture_mean< 2.834388 18 0 B (1.00000000 0.00000000) *
## 113) texture_mean>=2.834388 3 1 B (0.66666667 0.33333333) *
## 57) smoothness_mean>=-2.061717 1 0 M (0.00000000 1.00000000) *
## 29) symmetry_worst>=-1.524164 11 1 M (0.09090909 0.90909091)
## 58) texture_mean>=2.850705 1 0 B (1.00000000 0.00000000) *
## 59) texture_mean< 2.850705 10 0 M (0.00000000 1.00000000) *
## 15) smoothness_mean< -2.271585 111 29 M (0.26126126 0.73873874)
## 30) smoothness_mean< -2.447973 7 0 B (1.00000000 0.00000000) *
## 31) smoothness_mean>=-2.447973 104 22 M (0.21153846 0.78846154)
## 62) texture_mean< 2.758426 33 13 M (0.39393939 0.60606061)
## 124) compactness_se< -3.697394 10 0 B (1.00000000 0.00000000) *
## 125) compactness_se>=-3.697394 23 3 M (0.13043478 0.86956522) *
## 63) texture_mean>=2.758426 71 9 M (0.12676056 0.87323944)
## 126) compactness_se>=-3.483667 4 1 B (0.75000000 0.25000000) *
## 127) compactness_se< -3.483667 67 6 M (0.08955224 0.91044776) *
##
## $trees[[14]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 454 M (0.49780702 0.50219298)
## 2) smoothness_mean>=-2.275457 235 83 B (0.64680851 0.35319149)
## 4) compactness_se< -3.646366 137 26 B (0.81021898 0.18978102)
## 8) smoothness_worst< -1.459555 60 0 B (1.00000000 0.00000000) *
## 9) smoothness_worst>=-1.459555 77 26 B (0.66233766 0.33766234)
## 18) compactness_se< -4.030558 42 4 B (0.90476190 0.09523810)
## 36) texture_mean>=2.979048 33 0 B (1.00000000 0.00000000) *
## 37) texture_mean< 2.979048 9 4 B (0.55555556 0.44444444)
## 74) texture_mean< 2.950291 5 0 B (1.00000000 0.00000000) *
## 75) texture_mean>=2.950291 4 0 M (0.00000000 1.00000000) *
## 19) compactness_se>=-4.030558 35 13 M (0.37142857 0.62857143)
## 38) texture_mean< 2.900047 15 2 B (0.86666667 0.13333333)
## 76) texture_mean>=2.532482 13 0 B (1.00000000 0.00000000) *
## 77) texture_mean< 2.532482 2 0 M (0.00000000 1.00000000) *
## 39) texture_mean>=2.900047 20 0 M (0.00000000 1.00000000) *
## 5) compactness_se>=-3.646366 98 41 M (0.41836735 0.58163265)
## 10) smoothness_mean>=-2.093138 20 3 B (0.85000000 0.15000000)
## 20) symmetry_worst< -1.566249 17 0 B (1.00000000 0.00000000) *
## 21) symmetry_worst>=-1.566249 3 0 M (0.00000000 1.00000000) *
## 11) smoothness_mean< -2.093138 78 24 M (0.30769231 0.69230769)
## 22) texture_worst< 4.548911 44 20 B (0.54545455 0.45454545)
## 44) symmetry_worst< -1.814978 14 1 B (0.92857143 0.07142857)
## 88) compactness_se>=-3.557543 13 0 B (1.00000000 0.00000000) *
## 89) compactness_se< -3.557543 1 0 M (0.00000000 1.00000000) *
## 45) symmetry_worst>=-1.814978 30 11 M (0.36666667 0.63333333)
## 90) compactness_se>=-3.344528 18 7 B (0.61111111 0.38888889) *
## 91) compactness_se< -3.344528 12 0 M (0.00000000 1.00000000) *
## 23) texture_worst>=4.548911 34 0 M (0.00000000 1.00000000) *
## 3) smoothness_mean< -2.275457 677 302 M (0.44608567 0.55391433)
## 6) compactness_se< -4.705732 27 2 B (0.92592593 0.07407407)
## 12) symmetry_worst< -1.170399 25 0 B (1.00000000 0.00000000) *
## 13) symmetry_worst>=-1.170399 2 0 M (0.00000000 1.00000000) *
## 7) compactness_se>=-4.705732 650 277 M (0.42615385 0.57384615)
## 14) symmetry_worst>=-1.681676 237 103 B (0.56540084 0.43459916)
## 28) compactness_se>=-4.089478 159 51 B (0.67924528 0.32075472)
## 56) texture_worst< 5.003123 149 41 B (0.72483221 0.27516779)
## 112) compactness_se< -3.483184 88 12 B (0.86363636 0.13636364) *
## 113) compactness_se>=-3.483184 61 29 B (0.52459016 0.47540984) *
## 57) texture_worst>=5.003123 10 0 M (0.00000000 1.00000000) *
## 29) compactness_se< -4.089478 78 26 M (0.33333333 0.66666667)
## 58) compactness_se< -4.539406 22 7 B (0.68181818 0.31818182)
## 116) texture_worst>=4.62656 15 0 B (1.00000000 0.00000000) *
## 117) texture_worst< 4.62656 7 0 M (0.00000000 1.00000000) *
## 59) compactness_se>=-4.539406 56 11 M (0.19642857 0.80357143)
## 118) texture_worst< 4.496329 15 5 B (0.66666667 0.33333333) *
## 119) texture_worst>=4.496329 41 1 M (0.02439024 0.97560976) *
## 15) symmetry_worst< -1.681676 413 143 M (0.34624697 0.65375303)
## 30) smoothness_worst< -1.604009 63 22 B (0.65079365 0.34920635)
## 60) symmetry_worst< -1.895532 43 7 B (0.83720930 0.16279070)
## 120) smoothness_worst>=-1.694089 29 0 B (1.00000000 0.00000000) *
## 121) smoothness_worst< -1.694089 14 7 B (0.50000000 0.50000000) *
## 61) symmetry_worst>=-1.895532 20 5 M (0.25000000 0.75000000)
## 122) texture_mean< 2.923842 3 0 B (1.00000000 0.00000000) *
## 123) texture_mean>=2.923842 17 2 M (0.11764706 0.88235294) *
## 31) smoothness_worst>=-1.604009 350 102 M (0.29142857 0.70857143)
## 62) smoothness_mean< -2.488015 18 3 B (0.83333333 0.16666667)
## 124) smoothness_worst>=-1.572781 15 0 B (1.00000000 0.00000000) *
## 125) smoothness_worst< -1.572781 3 0 M (0.00000000 1.00000000) *
## 63) smoothness_mean>=-2.488015 332 87 M (0.26204819 0.73795181)
## 126) symmetry_worst< -2.25148 23 8 B (0.65217391 0.34782609) *
## 127) symmetry_worst>=-2.25148 309 72 M (0.23300971 0.76699029) *
##
## $trees[[15]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 397 B (0.56469298 0.43530702)
## 2) symmetry_worst< -2.048053 139 37 B (0.73381295 0.26618705)
## 4) smoothness_worst>=-1.493231 40 0 B (1.00000000 0.00000000) *
## 5) smoothness_worst< -1.493231 99 37 B (0.62626263 0.37373737)
## 10) smoothness_worst< -1.499051 91 29 B (0.68131868 0.31868132)
## 20) texture_worst< 4.371728 16 0 B (1.00000000 0.00000000) *
## 21) texture_worst>=4.371728 75 29 B (0.61333333 0.38666667)
## 42) texture_worst>=4.755481 42 9 B (0.78571429 0.21428571)
## 84) symmetry_worst< -2.063958 38 5 B (0.86842105 0.13157895) *
## 85) symmetry_worst>=-2.063958 4 0 M (0.00000000 1.00000000) *
## 43) texture_worst< 4.755481 33 13 M (0.39393939 0.60606061)
## 86) symmetry_worst>=-2.096646 5 0 B (1.00000000 0.00000000) *
## 87) symmetry_worst< -2.096646 28 8 M (0.28571429 0.71428571) *
## 11) smoothness_worst>=-1.499051 8 0 M (0.00000000 1.00000000) *
## 3) symmetry_worst>=-2.048053 773 360 B (0.53428202 0.46571798)
## 6) texture_mean< 3.058002 556 220 B (0.60431655 0.39568345)
## 12) texture_mean>=2.987952 162 37 B (0.77160494 0.22839506)
## 24) smoothness_worst>=-1.60795 148 25 B (0.83108108 0.16891892)
## 48) smoothness_mean< -2.203647 134 18 B (0.86567164 0.13432836)
## 96) texture_worst>=4.779866 57 1 B (0.98245614 0.01754386) *
## 97) texture_worst< 4.779866 77 17 B (0.77922078 0.22077922) *
## 49) smoothness_mean>=-2.203647 14 7 B (0.50000000 0.50000000)
## 98) texture_worst< 4.69039 7 0 B (1.00000000 0.00000000) *
## 99) texture_worst>=4.69039 7 0 M (0.00000000 1.00000000) *
## 25) smoothness_worst< -1.60795 14 2 M (0.14285714 0.85714286)
## 50) texture_mean< 2.999433 2 0 B (1.00000000 0.00000000) *
## 51) texture_mean>=2.999433 12 0 M (0.00000000 1.00000000) *
## 13) texture_mean< 2.987952 394 183 B (0.53553299 0.46446701)
## 26) texture_mean< 2.976294 378 167 B (0.55820106 0.44179894)
## 52) symmetry_worst>=-1.990832 346 142 B (0.58959538 0.41040462)
## 104) symmetry_worst< -1.861897 53 5 B (0.90566038 0.09433962) *
## 105) symmetry_worst>=-1.861897 293 137 B (0.53242321 0.46757679) *
## 53) symmetry_worst< -1.990832 32 7 M (0.21875000 0.78125000)
## 106) smoothness_mean< -2.445594 4 0 B (1.00000000 0.00000000) *
## 107) smoothness_mean>=-2.445594 28 3 M (0.10714286 0.89285714) *
## 27) texture_mean>=2.976294 16 0 M (0.00000000 1.00000000) *
## 7) texture_mean>=3.058002 217 77 M (0.35483871 0.64516129)
## 14) texture_worst>=4.753106 161 76 M (0.47204969 0.52795031)
## 28) compactness_se>=-3.902076 90 31 B (0.65555556 0.34444444)
## 56) compactness_se< -3.721197 30 1 B (0.96666667 0.03333333)
## 112) smoothness_worst< -1.446315 29 0 B (1.00000000 0.00000000) *
## 113) smoothness_worst>=-1.446315 1 0 M (0.00000000 1.00000000) *
## 57) compactness_se>=-3.721197 60 30 B (0.50000000 0.50000000)
## 114) texture_worst< 5.051039 45 15 B (0.66666667 0.33333333) *
## 115) texture_worst>=5.051039 15 0 M (0.00000000 1.00000000) *
## 29) compactness_se< -3.902076 71 17 M (0.23943662 0.76056338)
## 58) smoothness_worst< -1.622284 6 0 B (1.00000000 0.00000000) *
## 59) smoothness_worst>=-1.622284 65 11 M (0.16923077 0.83076923)
## 118) symmetry_worst>=-1.733593 36 11 M (0.30555556 0.69444444) *
## 119) symmetry_worst< -1.733593 29 0 M (0.00000000 1.00000000) *
## 15) texture_worst< 4.753106 56 1 M (0.01785714 0.98214286)
## 30) smoothness_mean< -2.498594 1 0 B (1.00000000 0.00000000) *
## 31) smoothness_mean>=-2.498594 55 0 M (0.00000000 1.00000000) *
##
## $trees[[16]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 451 M (0.49451754 0.50548246)
## 2) symmetry_worst< -1.424186 809 378 B (0.53275649 0.46724351)
## 4) smoothness_worst>=-1.49223 347 131 B (0.62247839 0.37752161)
## 8) texture_worst>=4.599485 199 53 B (0.73366834 0.26633166)
## 16) texture_mean< 3.083495 124 17 B (0.86290323 0.13709677)
## 32) compactness_se>=-4.280193 118 12 B (0.89830508 0.10169492)
## 64) smoothness_mean< -2.184141 108 6 B (0.94444444 0.05555556) *
## 65) smoothness_mean>=-2.184141 10 4 M (0.40000000 0.60000000) *
## 33) compactness_se< -4.280193 6 1 M (0.16666667 0.83333333)
## 66) texture_mean< 2.921626 1 0 B (1.00000000 0.00000000) *
## 67) texture_mean>=2.921626 5 0 M (0.00000000 1.00000000) *
## 17) texture_mean>=3.083495 75 36 B (0.52000000 0.48000000)
## 34) texture_mean>=3.192731 43 10 B (0.76744186 0.23255814)
## 68) texture_worst< 5.402766 37 4 B (0.89189189 0.10810811) *
## 69) texture_worst>=5.402766 6 0 M (0.00000000 1.00000000) *
## 35) texture_mean< 3.192731 32 6 M (0.18750000 0.81250000)
## 70) symmetry_worst< -2.063609 10 4 B (0.60000000 0.40000000) *
## 71) symmetry_worst>=-2.063609 22 0 M (0.00000000 1.00000000) *
## 9) texture_worst< 4.599485 148 70 M (0.47297297 0.52702703)
## 18) smoothness_worst< -1.450406 101 37 B (0.63366337 0.36633663)
## 36) compactness_se< -3.458298 70 11 B (0.84285714 0.15714286)
## 72) texture_worst>=4.180672 60 6 B (0.90000000 0.10000000) *
## 73) texture_worst< 4.180672 10 5 B (0.50000000 0.50000000) *
## 37) compactness_se>=-3.458298 31 5 M (0.16129032 0.83870968)
## 74) smoothness_mean>=-2.27605 10 5 B (0.50000000 0.50000000) *
## 75) smoothness_mean< -2.27605 21 0 M (0.00000000 1.00000000) *
## 19) smoothness_worst>=-1.450406 47 6 M (0.12765957 0.87234043)
## 38) texture_worst< 4.30106 10 4 B (0.60000000 0.40000000)
## 76) smoothness_worst>=-1.434633 6 0 B (1.00000000 0.00000000) *
## 77) smoothness_worst< -1.434633 4 0 M (0.00000000 1.00000000) *
## 39) texture_worst>=4.30106 37 0 M (0.00000000 1.00000000) *
## 5) smoothness_worst< -1.49223 462 215 M (0.46536797 0.53463203)
## 10) symmetry_worst< -1.815934 238 97 B (0.59243697 0.40756303)
## 20) smoothness_worst< -1.500665 222 82 B (0.63063063 0.36936937)
## 40) smoothness_worst>=-1.539367 63 10 B (0.84126984 0.15873016)
## 80) texture_worst< 4.566482 31 0 B (1.00000000 0.00000000) *
## 81) texture_worst>=4.566482 32 10 B (0.68750000 0.31250000) *
## 41) smoothness_worst< -1.539367 159 72 B (0.54716981 0.45283019)
## 82) smoothness_worst< -1.559798 126 47 B (0.62698413 0.37301587) *
## 83) smoothness_worst>=-1.559798 33 8 M (0.24242424 0.75757576) *
## 21) smoothness_worst>=-1.500665 16 1 M (0.06250000 0.93750000)
## 42) texture_worst< 4.593754 1 0 B (1.00000000 0.00000000) *
## 43) texture_worst>=4.593754 15 0 M (0.00000000 1.00000000) *
## 11) symmetry_worst>=-1.815934 224 74 M (0.33035714 0.66964286)
## 22) texture_mean< 2.824054 23 2 B (0.91304348 0.08695652)
## 44) symmetry_worst>=-1.792649 21 0 B (1.00000000 0.00000000) *
## 45) symmetry_worst< -1.792649 2 0 M (0.00000000 1.00000000) *
## 23) texture_mean>=2.824054 201 53 M (0.26368159 0.73631841)
## 46) texture_worst>=4.751723 50 25 B (0.50000000 0.50000000)
## 92) texture_worst< 4.996236 36 11 B (0.69444444 0.30555556) *
## 93) texture_worst>=4.996236 14 0 M (0.00000000 1.00000000) *
## 47) texture_worst< 4.751723 151 28 M (0.18543046 0.81456954)
## 94) smoothness_mean>=-2.231223 11 3 B (0.72727273 0.27272727) *
## 95) smoothness_mean< -2.231223 140 20 M (0.14285714 0.85714286) *
## 3) symmetry_worst>=-1.424186 103 20 M (0.19417476 0.80582524)
## 6) smoothness_worst< -1.501886 16 4 B (0.75000000 0.25000000)
## 12) texture_mean< 3.126045 12 0 B (1.00000000 0.00000000) *
## 13) texture_mean>=3.126045 4 0 M (0.00000000 1.00000000) *
## 7) smoothness_worst>=-1.501886 87 8 M (0.09195402 0.90804598)
## 14) texture_mean< 2.77286 11 5 M (0.45454545 0.54545455)
## 28) compactness_se< -3.173162 5 0 B (1.00000000 0.00000000) *
## 29) compactness_se>=-3.173162 6 0 M (0.00000000 1.00000000) *
## 15) texture_mean>=2.77286 76 3 M (0.03947368 0.96052632)
## 30) compactness_se>=-2.524297 3 1 B (0.66666667 0.33333333)
## 60) texture_mean< 2.915767 2 0 B (1.00000000 0.00000000) *
## 61) texture_mean>=2.915767 1 0 M (0.00000000 1.00000000) *
## 31) compactness_se< -2.524297 73 1 M (0.01369863 0.98630137)
## 62) compactness_se< -4.171724 2 1 B (0.50000000 0.50000000)
## 124) texture_mean< 3.068796 1 0 B (1.00000000 0.00000000) *
## 125) texture_mean>=3.068796 1 0 M (0.00000000 1.00000000) *
## 63) compactness_se>=-4.171724 71 0 M (0.00000000 1.00000000) *
##
## $trees[[17]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 403 M (0.44188596 0.55811404)
## 2) compactness_se< -4.706178 28 2 B (0.92857143 0.07142857)
## 4) symmetry_worst< -1.170399 26 0 B (1.00000000 0.00000000) *
## 5) symmetry_worst>=-1.170399 2 0 M (0.00000000 1.00000000) *
## 3) compactness_se>=-4.706178 884 377 M (0.42647059 0.57352941)
## 6) compactness_se>=-4.687525 855 375 M (0.43859649 0.56140351)
## 12) smoothness_worst< -1.500665 467 233 B (0.50107066 0.49892934)
## 24) texture_mean< 3.025285 237 89 B (0.62447257 0.37552743)
## 48) compactness_se>=-4.327955 200 61 B (0.69500000 0.30500000)
## 96) smoothness_worst>=-1.568787 143 29 B (0.79720280 0.20279720) *
## 97) smoothness_worst< -1.568787 57 25 M (0.43859649 0.56140351) *
## 49) compactness_se< -4.327955 37 9 M (0.24324324 0.75675676)
## 98) texture_mean< 2.85595 6 0 B (1.00000000 0.00000000) *
## 99) texture_mean>=2.85595 31 3 M (0.09677419 0.90322581) *
## 25) texture_mean>=3.025285 230 86 M (0.37391304 0.62608696)
## 50) smoothness_mean>=-2.317271 29 8 B (0.72413793 0.27586207)
## 100) smoothness_mean< -2.266276 19 0 B (1.00000000 0.00000000) *
## 101) smoothness_mean>=-2.266276 10 2 M (0.20000000 0.80000000) *
## 51) smoothness_mean< -2.317271 201 65 M (0.32338308 0.67661692)
## 102) smoothness_mean< -2.409448 104 51 M (0.49038462 0.50961538) *
## 103) smoothness_mean>=-2.409448 97 14 M (0.14432990 0.85567010) *
## 13) smoothness_worst>=-1.500665 388 141 M (0.36340206 0.63659794)
## 26) texture_worst>=4.628023 152 76 B (0.50000000 0.50000000)
## 52) texture_worst< 4.682677 25 2 B (0.92000000 0.08000000)
## 104) texture_mean>=2.836998 23 0 B (1.00000000 0.00000000) *
## 105) texture_mean< 2.836998 2 0 M (0.00000000 1.00000000) *
## 53) texture_worst>=4.682677 127 53 M (0.41732283 0.58267717)
## 106) smoothness_worst>=-1.430927 50 16 B (0.68000000 0.32000000) *
## 107) smoothness_worst< -1.430927 77 19 M (0.24675325 0.75324675) *
## 27) texture_worst< 4.628023 236 65 M (0.27542373 0.72457627)
## 54) compactness_se< -4.50262 10 0 B (1.00000000 0.00000000) *
## 55) compactness_se>=-4.50262 226 55 M (0.24336283 0.75663717)
## 110) symmetry_worst< -1.995212 21 7 B (0.66666667 0.33333333) *
## 111) symmetry_worst>=-1.995212 205 41 M (0.20000000 0.80000000) *
## 7) compactness_se< -4.687525 29 2 M (0.06896552 0.93103448)
## 14) smoothness_mean>=-2.441817 2 0 B (1.00000000 0.00000000) *
## 15) smoothness_mean< -2.441817 27 0 M (0.00000000 1.00000000) *
##
## $trees[[18]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 450 B (0.50657895 0.49342105)
## 2) texture_worst>=5.16917 76 18 B (0.76315789 0.23684211)
## 4) texture_worst< 5.636459 71 13 B (0.81690141 0.18309859)
## 8) smoothness_mean< -2.331159 67 9 B (0.86567164 0.13432836)
## 16) compactness_se< -3.328636 63 5 B (0.92063492 0.07936508)
## 32) symmetry_worst< -1.41032 62 4 B (0.93548387 0.06451613)
## 64) compactness_se>=-3.859901 36 0 B (1.00000000 0.00000000) *
## 65) compactness_se< -3.859901 26 4 B (0.84615385 0.15384615) *
## 33) symmetry_worst>=-1.41032 1 0 M (0.00000000 1.00000000) *
## 17) compactness_se>=-3.328636 4 0 M (0.00000000 1.00000000) *
## 9) smoothness_mean>=-2.331159 4 0 M (0.00000000 1.00000000) *
## 5) texture_worst>=5.636459 5 0 M (0.00000000 1.00000000) *
## 3) texture_worst< 5.16917 836 404 M (0.48325359 0.51674641)
## 6) texture_mean< 2.708379 31 3 B (0.90322581 0.09677419)
## 12) symmetry_worst< -1.112025 29 1 B (0.96551724 0.03448276)
## 24) smoothness_mean< -2.114874 24 0 B (1.00000000 0.00000000) *
## 25) smoothness_mean>=-2.114874 5 1 B (0.80000000 0.20000000)
## 50) smoothness_mean>=-2.060513 4 0 B (1.00000000 0.00000000) *
## 51) smoothness_mean< -2.060513 1 0 M (0.00000000 1.00000000) *
## 13) symmetry_worst>=-1.112025 2 0 M (0.00000000 1.00000000) *
## 7) texture_mean>=2.708379 805 376 M (0.46708075 0.53291925)
## 14) symmetry_worst< -1.427209 735 359 M (0.48843537 0.51156463)
## 28) compactness_se< -4.706178 13 0 B (1.00000000 0.00000000) *
## 29) compactness_se>=-4.706178 722 346 M (0.47922438 0.52077562)
## 58) compactness_se>=-4.681232 704 346 M (0.49147727 0.50852273)
## 116) texture_worst>=4.543638 408 180 B (0.55882353 0.44117647) *
## 117) texture_worst< 4.543638 296 118 M (0.39864865 0.60135135) *
## 59) compactness_se< -4.681232 18 0 M (0.00000000 1.00000000) *
## 15) symmetry_worst>=-1.427209 70 17 M (0.24285714 0.75714286)
## 30) texture_worst< 4.595658 31 14 M (0.45161290 0.54838710)
## 60) texture_worst>=4.251602 15 1 B (0.93333333 0.06666667)
## 120) smoothness_mean< -2.156267 14 0 B (1.00000000 0.00000000) *
## 121) smoothness_mean>=-2.156267 1 0 M (0.00000000 1.00000000) *
## 61) texture_worst< 4.251602 16 0 M (0.00000000 1.00000000) *
## 31) texture_worst>=4.595658 39 3 M (0.07692308 0.92307692)
## 62) texture_mean>=3.10949 11 3 M (0.27272727 0.72727273)
## 124) texture_mean< 3.116842 3 0 B (1.00000000 0.00000000) *
## 125) texture_mean>=3.116842 8 0 M (0.00000000 1.00000000) *
## 63) texture_mean< 3.10949 28 0 M (0.00000000 1.00000000) *
##
## $trees[[19]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 426 M (0.46710526 0.53289474)
## 2) symmetry_worst< -1.423936 816 407 M (0.49877451 0.50122549)
## 4) compactness_se< -3.648711 496 208 B (0.58064516 0.41935484)
## 8) symmetry_worst>=-1.749307 181 47 B (0.74033149 0.25966851)
## 16) smoothness_mean>=-2.483393 162 34 B (0.79012346 0.20987654)
## 32) compactness_se>=-4.676603 157 29 B (0.81528662 0.18471338)
## 64) texture_worst< 5.402766 152 24 B (0.84210526 0.15789474) *
## 65) texture_worst>=5.402766 5 0 M (0.00000000 1.00000000) *
## 33) compactness_se< -4.676603 5 0 M (0.00000000 1.00000000) *
## 17) smoothness_mean< -2.483393 19 6 M (0.31578947 0.68421053)
## 34) smoothness_mean< -2.536306 5 0 B (1.00000000 0.00000000) *
## 35) smoothness_mean>=-2.536306 14 1 M (0.07142857 0.92857143)
## 70) smoothness_worst>=-1.560846 1 0 B (1.00000000 0.00000000) *
## 71) smoothness_worst< -1.560846 13 0 M (0.00000000 1.00000000) *
## 9) symmetry_worst< -1.749307 315 154 M (0.48888889 0.51111111)
## 18) symmetry_worst< -1.789136 274 125 B (0.54379562 0.45620438)
## 36) symmetry_worst>=-1.855076 52 8 B (0.84615385 0.15384615)
## 72) smoothness_mean>=-2.433246 41 2 B (0.95121951 0.04878049) *
## 73) smoothness_mean< -2.433246 11 5 M (0.45454545 0.54545455) *
## 37) symmetry_worst< -1.855076 222 105 M (0.47297297 0.52702703)
## 74) smoothness_mean< -2.444437 39 7 B (0.82051282 0.17948718) *
## 75) smoothness_mean>=-2.444437 183 73 M (0.39890710 0.60109290) *
## 19) symmetry_worst>=-1.789136 41 5 M (0.12195122 0.87804878)
## 38) smoothness_mean< -2.429074 2 0 B (1.00000000 0.00000000) *
## 39) smoothness_mean>=-2.429074 39 3 M (0.07692308 0.92307692)
## 78) smoothness_worst< -1.550971 5 2 B (0.60000000 0.40000000) *
## 79) smoothness_worst>=-1.550971 34 0 M (0.00000000 1.00000000) *
## 5) compactness_se>=-3.648711 320 119 M (0.37187500 0.62812500)
## 10) symmetry_worst< -1.840831 128 58 B (0.54687500 0.45312500)
## 20) symmetry_worst>=-1.982941 43 0 B (1.00000000 0.00000000) *
## 21) symmetry_worst< -1.982941 85 27 M (0.31764706 0.68235294)
## 42) texture_worst>=5.255485 6 0 B (1.00000000 0.00000000) *
## 43) texture_worst< 5.255485 79 21 M (0.26582278 0.73417722)
## 86) texture_mean< 2.763153 4 0 B (1.00000000 0.00000000) *
## 87) texture_mean>=2.763153 75 17 M (0.22666667 0.77333333) *
## 11) symmetry_worst>=-1.840831 192 49 M (0.25520833 0.74479167)
## 22) smoothness_mean< -2.503795 10 0 B (1.00000000 0.00000000) *
## 23) smoothness_mean>=-2.503795 182 39 M (0.21428571 0.78571429)
## 46) symmetry_worst>=-1.471051 10 1 B (0.90000000 0.10000000)
## 92) texture_mean< 3.100749 9 0 B (1.00000000 0.00000000) *
## 93) texture_mean>=3.100749 1 0 M (0.00000000 1.00000000) *
## 47) symmetry_worst< -1.471051 172 30 M (0.17441860 0.82558140)
## 94) compactness_se>=-2.853699 19 9 B (0.52631579 0.47368421) *
## 95) compactness_se< -2.853699 153 20 M (0.13071895 0.86928105) *
## 3) symmetry_worst>=-1.423936 96 19 M (0.19791667 0.80208333)
## 6) texture_mean< 2.77286 10 2 B (0.80000000 0.20000000)
## 12) compactness_se< -3.173162 8 0 B (1.00000000 0.00000000) *
## 13) compactness_se>=-3.173162 2 0 M (0.00000000 1.00000000) *
## 7) texture_mean>=2.77286 86 11 M (0.12790698 0.87209302)
## 14) smoothness_worst< -1.501886 15 7 B (0.53333333 0.46666667)
## 28) texture_mean< 3.021535 6 0 B (1.00000000 0.00000000) *
## 29) texture_mean>=3.021535 9 2 M (0.22222222 0.77777778)
## 58) smoothness_mean>=-2.349952 2 0 B (1.00000000 0.00000000) *
## 59) smoothness_mean< -2.349952 7 0 M (0.00000000 1.00000000) *
## 15) smoothness_worst>=-1.501886 71 3 M (0.04225352 0.95774648)
## 30) compactness_se>=-2.567912 2 0 B (1.00000000 0.00000000) *
## 31) compactness_se< -2.567912 69 1 M (0.01449275 0.98550725)
## 62) compactness_se< -4.171724 5 1 M (0.20000000 0.80000000)
## 124) texture_mean< 3.006781 1 0 B (1.00000000 0.00000000) *
## 125) texture_mean>=3.006781 4 0 M (0.00000000 1.00000000) *
## 63) compactness_se>=-4.171724 64 0 M (0.00000000 1.00000000) *
##
## $trees[[20]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 450 B (0.50657895 0.49342105)
## 2) symmetry_worst< -1.840831 390 150 B (0.61538462 0.38461538)
## 4) compactness_se>=-4.434687 325 102 B (0.68615385 0.31384615)
## 8) symmetry_worst>=-1.9261 88 10 B (0.88636364 0.11363636)
## 16) texture_worst< 4.927821 82 6 B (0.92682927 0.07317073)
## 32) smoothness_worst< -1.424105 77 3 B (0.96103896 0.03896104)
## 64) smoothness_mean>=-2.390216 59 0 B (1.00000000 0.00000000) *
## 65) smoothness_mean< -2.390216 18 3 B (0.83333333 0.16666667) *
## 33) smoothness_worst>=-1.424105 5 2 M (0.40000000 0.60000000)
## 66) texture_mean>=2.876957 2 0 B (1.00000000 0.00000000) *
## 67) texture_mean< 2.876957 3 0 M (0.00000000 1.00000000) *
## 17) texture_worst>=4.927821 6 2 M (0.33333333 0.66666667)
## 34) texture_mean>=3.304052 2 0 B (1.00000000 0.00000000) *
## 35) texture_mean< 3.304052 4 0 M (0.00000000 1.00000000) *
## 9) symmetry_worst< -1.9261 237 92 B (0.61181435 0.38818565)
## 18) symmetry_worst< -1.964096 202 67 B (0.66831683 0.33168317)
## 36) smoothness_mean< -2.242666 189 55 B (0.70899471 0.29100529)
## 72) smoothness_mean>=-2.33454 44 3 B (0.93181818 0.06818182) *
## 73) smoothness_mean< -2.33454 145 52 B (0.64137931 0.35862069) *
## 37) smoothness_mean>=-2.242666 13 1 M (0.07692308 0.92307692)
## 74) texture_mean< 2.885158 1 0 B (1.00000000 0.00000000) *
## 75) texture_mean>=2.885158 12 0 M (0.00000000 1.00000000) *
## 19) symmetry_worst>=-1.964096 35 10 M (0.28571429 0.71428571)
## 38) smoothness_mean>=-2.225218 8 0 B (1.00000000 0.00000000) *
## 39) smoothness_mean< -2.225218 27 2 M (0.07407407 0.92592593)
## 78) smoothness_mean< -2.425835 2 0 B (1.00000000 0.00000000) *
## 79) smoothness_mean>=-2.425835 25 0 M (0.00000000 1.00000000) *
## 5) compactness_se< -4.434687 65 17 M (0.26153846 0.73846154)
## 10) compactness_se< -4.706178 11 0 B (1.00000000 0.00000000) *
## 11) compactness_se>=-4.706178 54 6 M (0.11111111 0.88888889)
## 22) texture_mean< 2.846651 2 0 B (1.00000000 0.00000000) *
## 23) texture_mean>=2.846651 52 4 M (0.07692308 0.92307692)
## 46) smoothness_mean>=-2.271294 2 0 B (1.00000000 0.00000000) *
## 47) smoothness_mean< -2.271294 50 2 M (0.04000000 0.96000000)
## 94) compactness_se>=-4.514873 18 2 M (0.11111111 0.88888889) *
## 95) compactness_se< -4.514873 32 0 M (0.00000000 1.00000000) *
## 3) symmetry_worst>=-1.840831 522 222 M (0.42528736 0.57471264)
## 6) compactness_se< -4.510773 39 9 B (0.76923077 0.23076923)
## 12) texture_worst>=4.622562 26 1 B (0.96153846 0.03846154)
## 24) smoothness_worst< -1.491257 19 0 B (1.00000000 0.00000000) *
## 25) smoothness_worst>=-1.491257 7 1 B (0.85714286 0.14285714)
## 50) compactness_se< -4.557422 6 0 B (1.00000000 0.00000000) *
## 51) compactness_se>=-4.557422 1 0 M (0.00000000 1.00000000) *
## 13) texture_worst< 4.622562 13 5 M (0.38461538 0.61538462)
## 26) texture_worst< 4.468479 5 0 B (1.00000000 0.00000000) *
## 27) texture_worst>=4.468479 8 0 M (0.00000000 1.00000000) *
## 7) compactness_se>=-4.510773 483 192 M (0.39751553 0.60248447)
## 14) smoothness_worst< -1.472307 322 149 M (0.46273292 0.53726708)
## 28) smoothness_worst>=-1.4768 36 6 B (0.83333333 0.16666667)
## 56) symmetry_worst>=-1.811141 30 0 B (1.00000000 0.00000000) *
## 57) symmetry_worst< -1.811141 6 0 M (0.00000000 1.00000000) *
## 29) smoothness_worst< -1.4768 286 119 M (0.41608392 0.58391608)
## 58) compactness_se< -3.711591 134 62 B (0.53731343 0.46268657)
## 116) compactness_se>=-4.159844 63 16 B (0.74603175 0.25396825) *
## 117) compactness_se< -4.159844 71 25 M (0.35211268 0.64788732) *
## 59) compactness_se>=-3.711591 152 47 M (0.30921053 0.69078947)
## 118) smoothness_mean< -2.424641 27 10 B (0.62962963 0.37037037) *
## 119) smoothness_mean>=-2.424641 125 30 M (0.24000000 0.76000000) *
## 15) smoothness_worst>=-1.472307 161 43 M (0.26708075 0.73291925)
## 30) smoothness_mean>=-2.093138 20 5 B (0.75000000 0.25000000)
## 60) texture_mean>=2.515298 16 1 B (0.93750000 0.06250000)
## 120) compactness_se< -2.887458 15 0 B (1.00000000 0.00000000) *
## 121) compactness_se>=-2.887458 1 0 M (0.00000000 1.00000000) *
## 61) texture_mean< 2.515298 4 0 M (0.00000000 1.00000000) *
## 31) smoothness_mean< -2.093138 141 28 M (0.19858156 0.80141844)
## 62) symmetry_worst< -1.780671 8 2 B (0.75000000 0.25000000)
## 124) smoothness_mean>=-2.223945 5 0 B (1.00000000 0.00000000) *
## 125) smoothness_mean< -2.223945 3 1 M (0.33333333 0.66666667) *
## 63) symmetry_worst>=-1.780671 133 22 M (0.16541353 0.83458647)
## 126) compactness_se>=-2.540721 3 0 B (1.00000000 0.00000000) *
## 127) compactness_se< -2.540721 130 19 M (0.14615385 0.85384615) *
##
## $trees[[21]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 435 B (0.52302632 0.47697368)
## 2) texture_worst< 4.609772 471 190 B (0.59660297 0.40339703)
## 4) compactness_se< -3.66733 304 95 B (0.68750000 0.31250000)
## 8) symmetry_worst< -1.786753 149 28 B (0.81208054 0.18791946)
## 16) symmetry_worst>=-2.49184 137 18 B (0.86861314 0.13138686)
## 32) symmetry_worst< -1.957488 57 0 B (1.00000000 0.00000000) *
## 33) symmetry_worst>=-1.957488 80 18 B (0.77500000 0.22500000)
## 66) symmetry_worst>=-1.919731 59 8 B (0.86440678 0.13559322) *
## 67) symmetry_worst< -1.919731 21 10 B (0.52380952 0.47619048) *
## 17) symmetry_worst< -2.49184 12 2 M (0.16666667 0.83333333)
## 34) texture_mean< 2.855865 2 0 B (1.00000000 0.00000000) *
## 35) texture_mean>=2.855865 10 0 M (0.00000000 1.00000000) *
## 9) symmetry_worst>=-1.786753 155 67 B (0.56774194 0.43225806)
## 18) symmetry_worst>=-1.749307 118 33 B (0.72033898 0.27966102)
## 36) smoothness_mean>=-2.418898 88 14 B (0.84090909 0.15909091)
## 72) smoothness_worst< -1.480927 39 0 B (1.00000000 0.00000000) *
## 73) smoothness_worst>=-1.480927 49 14 B (0.71428571 0.28571429) *
## 37) smoothness_mean< -2.418898 30 11 M (0.36666667 0.63333333)
## 74) smoothness_mean< -2.43698 14 3 B (0.78571429 0.21428571) *
## 75) smoothness_mean>=-2.43698 16 0 M (0.00000000 1.00000000) *
## 19) symmetry_worst< -1.749307 37 3 M (0.08108108 0.91891892)
## 38) texture_mean< 2.803754 2 0 B (1.00000000 0.00000000) *
## 39) texture_mean>=2.803754 35 1 M (0.02857143 0.97142857)
## 78) smoothness_worst< -1.550971 1 0 B (1.00000000 0.00000000) *
## 79) smoothness_worst>=-1.550971 34 0 M (0.00000000 1.00000000) *
## 5) compactness_se>=-3.66733 167 72 M (0.43113772 0.56886228)
## 10) compactness_se>=-3.483667 106 47 B (0.55660377 0.44339623)
## 20) smoothness_worst< -1.496438 60 12 B (0.80000000 0.20000000)
## 40) texture_mean< 3.049609 50 2 B (0.96000000 0.04000000)
## 80) compactness_se>=-3.392487 41 0 B (1.00000000 0.00000000) *
## 81) compactness_se< -3.392487 9 2 B (0.77777778 0.22222222) *
## 41) texture_mean>=3.049609 10 0 M (0.00000000 1.00000000) *
## 21) smoothness_worst>=-1.496438 46 11 M (0.23913043 0.76086957)
## 42) smoothness_worst>=-1.4665 15 5 B (0.66666667 0.33333333)
## 84) texture_worst>=4.250651 12 2 B (0.83333333 0.16666667) *
## 85) texture_worst< 4.250651 3 0 M (0.00000000 1.00000000) *
## 43) smoothness_worst< -1.4665 31 1 M (0.03225806 0.96774194)
## 86) texture_mean< 2.8622 7 1 M (0.14285714 0.85714286) *
## 87) texture_mean>=2.8622 24 0 M (0.00000000 1.00000000) *
## 11) compactness_se< -3.483667 61 13 M (0.21311475 0.78688525)
## 22) smoothness_mean< -2.322902 31 12 M (0.38709677 0.61290323)
## 44) smoothness_mean>=-2.40657 12 0 B (1.00000000 0.00000000) *
## 45) smoothness_mean< -2.40657 19 0 M (0.00000000 1.00000000) *
## 23) smoothness_mean>=-2.322902 30 1 M (0.03333333 0.96666667)
## 46) symmetry_worst< -1.900827 2 1 B (0.50000000 0.50000000)
## 92) texture_mean< 2.874386 1 0 B (1.00000000 0.00000000) *
## 93) texture_mean>=2.874386 1 0 M (0.00000000 1.00000000) *
## 47) symmetry_worst>=-1.900827 28 0 M (0.00000000 1.00000000) *
## 3) texture_worst>=4.609772 441 196 M (0.44444444 0.55555556)
## 6) compactness_se>=-4.671834 412 195 M (0.47330097 0.52669903)
## 12) texture_worst>=4.628023 381 189 B (0.50393701 0.49606299)
## 24) texture_mean< 2.91424 28 3 B (0.89285714 0.10714286)
## 48) symmetry_worst< -1.384729 25 0 B (1.00000000 0.00000000) *
## 49) symmetry_worst>=-1.384729 3 0 M (0.00000000 1.00000000) *
## 25) texture_mean>=2.91424 353 167 M (0.47308782 0.52691218)
## 50) texture_mean>=3.029409 277 129 B (0.53429603 0.46570397)
## 100) texture_mean< 3.058002 38 0 B (1.00000000 0.00000000) *
## 101) texture_mean>=3.058002 239 110 M (0.46025105 0.53974895) *
## 51) texture_mean< 3.029409 76 19 M (0.25000000 0.75000000)
## 102) smoothness_mean< -2.423157 8 0 B (1.00000000 0.00000000) *
## 103) smoothness_mean>=-2.423157 68 11 M (0.16176471 0.83823529) *
## 13) texture_worst< 4.628023 31 3 M (0.09677419 0.90322581)
## 26) smoothness_mean< -2.471714 2 0 B (1.00000000 0.00000000) *
## 27) smoothness_mean>=-2.471714 29 1 M (0.03448276 0.96551724)
## 54) symmetry_worst< -2.477165 1 0 B (1.00000000 0.00000000) *
## 55) symmetry_worst>=-2.477165 28 0 M (0.00000000 1.00000000) *
## 7) compactness_se< -4.671834 29 1 M (0.03448276 0.96551724)
## 14) compactness_se< -4.938351 1 0 B (1.00000000 0.00000000) *
## 15) compactness_se>=-4.938351 28 0 M (0.00000000 1.00000000) *
##
## $trees[[22]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 409 B (0.55153509 0.44846491)
## 2) symmetry_worst< -2.193154 105 24 B (0.77142857 0.22857143)
## 4) smoothness_mean< -2.217511 100 19 B (0.81000000 0.19000000)
## 8) symmetry_worst>=-2.957999 97 16 B (0.83505155 0.16494845)
## 16) texture_mean< 3.326618 89 11 B (0.87640449 0.12359551)
## 32) compactness_se>=-3.861191 63 2 B (0.96825397 0.03174603)
## 64) smoothness_mean>=-2.481712 57 0 B (1.00000000 0.00000000) *
## 65) smoothness_mean< -2.481712 6 2 B (0.66666667 0.33333333) *
## 33) compactness_se< -3.861191 26 9 B (0.65384615 0.34615385)
## 66) compactness_se< -3.941046 21 4 B (0.80952381 0.19047619) *
## 67) compactness_se>=-3.941046 5 0 M (0.00000000 1.00000000) *
## 17) texture_mean>=3.326618 8 3 M (0.37500000 0.62500000)
## 34) texture_mean>=3.379986 3 0 B (1.00000000 0.00000000) *
## 35) texture_mean< 3.379986 5 0 M (0.00000000 1.00000000) *
## 9) symmetry_worst< -2.957999 3 0 M (0.00000000 1.00000000) *
## 5) smoothness_mean>=-2.217511 5 0 M (0.00000000 1.00000000) *
## 3) symmetry_worst>=-2.193154 807 385 B (0.52292441 0.47707559)
## 6) texture_mean< 2.708379 30 2 B (0.93333333 0.06666667)
## 12) compactness_se< -2.990558 29 1 B (0.96551724 0.03448276)
## 24) symmetry_worst< -1.556816 22 0 B (1.00000000 0.00000000) *
## 25) symmetry_worst>=-1.556816 7 1 B (0.85714286 0.14285714)
## 50) smoothness_mean< -2.081877 6 0 B (1.00000000 0.00000000) *
## 51) smoothness_mean>=-2.081877 1 0 M (0.00000000 1.00000000) *
## 13) compactness_se>=-2.990558 1 0 M (0.00000000 1.00000000) *
## 7) texture_mean>=2.708379 777 383 B (0.50707851 0.49292149)
## 14) texture_worst>=4.982438 116 36 B (0.68965517 0.31034483)
## 28) smoothness_mean< -2.425205 69 7 B (0.89855072 0.10144928)
## 56) smoothness_worst< -1.490267 64 3 B (0.95312500 0.04687500)
## 112) symmetry_worst< -1.530091 63 2 B (0.96825397 0.03174603) *
## 113) symmetry_worst>=-1.530091 1 0 M (0.00000000 1.00000000) *
## 57) smoothness_worst>=-1.490267 5 1 M (0.20000000 0.80000000)
## 114) texture_mean>=3.23593 1 0 B (1.00000000 0.00000000) *
## 115) texture_mean< 3.23593 4 0 M (0.00000000 1.00000000) *
## 29) smoothness_mean>=-2.425205 47 18 M (0.38297872 0.61702128)
## 58) smoothness_worst>=-1.483884 29 11 B (0.62068966 0.37931034)
## 116) symmetry_worst< -1.65672 19 3 B (0.84210526 0.15789474) *
## 117) symmetry_worst>=-1.65672 10 2 M (0.20000000 0.80000000) *
## 59) smoothness_worst< -1.483884 18 0 M (0.00000000 1.00000000) *
## 15) texture_worst< 4.982438 661 314 M (0.47503782 0.52496218)
## 30) texture_worst< 4.976767 633 314 M (0.49605055 0.50394945)
## 60) smoothness_worst< -1.374083 614 301 B (0.50977199 0.49022801)
## 120) smoothness_mean>=-2.354774 327 135 B (0.58715596 0.41284404) *
## 121) smoothness_mean< -2.354774 287 121 M (0.42160279 0.57839721) *
## 61) smoothness_worst>=-1.374083 19 1 M (0.05263158 0.94736842)
## 122) symmetry_worst< -1.846189 1 0 B (1.00000000 0.00000000) *
## 123) symmetry_worst>=-1.846189 18 0 M (0.00000000 1.00000000) *
## 31) texture_worst>=4.976767 28 0 M (0.00000000 1.00000000) *
##
## $trees[[23]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 390 B (0.57236842 0.42763158)
## 2) smoothness_mean< -2.392182 336 110 B (0.67261905 0.32738095)
## 4) smoothness_mean>=-2.401687 39 0 B (1.00000000 0.00000000) *
## 5) smoothness_mean< -2.401687 297 110 B (0.62962963 0.37037037)
## 10) texture_mean>=3.198061 61 9 B (0.85245902 0.14754098)
## 20) symmetry_worst>=-2.242858 55 4 B (0.92727273 0.07272727)
## 40) texture_mean< 3.440257 52 1 B (0.98076923 0.01923077)
## 80) symmetry_worst< -1.345645 51 0 B (1.00000000 0.00000000) *
## 81) symmetry_worst>=-1.345645 1 0 M (0.00000000 1.00000000) *
## 41) texture_mean>=3.440257 3 0 M (0.00000000 1.00000000) *
## 21) symmetry_worst< -2.242858 6 1 M (0.16666667 0.83333333)
## 42) texture_mean>=3.357516 1 0 B (1.00000000 0.00000000) *
## 43) texture_mean< 3.357516 5 0 M (0.00000000 1.00000000) *
## 11) texture_mean< 3.198061 236 101 B (0.57203390 0.42796610)
## 22) texture_mean< 3.130673 216 82 B (0.62037037 0.37962963)
## 44) smoothness_mean< -2.408446 201 67 B (0.66666667 0.33333333)
## 88) smoothness_mean>=-2.495871 144 33 B (0.77083333 0.22916667) *
## 89) smoothness_mean< -2.495871 57 23 M (0.40350877 0.59649123) *
## 45) smoothness_mean>=-2.408446 15 0 M (0.00000000 1.00000000) *
## 23) texture_mean>=3.130673 20 1 M (0.05000000 0.95000000)
## 46) smoothness_mean>=-2.417513 1 0 B (1.00000000 0.00000000) *
## 47) smoothness_mean< -2.417513 19 0 M (0.00000000 1.00000000) *
## 3) smoothness_mean>=-2.392182 576 280 B (0.51388889 0.48611111)
## 6) texture_worst< 4.1745 38 5 B (0.86842105 0.13157895)
## 12) compactness_se< -3.032021 35 3 B (0.91428571 0.08571429)
## 24) smoothness_mean< -2.07745 29 1 B (0.96551724 0.03448276)
## 48) smoothness_worst>=-1.53208 26 0 B (1.00000000 0.00000000) *
## 49) smoothness_worst< -1.53208 3 1 B (0.66666667 0.33333333)
## 98) texture_mean>=2.744166 2 0 B (1.00000000 0.00000000) *
## 99) texture_mean< 2.744166 1 0 M (0.00000000 1.00000000) *
## 25) smoothness_mean>=-2.07745 6 2 B (0.66666667 0.33333333)
## 50) smoothness_mean>=-2.060513 4 0 B (1.00000000 0.00000000) *
## 51) smoothness_mean< -2.060513 2 0 M (0.00000000 1.00000000) *
## 13) compactness_se>=-3.032021 3 1 M (0.33333333 0.66666667)
## 26) smoothness_mean< -2.298748 1 0 B (1.00000000 0.00000000) *
## 27) smoothness_mean>=-2.298748 2 0 M (0.00000000 1.00000000) *
## 7) texture_worst>=4.1745 538 263 M (0.48884758 0.51115242)
## 14) texture_worst>=4.751723 202 74 B (0.63366337 0.36633663)
## 28) compactness_se< -3.352836 167 47 B (0.71856287 0.28143713)
## 56) texture_worst< 4.818867 52 2 B (0.96153846 0.03846154)
## 112) smoothness_worst< -1.425578 50 0 B (1.00000000 0.00000000) *
## 113) smoothness_worst>=-1.425578 2 0 M (0.00000000 1.00000000) *
## 57) texture_worst>=4.818867 115 45 B (0.60869565 0.39130435)
## 114) texture_mean>=3.032246 96 29 B (0.69791667 0.30208333) *
## 115) texture_mean< 3.032246 19 3 M (0.15789474 0.84210526) *
## 29) compactness_se>=-3.352836 35 8 M (0.22857143 0.77142857)
## 58) symmetry_worst>=-1.477364 10 2 B (0.80000000 0.20000000)
## 116) texture_mean< 3.05648 8 0 B (1.00000000 0.00000000) *
## 117) texture_mean>=3.05648 2 0 M (0.00000000 1.00000000) *
## 59) symmetry_worst< -1.477364 25 0 M (0.00000000 1.00000000) *
## 15) texture_worst< 4.751723 336 135 M (0.40178571 0.59821429)
## 30) texture_worst< 4.682677 287 132 M (0.45993031 0.54006969)
## 60) texture_worst>=4.626933 42 6 B (0.85714286 0.14285714)
## 120) smoothness_worst>=-1.505734 36 0 B (1.00000000 0.00000000) *
## 121) smoothness_worst< -1.505734 6 0 M (0.00000000 1.00000000) *
## 61) texture_worst< 4.626933 245 96 M (0.39183673 0.60816327)
## 122) texture_worst< 4.50835 143 70 B (0.51048951 0.48951049) *
## 123) texture_worst>=4.50835 102 23 M (0.22549020 0.77450980) *
## 31) texture_worst>=4.682677 49 3 M (0.06122449 0.93877551)
## 62) texture_mean< 2.909862 4 1 B (0.75000000 0.25000000)
## 124) texture_worst>=4.709072 3 0 B (1.00000000 0.00000000) *
## 125) texture_worst< 4.709072 1 0 M (0.00000000 1.00000000) *
## 63) texture_mean>=2.909862 45 0 M (0.00000000 1.00000000) *
##
## $trees[[24]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 432 B (0.52631579 0.47368421)
## 2) smoothness_worst< -1.482699 593 231 B (0.61045531 0.38954469)
## 4) smoothness_mean< -2.506908 68 13 B (0.80882353 0.19117647)
## 8) compactness_se>=-4.692873 59 5 B (0.91525424 0.08474576)
## 16) smoothness_worst>=-1.71076 52 2 B (0.96153846 0.03846154)
## 32) symmetry_worst< -1.627715 43 0 B (1.00000000 0.00000000) *
## 33) symmetry_worst>=-1.627715 9 2 B (0.77777778 0.22222222)
## 66) symmetry_worst>=-1.617577 7 0 B (1.00000000 0.00000000) *
## 67) symmetry_worst< -1.617577 2 0 M (0.00000000 1.00000000) *
## 17) smoothness_worst< -1.71076 7 3 B (0.57142857 0.42857143)
## 34) compactness_se< -3.013033 4 0 B (1.00000000 0.00000000) *
## 35) compactness_se>=-3.013033 3 0 M (0.00000000 1.00000000) *
## 9) compactness_se< -4.692873 9 1 M (0.11111111 0.88888889)
## 18) texture_mean>=3.172108 1 0 B (1.00000000 0.00000000) *
## 19) texture_mean< 3.172108 8 0 M (0.00000000 1.00000000) *
## 5) smoothness_mean>=-2.506908 525 218 B (0.58476190 0.41523810)
## 10) smoothness_mean>=-2.4986 511 204 B (0.60078278 0.39921722)
## 20) smoothness_worst< -1.618016 40 3 B (0.92500000 0.07500000)
## 40) smoothness_mean< -2.337942 38 1 B (0.97368421 0.02631579)
## 80) smoothness_worst>=-1.694089 37 0 B (1.00000000 0.00000000) *
## 81) smoothness_worst< -1.694089 1 0 M (0.00000000 1.00000000) *
## 41) smoothness_mean>=-2.337942 2 0 M (0.00000000 1.00000000) *
## 21) smoothness_worst>=-1.618016 471 201 B (0.57324841 0.42675159)
## 42) smoothness_worst>=-1.59596 423 168 B (0.60283688 0.39716312)
## 84) smoothness_worst< -1.584838 47 4 B (0.91489362 0.08510638) *
## 85) smoothness_worst>=-1.584838 376 164 B (0.56382979 0.43617021) *
## 43) smoothness_worst< -1.59596 48 15 M (0.31250000 0.68750000)
## 86) symmetry_worst>=-2.037346 22 9 B (0.59090909 0.40909091) *
## 87) symmetry_worst< -2.037346 26 2 M (0.07692308 0.92307692) *
## 11) smoothness_mean< -2.4986 14 0 M (0.00000000 1.00000000) *
## 3) smoothness_worst>=-1.482699 319 118 M (0.36990596 0.63009404)
## 6) symmetry_worst< -1.658843 165 79 B (0.52121212 0.47878788)
## 12) smoothness_worst>=-1.477976 132 47 B (0.64393939 0.35606061)
## 24) compactness_se< -3.294139 114 32 B (0.71929825 0.28070175)
## 48) texture_worst< 5.041355 108 27 B (0.75000000 0.25000000)
## 96) texture_worst< 4.373034 19 0 B (1.00000000 0.00000000) *
## 97) texture_worst>=4.373034 89 27 B (0.69662921 0.30337079) *
## 49) texture_worst>=5.041355 6 1 M (0.16666667 0.83333333)
## 98) texture_mean< 2.955358 1 0 B (1.00000000 0.00000000) *
## 99) texture_mean>=2.955358 5 0 M (0.00000000 1.00000000) *
## 25) compactness_se>=-3.294139 18 3 M (0.16666667 0.83333333)
## 50) texture_mean>=3.23593 3 0 B (1.00000000 0.00000000) *
## 51) texture_mean< 3.23593 15 0 M (0.00000000 1.00000000) *
## 13) smoothness_worst< -1.477976 33 1 M (0.03030303 0.96969697)
## 26) compactness_se< -3.967101 1 0 B (1.00000000 0.00000000) *
## 27) compactness_se>=-3.967101 32 0 M (0.00000000 1.00000000) *
## 7) symmetry_worst>=-1.658843 154 32 M (0.20779221 0.79220779)
## 14) smoothness_mean< -2.219198 101 31 M (0.30693069 0.69306931)
## 28) smoothness_mean>=-2.233531 11 0 B (1.00000000 0.00000000) *
## 29) smoothness_mean< -2.233531 90 20 M (0.22222222 0.77777778)
## 58) texture_mean< 2.735974 4 0 B (1.00000000 0.00000000) *
## 59) texture_mean>=2.735974 86 16 M (0.18604651 0.81395349)
## 118) compactness_se>=-3.05573 7 2 B (0.71428571 0.28571429) *
## 119) compactness_se< -3.05573 79 11 M (0.13924051 0.86075949) *
## 15) smoothness_mean>=-2.219198 53 1 M (0.01886792 0.98113208)
## 30) compactness_se< -4.032019 1 0 B (1.00000000 0.00000000) *
## 31) compactness_se>=-4.032019 52 0 M (0.00000000 1.00000000) *
##
## $trees[[25]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 411 M (0.45065789 0.54934211)
## 2) smoothness_worst< -1.604472 107 29 B (0.72897196 0.27102804)
## 4) compactness_se>=-4.507137 84 16 B (0.80952381 0.19047619)
## 8) texture_worst>=4.680896 41 1 B (0.97560976 0.02439024)
## 16) smoothness_mean< -2.337942 40 0 B (1.00000000 0.00000000) *
## 17) smoothness_mean>=-2.337942 1 0 M (0.00000000 1.00000000) *
## 9) texture_worst< 4.680896 43 15 B (0.65116279 0.34883721)
## 18) texture_worst< 4.491851 20 1 B (0.95000000 0.05000000)
## 36) smoothness_worst< -1.637109 18 0 B (1.00000000 0.00000000) *
## 37) smoothness_worst>=-1.637109 2 1 B (0.50000000 0.50000000)
## 74) texture_mean< 2.675349 1 0 B (1.00000000 0.00000000) *
## 75) texture_mean>=2.675349 1 0 M (0.00000000 1.00000000) *
## 19) texture_worst>=4.491851 23 9 M (0.39130435 0.60869565)
## 38) compactness_se< -4.234991 8 0 B (1.00000000 0.00000000) *
## 39) compactness_se>=-4.234991 15 1 M (0.06666667 0.93333333)
## 78) texture_worst>=4.619922 4 1 M (0.25000000 0.75000000) *
## 79) texture_worst< 4.619922 11 0 M (0.00000000 1.00000000) *
## 5) compactness_se< -4.507137 23 10 M (0.43478261 0.56521739)
## 10) smoothness_mean< -2.549773 7 0 B (1.00000000 0.00000000) *
## 11) smoothness_mean>=-2.549773 16 3 M (0.18750000 0.81250000)
## 22) symmetry_worst< -1.925408 6 3 B (0.50000000 0.50000000)
## 44) texture_mean< 3.149769 3 0 B (1.00000000 0.00000000) *
## 45) texture_mean>=3.149769 3 0 M (0.00000000 1.00000000) *
## 23) symmetry_worst>=-1.925408 10 0 M (0.00000000 1.00000000) *
## 3) smoothness_worst>=-1.604472 805 333 M (0.41366460 0.58633540)
## 6) texture_mean< 2.707375 28 3 B (0.89285714 0.10714286)
## 12) symmetry_worst< -1.577652 23 0 B (1.00000000 0.00000000) *
## 13) symmetry_worst>=-1.577652 5 2 M (0.40000000 0.60000000)
## 26) texture_mean>=2.553793 2 0 B (1.00000000 0.00000000) *
## 27) texture_mean< 2.553793 3 0 M (0.00000000 1.00000000) *
## 7) texture_mean>=2.707375 777 308 M (0.39639640 0.60360360)
## 14) compactness_se< -4.224388 116 48 B (0.58620690 0.41379310)
## 28) smoothness_mean>=-2.3007 25 0 B (1.00000000 0.00000000) *
## 29) smoothness_mean< -2.3007 91 43 M (0.47252747 0.52747253)
## 58) symmetry_worst>=-1.49608 11 0 B (1.00000000 0.00000000) *
## 59) symmetry_worst< -1.49608 80 32 M (0.40000000 0.60000000)
## 118) texture_worst>=4.876647 29 10 B (0.65517241 0.34482759) *
## 119) texture_worst< 4.876647 51 13 M (0.25490196 0.74509804) *
## 15) compactness_se>=-4.224388 661 240 M (0.36308623 0.63691377)
## 30) smoothness_mean< -2.2971 432 180 M (0.41666667 0.58333333)
## 60) compactness_se>=-3.93685 283 140 B (0.50530035 0.49469965)
## 120) smoothness_worst>=-1.565486 215 89 B (0.58604651 0.41395349) *
## 121) smoothness_worst< -1.565486 68 17 M (0.25000000 0.75000000) *
## 61) compactness_se< -3.93685 149 37 M (0.24832215 0.75167785)
## 122) smoothness_mean>=-2.312236 10 1 B (0.90000000 0.10000000) *
## 123) smoothness_mean< -2.312236 139 28 M (0.20143885 0.79856115) *
## 31) smoothness_mean>=-2.2971 229 60 M (0.26200873 0.73799127)
## 62) compactness_se< -4.032549 23 7 B (0.69565217 0.30434783)
## 124) compactness_se>=-4.156842 15 0 B (1.00000000 0.00000000) *
## 125) compactness_se< -4.156842 8 1 M (0.12500000 0.87500000) *
## 63) compactness_se>=-4.032549 206 44 M (0.21359223 0.78640777)
## 126) symmetry_worst< -1.660064 114 38 M (0.33333333 0.66666667) *
## 127) symmetry_worst>=-1.660064 92 6 M (0.06521739 0.93478261) *
##
## $trees[[26]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 412 M (0.45175439 0.54824561)
## 2) symmetry_worst< -1.816281 409 182 B (0.55501222 0.44498778)
## 4) smoothness_worst< -1.52112 245 85 B (0.65306122 0.34693878)
## 8) symmetry_worst>=-2.491275 229 71 B (0.68995633 0.31004367)
## 16) symmetry_worst>=-1.934101 75 11 B (0.85333333 0.14666667)
## 32) texture_mean>=2.718324 71 7 B (0.90140845 0.09859155)
## 64) smoothness_mean< -2.257258 68 4 B (0.94117647 0.05882353) *
## 65) smoothness_mean>=-2.257258 3 0 M (0.00000000 1.00000000) *
## 33) texture_mean< 2.718324 4 0 M (0.00000000 1.00000000) *
## 17) symmetry_worst< -1.934101 154 60 B (0.61038961 0.38961039)
## 34) smoothness_worst< -1.604936 33 1 B (0.96969697 0.03030303)
## 68) compactness_se< -2.951614 31 0 B (1.00000000 0.00000000) *
## 69) compactness_se>=-2.951614 2 1 B (0.50000000 0.50000000) *
## 35) smoothness_worst>=-1.604936 121 59 B (0.51239669 0.48760331)
## 70) smoothness_worst>=-1.540014 22 0 B (1.00000000 0.00000000) *
## 71) smoothness_worst< -1.540014 99 40 M (0.40404040 0.59595960) *
## 9) symmetry_worst< -2.491275 16 2 M (0.12500000 0.87500000)
## 18) compactness_se>=-3.572604 2 0 B (1.00000000 0.00000000) *
## 19) compactness_se< -3.572604 14 0 M (0.00000000 1.00000000) *
## 5) smoothness_worst>=-1.52112 164 67 M (0.40853659 0.59146341)
## 10) smoothness_mean>=-2.293133 58 21 B (0.63793103 0.36206897)
## 20) texture_mean< 3.104804 47 10 B (0.78723404 0.21276596)
## 40) compactness_se< -3.4389 35 2 B (0.94285714 0.05714286)
## 80) texture_worst< 4.85229 33 0 B (1.00000000 0.00000000) *
## 81) texture_worst>=4.85229 2 0 M (0.00000000 1.00000000) *
## 41) compactness_se>=-3.4389 12 4 M (0.33333333 0.66666667)
## 82) smoothness_worst< -1.495985 4 0 B (1.00000000 0.00000000) *
## 83) smoothness_worst>=-1.495985 8 0 M (0.00000000 1.00000000) *
## 21) texture_mean>=3.104804 11 0 M (0.00000000 1.00000000) *
## 11) smoothness_mean< -2.293133 106 30 M (0.28301887 0.71698113)
## 22) compactness_se>=-3.601238 20 6 B (0.70000000 0.30000000)
## 44) smoothness_worst< -1.473672 12 0 B (1.00000000 0.00000000) *
## 45) smoothness_worst>=-1.473672 8 2 M (0.25000000 0.75000000)
## 90) texture_mean< 2.896181 2 0 B (1.00000000 0.00000000) *
## 91) texture_mean>=2.896181 6 0 M (0.00000000 1.00000000) *
## 23) compactness_se< -3.601238 86 16 M (0.18604651 0.81395349)
## 46) smoothness_worst>=-1.479352 23 10 B (0.56521739 0.43478261)
## 92) smoothness_mean>=-2.35715 13 0 B (1.00000000 0.00000000) *
## 93) smoothness_mean< -2.35715 10 0 M (0.00000000 1.00000000) *
## 47) smoothness_worst< -1.479352 63 3 M (0.04761905 0.95238095)
## 94) texture_mean< 2.739678 1 0 B (1.00000000 0.00000000) *
## 95) texture_mean>=2.739678 62 2 M (0.03225806 0.96774194) *
## 3) symmetry_worst>=-1.816281 503 185 M (0.36779324 0.63220676)
## 6) compactness_se< -3.71586 269 120 M (0.44609665 0.55390335)
## 12) symmetry_worst>=-1.733593 179 81 B (0.54748603 0.45251397)
## 24) symmetry_worst< -1.688251 26 0 B (1.00000000 0.00000000) *
## 25) symmetry_worst>=-1.688251 153 72 M (0.47058824 0.52941176)
## 50) smoothness_worst< -1.488048 69 23 B (0.66666667 0.33333333)
## 100) smoothness_worst>=-1.576547 51 7 B (0.86274510 0.13725490) *
## 101) smoothness_worst< -1.576547 18 2 M (0.11111111 0.88888889) *
## 51) smoothness_worst>=-1.488048 84 26 M (0.30952381 0.69047619)
## 102) compactness_se< -4.480629 8 0 B (1.00000000 0.00000000) *
## 103) compactness_se>=-4.480629 76 18 M (0.23684211 0.76315789) *
## 13) symmetry_worst< -1.733593 90 22 M (0.24444444 0.75555556)
## 26) smoothness_worst>=-1.427418 12 1 B (0.91666667 0.08333333)
## 52) texture_mean>=3.039503 10 0 B (1.00000000 0.00000000) *
## 53) texture_mean< 3.039503 2 1 B (0.50000000 0.50000000)
## 106) texture_mean< 2.938081 1 0 B (1.00000000 0.00000000) *
## 107) texture_mean>=2.938081 1 0 M (0.00000000 1.00000000) *
## 27) smoothness_worst< -1.427418 78 11 M (0.14102564 0.85897436)
## 54) compactness_se>=-3.93685 8 0 B (1.00000000 0.00000000) *
## 55) compactness_se< -3.93685 70 3 M (0.04285714 0.95714286)
## 110) texture_mean< 2.803754 2 0 B (1.00000000 0.00000000) *
## 111) texture_mean>=2.803754 68 1 M (0.01470588 0.98529412) *
## 7) compactness_se>=-3.71586 234 65 M (0.27777778 0.72222222)
## 14) compactness_se>=-3.494301 160 57 M (0.35625000 0.64375000)
## 28) compactness_se< -3.484318 13 0 B (1.00000000 0.00000000) *
## 29) compactness_se>=-3.484318 147 44 M (0.29931973 0.70068027)
## 58) texture_mean< 2.96681 51 25 B (0.50980392 0.49019608)
## 116) smoothness_worst< -1.473088 24 2 B (0.91666667 0.08333333) *
## 117) smoothness_worst>=-1.473088 27 4 M (0.14814815 0.85185185) *
## 59) texture_mean>=2.96681 96 18 M (0.18750000 0.81250000)
## 118) symmetry_worst< -1.775603 11 2 B (0.81818182 0.18181818) *
## 119) symmetry_worst>=-1.775603 85 9 M (0.10588235 0.89411765) *
## 15) compactness_se< -3.494301 74 8 M (0.10810811 0.89189189)
## 30) texture_worst>=4.900864 8 2 B (0.75000000 0.25000000)
## 60) smoothness_mean< -2.358194 6 0 B (1.00000000 0.00000000) *
## 61) smoothness_mean>=-2.358194 2 0 M (0.00000000 1.00000000) *
## 31) texture_worst< 4.900864 66 2 M (0.03030303 0.96969697)
## 62) texture_mean< 2.671633 2 0 B (1.00000000 0.00000000) *
## 63) texture_mean>=2.671633 64 0 M (0.00000000 1.00000000) *
##
## $trees[[27]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 445 B (0.51206140 0.48793860)
## 2) compactness_se< -3.969125 357 134 B (0.62464986 0.37535014)
## 4) texture_mean< 2.81988 42 0 B (1.00000000 0.00000000) *
## 5) texture_mean>=2.81988 315 134 B (0.57460317 0.42539683)
## 10) smoothness_mean>=-2.294121 66 13 B (0.80303030 0.19696970)
## 20) texture_worst< 5.021606 59 6 B (0.89830508 0.10169492)
## 40) symmetry_worst< -1.463197 52 2 B (0.96153846 0.03846154)
## 80) smoothness_mean< -2.222419 47 1 B (0.97872340 0.02127660) *
## 81) smoothness_mean>=-2.222419 5 1 B (0.80000000 0.20000000) *
## 41) symmetry_worst>=-1.463197 7 3 M (0.42857143 0.57142857)
## 82) texture_mean< 2.946426 3 0 B (1.00000000 0.00000000) *
## 83) texture_mean>=2.946426 4 0 M (0.00000000 1.00000000) *
## 21) texture_worst>=5.021606 7 0 M (0.00000000 1.00000000) *
## 11) smoothness_mean< -2.294121 249 121 B (0.51405622 0.48594378)
## 22) smoothness_worst< -1.556321 124 37 B (0.70161290 0.29838710)
## 44) compactness_se>=-4.563271 84 10 B (0.88095238 0.11904762)
## 88) texture_mean< 2.977058 38 0 B (1.00000000 0.00000000) *
## 89) texture_mean>=2.977058 46 10 B (0.78260870 0.21739130) *
## 45) compactness_se< -4.563271 40 13 M (0.32500000 0.67500000)
## 90) smoothness_worst>=-1.595505 9 0 B (1.00000000 0.00000000) *
## 91) smoothness_worst< -1.595505 31 4 M (0.12903226 0.87096774) *
## 23) smoothness_worst>=-1.556321 125 41 M (0.32800000 0.67200000)
## 46) smoothness_mean< -2.473387 11 0 B (1.00000000 0.00000000) *
## 47) smoothness_mean>=-2.473387 114 30 M (0.26315789 0.73684211)
## 94) symmetry_worst>=-1.52618 20 7 B (0.65000000 0.35000000) *
## 95) symmetry_worst< -1.52618 94 17 M (0.18085106 0.81914894) *
## 3) compactness_se>=-3.969125 555 244 M (0.43963964 0.56036036)
## 6) smoothness_worst< -1.586874 97 34 B (0.64948454 0.35051546)
## 12) smoothness_worst>=-1.59459 25 0 B (1.00000000 0.00000000) *
## 13) smoothness_worst< -1.59459 72 34 B (0.52777778 0.47222222)
## 26) smoothness_worst< -1.616129 47 12 B (0.74468085 0.25531915)
## 52) smoothness_worst>=-1.720903 38 5 B (0.86842105 0.13157895)
## 104) compactness_se>=-3.570653 28 1 B (0.96428571 0.03571429) *
## 105) compactness_se< -3.570653 10 4 B (0.60000000 0.40000000) *
## 53) smoothness_worst< -1.720903 9 2 M (0.22222222 0.77777778)
## 106) texture_mean>=3.103494 2 0 B (1.00000000 0.00000000) *
## 107) texture_mean< 3.103494 7 0 M (0.00000000 1.00000000) *
## 27) smoothness_worst>=-1.616129 25 3 M (0.12000000 0.88000000)
## 54) texture_mean< 2.755158 2 0 B (1.00000000 0.00000000) *
## 55) texture_mean>=2.755158 23 1 M (0.04347826 0.95652174)
## 110) compactness_se>=-3.126751 4 1 M (0.25000000 0.75000000) *
## 111) compactness_se< -3.126751 19 0 M (0.00000000 1.00000000) *
## 7) smoothness_worst>=-1.586874 458 181 M (0.39519651 0.60480349)
## 14) texture_mean>=3.192731 83 29 B (0.65060241 0.34939759)
## 28) compactness_se< -3.055765 76 22 B (0.71052632 0.28947368)
## 56) compactness_se>=-3.859901 57 10 B (0.82456140 0.17543860)
## 112) smoothness_mean>=-2.473552 50 5 B (0.90000000 0.10000000) *
## 113) smoothness_mean< -2.473552 7 2 M (0.28571429 0.71428571) *
## 57) compactness_se< -3.859901 19 7 M (0.36842105 0.63157895)
## 114) texture_mean< 3.216671 7 0 B (1.00000000 0.00000000) *
## 115) texture_mean>=3.216671 12 0 M (0.00000000 1.00000000) *
## 29) compactness_se>=-3.055765 7 0 M (0.00000000 1.00000000) *
## 15) texture_mean< 3.192731 375 127 M (0.33866667 0.66133333)
## 30) smoothness_worst>=-1.477976 133 63 B (0.52631579 0.47368421)
## 60) texture_worst< 4.879822 117 47 B (0.59829060 0.40170940)
## 120) symmetry_worst< -1.895488 25 0 B (1.00000000 0.00000000) *
## 121) symmetry_worst>=-1.895488 92 45 M (0.48913043 0.51086957) *
## 61) texture_worst>=4.879822 16 0 M (0.00000000 1.00000000) *
## 31) smoothness_worst< -1.477976 242 57 M (0.23553719 0.76446281)
## 62) compactness_se< -3.714286 57 26 M (0.45614035 0.54385965)
## 124) compactness_se>=-3.869459 24 1 B (0.95833333 0.04166667) *
## 125) compactness_se< -3.869459 33 3 M (0.09090909 0.90909091) *
## 63) compactness_se>=-3.714286 185 31 M (0.16756757 0.83243243)
## 126) smoothness_mean< -2.486577 6 0 B (1.00000000 0.00000000) *
## 127) smoothness_mean>=-2.486577 179 25 M (0.13966480 0.86033520) *
##
## $trees[[28]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 423 B (0.53618421 0.46381579)
## 2) symmetry_worst< -1.966052 223 68 B (0.69506726 0.30493274)
## 4) smoothness_worst< -1.523408 163 34 B (0.79141104 0.20858896)
## 8) compactness_se< -3.004445 149 25 B (0.83221477 0.16778523)
## 16) smoothness_mean< -2.394379 90 8 B (0.91111111 0.08888889)
## 32) compactness_se< -3.554993 65 1 B (0.98461538 0.01538462)
## 64) smoothness_worst< -1.552639 54 0 B (1.00000000 0.00000000) *
## 65) smoothness_worst>=-1.552639 11 1 B (0.90909091 0.09090909) *
## 33) compactness_se>=-3.554993 25 7 B (0.72000000 0.28000000)
## 66) texture_mean< 3.228271 21 3 B (0.85714286 0.14285714) *
## 67) texture_mean>=3.228271 4 0 M (0.00000000 1.00000000) *
## 17) smoothness_mean>=-2.394379 59 17 B (0.71186441 0.28813559)
## 34) smoothness_mean>=-2.382983 48 6 B (0.87500000 0.12500000)
## 68) symmetry_worst>=-2.424439 42 0 B (1.00000000 0.00000000) *
## 69) symmetry_worst< -2.424439 6 0 M (0.00000000 1.00000000) *
## 35) smoothness_mean< -2.382983 11 0 M (0.00000000 1.00000000) *
## 9) compactness_se>=-3.004445 14 5 M (0.35714286 0.64285714)
## 18) texture_mean< 3.076827 5 0 B (1.00000000 0.00000000) *
## 19) texture_mean>=3.076827 9 0 M (0.00000000 1.00000000) *
## 5) smoothness_worst>=-1.523408 60 26 M (0.43333333 0.56666667)
## 10) smoothness_worst>=-1.493231 26 5 B (0.80769231 0.19230769)
## 20) smoothness_mean< -2.243629 22 1 B (0.95454545 0.04545455)
## 40) compactness_se< -3.443758 20 0 B (1.00000000 0.00000000) *
## 41) compactness_se>=-3.443758 2 1 B (0.50000000 0.50000000)
## 82) texture_mean>=2.866189 1 0 B (1.00000000 0.00000000) *
## 83) texture_mean< 2.866189 1 0 M (0.00000000 1.00000000) *
## 21) smoothness_mean>=-2.243629 4 0 M (0.00000000 1.00000000) *
## 11) smoothness_worst< -1.493231 34 5 M (0.14705882 0.85294118)
## 22) texture_mean< 2.965389 7 2 B (0.71428571 0.28571429)
## 44) texture_mean>=2.856718 5 0 B (1.00000000 0.00000000) *
## 45) texture_mean< 2.856718 2 0 M (0.00000000 1.00000000) *
## 23) texture_mean>=2.965389 27 0 M (0.00000000 1.00000000) *
## 3) symmetry_worst>=-1.966052 689 334 M (0.48476052 0.51523948)
## 6) texture_mean< 3.058002 520 231 B (0.55576923 0.44423077)
## 12) texture_worst< 4.858219 490 204 B (0.58367347 0.41632653)
## 24) smoothness_worst>=-1.477389 184 51 B (0.72282609 0.27717391)
## 48) smoothness_worst< -1.472307 42 0 B (1.00000000 0.00000000) *
## 49) smoothness_worst>=-1.472307 142 51 B (0.64084507 0.35915493)
## 98) smoothness_worst>=-1.468619 133 42 B (0.68421053 0.31578947) *
## 99) smoothness_worst< -1.468619 9 0 M (0.00000000 1.00000000) *
## 25) smoothness_worst< -1.477389 306 153 B (0.50000000 0.50000000)
## 50) smoothness_worst< -1.496036 235 99 B (0.57872340 0.42127660)
## 100) smoothness_worst>=-1.519671 34 1 B (0.97058824 0.02941176) *
## 101) smoothness_worst< -1.519671 201 98 B (0.51243781 0.48756219) *
## 51) smoothness_worst>=-1.496036 71 17 M (0.23943662 0.76056338)
## 102) compactness_se< -4.216002 8 0 B (1.00000000 0.00000000) *
## 103) compactness_se>=-4.216002 63 9 M (0.14285714 0.85714286) *
## 13) texture_worst>=4.858219 30 3 M (0.10000000 0.90000000)
## 26) texture_mean>=3.04476 3 0 B (1.00000000 0.00000000) *
## 27) texture_mean< 3.04476 27 0 M (0.00000000 1.00000000) *
## 7) texture_mean>=3.058002 169 45 M (0.26627219 0.73372781)
## 14) smoothness_worst< -1.618721 11 1 B (0.90909091 0.09090909)
## 28) smoothness_mean< -2.337942 10 0 B (1.00000000 0.00000000) *
## 29) smoothness_mean>=-2.337942 1 0 M (0.00000000 1.00000000) *
## 15) smoothness_worst>=-1.618721 158 35 M (0.22151899 0.77848101)
## 30) smoothness_mean< -2.41714 37 17 M (0.45945946 0.54054054)
## 60) smoothness_mean>=-2.453967 16 0 B (1.00000000 0.00000000) *
## 61) smoothness_mean< -2.453967 21 1 M (0.04761905 0.95238095)
## 122) smoothness_mean< -2.521804 1 0 B (1.00000000 0.00000000) *
## 123) smoothness_mean>=-2.521804 20 0 M (0.00000000 1.00000000) *
## 31) smoothness_mean>=-2.41714 121 18 M (0.14876033 0.85123967)
## 62) texture_mean>=3.36829 5 1 B (0.80000000 0.20000000)
## 124) texture_mean< 3.407548 4 0 B (1.00000000 0.00000000) *
## 125) texture_mean>=3.407548 1 0 M (0.00000000 1.00000000) *
## 63) texture_mean< 3.36829 116 14 M (0.12068966 0.87931034)
## 126) smoothness_mean>=-2.301586 29 11 M (0.37931034 0.62068966) *
## 127) smoothness_mean< -2.301586 87 3 M (0.03448276 0.96551724) *
##
## $trees[[29]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 421 B (0.53837719 0.46162281)
## 2) symmetry_worst< -1.815238 382 135 B (0.64659686 0.35340314)
## 4) texture_worst< 4.897936 285 79 B (0.72280702 0.27719298)
## 8) texture_worst>=4.68481 73 6 B (0.91780822 0.08219178)
## 16) compactness_se< -2.72933 69 2 B (0.97101449 0.02898551)
## 32) texture_mean>=2.958239 59 0 B (1.00000000 0.00000000) *
## 33) texture_mean< 2.958239 10 2 B (0.80000000 0.20000000)
## 66) texture_mean< 2.946804 8 0 B (1.00000000 0.00000000) *
## 67) texture_mean>=2.946804 2 0 M (0.00000000 1.00000000) *
## 17) compactness_se>=-2.72933 4 0 M (0.00000000 1.00000000) *
## 9) texture_worst< 4.68481 212 73 B (0.65566038 0.34433962)
## 18) compactness_se>=-3.483667 68 9 B (0.86764706 0.13235294)
## 36) texture_mean< 3.064089 63 4 B (0.93650794 0.06349206)
## 72) smoothness_worst< -1.482504 49 0 B (1.00000000 0.00000000) *
## 73) smoothness_worst>=-1.482504 14 4 B (0.71428571 0.28571429) *
## 37) texture_mean>=3.064089 5 0 M (0.00000000 1.00000000) *
## 19) compactness_se< -3.483667 144 64 B (0.55555556 0.44444444)
## 38) texture_worst< 4.612323 117 42 B (0.64102564 0.35897436)
## 76) smoothness_worst< -1.427424 108 33 B (0.69444444 0.30555556) *
## 77) smoothness_worst>=-1.427424 9 0 M (0.00000000 1.00000000) *
## 39) texture_worst>=4.612323 27 5 M (0.18518519 0.81481481)
## 78) smoothness_worst>=-1.452987 5 0 B (1.00000000 0.00000000) *
## 79) smoothness_worst< -1.452987 22 0 M (0.00000000 1.00000000) *
## 5) texture_worst>=4.897936 97 41 M (0.42268041 0.57731959)
## 10) texture_worst>=5.011215 66 26 B (0.60606061 0.39393939)
## 20) compactness_se< -3.413706 54 14 B (0.74074074 0.25925926)
## 40) smoothness_mean< -2.397526 35 3 B (0.91428571 0.08571429)
## 80) texture_mean< 3.431166 33 1 B (0.96969697 0.03030303) *
## 81) texture_mean>=3.431166 2 0 M (0.00000000 1.00000000) *
## 41) smoothness_mean>=-2.397526 19 8 M (0.42105263 0.57894737)
## 82) symmetry_worst< -2.133872 8 0 B (1.00000000 0.00000000) *
## 83) symmetry_worst>=-2.133872 11 0 M (0.00000000 1.00000000) *
## 21) compactness_se>=-3.413706 12 0 M (0.00000000 1.00000000) *
## 11) texture_worst< 5.011215 31 1 M (0.03225806 0.96774194)
## 22) compactness_se< -4.706178 1 0 B (1.00000000 0.00000000) *
## 23) compactness_se>=-4.706178 30 0 M (0.00000000 1.00000000) *
## 3) symmetry_worst>=-1.815238 530 244 M (0.46037736 0.53962264)
## 6) smoothness_worst>=-1.537035 367 170 B (0.53678474 0.46321526)
## 12) compactness_se< -3.955455 133 42 B (0.68421053 0.31578947)
## 24) texture_mean< 2.956197 68 6 B (0.91176471 0.08823529)
## 48) texture_worst< 4.514456 45 0 B (1.00000000 0.00000000) *
## 49) texture_worst>=4.514456 23 6 B (0.73913043 0.26086957)
## 98) texture_worst>=4.527768 18 1 B (0.94444444 0.05555556) *
## 99) texture_worst< 4.527768 5 0 M (0.00000000 1.00000000) *
## 25) texture_mean>=2.956197 65 29 M (0.44615385 0.55384615)
## 50) texture_mean>=2.982883 51 22 B (0.56862745 0.43137255)
## 100) texture_mean< 3.082128 30 6 B (0.80000000 0.20000000) *
## 101) texture_mean>=3.082128 21 5 M (0.23809524 0.76190476) *
## 51) texture_mean< 2.982883 14 0 M (0.00000000 1.00000000) *
## 13) compactness_se>=-3.955455 234 106 M (0.45299145 0.54700855)
## 26) compactness_se>=-3.92342 212 106 B (0.50000000 0.50000000)
## 52) smoothness_mean< -2.36186 47 10 B (0.78723404 0.21276596)
## 104) smoothness_mean>=-2.453967 41 5 B (0.87804878 0.12195122) *
## 105) smoothness_mean< -2.453967 6 1 M (0.16666667 0.83333333) *
## 53) smoothness_mean>=-2.36186 165 69 M (0.41818182 0.58181818)
## 106) texture_mean< 2.927988 76 28 B (0.63157895 0.36842105) *
## 107) texture_mean>=2.927988 89 21 M (0.23595506 0.76404494) *
## 27) compactness_se< -3.92342 22 0 M (0.00000000 1.00000000) *
## 7) smoothness_worst< -1.537035 163 47 M (0.28834356 0.71165644)
## 14) smoothness_mean< -2.528181 8 0 B (1.00000000 0.00000000) *
## 15) smoothness_mean>=-2.528181 155 39 M (0.25161290 0.74838710)
## 30) symmetry_worst>=-1.750623 89 32 M (0.35955056 0.64044944)
## 60) symmetry_worst< -1.549426 43 16 B (0.62790698 0.37209302)
## 120) compactness_se>=-4.283814 29 5 B (0.82758621 0.17241379) *
## 121) compactness_se< -4.283814 14 3 M (0.21428571 0.78571429) *
## 61) symmetry_worst>=-1.549426 46 5 M (0.10869565 0.89130435)
## 122) texture_worst< 4.496329 2 0 B (1.00000000 0.00000000) *
## 123) texture_worst>=4.496329 44 3 M (0.06818182 0.93181818) *
## 31) symmetry_worst< -1.750623 66 7 M (0.10606061 0.89393939)
## 62) smoothness_mean>=-2.406089 17 7 M (0.41176471 0.58823529)
## 124) smoothness_mean< -2.38134 7 0 B (1.00000000 0.00000000) *
## 125) smoothness_mean>=-2.38134 10 0 M (0.00000000 1.00000000) *
## 63) smoothness_mean< -2.406089 49 0 M (0.00000000 1.00000000) *
##
## $trees[[30]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 452 M (0.49561404 0.50438596)
## 2) compactness_se< -4.706178 32 3 B (0.90625000 0.09375000)
## 4) symmetry_worst< -1.170399 29 0 B (1.00000000 0.00000000) *
## 5) symmetry_worst>=-1.170399 3 0 M (0.00000000 1.00000000) *
## 3) compactness_se>=-4.706178 880 423 M (0.48068182 0.51931818)
## 6) texture_worst< 4.168738 85 26 B (0.69411765 0.30588235)
## 12) symmetry_worst< -1.086115 75 16 B (0.78666667 0.21333333)
## 24) smoothness_worst>=-1.59351 64 9 B (0.85937500 0.14062500)
## 48) texture_mean>=2.515298 60 6 B (0.90000000 0.10000000)
## 96) texture_mean< 2.805441 56 4 B (0.92857143 0.07142857) *
## 97) texture_mean>=2.805441 4 2 B (0.50000000 0.50000000) *
## 49) texture_mean< 2.515298 4 1 M (0.25000000 0.75000000)
## 98) texture_mean< 2.434062 1 0 B (1.00000000 0.00000000) *
## 99) texture_mean>=2.434062 3 0 M (0.00000000 1.00000000) *
## 25) smoothness_worst< -1.59351 11 4 M (0.36363636 0.63636364)
## 50) smoothness_worst< -1.607498 4 0 B (1.00000000 0.00000000) *
## 51) smoothness_worst>=-1.607498 7 0 M (0.00000000 1.00000000) *
## 13) symmetry_worst>=-1.086115 10 0 M (0.00000000 1.00000000) *
## 7) texture_worst>=4.168738 795 364 M (0.45786164 0.54213836)
## 14) texture_mean>=2.761589 743 358 M (0.48183042 0.51816958)
## 28) compactness_se>=-4.676462 714 355 M (0.49719888 0.50280112)
## 56) symmetry_worst< -1.366937 661 317 B (0.52042360 0.47957640)
## 112) texture_mean< 2.824054 19 0 B (1.00000000 0.00000000) *
## 113) texture_mean>=2.824054 642 317 B (0.50623053 0.49376947) *
## 57) symmetry_worst>=-1.366937 53 11 M (0.20754717 0.79245283)
## 114) smoothness_worst< -1.49848 16 6 B (0.62500000 0.37500000) *
## 115) smoothness_worst>=-1.49848 37 1 M (0.02702703 0.97297297) *
## 29) compactness_se< -4.676462 29 3 M (0.10344828 0.89655172)
## 58) smoothness_mean>=-2.443464 3 0 B (1.00000000 0.00000000) *
## 59) smoothness_mean< -2.443464 26 0 M (0.00000000 1.00000000) *
## 15) texture_mean< 2.761589 52 6 M (0.11538462 0.88461538)
## 30) compactness_se< -3.892047 6 0 B (1.00000000 0.00000000) *
## 31) compactness_se>=-3.892047 46 0 M (0.00000000 1.00000000) *
##
## $trees[[31]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 399 M (0.43750000 0.56250000)
## 2) smoothness_mean< -2.506908 50 11 B (0.78000000 0.22000000)
## 4) texture_mean< 2.960617 18 0 B (1.00000000 0.00000000) *
## 5) texture_mean>=2.960617 32 11 B (0.65625000 0.34375000)
## 10) texture_mean>=2.986158 21 3 B (0.85714286 0.14285714)
## 20) smoothness_worst>=-1.714091 17 0 B (1.00000000 0.00000000) *
## 21) smoothness_worst< -1.714091 4 1 M (0.25000000 0.75000000)
## 42) texture_mean>=3.103494 1 0 B (1.00000000 0.00000000) *
## 43) texture_mean< 3.103494 3 0 M (0.00000000 1.00000000) *
## 11) texture_mean< 2.986158 11 3 M (0.27272727 0.72727273)
## 22) smoothness_mean< -2.546123 3 0 B (1.00000000 0.00000000) *
## 23) smoothness_mean>=-2.546123 8 0 M (0.00000000 1.00000000) *
## 3) smoothness_mean>=-2.506908 862 360 M (0.41763341 0.58236659)
## 6) texture_worst>=4.626933 360 178 B (0.50555556 0.49444444)
## 12) smoothness_worst>=-1.400053 52 8 B (0.84615385 0.15384615)
## 24) texture_mean< 3.222134 48 4 B (0.91666667 0.08333333)
## 48) symmetry_worst< -1.405153 45 1 B (0.97777778 0.02222222)
## 96) compactness_se< -2.783552 44 0 B (1.00000000 0.00000000) *
## 97) compactness_se>=-2.783552 1 0 M (0.00000000 1.00000000) *
## 49) symmetry_worst>=-1.405153 3 0 M (0.00000000 1.00000000) *
## 25) texture_mean>=3.222134 4 0 M (0.00000000 1.00000000) *
## 13) smoothness_worst< -1.400053 308 138 M (0.44805195 0.55194805)
## 26) compactness_se< -3.379083 251 123 B (0.50996016 0.49003984)
## 52) compactness_se>=-3.502612 47 7 B (0.85106383 0.14893617)
## 104) smoothness_mean>=-2.355655 37 0 B (1.00000000 0.00000000) *
## 105) smoothness_mean< -2.355655 10 3 M (0.30000000 0.70000000) *
## 53) compactness_se< -3.502612 204 88 M (0.43137255 0.56862745)
## 106) symmetry_worst< -2.207988 19 0 B (1.00000000 0.00000000) *
## 107) symmetry_worst>=-2.207988 185 69 M (0.37297297 0.62702703) *
## 27) compactness_se>=-3.379083 57 10 M (0.17543860 0.82456140)
## 54) smoothness_worst< -1.647098 3 0 B (1.00000000 0.00000000) *
## 55) smoothness_worst>=-1.647098 54 7 M (0.12962963 0.87037037)
## 110) texture_mean< 3.038537 13 5 M (0.38461538 0.61538462) *
## 111) texture_mean>=3.038537 41 2 M (0.04878049 0.95121951) *
## 7) texture_worst< 4.626933 502 178 M (0.35458167 0.64541833)
## 14) texture_mean< 2.708379 28 5 B (0.82142857 0.17857143)
## 28) symmetry_worst< -1.577652 19 0 B (1.00000000 0.00000000) *
## 29) symmetry_worst>=-1.577652 9 4 M (0.44444444 0.55555556)
## 58) texture_mean>=2.553793 5 1 B (0.80000000 0.20000000)
## 116) compactness_se< -3.3026 4 0 B (1.00000000 0.00000000) *
## 117) compactness_se>=-3.3026 1 0 M (0.00000000 1.00000000) *
## 59) texture_mean< 2.553793 4 0 M (0.00000000 1.00000000) *
## 15) texture_mean>=2.708379 474 155 M (0.32700422 0.67299578)
## 30) compactness_se< -3.673868 260 112 M (0.43076923 0.56923077)
## 60) smoothness_mean>=-2.233531 36 5 B (0.86111111 0.13888889)
## 120) texture_worst< 4.489843 25 0 B (1.00000000 0.00000000) *
## 121) texture_worst>=4.489843 11 5 B (0.54545455 0.45454545) *
## 61) smoothness_mean< -2.233531 224 81 M (0.36160714 0.63839286)
## 122) texture_mean< 2.756519 12 0 B (1.00000000 0.00000000) *
## 123) texture_mean>=2.756519 212 69 M (0.32547170 0.67452830) *
## 31) compactness_se>=-3.673868 214 43 M (0.20093458 0.79906542)
## 62) compactness_se>=-2.716917 6 0 B (1.00000000 0.00000000) *
## 63) compactness_se< -2.716917 208 37 M (0.17788462 0.82211538)
## 126) symmetry_worst< -1.816281 71 22 M (0.30985915 0.69014085) *
## 127) symmetry_worst>=-1.816281 137 15 M (0.10948905 0.89051095) *
##
## $trees[[32]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 428 B (0.53070175 0.46929825)
## 2) texture_mean>=3.192731 99 27 B (0.72727273 0.27272727)
## 4) texture_worst>=4.881566 92 20 B (0.78260870 0.21739130)
## 8) symmetry_worst< -1.41032 88 16 B (0.81818182 0.18181818)
## 16) texture_worst< 5.194184 67 8 B (0.88059701 0.11940299)
## 32) symmetry_worst>=-2.242858 65 6 B (0.90769231 0.09230769)
## 64) smoothness_worst< -1.441559 49 2 B (0.95918367 0.04081633) *
## 65) smoothness_worst>=-1.441559 16 4 B (0.75000000 0.25000000) *
## 33) symmetry_worst< -2.242858 2 0 M (0.00000000 1.00000000) *
## 17) texture_worst>=5.194184 21 8 B (0.61904762 0.38095238)
## 34) texture_mean>=3.332536 16 3 B (0.81250000 0.18750000)
## 68) symmetry_worst< -1.612163 14 1 B (0.92857143 0.07142857) *
## 69) symmetry_worst>=-1.612163 2 0 M (0.00000000 1.00000000) *
## 35) texture_mean< 3.332536 5 0 M (0.00000000 1.00000000) *
## 9) symmetry_worst>=-1.41032 4 0 M (0.00000000 1.00000000) *
## 5) texture_worst< 4.881566 7 0 M (0.00000000 1.00000000) *
## 3) texture_mean< 3.192731 813 401 B (0.50676507 0.49323493)
## 6) symmetry_worst< -1.863339 253 98 B (0.61264822 0.38735178)
## 12) texture_worst< 4.895326 231 77 B (0.66666667 0.33333333)
## 24) texture_mean>=2.775685 187 49 B (0.73796791 0.26203209)
## 48) texture_mean< 2.976803 87 9 B (0.89655172 0.10344828)
## 96) symmetry_worst>=-1.990832 52 0 B (1.00000000 0.00000000) *
## 97) symmetry_worst< -1.990832 35 9 B (0.74285714 0.25714286) *
## 49) texture_mean>=2.976803 100 40 B (0.60000000 0.40000000)
## 98) texture_worst>=4.530419 78 20 B (0.74358974 0.25641026) *
## 99) texture_worst< 4.530419 22 2 M (0.09090909 0.90909091) *
## 25) texture_mean< 2.775685 44 16 M (0.36363636 0.63636364)
## 50) texture_mean< 2.758426 15 0 B (1.00000000 0.00000000) *
## 51) texture_mean>=2.758426 29 1 M (0.03448276 0.96551724)
## 102) smoothness_mean< -2.479158 1 0 B (1.00000000 0.00000000) *
## 103) smoothness_mean>=-2.479158 28 0 M (0.00000000 1.00000000) *
## 13) texture_worst>=4.895326 22 1 M (0.04545455 0.95454545)
## 26) texture_worst>=5.15394 1 0 B (1.00000000 0.00000000) *
## 27) texture_worst< 5.15394 21 0 M (0.00000000 1.00000000) *
## 7) symmetry_worst>=-1.863339 560 257 M (0.45892857 0.54107143)
## 14) symmetry_worst>=-1.749963 396 190 B (0.52020202 0.47979798)
## 28) smoothness_worst< -1.388752 371 167 B (0.54986523 0.45013477)
## 56) smoothness_worst>=-1.439294 72 13 B (0.81944444 0.18055556)
## 112) symmetry_worst< -1.36527 67 8 B (0.88059701 0.11940299) *
## 113) symmetry_worst>=-1.36527 5 0 M (0.00000000 1.00000000) *
## 57) smoothness_worst< -1.439294 299 145 M (0.48494983 0.51505017)
## 114) smoothness_worst< -1.451541 269 125 B (0.53531599 0.46468401) *
## 115) smoothness_worst>=-1.451541 30 1 M (0.03333333 0.96666667) *
## 29) smoothness_worst>=-1.388752 25 2 M (0.08000000 0.92000000)
## 58) texture_mean< 2.692775 2 0 B (1.00000000 0.00000000) *
## 59) texture_mean>=2.692775 23 0 M (0.00000000 1.00000000) *
## 15) symmetry_worst< -1.749963 164 51 M (0.31097561 0.68902439)
## 30) symmetry_worst< -1.786753 98 48 M (0.48979592 0.51020408)
## 60) symmetry_worst>=-1.85615 82 34 B (0.58536585 0.41463415)
## 120) smoothness_mean< -2.327576 41 6 B (0.85365854 0.14634146) *
## 121) smoothness_mean>=-2.327576 41 13 M (0.31707317 0.68292683) *
## 61) symmetry_worst< -1.85615 16 0 M (0.00000000 1.00000000) *
## 31) symmetry_worst>=-1.786753 66 3 M (0.04545455 0.95454545)
## 62) smoothness_worst>=-1.385102 1 0 B (1.00000000 0.00000000) *
## 63) smoothness_worst< -1.385102 65 2 M (0.03076923 0.96923077)
## 126) texture_worst< 4.422428 10 2 M (0.20000000 0.80000000) *
## 127) texture_worst>=4.422428 55 0 M (0.00000000 1.00000000) *
##
## $trees[[33]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 401 M (0.43969298 0.56030702)
## 2) smoothness_worst>=-1.401479 74 24 B (0.67567568 0.32432432)
## 4) symmetry_worst< -1.607739 46 6 B (0.86956522 0.13043478)
## 8) compactness_se< -3.001392 42 2 B (0.95238095 0.04761905)
## 16) texture_worst< 5.106195 40 0 B (1.00000000 0.00000000) *
## 17) texture_worst>=5.106195 2 0 M (0.00000000 1.00000000) *
## 9) compactness_se>=-3.001392 4 0 M (0.00000000 1.00000000) *
## 5) symmetry_worst>=-1.607739 28 10 M (0.35714286 0.64285714)
## 10) smoothness_mean< -2.219224 10 1 B (0.90000000 0.10000000)
## 20) texture_mean>=2.926371 9 0 B (1.00000000 0.00000000) *
## 21) texture_mean< 2.926371 1 0 M (0.00000000 1.00000000) *
## 11) smoothness_mean>=-2.219224 18 1 M (0.05555556 0.94444444)
## 22) texture_mean< 2.688296 1 0 B (1.00000000 0.00000000) *
## 23) texture_mean>=2.688296 17 0 M (0.00000000 1.00000000) *
## 3) smoothness_worst< -1.401479 838 351 M (0.41885442 0.58114558)
## 6) symmetry_worst< -1.713275 434 211 M (0.48617512 0.51382488)
## 12) smoothness_mean>=-2.28279 86 25 B (0.70930233 0.29069767)
## 24) symmetry_worst>=-1.994978 63 10 B (0.84126984 0.15873016)
## 48) smoothness_worst>=-1.524868 61 8 B (0.86885246 0.13114754)
## 96) smoothness_worst< -1.433156 56 5 B (0.91071429 0.08928571) *
## 97) smoothness_worst>=-1.433156 5 2 M (0.40000000 0.60000000) *
## 49) smoothness_worst< -1.524868 2 0 M (0.00000000 1.00000000) *
## 25) symmetry_worst< -1.994978 23 8 M (0.34782609 0.65217391)
## 50) texture_mean< 3.018626 11 3 B (0.72727273 0.27272727)
## 100) smoothness_worst>=-1.56036 8 0 B (1.00000000 0.00000000) *
## 101) smoothness_worst< -1.56036 3 0 M (0.00000000 1.00000000) *
## 51) texture_mean>=3.018626 12 0 M (0.00000000 1.00000000) *
## 13) smoothness_mean< -2.28279 348 150 M (0.43103448 0.56896552)
## 26) compactness_se< -4.705732 14 0 B (1.00000000 0.00000000) *
## 27) compactness_se>=-4.705732 334 136 M (0.40718563 0.59281437)
## 54) compactness_se>=-4.493635 286 130 M (0.45454545 0.54545455)
## 108) symmetry_worst< -2.202388 24 3 B (0.87500000 0.12500000) *
## 109) symmetry_worst>=-2.202388 262 109 M (0.41603053 0.58396947) *
## 55) compactness_se< -4.493635 48 6 M (0.12500000 0.87500000)
## 110) texture_mean< 2.846651 3 0 B (1.00000000 0.00000000) *
## 111) texture_mean>=2.846651 45 3 M (0.06666667 0.93333333) *
## 7) symmetry_worst>=-1.713275 404 140 M (0.34653465 0.65346535)
## 14) texture_mean>=3.21466 36 8 B (0.77777778 0.22222222)
## 28) texture_mean< 3.260913 28 0 B (1.00000000 0.00000000) *
## 29) texture_mean>=3.260913 8 0 M (0.00000000 1.00000000) *
## 15) texture_mean< 3.21466 368 112 M (0.30434783 0.69565217)
## 30) texture_worst< 4.818867 311 110 M (0.35369775 0.64630225)
## 60) texture_mean>=3.110176 17 1 B (0.94117647 0.05882353)
## 120) texture_mean< 3.137421 16 0 B (1.00000000 0.00000000) *
## 121) texture_mean>=3.137421 1 0 M (0.00000000 1.00000000) *
## 61) texture_mean< 3.110176 294 94 M (0.31972789 0.68027211)
## 122) compactness_se< -3.821965 123 60 M (0.48780488 0.51219512) *
## 123) compactness_se>=-3.821965 171 34 M (0.19883041 0.80116959) *
## 31) texture_worst>=4.818867 57 2 M (0.03508772 0.96491228)
## 62) smoothness_mean< -2.54971 1 0 B (1.00000000 0.00000000) *
## 63) smoothness_mean>=-2.54971 56 1 M (0.01785714 0.98214286)
## 126) symmetry_worst< -1.664056 8 1 M (0.12500000 0.87500000) *
## 127) symmetry_worst>=-1.664056 48 0 M (0.00000000 1.00000000) *
##
## $trees[[34]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 430 B (0.52850877 0.47149123)
## 2) smoothness_mean>=-2.350891 461 191 B (0.58568330 0.41431670)
## 4) smoothness_mean< -2.332581 44 2 B (0.95454545 0.04545455)
## 8) smoothness_worst< -1.435092 43 1 B (0.97674419 0.02325581)
## 16) symmetry_worst>=-2.189951 42 0 B (1.00000000 0.00000000) *
## 17) symmetry_worst< -2.189951 1 0 M (0.00000000 1.00000000) *
## 9) smoothness_worst>=-1.435092 1 0 M (0.00000000 1.00000000) *
## 5) smoothness_mean>=-2.332581 417 189 B (0.54676259 0.45323741)
## 10) smoothness_worst>=-1.562856 392 165 B (0.57908163 0.42091837)
## 20) symmetry_worst< -1.529476 294 101 B (0.65646259 0.34353741)
## 40) texture_worst< 5.073596 284 91 B (0.67957746 0.32042254)
## 80) smoothness_mean>=-2.328057 275 82 B (0.70181818 0.29818182) *
## 81) smoothness_mean< -2.328057 9 0 M (0.00000000 1.00000000) *
## 41) texture_worst>=5.073596 10 0 M (0.00000000 1.00000000) *
## 21) symmetry_worst>=-1.529476 98 34 M (0.34693878 0.65306122)
## 42) texture_mean< 2.77286 24 8 B (0.66666667 0.33333333)
## 84) smoothness_mean< -2.081877 16 1 B (0.93750000 0.06250000) *
## 85) smoothness_mean>=-2.081877 8 1 M (0.12500000 0.87500000) *
## 43) texture_mean>=2.77286 74 18 M (0.24324324 0.75675676)
## 86) symmetry_worst>=-1.120651 11 2 B (0.81818182 0.18181818) *
## 87) symmetry_worst< -1.120651 63 9 M (0.14285714 0.85714286) *
## 11) smoothness_worst< -1.562856 25 1 M (0.04000000 0.96000000)
## 22) smoothness_mean>=-2.277089 6 1 M (0.16666667 0.83333333)
## 44) texture_mean< 2.898795 1 0 B (1.00000000 0.00000000) *
## 45) texture_mean>=2.898795 5 0 M (0.00000000 1.00000000) *
## 23) smoothness_mean< -2.277089 19 0 M (0.00000000 1.00000000) *
## 3) smoothness_mean< -2.350891 451 212 M (0.47006652 0.52993348)
## 6) smoothness_mean< -2.360532 411 200 B (0.51338200 0.48661800)
## 12) texture_mean< 2.868712 72 16 B (0.77777778 0.22222222)
## 24) smoothness_worst>=-1.602623 62 9 B (0.85483871 0.14516129)
## 48) smoothness_worst< -1.452493 58 6 B (0.89655172 0.10344828)
## 96) compactness_se>=-4.133653 32 0 B (1.00000000 0.00000000) *
## 97) compactness_se< -4.133653 26 6 B (0.76923077 0.23076923) *
## 49) smoothness_worst>=-1.452493 4 1 M (0.25000000 0.75000000)
## 98) texture_mean< 2.764784 1 0 B (1.00000000 0.00000000) *
## 99) texture_mean>=2.764784 3 0 M (0.00000000 1.00000000) *
## 25) smoothness_worst< -1.602623 10 3 M (0.30000000 0.70000000)
## 50) compactness_se< -3.815858 3 0 B (1.00000000 0.00000000) *
## 51) compactness_se>=-3.815858 7 0 M (0.00000000 1.00000000) *
## 13) texture_mean>=2.868712 339 155 M (0.45722714 0.54277286)
## 26) texture_worst>=5.329405 16 0 B (1.00000000 0.00000000) *
## 27) texture_worst< 5.329405 323 139 M (0.43034056 0.56965944)
## 54) smoothness_worst>=-1.427204 15 0 B (1.00000000 0.00000000) *
## 55) smoothness_worst< -1.427204 308 124 M (0.40259740 0.59740260)
## 110) smoothness_worst< -1.584388 105 41 B (0.60952381 0.39047619) *
## 111) smoothness_worst>=-1.584388 203 60 M (0.29556650 0.70443350) *
## 7) smoothness_mean>=-2.360532 40 1 M (0.02500000 0.97500000)
## 14) texture_mean< 2.698419 1 0 B (1.00000000 0.00000000) *
## 15) texture_mean>=2.698419 39 0 M (0.00000000 1.00000000) *
##
## $trees[[35]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 450 M (0.49342105 0.50657895)
## 2) compactness_se< -3.66733 515 220 B (0.57281553 0.42718447)
## 4) texture_mean< 2.956197 264 85 B (0.67803030 0.32196970)
## 8) texture_mean>=2.922355 66 7 B (0.89393939 0.10606061)
## 16) compactness_se< -3.747374 63 4 B (0.93650794 0.06349206)
## 32) texture_worst< 4.705422 61 2 B (0.96721311 0.03278689)
## 64) smoothness_worst< -1.473478 59 0 B (1.00000000 0.00000000) *
## 65) smoothness_worst>=-1.473478 2 0 M (0.00000000 1.00000000) *
## 33) texture_worst>=4.705422 2 0 M (0.00000000 1.00000000) *
## 17) compactness_se>=-3.747374 3 0 M (0.00000000 1.00000000) *
## 9) texture_mean< 2.922355 198 78 B (0.60606061 0.39393939)
## 18) smoothness_mean>=-2.28772 46 4 B (0.91304348 0.08695652)
## 36) texture_worst< 4.740635 43 1 B (0.97674419 0.02325581)
## 72) smoothness_mean< -2.11834 41 0 B (1.00000000 0.00000000) *
## 73) smoothness_mean>=-2.11834 2 1 B (0.50000000 0.50000000) *
## 37) texture_worst>=4.740635 3 0 M (0.00000000 1.00000000) *
## 19) smoothness_mean< -2.28772 152 74 B (0.51315789 0.48684211)
## 38) texture_mean< 2.756519 24 0 B (1.00000000 0.00000000) *
## 39) texture_mean>=2.756519 128 54 M (0.42187500 0.57812500)
## 78) smoothness_worst< -1.501474 89 38 B (0.57303371 0.42696629) *
## 79) smoothness_worst>=-1.501474 39 3 M (0.07692308 0.92307692) *
## 5) texture_mean>=2.956197 251 116 M (0.46215139 0.53784861)
## 10) compactness_se>=-3.869459 43 9 B (0.79069767 0.20930233)
## 20) smoothness_worst< -1.417195 40 6 B (0.85000000 0.15000000)
## 40) compactness_se< -3.721197 27 1 B (0.96296296 0.03703704)
## 80) symmetry_worst< -1.482402 26 0 B (1.00000000 0.00000000) *
## 81) symmetry_worst>=-1.482402 1 0 M (0.00000000 1.00000000) *
## 41) compactness_se>=-3.721197 13 5 B (0.61538462 0.38461538)
## 82) texture_mean< 3.083513 8 0 B (1.00000000 0.00000000) *
## 83) texture_mean>=3.083513 5 0 M (0.00000000 1.00000000) *
## 21) smoothness_worst>=-1.417195 3 0 M (0.00000000 1.00000000) *
## 11) compactness_se< -3.869459 208 82 M (0.39423077 0.60576923)
## 22) texture_worst>=4.749969 130 64 B (0.50769231 0.49230769)
## 44) texture_worst< 4.822896 23 0 B (1.00000000 0.00000000) *
## 45) texture_worst>=4.822896 107 43 M (0.40186916 0.59813084)
## 90) texture_mean>=3.210432 49 17 B (0.65306122 0.34693878) *
## 91) texture_mean< 3.210432 58 11 M (0.18965517 0.81034483) *
## 23) texture_worst< 4.749969 78 16 M (0.20512821 0.79487179)
## 46) symmetry_worst< -1.953246 22 11 B (0.50000000 0.50000000)
## 92) texture_mean< 3.183122 16 5 B (0.68750000 0.31250000) *
## 93) texture_mean>=3.183122 6 0 M (0.00000000 1.00000000) *
## 47) symmetry_worst>=-1.953246 56 5 M (0.08928571 0.91071429)
## 94) symmetry_worst>=-1.490299 11 4 M (0.36363636 0.63636364) *
## 95) symmetry_worst< -1.490299 45 1 M (0.02222222 0.97777778) *
## 3) compactness_se>=-3.66733 397 155 M (0.39042821 0.60957179)
## 6) symmetry_worst< -1.816281 139 62 B (0.55395683 0.44604317)
## 12) smoothness_mean< -2.377849 74 19 B (0.74324324 0.25675676)
## 24) smoothness_mean>=-2.438756 39 0 B (1.00000000 0.00000000) *
## 25) smoothness_mean< -2.438756 35 16 M (0.45714286 0.54285714)
## 50) symmetry_worst>=-2.044741 14 2 B (0.85714286 0.14285714)
## 100) smoothness_worst< -1.493511 12 0 B (1.00000000 0.00000000) *
## 101) smoothness_worst>=-1.493511 2 0 M (0.00000000 1.00000000) *
## 51) symmetry_worst< -2.044741 21 4 M (0.19047619 0.80952381)
## 102) compactness_se< -3.514597 2 0 B (1.00000000 0.00000000) *
## 103) compactness_se>=-3.514597 19 2 M (0.10526316 0.89473684) *
## 13) smoothness_mean>=-2.377849 65 22 M (0.33846154 0.66153846)
## 26) texture_worst< 4.248666 12 2 B (0.83333333 0.16666667)
## 52) texture_mean< 2.874386 10 0 B (1.00000000 0.00000000) *
## 53) texture_mean>=2.874386 2 0 M (0.00000000 1.00000000) *
## 27) texture_worst>=4.248666 53 12 M (0.22641509 0.77358491)
## 54) texture_mean>=3.284902 3 0 B (1.00000000 0.00000000) *
## 55) texture_mean< 3.284902 50 9 M (0.18000000 0.82000000)
## 110) smoothness_worst>=-1.474648 5 1 B (0.80000000 0.20000000) *
## 111) smoothness_worst< -1.474648 45 5 M (0.11111111 0.88888889) *
## 7) symmetry_worst>=-1.816281 258 78 M (0.30232558 0.69767442)
## 14) compactness_se>=-3.494301 191 78 M (0.40837696 0.59162304)
## 28) compactness_se< -3.484318 15 0 B (1.00000000 0.00000000) *
## 29) compactness_se>=-3.484318 176 63 M (0.35795455 0.64204545)
## 58) compactness_se>=-2.759266 15 1 B (0.93333333 0.06666667)
## 116) smoothness_mean< -2.093543 14 0 B (1.00000000 0.00000000) *
## 117) smoothness_mean>=-2.093543 1 0 M (0.00000000 1.00000000) *
## 59) compactness_se< -2.759266 161 49 M (0.30434783 0.69565217)
## 118) smoothness_mean>=-2.106736 27 9 B (0.66666667 0.33333333) *
## 119) smoothness_mean< -2.106736 134 31 M (0.23134328 0.76865672) *
## 15) compactness_se< -3.494301 67 0 M (0.00000000 1.00000000) *
##
## $trees[[36]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 443 B (0.51425439 0.48574561)
## 2) smoothness_mean< -2.21595 809 372 B (0.54017305 0.45982695)
## 4) smoothness_mean>=-2.231196 40 2 B (0.95000000 0.05000000)
## 8) texture_mean< 3.093624 38 0 B (1.00000000 0.00000000) *
## 9) texture_mean>=3.093624 2 0 M (0.00000000 1.00000000) *
## 5) smoothness_mean< -2.231196 769 370 B (0.51885566 0.48114434)
## 10) smoothness_mean< -2.367284 399 168 B (0.57894737 0.42105263)
## 20) compactness_se>=-3.230243 50 6 B (0.88000000 0.12000000)
## 40) smoothness_worst>=-1.720903 47 3 B (0.93617021 0.06382979)
## 80) texture_mean< 3.297828 46 2 B (0.95652174 0.04347826) *
## 81) texture_mean>=3.297828 1 0 M (0.00000000 1.00000000) *
## 41) smoothness_worst< -1.720903 3 0 M (0.00000000 1.00000000) *
## 21) compactness_se< -3.230243 349 162 B (0.53581662 0.46418338)
## 42) symmetry_worst< -1.815934 159 52 B (0.67295597 0.32704403)
## 84) texture_mean< 2.963209 52 7 B (0.86538462 0.13461538) *
## 85) texture_mean>=2.963209 107 45 B (0.57943925 0.42056075) *
## 43) symmetry_worst>=-1.815934 190 80 M (0.42105263 0.57894737)
## 86) texture_worst>=4.901515 29 6 B (0.79310345 0.20689655) *
## 87) texture_worst< 4.901515 161 57 M (0.35403727 0.64596273) *
## 11) smoothness_mean>=-2.367284 370 168 M (0.45405405 0.54594595)
## 22) compactness_se< -3.991189 119 44 B (0.63025210 0.36974790)
## 44) compactness_se>=-4.353745 82 20 B (0.75609756 0.24390244)
## 88) symmetry_worst< -1.614622 59 3 B (0.94915254 0.05084746) *
## 89) symmetry_worst>=-1.614622 23 6 M (0.26086957 0.73913043) *
## 45) compactness_se< -4.353745 37 13 M (0.35135135 0.64864865)
## 90) symmetry_worst>=-1.506254 10 0 B (1.00000000 0.00000000) *
## 91) symmetry_worst< -1.506254 27 3 M (0.11111111 0.88888889) *
## 23) compactness_se>=-3.991189 251 93 M (0.37051793 0.62948207)
## 46) symmetry_worst>=-1.770907 151 75 B (0.50331126 0.49668874)
## 92) smoothness_mean< -2.296604 92 29 B (0.68478261 0.31521739) *
## 93) smoothness_mean>=-2.296604 59 13 M (0.22033898 0.77966102) *
## 47) symmetry_worst< -1.770907 100 17 M (0.17000000 0.83000000)
## 94) symmetry_worst< -1.894024 51 16 M (0.31372549 0.68627451) *
## 95) symmetry_worst>=-1.894024 49 1 M (0.02040816 0.97959184) *
## 3) smoothness_mean>=-2.21595 103 32 M (0.31067961 0.68932039)
## 6) symmetry_worst< -1.653707 49 22 B (0.55102041 0.44897959)
## 12) smoothness_worst>=-1.427418 23 1 B (0.95652174 0.04347826)
## 24) texture_worst< 5.106195 22 0 B (1.00000000 0.00000000) *
## 25) texture_worst>=5.106195 1 0 M (0.00000000 1.00000000) *
## 13) smoothness_worst< -1.427418 26 5 M (0.19230769 0.80769231)
## 26) texture_worst< 4.490422 7 2 B (0.71428571 0.28571429)
## 52) smoothness_worst>=-1.56036 5 0 B (1.00000000 0.00000000) *
## 53) smoothness_worst< -1.56036 2 0 M (0.00000000 1.00000000) *
## 27) texture_worst>=4.490422 19 0 M (0.00000000 1.00000000) *
## 7) symmetry_worst>=-1.653707 54 5 M (0.09259259 0.90740741)
## 14) smoothness_worst>=-1.333822 6 2 B (0.66666667 0.33333333)
## 28) texture_mean< 2.838682 4 0 B (1.00000000 0.00000000) *
## 29) texture_mean>=2.838682 2 0 M (0.00000000 1.00000000) *
## 15) smoothness_worst< -1.333822 48 1 M (0.02083333 0.97916667)
## 30) compactness_se< -4.19021 1 0 B (1.00000000 0.00000000) *
## 31) compactness_se>=-4.19021 47 0 M (0.00000000 1.00000000) *
##
## $trees[[37]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 449 B (0.50767544 0.49232456)
## 2) compactness_se< -4.219581 216 71 B (0.67129630 0.32870370)
## 4) symmetry_worst>=-1.508268 61 5 B (0.91803279 0.08196721)
## 8) symmetry_worst< -1.312214 57 1 B (0.98245614 0.01754386)
## 16) texture_worst< 5.204837 56 0 B (1.00000000 0.00000000) *
## 17) texture_worst>=5.204837 1 0 M (0.00000000 1.00000000) *
## 9) symmetry_worst>=-1.312214 4 0 M (0.00000000 1.00000000) *
## 5) symmetry_worst< -1.508268 155 66 B (0.57419355 0.42580645)
## 10) symmetry_worst< -1.533879 133 47 B (0.64661654 0.35338346)
## 20) smoothness_worst>=-1.596418 98 24 B (0.75510204 0.24489796)
## 40) texture_worst< 5.05366 82 15 B (0.81707317 0.18292683)
## 80) smoothness_worst< -1.555675 26 0 B (1.00000000 0.00000000) *
## 81) smoothness_worst>=-1.555675 56 15 B (0.73214286 0.26785714) *
## 41) texture_worst>=5.05366 16 7 M (0.43750000 0.56250000)
## 82) texture_mean>=3.289936 7 0 B (1.00000000 0.00000000) *
## 83) texture_mean< 3.289936 9 0 M (0.00000000 1.00000000) *
## 21) smoothness_worst< -1.596418 35 12 M (0.34285714 0.65714286)
## 42) compactness_se< -4.711555 8 0 B (1.00000000 0.00000000) *
## 43) compactness_se>=-4.711555 27 4 M (0.14814815 0.85185185)
## 86) texture_mean>=3.23119 3 0 B (1.00000000 0.00000000) *
## 87) texture_mean< 3.23119 24 1 M (0.04166667 0.95833333) *
## 11) symmetry_worst>=-1.533879 22 3 M (0.13636364 0.86363636)
## 22) texture_mean< 2.906784 3 0 B (1.00000000 0.00000000) *
## 23) texture_mean>=2.906784 19 0 M (0.00000000 1.00000000) *
## 3) compactness_se>=-4.219581 696 318 M (0.45689655 0.54310345)
## 6) smoothness_mean< -2.478548 37 7 B (0.81081081 0.18918919)
## 12) symmetry_worst>=-1.750953 20 0 B (1.00000000 0.00000000) *
## 13) symmetry_worst< -1.750953 17 7 B (0.58823529 0.41176471)
## 26) smoothness_mean< -2.504718 10 0 B (1.00000000 0.00000000) *
## 27) smoothness_mean>=-2.504718 7 0 M (0.00000000 1.00000000) *
## 7) smoothness_mean>=-2.478548 659 288 M (0.43702580 0.56297420)
## 14) smoothness_worst>=-1.565486 552 265 M (0.48007246 0.51992754)
## 28) smoothness_worst< -1.560016 17 0 B (1.00000000 0.00000000) *
## 29) smoothness_worst>=-1.560016 535 248 M (0.46355140 0.53644860)
## 58) compactness_se>=-4.113227 497 243 M (0.48893360 0.51106640)
## 116) compactness_se< -4.024085 60 12 B (0.80000000 0.20000000) *
## 117) compactness_se>=-4.024085 437 195 M (0.44622426 0.55377574) *
## 59) compactness_se< -4.113227 38 5 M (0.13157895 0.86842105)
## 118) symmetry_worst< -1.75757 8 3 B (0.62500000 0.37500000) *
## 119) symmetry_worst>=-1.75757 30 0 M (0.00000000 1.00000000) *
## 15) smoothness_worst< -1.565486 107 23 M (0.21495327 0.78504673)
## 30) symmetry_worst< -2.25148 7 1 B (0.85714286 0.14285714)
## 60) smoothness_mean< -2.242079 6 0 B (1.00000000 0.00000000) *
## 61) smoothness_mean>=-2.242079 1 0 M (0.00000000 1.00000000) *
## 31) symmetry_worst>=-2.25148 100 17 M (0.17000000 0.83000000)
## 62) smoothness_worst< -1.618016 7 2 B (0.71428571 0.28571429)
## 124) texture_mean< 3.160844 5 0 B (1.00000000 0.00000000) *
## 125) texture_mean>=3.160844 2 0 M (0.00000000 1.00000000) *
## 63) smoothness_worst>=-1.618016 93 12 M (0.12903226 0.87096774)
## 126) texture_worst< 4.585652 36 10 M (0.27777778 0.72222222) *
## 127) texture_worst>=4.585652 57 2 M (0.03508772 0.96491228) *
##
## $trees[[38]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 418 B (0.54166667 0.45833333)
## 2) smoothness_worst>=-1.4768 272 90 B (0.66911765 0.33088235)
## 4) smoothness_mean< -2.079457 255 74 B (0.70980392 0.29019608)
## 8) texture_mean< 3.081214 211 50 B (0.76303318 0.23696682)
## 16) smoothness_worst< -1.473476 48 0 B (1.00000000 0.00000000) *
## 17) smoothness_worst>=-1.473476 163 50 B (0.69325153 0.30674847)
## 34) smoothness_worst>=-1.441166 83 13 B (0.84337349 0.15662651)
## 68) symmetry_worst< -1.36527 80 10 B (0.87500000 0.12500000) *
## 69) symmetry_worst>=-1.36527 3 0 M (0.00000000 1.00000000) *
## 35) smoothness_worst< -1.441166 80 37 B (0.53750000 0.46250000)
## 70) compactness_se< -3.652905 52 16 B (0.69230769 0.30769231) *
## 71) compactness_se>=-3.652905 28 7 M (0.25000000 0.75000000) *
## 9) texture_mean>=3.081214 44 20 M (0.45454545 0.54545455)
## 18) compactness_se>=-3.540614 23 5 B (0.78260870 0.21739130)
## 36) texture_mean>=3.099415 19 1 B (0.94736842 0.05263158)
## 72) texture_mean< 3.256167 18 0 B (1.00000000 0.00000000) *
## 73) texture_mean>=3.256167 1 0 M (0.00000000 1.00000000) *
## 37) texture_mean< 3.099415 4 0 M (0.00000000 1.00000000) *
## 19) compactness_se< -3.540614 21 2 M (0.09523810 0.90476190)
## 38) compactness_se< -4.494315 2 0 B (1.00000000 0.00000000) *
## 39) compactness_se>=-4.494315 19 0 M (0.00000000 1.00000000) *
## 5) smoothness_mean>=-2.079457 17 1 M (0.05882353 0.94117647)
## 10) smoothness_mean>=-1.879984 1 0 B (1.00000000 0.00000000) *
## 11) smoothness_mean< -1.879984 16 0 M (0.00000000 1.00000000) *
## 3) smoothness_worst< -1.4768 640 312 M (0.48750000 0.51250000)
## 6) smoothness_worst< -1.482699 603 294 B (0.51243781 0.48756219)
## 12) texture_mean< 2.874407 110 34 B (0.69090909 0.30909091)
## 24) symmetry_worst>=-1.74309 49 4 B (0.91836735 0.08163265)
## 48) symmetry_worst< -1.129539 45 0 B (1.00000000 0.00000000) *
## 49) symmetry_worst>=-1.129539 4 0 M (0.00000000 1.00000000) *
## 25) symmetry_worst< -1.74309 61 30 B (0.50819672 0.49180328)
## 50) texture_worst>=4.320273 17 2 B (0.88235294 0.11764706)
## 100) compactness_se>=-4.394685 14 0 B (1.00000000 0.00000000) *
## 101) compactness_se< -4.394685 3 1 M (0.33333333 0.66666667) *
## 51) texture_worst< 4.320273 44 16 M (0.36363636 0.63636364)
## 102) compactness_se< -4.18512 8 0 B (1.00000000 0.00000000) *
## 103) compactness_se>=-4.18512 36 8 M (0.22222222 0.77777778) *
## 13) texture_mean>=2.874407 493 233 M (0.47261663 0.52738337)
## 26) compactness_se>=-4.514873 442 219 B (0.50452489 0.49547511)
## 52) texture_mean>=2.882272 427 204 B (0.52224824 0.47775176)
## 104) smoothness_mean< -2.424301 140 42 B (0.70000000 0.30000000) *
## 105) smoothness_mean>=-2.424301 287 125 M (0.43554007 0.56445993) *
## 53) texture_mean< 2.882272 15 0 M (0.00000000 1.00000000) *
## 27) compactness_se< -4.514873 51 10 M (0.19607843 0.80392157)
## 54) texture_worst>=4.62656 29 10 M (0.34482759 0.65517241)
## 108) texture_mean< 2.957227 8 0 B (1.00000000 0.00000000) *
## 109) texture_mean>=2.957227 21 2 M (0.09523810 0.90476190) *
## 55) texture_worst< 4.62656 22 0 M (0.00000000 1.00000000) *
## 7) smoothness_worst>=-1.482699 37 3 M (0.08108108 0.91891892)
## 14) texture_mean< 2.755881 2 0 B (1.00000000 0.00000000) *
## 15) texture_mean>=2.755881 35 1 M (0.02857143 0.97142857)
## 30) compactness_se< -3.967101 7 1 M (0.14285714 0.85714286)
## 60) smoothness_mean>=-2.254596 1 0 B (1.00000000 0.00000000) *
## 61) smoothness_mean< -2.254596 6 0 M (0.00000000 1.00000000) *
## 31) compactness_se>=-3.967101 28 0 M (0.00000000 1.00000000) *
##
## $trees[[39]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 375 B (0.58881579 0.41118421)
## 2) symmetry_worst>=-1.490299 141 27 B (0.80851064 0.19148936)
## 4) symmetry_worst< -1.051331 135 21 B (0.84444444 0.15555556)
## 8) smoothness_mean< -2.334751 75 3 B (0.96000000 0.04000000)
## 16) texture_mean< 3.116237 67 0 B (1.00000000 0.00000000) *
## 17) texture_mean>=3.116237 8 3 B (0.62500000 0.37500000)
## 34) smoothness_mean>=-2.350921 5 0 B (1.00000000 0.00000000) *
## 35) smoothness_mean< -2.350921 3 0 M (0.00000000 1.00000000) *
## 9) smoothness_mean>=-2.334751 60 18 B (0.70000000 0.30000000)
## 18) texture_mean< 2.777879 27 0 B (1.00000000 0.00000000) *
## 19) texture_mean>=2.777879 33 15 M (0.45454545 0.54545455)
## 38) symmetry_worst>=-1.124686 10 0 B (1.00000000 0.00000000) *
## 39) symmetry_worst< -1.124686 23 5 M (0.21739130 0.78260870)
## 78) compactness_se< -4.171724 5 0 B (1.00000000 0.00000000) *
## 79) compactness_se>=-4.171724 18 0 M (0.00000000 1.00000000) *
## 5) symmetry_worst>=-1.051331 6 0 M (0.00000000 1.00000000) *
## 3) symmetry_worst< -1.490299 771 348 B (0.54863813 0.45136187)
## 6) texture_worst< 4.389172 182 55 B (0.69780220 0.30219780)
## 12) texture_mean>=2.857325 67 6 B (0.91044776 0.08955224)
## 24) smoothness_mean>=-2.515683 62 2 B (0.96774194 0.03225806)
## 48) smoothness_mean< -2.178638 61 1 B (0.98360656 0.01639344)
## 96) smoothness_mean>=-2.27605 51 0 B (1.00000000 0.00000000) *
## 97) smoothness_mean< -2.27605 10 1 B (0.90000000 0.10000000) *
## 49) smoothness_mean>=-2.178638 1 0 M (0.00000000 1.00000000) *
## 25) smoothness_mean< -2.515683 5 1 M (0.20000000 0.80000000)
## 50) texture_mean>=2.986158 1 0 B (1.00000000 0.00000000) *
## 51) texture_mean< 2.986158 4 0 M (0.00000000 1.00000000) *
## 13) texture_mean< 2.857325 115 49 B (0.57391304 0.42608696)
## 26) texture_mean< 2.824054 94 30 B (0.68085106 0.31914894)
## 52) texture_mean>=2.771335 23 0 B (1.00000000 0.00000000) *
## 53) texture_mean< 2.771335 71 30 B (0.57746479 0.42253521)
## 106) texture_mean< 2.758426 57 17 B (0.70175439 0.29824561) *
## 107) texture_mean>=2.758426 14 1 M (0.07142857 0.92857143) *
## 27) texture_mean>=2.824054 21 2 M (0.09523810 0.90476190)
## 54) smoothness_mean< -2.415139 2 0 B (1.00000000 0.00000000) *
## 55) smoothness_mean>=-2.415139 19 0 M (0.00000000 1.00000000) *
## 7) texture_worst>=4.389172 589 293 B (0.50254669 0.49745331)
## 14) texture_mean< 2.876103 37 5 B (0.86486486 0.13513514)
## 28) symmetry_worst< -1.596157 32 0 B (1.00000000 0.00000000) *
## 29) symmetry_worst>=-1.596157 5 0 M (0.00000000 1.00000000) *
## 15) texture_mean>=2.876103 552 264 M (0.47826087 0.52173913)
## 30) texture_worst>=4.406766 526 261 M (0.49619772 0.50380228)
## 60) texture_mean< 2.892591 12 0 B (1.00000000 0.00000000) *
## 61) texture_mean>=2.892591 514 249 M (0.48443580 0.51556420)
## 122) symmetry_worst< -1.893233 179 74 B (0.58659218 0.41340782) *
## 123) symmetry_worst>=-1.893233 335 144 M (0.42985075 0.57014925) *
## 31) texture_worst< 4.406766 26 3 M (0.11538462 0.88461538)
## 62) texture_mean>=2.918641 3 0 B (1.00000000 0.00000000) *
## 63) texture_mean< 2.918641 23 0 M (0.00000000 1.00000000) *
##
## $trees[[40]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 379 B (0.58442982 0.41557018)
## 2) smoothness_mean>=-2.441446 728 266 B (0.63461538 0.36538462)
## 4) smoothness_mean< -2.425205 60 6 B (0.90000000 0.10000000)
## 8) symmetry_worst< -1.496954 52 0 B (1.00000000 0.00000000) *
## 9) symmetry_worst>=-1.496954 8 2 M (0.25000000 0.75000000)
## 18) texture_mean< 2.97943 2 0 B (1.00000000 0.00000000) *
## 19) texture_mean>=2.97943 6 0 M (0.00000000 1.00000000) *
## 5) smoothness_mean>=-2.425205 668 260 B (0.61077844 0.38922156)
## 10) texture_mean< 3.054236 489 166 B (0.66053170 0.33946830)
## 20) texture_mean>=2.760642 418 123 B (0.70574163 0.29425837)
## 40) smoothness_worst>=-1.593678 404 111 B (0.72524752 0.27475248)
## 80) smoothness_worst< -1.551993 50 2 B (0.96000000 0.04000000) *
## 81) smoothness_worst>=-1.551993 354 109 B (0.69209040 0.30790960) *
## 41) smoothness_worst< -1.593678 14 2 M (0.14285714 0.85714286)
## 82) texture_mean< 2.889781 2 0 B (1.00000000 0.00000000) *
## 83) texture_mean>=2.889781 12 0 M (0.00000000 1.00000000) *
## 21) texture_mean< 2.760642 71 28 M (0.39436620 0.60563380)
## 42) texture_worst< 4.16384 35 11 B (0.68571429 0.31428571)
## 84) texture_mean>=2.715678 16 0 B (1.00000000 0.00000000) *
## 85) texture_mean< 2.715678 19 8 M (0.42105263 0.57894737) *
## 43) texture_worst>=4.16384 36 4 M (0.11111111 0.88888889)
## 86) compactness_se< -3.892047 3 0 B (1.00000000 0.00000000) *
## 87) compactness_se>=-3.892047 33 1 M (0.03030303 0.96969697) *
## 11) texture_mean>=3.054236 179 85 M (0.47486034 0.52513966)
## 22) smoothness_mean>=-2.383798 138 58 B (0.57971014 0.42028986)
## 44) compactness_se< -3.304429 126 46 B (0.63492063 0.36507937)
## 88) compactness_se>=-3.902076 89 20 B (0.77528090 0.22471910) *
## 89) compactness_se< -3.902076 37 11 M (0.29729730 0.70270270) *
## 45) compactness_se>=-3.304429 12 0 M (0.00000000 1.00000000) *
## 23) smoothness_mean< -2.383798 41 5 M (0.12195122 0.87804878)
## 46) symmetry_worst< -2.145206 3 0 B (1.00000000 0.00000000) *
## 47) symmetry_worst>=-2.145206 38 2 M (0.05263158 0.94736842)
## 94) smoothness_worst< -1.602866 2 0 B (1.00000000 0.00000000) *
## 95) smoothness_worst>=-1.602866 36 0 M (0.00000000 1.00000000) *
## 3) smoothness_mean< -2.441446 184 71 M (0.38586957 0.61413043)
## 6) compactness_se< -4.356557 63 25 B (0.60317460 0.39682540)
## 12) symmetry_worst< -1.574457 50 12 B (0.76000000 0.24000000)
## 24) smoothness_mean< -2.44767 46 8 B (0.82608696 0.17391304)
## 48) smoothness_mean>=-2.496965 26 0 B (1.00000000 0.00000000) *
## 49) smoothness_mean< -2.496965 20 8 B (0.60000000 0.40000000)
## 98) smoothness_mean< -2.507092 16 4 B (0.75000000 0.25000000) *
## 99) smoothness_mean>=-2.507092 4 0 M (0.00000000 1.00000000) *
## 25) smoothness_mean>=-2.44767 4 0 M (0.00000000 1.00000000) *
## 13) symmetry_worst>=-1.574457 13 0 M (0.00000000 1.00000000) *
## 7) compactness_se>=-4.356557 121 33 M (0.27272727 0.72727273)
## 14) texture_mean< 2.754836 8 0 B (1.00000000 0.00000000) *
## 15) texture_mean>=2.754836 113 25 M (0.22123894 0.77876106)
## 30) compactness_se>=-2.839112 7 0 B (1.00000000 0.00000000) *
## 31) compactness_se< -2.839112 106 18 M (0.16981132 0.83018868)
## 62) texture_worst>=5.316369 3 0 B (1.00000000 0.00000000) *
## 63) texture_worst< 5.316369 103 15 M (0.14563107 0.85436893)
## 126) smoothness_mean>=-2.467755 33 10 M (0.30303030 0.69696970) *
## 127) smoothness_mean< -2.467755 70 5 M (0.07142857 0.92857143) *
##
## $trees[[41]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 452 B (0.50438596 0.49561404)
## 2) symmetry_worst>=-1.930267 653 296 B (0.54670750 0.45329250)
## 4) smoothness_mean< -2.079457 631 277 B (0.56101426 0.43898574)
## 8) smoothness_mean>=-2.235394 126 35 B (0.72222222 0.27777778)
## 16) smoothness_mean< -2.222851 43 2 B (0.95348837 0.04651163)
## 32) compactness_se< -2.985939 41 0 B (1.00000000 0.00000000) *
## 33) compactness_se>=-2.985939 2 0 M (0.00000000 1.00000000) *
## 17) smoothness_mean>=-2.222851 83 33 B (0.60240964 0.39759036)
## 34) symmetry_worst< -1.765932 29 3 B (0.89655172 0.10344828)
## 68) texture_mean< 3.232324 27 1 B (0.96296296 0.03703704) *
## 69) texture_mean>=3.232324 2 0 M (0.00000000 1.00000000) *
## 35) symmetry_worst>=-1.765932 54 24 M (0.44444444 0.55555556)
## 70) smoothness_mean>=-2.092733 9 0 B (1.00000000 0.00000000) *
## 71) smoothness_mean< -2.092733 45 15 M (0.33333333 0.66666667) *
## 9) smoothness_mean< -2.235394 505 242 B (0.52079208 0.47920792)
## 18) smoothness_mean< -2.36186 287 110 B (0.61672474 0.38327526)
## 36) texture_mean< 2.956199 143 38 B (0.73426573 0.26573427)
## 72) symmetry_worst>=-1.748649 78 7 B (0.91025641 0.08974359) *
## 73) symmetry_worst< -1.748649 65 31 B (0.52307692 0.47692308) *
## 37) texture_mean>=2.956199 144 72 B (0.50000000 0.50000000)
## 74) texture_worst>=4.781424 82 24 B (0.70731707 0.29268293) *
## 75) texture_worst< 4.781424 62 14 M (0.22580645 0.77419355) *
## 19) smoothness_mean>=-2.36186 218 86 M (0.39449541 0.60550459)
## 38) symmetry_worst>=-1.128751 15 1 B (0.93333333 0.06666667)
## 76) smoothness_mean>=-2.321477 14 0 B (1.00000000 0.00000000) *
## 77) smoothness_mean< -2.321477 1 0 M (0.00000000 1.00000000) *
## 39) symmetry_worst< -1.128751 203 72 M (0.35467980 0.64532020)
## 78) compactness_se< -4.025757 49 18 B (0.63265306 0.36734694) *
## 79) compactness_se>=-4.025757 154 41 M (0.26623377 0.73376623) *
## 5) smoothness_mean>=-2.079457 22 3 M (0.13636364 0.86363636)
## 10) smoothness_mean>=-2.000349 5 2 B (0.60000000 0.40000000)
## 20) texture_mean< 2.688296 3 0 B (1.00000000 0.00000000) *
## 21) texture_mean>=2.688296 2 0 M (0.00000000 1.00000000) *
## 11) smoothness_mean< -2.000349 17 0 M (0.00000000 1.00000000) *
## 3) symmetry_worst< -1.930267 259 103 M (0.39768340 0.60231660)
## 6) texture_worst< 4.605004 85 31 B (0.63529412 0.36470588)
## 12) compactness_se>=-4.49319 71 19 B (0.73239437 0.26760563)
## 24) symmetry_worst< -2.048468 38 2 B (0.94736842 0.05263158)
## 48) smoothness_worst>=-1.720903 36 0 B (1.00000000 0.00000000) *
## 49) smoothness_worst< -1.720903 2 0 M (0.00000000 1.00000000) *
## 25) symmetry_worst>=-2.048468 33 16 M (0.48484848 0.51515152)
## 50) compactness_se< -3.88564 10 0 B (1.00000000 0.00000000) *
## 51) compactness_se>=-3.88564 23 6 M (0.26086957 0.73913043)
## 102) texture_mean< 2.753964 2 0 B (1.00000000 0.00000000) *
## 103) texture_mean>=2.753964 21 4 M (0.19047619 0.80952381) *
## 13) compactness_se< -4.49319 14 2 M (0.14285714 0.85714286)
## 26) compactness_se< -4.635639 2 0 B (1.00000000 0.00000000) *
## 27) compactness_se>=-4.635639 12 0 M (0.00000000 1.00000000) *
## 7) texture_worst>=4.605004 174 49 M (0.28160920 0.71839080)
## 14) smoothness_mean< -2.486268 11 0 B (1.00000000 0.00000000) *
## 15) smoothness_mean>=-2.486268 163 38 M (0.23312883 0.76687117)
## 30) smoothness_worst< -1.576561 22 9 B (0.59090909 0.40909091)
## 60) texture_mean< 3.256972 10 0 B (1.00000000 0.00000000) *
## 61) texture_mean>=3.256972 12 3 M (0.25000000 0.75000000)
## 122) texture_mean>=3.383004 3 0 B (1.00000000 0.00000000) *
## 123) texture_mean< 3.383004 9 0 M (0.00000000 1.00000000) *
## 31) smoothness_worst>=-1.576561 141 25 M (0.17730496 0.82269504)
## 62) symmetry_worst< -2.233349 10 3 B (0.70000000 0.30000000)
## 124) smoothness_mean< -2.337981 6 0 B (1.00000000 0.00000000) *
## 125) smoothness_mean>=-2.337981 4 1 M (0.25000000 0.75000000) *
## 63) symmetry_worst>=-2.233349 131 18 M (0.13740458 0.86259542)
## 126) smoothness_mean>=-2.352488 31 13 M (0.41935484 0.58064516) *
## 127) smoothness_mean< -2.352488 100 5 M (0.05000000 0.95000000) *
##
## $trees[[42]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 438 B (0.51973684 0.48026316)
## 2) smoothness_mean>=-2.441446 720 315 B (0.56250000 0.43750000)
## 4) smoothness_worst< -1.472307 493 183 B (0.62880325 0.37119675)
## 8) smoothness_worst>=-1.476409 51 1 B (0.98039216 0.01960784)
## 16) texture_worst< 4.844547 50 0 B (1.00000000 0.00000000) *
## 17) texture_worst>=4.844547 1 0 M (0.00000000 1.00000000) *
## 9) smoothness_worst< -1.476409 442 182 B (0.58823529 0.41176471)
## 18) smoothness_worst< -1.482107 410 157 B (0.61707317 0.38292683)
## 36) symmetry_worst>=-1.595052 106 21 B (0.80188679 0.19811321)
## 72) smoothness_worst>=-1.607486 95 12 B (0.87368421 0.12631579) *
## 73) smoothness_worst< -1.607486 11 2 M (0.18181818 0.81818182) *
## 37) symmetry_worst< -1.595052 304 136 B (0.55263158 0.44736842)
## 74) compactness_se>=-4.100467 214 79 B (0.63084112 0.36915888) *
## 75) compactness_se< -4.100467 90 33 M (0.36666667 0.63333333) *
## 19) smoothness_worst>=-1.482107 32 7 M (0.21875000 0.78125000)
## 38) texture_mean< 2.755881 4 0 B (1.00000000 0.00000000) *
## 39) texture_mean>=2.755881 28 3 M (0.10714286 0.89285714)
## 78) smoothness_mean>=-2.253991 7 3 M (0.42857143 0.57142857) *
## 79) smoothness_mean< -2.253991 21 0 M (0.00000000 1.00000000) *
## 5) smoothness_worst>=-1.472307 227 95 M (0.41850220 0.58149780)
## 10) compactness_se< -4.040144 66 23 B (0.65151515 0.34848485)
## 20) texture_worst>=4.752437 48 9 B (0.81250000 0.18750000)
## 40) compactness_se>=-4.348305 30 0 B (1.00000000 0.00000000) *
## 41) compactness_se< -4.348305 18 9 B (0.50000000 0.50000000)
## 82) smoothness_mean< -2.333927 9 0 B (1.00000000 0.00000000) *
## 83) smoothness_mean>=-2.333927 9 0 M (0.00000000 1.00000000) *
## 21) texture_worst< 4.752437 18 4 M (0.22222222 0.77777778)
## 42) texture_mean< 2.934384 4 0 B (1.00000000 0.00000000) *
## 43) texture_mean>=2.934384 14 0 M (0.00000000 1.00000000) *
## 11) compactness_se>=-4.040144 161 52 M (0.32298137 0.67701863)
## 22) compactness_se>=-3.938669 124 52 M (0.41935484 0.58064516)
## 44) smoothness_worst>=-1.434633 62 26 B (0.58064516 0.41935484)
## 88) compactness_se< -3.311998 42 12 B (0.71428571 0.28571429) *
## 89) compactness_se>=-3.311998 20 6 M (0.30000000 0.70000000) *
## 45) smoothness_worst< -1.434633 62 16 M (0.25806452 0.74193548)
## 90) symmetry_worst< -2.030418 7 0 B (1.00000000 0.00000000) *
## 91) symmetry_worst>=-2.030418 55 9 M (0.16363636 0.83636364) *
## 23) compactness_se< -3.938669 37 0 M (0.00000000 1.00000000) *
## 3) smoothness_mean< -2.441446 192 69 M (0.35937500 0.64062500)
## 6) texture_mean< 2.869285 17 3 B (0.82352941 0.17647059)
## 12) smoothness_mean< -2.448147 14 0 B (1.00000000 0.00000000) *
## 13) smoothness_mean>=-2.448147 3 0 M (0.00000000 1.00000000) *
## 7) texture_mean>=2.869285 175 55 M (0.31428571 0.68571429)
## 14) compactness_se>=-2.839112 7 0 B (1.00000000 0.00000000) *
## 15) compactness_se< -2.839112 168 48 M (0.28571429 0.71428571)
## 30) texture_mean>=2.881435 141 48 M (0.34042553 0.65957447)
## 60) texture_mean< 2.921008 13 0 B (1.00000000 0.00000000) *
## 61) texture_mean>=2.921008 128 35 M (0.27343750 0.72656250)
## 122) symmetry_worst< -1.54778 102 35 M (0.34313725 0.65686275) *
## 123) symmetry_worst>=-1.54778 26 0 M (0.00000000 1.00000000) *
## 31) texture_mean< 2.881435 27 0 M (0.00000000 1.00000000) *
##
## $trees[[43]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 436 B (0.52192982 0.47807018)
## 2) texture_mean< 2.876103 198 63 B (0.68181818 0.31818182)
## 4) texture_mean>=2.850128 50 3 B (0.94000000 0.06000000)
## 8) smoothness_mean< -2.220156 48 1 B (0.97916667 0.02083333)
## 16) smoothness_worst< -1.438497 47 0 B (1.00000000 0.00000000) *
## 17) smoothness_worst>=-1.438497 1 0 M (0.00000000 1.00000000) *
## 9) smoothness_mean>=-2.220156 2 0 M (0.00000000 1.00000000) *
## 5) texture_mean< 2.850128 148 60 B (0.59459459 0.40540541)
## 10) texture_worst< 4.260219 78 19 B (0.75641026 0.24358974)
## 20) symmetry_worst< -1.429489 69 13 B (0.81159420 0.18840580)
## 40) smoothness_worst>=-1.54469 50 5 B (0.90000000 0.10000000)
## 80) texture_worst< 4.176708 39 1 B (0.97435897 0.02564103) *
## 81) texture_worst>=4.176708 11 4 B (0.63636364 0.36363636) *
## 41) smoothness_worst< -1.54469 19 8 B (0.57894737 0.42105263)
## 82) compactness_se< -3.541332 10 0 B (1.00000000 0.00000000) *
## 83) compactness_se>=-3.541332 9 1 M (0.11111111 0.88888889) *
## 21) symmetry_worst>=-1.429489 9 3 M (0.33333333 0.66666667)
## 42) texture_worst>=4.136225 3 0 B (1.00000000 0.00000000) *
## 43) texture_worst< 4.136225 6 0 M (0.00000000 1.00000000) *
## 11) texture_worst>=4.260219 70 29 M (0.41428571 0.58571429)
## 22) texture_worst>=4.338767 42 14 B (0.66666667 0.33333333)
## 44) texture_worst< 4.517878 25 1 B (0.96000000 0.04000000)
## 88) smoothness_mean< -2.137307 24 0 B (1.00000000 0.00000000) *
## 89) smoothness_mean>=-2.137307 1 0 M (0.00000000 1.00000000) *
## 45) texture_worst>=4.517878 17 4 M (0.23529412 0.76470588)
## 90) smoothness_mean< -2.3918 3 0 B (1.00000000 0.00000000) *
## 91) smoothness_mean>=-2.3918 14 1 M (0.07142857 0.92857143) *
## 23) texture_worst< 4.338767 28 1 M (0.03571429 0.96428571)
## 46) smoothness_mean< -2.417779 1 0 B (1.00000000 0.00000000) *
## 47) smoothness_mean>=-2.417779 27 0 M (0.00000000 1.00000000) *
## 3) texture_mean>=2.876103 714 341 M (0.47759104 0.52240896)
## 6) symmetry_worst< -1.317839 685 337 M (0.49197080 0.50802920)
## 12) smoothness_worst>=-1.614253 623 302 B (0.51524880 0.48475120)
## 24) compactness_se< -3.816486 309 124 B (0.59870550 0.40129450)
## 48) smoothness_worst< -1.559798 93 18 B (0.80645161 0.19354839)
## 96) texture_worst< 5.083395 82 9 B (0.89024390 0.10975610) *
## 97) texture_worst>=5.083395 11 2 M (0.18181818 0.81818182) *
## 49) smoothness_worst>=-1.559798 216 106 B (0.50925926 0.49074074)
## 98) smoothness_worst>=-1.546619 168 67 B (0.60119048 0.39880952) *
## 99) smoothness_worst< -1.546619 48 9 M (0.18750000 0.81250000) *
## 25) compactness_se>=-3.816486 314 136 M (0.43312102 0.56687898)
## 50) texture_mean< 2.925574 26 1 B (0.96153846 0.03846154)
## 100) texture_worst>=4.12485 25 0 B (1.00000000 0.00000000) *
## 101) texture_worst< 4.12485 1 0 M (0.00000000 1.00000000) *
## 51) texture_mean>=2.925574 288 111 M (0.38541667 0.61458333)
## 102) texture_mean>=2.936509 256 111 M (0.43359375 0.56640625) *
## 103) texture_mean< 2.936509 32 0 M (0.00000000 1.00000000) *
## 13) smoothness_worst< -1.614253 62 16 M (0.25806452 0.74193548)
## 26) texture_mean>=3.089887 8 1 B (0.87500000 0.12500000)
## 52) compactness_se>=-4.480894 7 0 B (1.00000000 0.00000000) *
## 53) compactness_se< -4.480894 1 0 M (0.00000000 1.00000000) *
## 27) texture_mean< 3.089887 54 9 M (0.16666667 0.83333333)
## 54) compactness_se>=-2.870592 3 0 B (1.00000000 0.00000000) *
## 55) compactness_se< -2.870592 51 6 M (0.11764706 0.88235294)
## 110) texture_mean< 2.935975 1 0 B (1.00000000 0.00000000) *
## 111) texture_mean>=2.935975 50 5 M (0.10000000 0.90000000) *
## 7) symmetry_worst>=-1.317839 29 4 M (0.13793103 0.86206897)
## 14) texture_mean>=3.099059 6 2 B (0.66666667 0.33333333)
## 28) texture_mean< 3.163269 4 0 B (1.00000000 0.00000000) *
## 29) texture_mean>=3.163269 2 0 M (0.00000000 1.00000000) *
## 15) texture_mean< 3.099059 23 0 M (0.00000000 1.00000000) *
##
## $trees[[44]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 432 B (0.52631579 0.47368421)
## 2) compactness_se>=-3.494301 297 98 B (0.67003367 0.32996633)
## 4) texture_mean< 3.059388 228 50 B (0.78070175 0.21929825)
## 8) smoothness_mean< -2.066369 222 44 B (0.80180180 0.19819820)
## 16) smoothness_mean< -2.395314 44 1 B (0.97727273 0.02272727)
## 32) compactness_se>=-3.483667 43 0 B (1.00000000 0.00000000) *
## 33) compactness_se< -3.483667 1 0 M (0.00000000 1.00000000) *
## 17) smoothness_mean>=-2.395314 178 43 B (0.75842697 0.24157303)
## 34) smoothness_mean>=-2.369527 172 37 B (0.78488372 0.21511628)
## 68) smoothness_worst>=-1.477195 80 8 B (0.90000000 0.10000000) *
## 69) smoothness_worst< -1.477195 92 29 B (0.68478261 0.31521739) *
## 35) smoothness_mean< -2.369527 6 0 M (0.00000000 1.00000000) *
## 9) smoothness_mean>=-2.066369 6 0 M (0.00000000 1.00000000) *
## 5) texture_mean>=3.059388 69 21 M (0.30434783 0.69565217)
## 10) smoothness_worst>=-1.513087 32 12 B (0.62500000 0.37500000)
## 20) texture_worst>=4.744846 27 7 B (0.74074074 0.25925926)
## 40) texture_mean< 3.220473 24 4 B (0.83333333 0.16666667)
## 80) texture_worst< 5.016194 21 1 B (0.95238095 0.04761905) *
## 81) texture_worst>=5.016194 3 0 M (0.00000000 1.00000000) *
## 41) texture_mean>=3.220473 3 0 M (0.00000000 1.00000000) *
## 21) texture_worst< 4.744846 5 0 M (0.00000000 1.00000000) *
## 11) smoothness_worst< -1.513087 37 1 M (0.02702703 0.97297297)
## 22) smoothness_mean< -2.638103 1 0 B (1.00000000 0.00000000) *
## 23) smoothness_mean>=-2.638103 36 0 M (0.00000000 1.00000000) *
## 3) compactness_se< -3.494301 615 281 M (0.45691057 0.54308943)
## 6) smoothness_worst< -1.520292 340 155 B (0.54411765 0.45588235)
## 12) texture_worst>=4.465917 249 92 B (0.63052209 0.36947791)
## 24) smoothness_worst>=-1.570555 165 43 B (0.73939394 0.26060606)
## 48) compactness_se< -3.512408 157 35 B (0.77707006 0.22292994)
## 96) texture_worst< 4.949112 119 17 B (0.85714286 0.14285714) *
## 97) texture_worst>=4.949112 38 18 B (0.52631579 0.47368421) *
## 49) compactness_se>=-3.512408 8 0 M (0.00000000 1.00000000) *
## 25) smoothness_worst< -1.570555 84 35 M (0.41666667 0.58333333)
## 50) smoothness_worst< -1.584838 59 24 B (0.59322034 0.40677966)
## 100) smoothness_worst>=-1.607486 22 3 B (0.86363636 0.13636364) *
## 101) smoothness_worst< -1.607486 37 16 M (0.43243243 0.56756757) *
## 51) smoothness_worst>=-1.584838 25 0 M (0.00000000 1.00000000) *
## 13) texture_worst< 4.465917 91 28 M (0.30769231 0.69230769)
## 26) symmetry_worst< -1.948608 23 6 B (0.73913043 0.26086957)
## 52) symmetry_worst>=-2.391709 17 0 B (1.00000000 0.00000000) *
## 53) symmetry_worst< -2.391709 6 0 M (0.00000000 1.00000000) *
## 27) symmetry_worst>=-1.948608 68 11 M (0.16176471 0.83823529)
## 54) symmetry_worst>=-1.603839 5 0 B (1.00000000 0.00000000) *
## 55) symmetry_worst< -1.603839 63 6 M (0.09523810 0.90476190)
## 110) smoothness_worst< -1.556329 13 6 M (0.46153846 0.53846154) *
## 111) smoothness_worst>=-1.556329 50 0 M (0.00000000 1.00000000) *
## 7) smoothness_worst>=-1.520292 275 96 M (0.34909091 0.65090909)
## 14) texture_worst< 4.469369 51 15 B (0.70588235 0.29411765)
## 28) smoothness_worst< -1.451541 29 3 B (0.89655172 0.10344828)
## 56) symmetry_worst>=-1.886625 23 0 B (1.00000000 0.00000000) *
## 57) symmetry_worst< -1.886625 6 3 B (0.50000000 0.50000000)
## 114) texture_mean>=2.796001 3 0 B (1.00000000 0.00000000) *
## 115) texture_mean< 2.796001 3 0 M (0.00000000 1.00000000) *
## 29) smoothness_worst>=-1.451541 22 10 M (0.45454545 0.54545455)
## 58) smoothness_worst>=-1.434633 13 4 B (0.69230769 0.30769231)
## 116) texture_worst< 4.30106 9 0 B (1.00000000 0.00000000) *
## 117) texture_worst>=4.30106 4 0 M (0.00000000 1.00000000) *
## 59) smoothness_worst< -1.434633 9 1 M (0.11111111 0.88888889)
## 118) compactness_se< -4.188107 1 0 B (1.00000000 0.00000000) *
## 119) compactness_se>=-4.188107 8 0 M (0.00000000 1.00000000) *
## 15) texture_worst>=4.469369 224 60 M (0.26785714 0.73214286)
## 30) texture_mean< 2.88392 26 8 B (0.69230769 0.30769231)
## 60) texture_mean>=2.849548 17 0 B (1.00000000 0.00000000) *
## 61) texture_mean< 2.849548 9 1 M (0.11111111 0.88888889)
## 122) texture_mean< 2.79419 1 0 B (1.00000000 0.00000000) *
## 123) texture_mean>=2.79419 8 0 M (0.00000000 1.00000000) *
## 31) texture_mean>=2.88392 198 42 M (0.21212121 0.78787879)
## 62) texture_mean>=3.309778 17 8 B (0.52941176 0.47058824)
## 124) texture_mean< 3.407548 12 3 B (0.75000000 0.25000000) *
## 125) texture_mean>=3.407548 5 0 M (0.00000000 1.00000000) *
## 63) texture_mean< 3.309778 181 33 M (0.18232044 0.81767956)
## 126) smoothness_mean>=-2.311929 78 25 M (0.32051282 0.67948718) *
## 127) smoothness_mean< -2.311929 103 8 M (0.07766990 0.92233010) *
##
## $trees[[45]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 446 M (0.48903509 0.51096491)
## 2) smoothness_worst< -1.558926 239 80 B (0.66527197 0.33472803)
## 4) smoothness_worst>=-1.565486 37 0 B (1.00000000 0.00000000) *
## 5) smoothness_worst< -1.565486 202 80 B (0.60396040 0.39603960)
## 10) smoothness_worst< -1.584838 149 44 B (0.70469799 0.29530201)
## 20) texture_mean< 3.204554 133 30 B (0.77443609 0.22556391)
## 40) compactness_se< -2.951614 121 21 B (0.82644628 0.17355372)
## 80) symmetry_worst>=-2.391709 116 16 B (0.86206897 0.13793103) *
## 81) symmetry_worst< -2.391709 5 0 M (0.00000000 1.00000000) *
## 41) compactness_se>=-2.951614 12 3 M (0.25000000 0.75000000)
## 82) texture_mean< 3.045208 3 0 B (1.00000000 0.00000000) *
## 83) texture_mean>=3.045208 9 0 M (0.00000000 1.00000000) *
## 21) texture_mean>=3.204554 16 2 M (0.12500000 0.87500000)
## 42) smoothness_mean< -2.520061 2 0 B (1.00000000 0.00000000) *
## 43) smoothness_mean>=-2.520061 14 0 M (0.00000000 1.00000000) *
## 11) smoothness_worst>=-1.584838 53 17 M (0.32075472 0.67924528)
## 22) smoothness_mean< -2.444611 10 1 B (0.90000000 0.10000000)
## 44) texture_mean< 3.074043 9 0 B (1.00000000 0.00000000) *
## 45) texture_mean>=3.074043 1 0 M (0.00000000 1.00000000) *
## 23) smoothness_mean>=-2.444611 43 8 M (0.18604651 0.81395349)
## 46) compactness_se< -4.144493 5 0 B (1.00000000 0.00000000) *
## 47) compactness_se>=-4.144493 38 3 M (0.07894737 0.92105263)
## 94) texture_mean< 2.869214 6 3 B (0.50000000 0.50000000) *
## 95) texture_mean>=2.869214 32 0 M (0.00000000 1.00000000) *
## 3) smoothness_worst>=-1.558926 673 287 M (0.42644874 0.57355126)
## 6) smoothness_worst>=-1.551128 616 280 M (0.45454545 0.54545455)
## 12) smoothness_mean< -2.416986 80 26 B (0.67500000 0.32500000)
## 24) texture_worst< 4.998431 67 13 B (0.80597015 0.19402985)
## 48) symmetry_worst>=-1.995409 57 4 B (0.92982456 0.07017544)
## 96) symmetry_worst< -1.429489 53 0 B (1.00000000 0.00000000) *
## 97) symmetry_worst>=-1.429489 4 0 M (0.00000000 1.00000000) *
## 49) symmetry_worst< -1.995409 10 1 M (0.10000000 0.90000000)
## 98) texture_mean< 2.768852 1 0 B (1.00000000 0.00000000) *
## 99) texture_mean>=2.768852 9 0 M (0.00000000 1.00000000) *
## 25) texture_worst>=4.998431 13 0 M (0.00000000 1.00000000) *
## 13) smoothness_mean>=-2.416986 536 226 M (0.42164179 0.57835821)
## 26) smoothness_mean>=-2.326878 361 180 B (0.50138504 0.49861496)
## 52) symmetry_worst< -1.640882 188 68 B (0.63829787 0.36170213)
## 104) smoothness_mean< -2.304488 43 2 B (0.95348837 0.04651163) *
## 105) smoothness_mean>=-2.304488 145 66 B (0.54482759 0.45517241) *
## 53) symmetry_worst>=-1.640882 173 61 M (0.35260116 0.64739884)
## 106) smoothness_mean< -2.322588 11 0 B (1.00000000 0.00000000) *
## 107) smoothness_mean>=-2.322588 162 50 M (0.30864198 0.69135802) *
## 27) smoothness_mean< -2.326878 175 45 M (0.25714286 0.74285714)
## 54) symmetry_worst>=-1.50256 27 6 B (0.77777778 0.22222222)
## 108) symmetry_worst< -1.291518 21 1 B (0.95238095 0.04761905) *
## 109) symmetry_worst>=-1.291518 6 1 M (0.16666667 0.83333333) *
## 55) symmetry_worst< -1.50256 148 24 M (0.16216216 0.83783784)
## 110) texture_worst< 4.242051 3 0 B (1.00000000 0.00000000) *
## 111) texture_worst>=4.242051 145 21 M (0.14482759 0.85517241) *
## 7) smoothness_worst< -1.551128 57 7 M (0.12280702 0.87719298)
## 14) texture_mean>=3.344965 4 0 B (1.00000000 0.00000000) *
## 15) texture_mean< 3.344965 53 3 M (0.05660377 0.94339623)
## 30) texture_mean< 2.850634 1 0 B (1.00000000 0.00000000) *
## 31) texture_mean>=2.850634 52 2 M (0.03846154 0.96153846)
## 62) texture_mean< 2.919658 9 2 M (0.22222222 0.77777778)
## 124) texture_mean>=2.897371 2 0 B (1.00000000 0.00000000) *
## 125) texture_mean< 2.897371 7 0 M (0.00000000 1.00000000) *
## 63) texture_mean>=2.919658 43 0 M (0.00000000 1.00000000) *
##
## $trees[[46]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 448 B (0.50877193 0.49122807)
## 2) texture_worst< 4.905415 705 310 B (0.56028369 0.43971631)
## 4) smoothness_worst< -1.557621 154 41 B (0.73376623 0.26623377)
## 8) symmetry_worst< -1.838755 69 7 B (0.89855072 0.10144928)
## 16) smoothness_mean>=-2.603563 63 4 B (0.93650794 0.06349206)
## 32) texture_worst>=3.965685 58 2 B (0.96551724 0.03448276)
## 64) smoothness_worst< -1.575665 42 0 B (1.00000000 0.00000000) *
## 65) smoothness_worst>=-1.575665 16 2 B (0.87500000 0.12500000) *
## 33) texture_worst< 3.965685 5 2 B (0.60000000 0.40000000)
## 66) texture_mean< 2.754513 3 0 B (1.00000000 0.00000000) *
## 67) texture_mean>=2.754513 2 0 M (0.00000000 1.00000000) *
## 17) smoothness_mean< -2.603563 6 3 B (0.50000000 0.50000000)
## 34) texture_mean< 2.993132 3 0 B (1.00000000 0.00000000) *
## 35) texture_mean>=2.993132 3 0 M (0.00000000 1.00000000) *
## 9) symmetry_worst>=-1.838755 85 34 B (0.60000000 0.40000000)
## 18) compactness_se>=-3.586422 35 5 B (0.85714286 0.14285714)
## 36) symmetry_worst< -1.528105 33 3 B (0.90909091 0.09090909)
## 72) compactness_se< -3.430879 25 0 B (1.00000000 0.00000000) *
## 73) compactness_se>=-3.430879 8 3 B (0.62500000 0.37500000) *
## 37) symmetry_worst>=-1.528105 2 0 M (0.00000000 1.00000000) *
## 19) compactness_se< -3.586422 50 21 M (0.42000000 0.58000000)
## 38) smoothness_worst>=-1.567699 16 0 B (1.00000000 0.00000000) *
## 39) smoothness_worst< -1.567699 34 5 M (0.14705882 0.85294118)
## 78) texture_worst>=4.658002 4 1 B (0.75000000 0.25000000) *
## 79) texture_worst< 4.658002 30 2 M (0.06666667 0.93333333) *
## 5) smoothness_worst>=-1.557621 551 269 B (0.51179673 0.48820327)
## 10) smoothness_worst>=-1.537035 460 194 B (0.57826087 0.42173913)
## 20) smoothness_worst< -1.501069 157 41 B (0.73885350 0.26114650)
## 40) texture_mean< 3.065024 122 20 B (0.83606557 0.16393443)
## 80) texture_mean>=2.893423 92 8 B (0.91304348 0.08695652) *
## 81) texture_mean< 2.893423 30 12 B (0.60000000 0.40000000) *
## 41) texture_mean>=3.065024 35 14 M (0.40000000 0.60000000)
## 82) texture_worst>=4.864642 10 0 B (1.00000000 0.00000000) *
## 83) texture_worst< 4.864642 25 4 M (0.16000000 0.84000000) *
## 21) smoothness_worst>=-1.501069 303 150 M (0.49504950 0.50495050)
## 42) smoothness_worst>=-1.477976 202 71 B (0.64851485 0.35148515)
## 84) smoothness_worst< -1.472307 40 1 B (0.97500000 0.02500000) *
## 85) smoothness_worst>=-1.472307 162 70 B (0.56790123 0.43209877) *
## 43) smoothness_worst< -1.477976 101 19 M (0.18811881 0.81188119)
## 86) smoothness_mean>=-2.231196 10 3 B (0.70000000 0.30000000) *
## 87) smoothness_mean< -2.231196 91 12 M (0.13186813 0.86813187) *
## 11) smoothness_worst< -1.537035 91 16 M (0.17582418 0.82417582)
## 22) texture_mean< 2.919658 37 14 M (0.37837838 0.62162162)
## 44) texture_worst>=4.403188 14 0 B (1.00000000 0.00000000) *
## 45) texture_worst< 4.403188 23 0 M (0.00000000 1.00000000) *
## 23) texture_mean>=2.919658 54 2 M (0.03703704 0.96296296)
## 46) smoothness_mean< -2.457256 11 2 M (0.18181818 0.81818182)
## 92) texture_mean< 3.006366 2 0 B (1.00000000 0.00000000) *
## 93) texture_mean>=3.006366 9 0 M (0.00000000 1.00000000) *
## 47) smoothness_mean>=-2.457256 43 0 M (0.00000000 1.00000000) *
## 3) texture_worst>=4.905415 207 69 M (0.33333333 0.66666667)
## 6) compactness_se>=-3.781676 80 39 B (0.51250000 0.48750000)
## 12) smoothness_mean< -2.3667 45 12 B (0.73333333 0.26666667)
## 24) symmetry_worst< -1.541072 39 6 B (0.84615385 0.15384615)
## 48) smoothness_mean>=-2.473552 32 1 B (0.96875000 0.03125000)
## 96) smoothness_worst>=-1.580728 28 0 B (1.00000000 0.00000000) *
## 97) smoothness_worst< -1.580728 4 1 B (0.75000000 0.25000000) *
## 49) smoothness_mean< -2.473552 7 2 M (0.28571429 0.71428571)
## 98) smoothness_mean< -2.491711 2 0 B (1.00000000 0.00000000) *
## 99) smoothness_mean>=-2.491711 5 0 M (0.00000000 1.00000000) *
## 25) symmetry_worst>=-1.541072 6 0 M (0.00000000 1.00000000) *
## 13) smoothness_mean>=-2.3667 35 8 M (0.22857143 0.77142857)
## 26) smoothness_worst>=-1.362317 5 1 B (0.80000000 0.20000000)
## 52) texture_mean>=3.181902 4 0 B (1.00000000 0.00000000) *
## 53) texture_mean< 3.181902 1 0 M (0.00000000 1.00000000) *
## 27) smoothness_worst< -1.362317 30 4 M (0.13333333 0.86666667)
## 54) symmetry_worst< -2.116233 7 3 B (0.57142857 0.42857143)
## 108) texture_mean>=3.253685 4 0 B (1.00000000 0.00000000) *
## 109) texture_mean< 3.253685 3 0 M (0.00000000 1.00000000) *
## 55) symmetry_worst>=-2.116233 23 0 M (0.00000000 1.00000000) *
## 7) compactness_se< -3.781676 127 28 M (0.22047244 0.77952756)
## 14) compactness_se< -4.030876 73 26 M (0.35616438 0.64383562)
## 28) smoothness_worst>=-1.452317 13 0 B (1.00000000 0.00000000) *
## 29) smoothness_worst< -1.452317 60 13 M (0.21666667 0.78333333)
## 58) smoothness_worst< -1.624406 4 0 B (1.00000000 0.00000000) *
## 59) smoothness_worst>=-1.624406 56 9 M (0.16071429 0.83928571)
## 118) texture_mean< 2.915217 2 0 B (1.00000000 0.00000000) *
## 119) texture_mean>=2.915217 54 7 M (0.12962963 0.87037037) *
## 15) compactness_se>=-4.030876 54 2 M (0.03703704 0.96296296)
## 30) texture_mean>=3.348904 2 0 B (1.00000000 0.00000000) *
## 31) texture_mean< 3.348904 52 0 M (0.00000000 1.00000000) *
##
## $trees[[47]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 455 B (0.50109649 0.49890351)
## 2) smoothness_mean< -2.424301 214 76 B (0.64485981 0.35514019)
## 4) symmetry_worst< -1.541072 166 44 B (0.73493976 0.26506024)
## 8) smoothness_mean>=-2.439727 35 0 B (1.00000000 0.00000000) *
## 9) smoothness_mean< -2.439727 131 44 B (0.66412214 0.33587786)
## 18) smoothness_mean< -2.444322 117 30 B (0.74358974 0.25641026)
## 36) compactness_se>=-4.296297 63 9 B (0.85714286 0.14285714)
## 72) symmetry_worst>=-2.081488 55 5 B (0.90909091 0.09090909) *
## 73) symmetry_worst< -2.081488 8 4 B (0.50000000 0.50000000) *
## 37) compactness_se< -4.296297 54 21 B (0.61111111 0.38888889)
## 74) compactness_se< -4.510773 29 5 B (0.82758621 0.17241379) *
## 75) compactness_se>=-4.510773 25 9 M (0.36000000 0.64000000) *
## 19) smoothness_mean>=-2.444322 14 0 M (0.00000000 1.00000000) *
## 5) symmetry_worst>=-1.541072 48 16 M (0.33333333 0.66666667)
## 10) texture_mean< 2.973222 22 7 B (0.68181818 0.31818182)
## 20) smoothness_worst< -1.556321 15 0 B (1.00000000 0.00000000) *
## 21) smoothness_worst>=-1.556321 7 0 M (0.00000000 1.00000000) *
## 11) texture_mean>=2.973222 26 1 M (0.03846154 0.96153846)
## 22) smoothness_mean< -2.540124 1 0 B (1.00000000 0.00000000) *
## 23) smoothness_mean>=-2.540124 25 0 M (0.00000000 1.00000000) *
## 3) smoothness_mean>=-2.424301 698 319 M (0.45702006 0.54297994)
## 6) compactness_se< -4.219581 71 21 B (0.70422535 0.29577465)
## 12) texture_mean< 2.99373 37 3 B (0.91891892 0.08108108)
## 24) compactness_se>=-4.469757 29 0 B (1.00000000 0.00000000) *
## 25) compactness_se< -4.469757 8 3 B (0.62500000 0.37500000)
## 50) texture_mean< 2.835488 3 0 B (1.00000000 0.00000000) *
## 51) texture_mean>=2.835488 5 2 M (0.40000000 0.60000000)
## 102) smoothness_mean>=-2.277891 2 0 B (1.00000000 0.00000000) *
## 103) smoothness_mean< -2.277891 3 0 M (0.00000000 1.00000000) *
## 13) texture_mean>=2.99373 34 16 M (0.47058824 0.52941176)
## 26) texture_mean>=3.227241 11 0 B (1.00000000 0.00000000) *
## 27) texture_mean< 3.227241 23 5 M (0.21739130 0.78260870)
## 54) smoothness_mean>=-2.30036 3 0 B (1.00000000 0.00000000) *
## 55) smoothness_mean< -2.30036 20 2 M (0.10000000 0.90000000)
## 110) compactness_se>=-4.332241 4 2 B (0.50000000 0.50000000) *
## 111) compactness_se< -4.332241 16 0 M (0.00000000 1.00000000) *
## 7) compactness_se>=-4.219581 627 269 M (0.42902711 0.57097289)
## 14) compactness_se>=-4.09685 556 256 M (0.46043165 0.53956835)
## 28) compactness_se< -4.025757 35 3 B (0.91428571 0.08571429)
## 56) texture_worst< 5.105262 32 0 B (1.00000000 0.00000000) *
## 57) texture_worst>=5.105262 3 0 M (0.00000000 1.00000000) *
## 29) compactness_se>=-4.025757 521 224 M (0.42994242 0.57005758)
## 58) smoothness_worst< -1.451541 383 183 M (0.47780679 0.52219321)
## 116) symmetry_worst>=-1.609029 107 31 B (0.71028037 0.28971963) *
## 117) symmetry_worst< -1.609029 276 107 M (0.38768116 0.61231884) *
## 59) smoothness_worst>=-1.451541 138 41 M (0.29710145 0.70289855)
## 118) compactness_se>=-3.68868 74 36 B (0.51351351 0.48648649) *
## 119) compactness_se< -3.68868 64 3 M (0.04687500 0.95312500) *
## 15) compactness_se< -4.09685 71 13 M (0.18309859 0.81690141)
## 30) texture_mean< 2.936778 24 11 B (0.54166667 0.45833333)
## 60) symmetry_worst< -1.589658 17 4 B (0.76470588 0.23529412)
## 120) smoothness_mean>=-2.390594 13 0 B (1.00000000 0.00000000) *
## 121) smoothness_mean< -2.390594 4 0 M (0.00000000 1.00000000) *
## 61) symmetry_worst>=-1.589658 7 0 M (0.00000000 1.00000000) *
## 31) texture_mean>=2.936778 47 0 M (0.00000000 1.00000000) *
##
## $trees[[48]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 449 M (0.49232456 0.50767544)
## 2) smoothness_mean< -2.21595 785 368 B (0.53121019 0.46878981)
## 4) symmetry_worst< -1.886611 259 88 B (0.66023166 0.33976834)
## 8) symmetry_worst>=-1.926862 47 1 B (0.97872340 0.02127660)
## 16) texture_mean< 3.241447 46 0 B (1.00000000 0.00000000) *
## 17) texture_mean>=3.241447 1 0 M (0.00000000 1.00000000) *
## 9) symmetry_worst< -1.926862 212 87 B (0.58962264 0.41037736)
## 18) symmetry_worst< -2.052205 106 28 B (0.73584906 0.26415094)
## 36) texture_worst>=5.15236 20 0 B (1.00000000 0.00000000) *
## 37) texture_worst< 5.15236 86 28 B (0.67441860 0.32558140)
## 74) texture_mean< 3.14457 73 18 B (0.75342466 0.24657534) *
## 75) texture_mean>=3.14457 13 3 M (0.23076923 0.76923077) *
## 19) symmetry_worst>=-2.052205 106 47 M (0.44339623 0.55660377)
## 38) smoothness_worst>=-1.479155 13 0 B (1.00000000 0.00000000) *
## 39) smoothness_worst< -1.479155 93 34 M (0.36559140 0.63440860)
## 78) smoothness_mean< -2.408892 49 18 B (0.63265306 0.36734694) *
## 79) smoothness_mean>=-2.408892 44 3 M (0.06818182 0.93181818) *
## 5) symmetry_worst>=-1.886611 526 246 M (0.46768061 0.53231939)
## 10) smoothness_mean>=-2.235862 31 2 B (0.93548387 0.06451613)
## 20) compactness_se< -3.143422 29 0 B (1.00000000 0.00000000) *
## 21) compactness_se>=-3.143422 2 0 M (0.00000000 1.00000000) *
## 11) smoothness_mean< -2.235862 495 217 M (0.43838384 0.56161616)
## 22) smoothness_mean< -2.333148 280 129 B (0.53928571 0.46071429)
## 44) compactness_se>=-4.663537 253 106 B (0.58102767 0.41897233)
## 88) smoothness_mean>=-2.354616 22 1 B (0.95454545 0.04545455) *
## 89) smoothness_mean< -2.354616 231 105 B (0.54545455 0.45454545) *
## 45) compactness_se< -4.663537 27 4 M (0.14814815 0.85185185)
## 90) smoothness_mean>=-2.441817 3 0 B (1.00000000 0.00000000) *
## 91) smoothness_mean< -2.441817 24 1 M (0.04166667 0.95833333) *
## 23) smoothness_mean>=-2.333148 215 66 M (0.30697674 0.69302326)
## 46) symmetry_worst>=-1.769229 144 64 M (0.44444444 0.55555556)
## 92) symmetry_worst< -1.606092 78 28 B (0.64102564 0.35897436) *
## 93) symmetry_worst>=-1.606092 66 14 M (0.21212121 0.78787879) *
## 47) symmetry_worst< -1.769229 71 2 M (0.02816901 0.97183099)
## 94) symmetry_worst< -1.845834 1 0 B (1.00000000 0.00000000) *
## 95) symmetry_worst>=-1.845834 70 1 M (0.01428571 0.98571429) *
## 3) smoothness_mean>=-2.21595 127 32 M (0.25196850 0.74803150)
## 6) texture_worst< 3.952268 20 8 B (0.60000000 0.40000000)
## 12) texture_mean>=2.515298 12 0 B (1.00000000 0.00000000) *
## 13) texture_mean< 2.515298 8 0 M (0.00000000 1.00000000) *
## 7) texture_worst>=3.952268 107 20 M (0.18691589 0.81308411)
## 14) texture_mean>=2.999709 45 17 M (0.37777778 0.62222222)
## 28) smoothness_mean>=-2.093138 9 1 B (0.88888889 0.11111111)
## 56) smoothness_mean< -2.073133 8 0 B (1.00000000 0.00000000) *
## 57) smoothness_mean>=-2.073133 1 0 M (0.00000000 1.00000000) *
## 29) smoothness_mean< -2.093138 36 9 M (0.25000000 0.75000000)
## 58) compactness_se< -4.054302 6 0 B (1.00000000 0.00000000) *
## 59) compactness_se>=-4.054302 30 3 M (0.10000000 0.90000000)
## 118) texture_mean< 3.017902 3 0 B (1.00000000 0.00000000) *
## 119) texture_mean>=3.017902 27 0 M (0.00000000 1.00000000) *
## 15) texture_mean< 2.999709 62 3 M (0.04838710 0.95161290)
## 30) texture_mean< 2.825779 10 3 M (0.30000000 0.70000000)
## 60) smoothness_mean< -2.143945 3 0 B (1.00000000 0.00000000) *
## 61) smoothness_mean>=-2.143945 7 0 M (0.00000000 1.00000000) *
## 31) texture_mean>=2.825779 52 0 M (0.00000000 1.00000000) *
##
## $trees[[49]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 396 M (0.43421053 0.56578947)
## 2) compactness_se< -4.705732 22 1 B (0.95454545 0.04545455)
## 4) symmetry_worst< -1.170399 21 0 B (1.00000000 0.00000000) *
## 5) symmetry_worst>=-1.170399 1 0 M (0.00000000 1.00000000) *
## 3) compactness_se>=-4.705732 890 375 M (0.42134831 0.57865169)
## 6) texture_worst< 4.12321 41 11 B (0.73170732 0.26829268)
## 12) texture_mean>=2.714689 25 2 B (0.92000000 0.08000000)
## 24) smoothness_worst>=-1.590596 21 0 B (1.00000000 0.00000000) *
## 25) smoothness_worst< -1.590596 4 2 B (0.50000000 0.50000000)
## 50) smoothness_mean< -2.465605 2 0 B (1.00000000 0.00000000) *
## 51) smoothness_mean>=-2.465605 2 0 M (0.00000000 1.00000000) *
## 13) texture_mean< 2.714689 16 7 M (0.43750000 0.56250000)
## 26) texture_worst< 3.781157 5 0 B (1.00000000 0.00000000) *
## 27) texture_worst>=3.781157 11 2 M (0.18181818 0.81818182)
## 54) compactness_se< -3.869334 2 0 B (1.00000000 0.00000000) *
## 55) compactness_se>=-3.869334 9 0 M (0.00000000 1.00000000) *
## 7) texture_worst>=4.12321 849 345 M (0.40636042 0.59363958)
## 14) smoothness_mean< -2.21595 748 323 M (0.43181818 0.56818182)
## 28) smoothness_mean>=-2.235394 37 5 B (0.86486486 0.13513514)
## 56) texture_mean< 3.035465 32 0 B (1.00000000 0.00000000) *
## 57) texture_mean>=3.035465 5 0 M (0.00000000 1.00000000) *
## 29) smoothness_mean< -2.235394 711 291 M (0.40928270 0.59071730)
## 58) symmetry_worst>=-1.128751 16 1 B (0.93750000 0.06250000)
## 116) compactness_se>=-3.52227 15 0 B (1.00000000 0.00000000) *
## 117) compactness_se< -3.52227 1 0 M (0.00000000 1.00000000) *
## 59) symmetry_worst< -1.128751 695 276 M (0.39712230 0.60287770)
## 118) smoothness_mean< -2.242902 667 276 M (0.41379310 0.58620690) *
## 119) smoothness_mean>=-2.242902 28 0 M (0.00000000 1.00000000) *
## 15) smoothness_mean>=-2.21595 101 22 M (0.21782178 0.78217822)
## 30) texture_mean>=3.033028 28 13 B (0.53571429 0.46428571)
## 60) smoothness_mean>=-2.093138 9 0 B (1.00000000 0.00000000) *
## 61) smoothness_mean< -2.093138 19 6 M (0.31578947 0.68421053)
## 122) compactness_se< -4.054302 6 0 B (1.00000000 0.00000000) *
## 123) compactness_se>=-4.054302 13 0 M (0.00000000 1.00000000) *
## 31) texture_mean< 3.033028 73 7 M (0.09589041 0.90410959)
## 62) symmetry_worst< -1.816375 10 5 B (0.50000000 0.50000000)
## 124) texture_mean< 3.018626 5 0 B (1.00000000 0.00000000) *
## 125) texture_mean>=3.018626 5 0 M (0.00000000 1.00000000) *
## 63) symmetry_worst>=-1.816375 63 2 M (0.03174603 0.96825397)
## 126) texture_mean< 2.657764 1 0 B (1.00000000 0.00000000) *
## 127) texture_mean>=2.657764 62 1 M (0.01612903 0.98387097) *
##
## $trees[[50]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 436 B (0.52192982 0.47807018)
## 2) smoothness_worst< -1.473476 668 269 B (0.59730539 0.40269461)
## 4) smoothness_worst>=-1.4768 51 0 B (1.00000000 0.00000000) *
## 5) smoothness_worst< -1.4768 617 269 B (0.56401945 0.43598055)
## 10) smoothness_worst< -1.48191 589 245 B (0.58404075 0.41595925)
## 20) compactness_se< -3.021724 542 212 B (0.60885609 0.39114391)
## 40) smoothness_mean>=-2.301086 93 17 B (0.81720430 0.18279570)
## 80) smoothness_worst>=-1.533868 79 7 B (0.91139241 0.08860759) *
## 81) smoothness_worst< -1.533868 14 4 M (0.28571429 0.71428571) *
## 41) smoothness_mean< -2.301086 449 195 B (0.56570156 0.43429844)
## 82) smoothness_mean< -2.303285 434 180 B (0.58525346 0.41474654) *
## 83) smoothness_mean>=-2.303285 15 0 M (0.00000000 1.00000000) *
## 21) compactness_se>=-3.021724 47 14 M (0.29787234 0.70212766)
## 42) texture_mean< 3.038537 23 9 B (0.60869565 0.39130435)
## 84) smoothness_mean< -2.291354 14 0 B (1.00000000 0.00000000) *
## 85) smoothness_mean>=-2.291354 9 0 M (0.00000000 1.00000000) *
## 43) texture_mean>=3.038537 24 0 M (0.00000000 1.00000000) *
## 11) smoothness_worst>=-1.48191 28 4 M (0.14285714 0.85714286)
## 22) texture_mean< 2.755881 3 0 B (1.00000000 0.00000000) *
## 23) texture_mean>=2.755881 25 1 M (0.04000000 0.96000000)
## 46) compactness_se< -3.967101 3 1 M (0.33333333 0.66666667)
## 92) texture_mean>=2.844596 1 0 B (1.00000000 0.00000000) *
## 93) texture_mean< 2.844596 2 0 M (0.00000000 1.00000000) *
## 47) compactness_se>=-3.967101 22 0 M (0.00000000 1.00000000) *
## 3) smoothness_worst>=-1.473476 244 77 M (0.31557377 0.68442623)
## 6) texture_worst>=4.76475 67 31 B (0.53731343 0.46268657)
## 12) symmetry_worst>=-1.537481 30 5 B (0.83333333 0.16666667)
## 24) symmetry_worst< -1.362966 27 2 B (0.92592593 0.07407407)
## 48) smoothness_mean< -2.252478 26 1 B (0.96153846 0.03846154)
## 96) smoothness_worst< -1.426496 19 0 B (1.00000000 0.00000000) *
## 97) smoothness_worst>=-1.426496 7 1 B (0.85714286 0.14285714) *
## 49) smoothness_mean>=-2.252478 1 0 M (0.00000000 1.00000000) *
## 25) symmetry_worst>=-1.362966 3 0 M (0.00000000 1.00000000) *
## 13) symmetry_worst< -1.537481 37 11 M (0.29729730 0.70270270)
## 26) compactness_se>=-3.490357 9 3 B (0.66666667 0.33333333)
## 52) symmetry_worst< -1.763957 6 0 B (1.00000000 0.00000000) *
## 53) symmetry_worst>=-1.763957 3 0 M (0.00000000 1.00000000) *
## 27) compactness_se< -3.490357 28 5 M (0.17857143 0.82142857)
## 54) compactness_se< -4.054302 11 5 M (0.45454545 0.54545455)
## 108) symmetry_worst< -1.650994 5 0 B (1.00000000 0.00000000) *
## 109) symmetry_worst>=-1.650994 6 0 M (0.00000000 1.00000000) *
## 55) compactness_se>=-4.054302 17 0 M (0.00000000 1.00000000) *
## 7) texture_worst< 4.76475 177 41 M (0.23163842 0.76836158)
## 14) symmetry_worst< -1.895488 12 0 B (1.00000000 0.00000000) *
## 15) symmetry_worst>=-1.895488 165 29 M (0.17575758 0.82424242)
## 30) texture_worst< 4.398698 62 25 M (0.40322581 0.59677419)
## 60) smoothness_mean>=-2.326212 43 19 B (0.55813953 0.44186047)
## 120) compactness_se< -3.786997 13 0 B (1.00000000 0.00000000) *
## 121) compactness_se>=-3.786997 30 11 M (0.36666667 0.63333333) *
## 61) smoothness_mean< -2.326212 19 1 M (0.05263158 0.94736842)
## 122) texture_mean< 2.561441 1 0 B (1.00000000 0.00000000) *
## 123) texture_mean>=2.561441 18 0 M (0.00000000 1.00000000) *
## 31) texture_worst>=4.398698 103 4 M (0.03883495 0.96116505)
## 62) compactness_se< -4.224437 11 3 M (0.27272727 0.72727273)
## 124) texture_mean< 2.962963 3 0 B (1.00000000 0.00000000) *
## 125) texture_mean>=2.962963 8 0 M (0.00000000 1.00000000) *
## 63) compactness_se>=-4.224437 92 1 M (0.01086957 0.98913043)
## 126) smoothness_mean>=-2.090314 4 1 M (0.25000000 0.75000000) *
## 127) smoothness_mean< -2.090314 88 0 M (0.00000000 1.00000000) *
##
## $trees[[51]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 408 M (0.44736842 0.55263158)
## 2) smoothness_mean>=-2.328057 396 176 B (0.55555556 0.44444444)
## 4) smoothness_mean< -2.305941 65 10 B (0.84615385 0.15384615)
## 8) compactness_se< -3.202039 61 6 B (0.90163934 0.09836066)
## 16) symmetry_worst< -1.40737 59 4 B (0.93220339 0.06779661)
## 32) texture_mean< 3.321235 58 3 B (0.94827586 0.05172414)
## 64) compactness_se>=-4.276389 51 1 B (0.98039216 0.01960784) *
## 65) compactness_se< -4.276389 7 2 B (0.71428571 0.28571429) *
## 33) texture_mean>=3.321235 1 0 M (0.00000000 1.00000000) *
## 17) symmetry_worst>=-1.40737 2 0 M (0.00000000 1.00000000) *
## 9) compactness_se>=-3.202039 4 0 M (0.00000000 1.00000000) *
## 5) smoothness_mean>=-2.305941 331 165 M (0.49848943 0.50151057)
## 10) symmetry_worst>=-1.930267 267 116 B (0.56554307 0.43445693)
## 20) symmetry_worst< -1.825003 41 3 B (0.92682927 0.07317073)
## 40) texture_worst< 4.927821 38 0 B (1.00000000 0.00000000) *
## 41) texture_worst>=4.927821 3 0 M (0.00000000 1.00000000) *
## 21) symmetry_worst>=-1.825003 226 113 B (0.50000000 0.50000000)
## 42) texture_worst< 4.400395 86 22 B (0.74418605 0.25581395)
## 84) symmetry_worst< -1.532237 44 2 B (0.95454545 0.04545455) *
## 85) symmetry_worst>=-1.532237 42 20 B (0.52380952 0.47619048) *
## 43) texture_worst>=4.400395 140 49 M (0.35000000 0.65000000)
## 86) texture_worst>=4.769176 44 11 B (0.75000000 0.25000000) *
## 87) texture_worst< 4.769176 96 16 M (0.16666667 0.83333333) *
## 11) symmetry_worst< -1.930267 64 14 M (0.21875000 0.78125000)
## 22) symmetry_worst< -2.188127 25 11 M (0.44000000 0.56000000)
## 44) symmetry_worst>=-2.270701 11 0 B (1.00000000 0.00000000) *
## 45) symmetry_worst< -2.270701 14 0 M (0.00000000 1.00000000) *
## 23) symmetry_worst>=-2.188127 39 3 M (0.07692308 0.92307692)
## 46) compactness_se< -4.001166 1 0 B (1.00000000 0.00000000) *
## 47) compactness_se>=-4.001166 38 2 M (0.05263158 0.94736842)
## 94) compactness_se>=-3.425209 4 2 B (0.50000000 0.50000000) *
## 95) compactness_se< -3.425209 34 0 M (0.00000000 1.00000000) *
## 3) smoothness_mean< -2.328057 516 188 M (0.36434109 0.63565891)
## 6) texture_mean< 2.74084 13 0 B (1.00000000 0.00000000) *
## 7) texture_mean>=2.74084 503 175 M (0.34791252 0.65208748)
## 14) smoothness_mean< -2.564314 14 2 B (0.85714286 0.14285714)
## 28) compactness_se< -3.021883 12 0 B (1.00000000 0.00000000) *
## 29) compactness_se>=-3.021883 2 0 M (0.00000000 1.00000000) *
## 15) smoothness_mean>=-2.564314 489 163 M (0.33333333 0.66666667)
## 30) symmetry_worst< -2.25148 6 0 B (1.00000000 0.00000000) *
## 31) symmetry_worst>=-2.25148 483 157 M (0.32505176 0.67494824)
## 62) smoothness_mean< -2.367284 359 132 M (0.36768802 0.63231198)
## 124) smoothness_worst>=-1.512058 73 29 B (0.60273973 0.39726027) *
## 125) smoothness_worst< -1.512058 286 88 M (0.30769231 0.69230769) *
## 63) smoothness_mean>=-2.367284 124 25 M (0.20161290 0.79838710)
## 126) smoothness_worst< -1.544057 11 3 B (0.72727273 0.27272727) *
## 127) smoothness_worst>=-1.544057 113 17 M (0.15044248 0.84955752) *
##
## $trees[[52]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 451 B (0.50548246 0.49451754)
## 2) symmetry_worst< -1.816281 352 139 B (0.60511364 0.39488636)
## 4) texture_mean< 2.963209 114 26 B (0.77192982 0.22807018)
## 8) smoothness_worst>=-1.595961 90 10 B (0.88888889 0.11111111)
## 16) texture_worst< 4.734027 87 8 B (0.90804598 0.09195402)
## 32) smoothness_worst< -1.441362 86 7 B (0.91860465 0.08139535)
## 64) smoothness_mean< -2.30802 61 1 B (0.98360656 0.01639344) *
## 65) smoothness_mean>=-2.30802 25 6 B (0.76000000 0.24000000) *
## 33) smoothness_worst>=-1.441362 1 0 M (0.00000000 1.00000000) *
## 17) texture_worst>=4.734027 3 1 M (0.33333333 0.66666667)
## 34) texture_mean< 2.946804 1 0 B (1.00000000 0.00000000) *
## 35) texture_mean>=2.946804 2 0 M (0.00000000 1.00000000) *
## 9) smoothness_worst< -1.595961 24 8 M (0.33333333 0.66666667)
## 18) smoothness_worst< -1.609607 8 0 B (1.00000000 0.00000000) *
## 19) smoothness_worst>=-1.609607 16 0 M (0.00000000 1.00000000) *
## 5) texture_mean>=2.963209 238 113 B (0.52521008 0.47478992)
## 10) smoothness_worst< -1.52112 165 61 B (0.63030303 0.36969697)
## 20) texture_mean>=3.087399 81 14 B (0.82716049 0.17283951)
## 40) compactness_se< -3.400535 72 9 B (0.87500000 0.12500000)
## 80) texture_worst< 5.309872 56 3 B (0.94642857 0.05357143) *
## 81) texture_worst>=5.309872 16 6 B (0.62500000 0.37500000) *
## 41) compactness_se>=-3.400535 9 4 M (0.44444444 0.55555556)
## 82) texture_mean< 3.136493 4 0 B (1.00000000 0.00000000) *
## 83) texture_mean>=3.136493 5 0 M (0.00000000 1.00000000) *
## 21) texture_mean< 3.087399 84 37 M (0.44047619 0.55952381)
## 42) compactness_se>=-3.439035 23 3 B (0.86956522 0.13043478)
## 84) texture_mean< 3.076827 20 0 B (1.00000000 0.00000000) *
## 85) texture_mean>=3.076827 3 0 M (0.00000000 1.00000000) *
## 43) compactness_se< -3.439035 61 17 M (0.27868852 0.72131148)
## 86) symmetry_worst< -1.969194 20 7 B (0.65000000 0.35000000) *
## 87) symmetry_worst>=-1.969194 41 4 M (0.09756098 0.90243902) *
## 11) smoothness_worst>=-1.52112 73 21 M (0.28767123 0.71232877)
## 22) texture_mean< 2.975782 8 0 B (1.00000000 0.00000000) *
## 23) texture_mean>=2.975782 65 13 M (0.20000000 0.80000000)
## 46) texture_worst< 4.469369 4 0 B (1.00000000 0.00000000) *
## 47) texture_worst>=4.469369 61 9 M (0.14754098 0.85245902)
## 94) symmetry_worst< -2.188127 7 3 B (0.57142857 0.42857143) *
## 95) symmetry_worst>=-2.188127 54 5 M (0.09259259 0.90740741) *
## 3) symmetry_worst>=-1.816281 560 248 M (0.44285714 0.55714286)
## 6) texture_worst< 4.514456 211 90 B (0.57345972 0.42654028)
## 12) smoothness_mean< -2.173316 173 56 B (0.67630058 0.32369942)
## 24) texture_worst>=4.463188 49 0 B (1.00000000 0.00000000) *
## 25) texture_worst< 4.463188 124 56 B (0.54838710 0.45161290)
## 50) smoothness_mean>=-2.353616 70 15 B (0.78571429 0.21428571)
## 100) smoothness_worst< -1.430373 66 11 B (0.83333333 0.16666667) *
## 101) smoothness_worst>=-1.430373 4 0 M (0.00000000 1.00000000) *
## 51) smoothness_mean< -2.353616 54 13 M (0.24074074 0.75925926)
## 102) compactness_se< -4.559289 4 0 B (1.00000000 0.00000000) *
## 103) compactness_se>=-4.559289 50 9 M (0.18000000 0.82000000) *
## 13) smoothness_mean>=-2.173316 38 4 M (0.10526316 0.89473684)
## 26) smoothness_mean>=-1.889548 3 0 B (1.00000000 0.00000000) *
## 27) smoothness_mean< -1.889548 35 1 M (0.02857143 0.97142857)
## 54) smoothness_mean>=-2.000349 6 1 M (0.16666667 0.83333333)
## 108) texture_mean< 2.688296 1 0 B (1.00000000 0.00000000) *
## 109) texture_mean>=2.688296 5 0 M (0.00000000 1.00000000) *
## 55) smoothness_mean< -2.000349 29 0 M (0.00000000 1.00000000) *
## 7) texture_worst>=4.514456 349 127 M (0.36389685 0.63610315)
## 14) symmetry_worst>=-1.749963 285 121 M (0.42456140 0.57543860)
## 28) symmetry_worst< -1.716176 37 7 B (0.81081081 0.18918919)
## 56) texture_mean< 3.407548 32 2 B (0.93750000 0.06250000)
## 112) compactness_se>=-4.528789 29 0 B (1.00000000 0.00000000) *
## 113) compactness_se< -4.528789 3 1 M (0.33333333 0.66666667) *
## 57) texture_mean>=3.407548 5 0 M (0.00000000 1.00000000) *
## 29) symmetry_worst>=-1.716176 248 91 M (0.36693548 0.63306452)
## 58) compactness_se< -3.447524 191 82 M (0.42931937 0.57068063)
## 116) compactness_se>=-3.494961 28 4 B (0.85714286 0.14285714) *
## 117) compactness_se< -3.494961 163 58 M (0.35582822 0.64417178) *
## 59) compactness_se>=-3.447524 57 9 M (0.15789474 0.84210526)
## 118) compactness_se>=-3.18382 19 9 M (0.47368421 0.52631579) *
## 119) compactness_se< -3.18382 38 0 M (0.00000000 1.00000000) *
## 15) symmetry_worst< -1.749963 64 6 M (0.09375000 0.90625000)
## 30) texture_worst>=4.897895 6 1 B (0.83333333 0.16666667)
## 60) compactness_se>=-4.246101 5 0 B (1.00000000 0.00000000) *
## 61) compactness_se< -4.246101 1 0 M (0.00000000 1.00000000) *
## 31) texture_worst< 4.897895 58 1 M (0.01724138 0.98275862)
## 62) texture_mean< 2.875669 1 0 B (1.00000000 0.00000000) *
## 63) texture_mean>=2.875669 57 0 M (0.00000000 1.00000000) *
##
## $trees[[53]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 409 B (0.55153509 0.44846491)
## 2) compactness_se< -3.672219 541 208 B (0.61552680 0.38447320)
## 4) compactness_se>=-3.761452 65 4 B (0.93846154 0.06153846)
## 8) smoothness_worst>=-1.534142 58 0 B (1.00000000 0.00000000) *
## 9) smoothness_worst< -1.534142 7 3 M (0.42857143 0.57142857)
## 18) smoothness_mean< -2.477152 3 0 B (1.00000000 0.00000000) *
## 19) smoothness_mean>=-2.477152 4 0 M (0.00000000 1.00000000) *
## 5) compactness_se< -3.761452 476 204 B (0.57142857 0.42857143)
## 10) texture_mean< 2.976294 246 77 B (0.68699187 0.31300813)
## 20) symmetry_worst>=-1.749307 135 21 B (0.84444444 0.15555556)
## 40) smoothness_worst< -1.451541 112 6 B (0.94642857 0.05357143)
## 80) texture_worst< 4.707428 107 3 B (0.97196262 0.02803738) *
## 81) texture_worst>=4.707428 5 2 M (0.40000000 0.60000000) *
## 41) smoothness_worst>=-1.451541 23 8 M (0.34782609 0.65217391)
## 82) compactness_se< -4.023814 7 0 B (1.00000000 0.00000000) *
## 83) compactness_se>=-4.023814 16 1 M (0.06250000 0.93750000) *
## 21) symmetry_worst< -1.749307 111 55 M (0.49549550 0.50450450)
## 42) symmetry_worst< -1.787433 93 39 B (0.58064516 0.41935484)
## 84) texture_worst>=4.4131 56 13 B (0.76785714 0.23214286) *
## 85) texture_worst< 4.4131 37 11 M (0.29729730 0.70270270) *
## 43) symmetry_worst>=-1.787433 18 1 M (0.05555556 0.94444444)
## 86) texture_mean< 2.788049 1 0 B (1.00000000 0.00000000) *
## 87) texture_mean>=2.788049 17 0 M (0.00000000 1.00000000) *
## 11) texture_mean>=2.976294 230 103 M (0.44782609 0.55217391)
## 22) smoothness_mean>=-2.295291 48 13 B (0.72916667 0.27083333)
## 44) compactness_se< -4.030876 28 0 B (1.00000000 0.00000000) *
## 45) compactness_se>=-4.030876 20 7 M (0.35000000 0.65000000)
## 90) smoothness_worst< -1.475287 7 0 B (1.00000000 0.00000000) *
## 91) smoothness_worst>=-1.475287 13 0 M (0.00000000 1.00000000) *
## 23) smoothness_mean< -2.295291 182 68 M (0.37362637 0.62637363)
## 46) compactness_se< -4.557422 22 4 B (0.81818182 0.18181818)
## 92) texture_mean>=3.186756 12 0 B (1.00000000 0.00000000) *
## 93) texture_mean< 3.186756 10 4 B (0.60000000 0.40000000) *
## 47) compactness_se>=-4.557422 160 50 M (0.31250000 0.68750000)
## 94) symmetry_worst< -1.670808 104 50 M (0.48076923 0.51923077) *
## 95) symmetry_worst>=-1.670808 56 0 M (0.00000000 1.00000000) *
## 3) compactness_se>=-3.672219 371 170 M (0.45822102 0.54177898)
## 6) symmetry_worst< -1.816281 135 54 B (0.60000000 0.40000000)
## 12) symmetry_worst>=-1.982941 64 13 B (0.79687500 0.20312500)
## 24) texture_mean< 3.058819 56 6 B (0.89285714 0.10714286)
## 48) compactness_se>=-3.596781 51 1 B (0.98039216 0.01960784)
## 96) texture_worst< 4.806084 50 0 B (1.00000000 0.00000000) *
## 97) texture_worst>=4.806084 1 0 M (0.00000000 1.00000000) *
## 49) compactness_se< -3.596781 5 0 M (0.00000000 1.00000000) *
## 25) texture_mean>=3.058819 8 1 M (0.12500000 0.87500000)
## 50) smoothness_mean< -2.497464 1 0 B (1.00000000 0.00000000) *
## 51) smoothness_mean>=-2.497464 7 0 M (0.00000000 1.00000000) *
## 13) symmetry_worst< -1.982941 71 30 M (0.42253521 0.57746479)
## 26) texture_mean>=3.268457 14 0 B (1.00000000 0.00000000) *
## 27) texture_mean< 3.268457 57 16 M (0.28070175 0.71929825)
## 54) compactness_se>=-3.41277 22 9 B (0.59090909 0.40909091)
## 108) texture_mean< 3.076827 10 0 B (1.00000000 0.00000000) *
## 109) texture_mean>=3.076827 12 3 M (0.25000000 0.75000000) *
## 55) compactness_se< -3.41277 35 3 M (0.08571429 0.91428571)
## 110) texture_mean>=3.07129 4 1 B (0.75000000 0.25000000) *
## 111) texture_mean< 3.07129 31 0 M (0.00000000 1.00000000) *
## 7) symmetry_worst>=-1.816281 236 89 M (0.37711864 0.62288136)
## 14) compactness_se>=-3.494301 175 82 M (0.46857143 0.53142857)
## 28) symmetry_worst< -1.001713 164 82 B (0.50000000 0.50000000)
## 56) texture_mean< 2.927442 68 23 B (0.66176471 0.33823529)
## 112) symmetry_worst< -1.343592 50 9 B (0.82000000 0.18000000) *
## 113) symmetry_worst>=-1.343592 18 4 M (0.22222222 0.77777778) *
## 57) texture_mean>=2.927442 96 37 M (0.38541667 0.61458333)
## 114) symmetry_worst>=-1.694861 61 27 B (0.55737705 0.44262295) *
## 115) symmetry_worst< -1.694861 35 3 M (0.08571429 0.91428571) *
## 29) symmetry_worst>=-1.001713 11 0 M (0.00000000 1.00000000) *
## 15) compactness_se< -3.494301 61 7 M (0.11475410 0.88524590)
## 30) texture_mean>=3.094444 7 1 B (0.85714286 0.14285714)
## 60) texture_mean< 3.383373 6 0 B (1.00000000 0.00000000) *
## 61) texture_mean>=3.383373 1 0 M (0.00000000 1.00000000) *
## 31) texture_mean< 3.094444 54 1 M (0.01851852 0.98148148)
## 62) symmetry_worst>=-1.47212 6 1 M (0.16666667 0.83333333)
## 124) texture_mean< 2.800842 1 0 B (1.00000000 0.00000000) *
## 125) texture_mean>=2.800842 5 0 M (0.00000000 1.00000000) *
## 63) symmetry_worst< -1.47212 48 0 M (0.00000000 1.00000000) *
##
## $trees[[54]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 452 M (0.49561404 0.50438596)
## 2) compactness_se< -3.648711 530 221 B (0.58301887 0.41698113)
## 4) compactness_se>=-4.676462 467 176 B (0.62312634 0.37687366)
## 8) texture_mean< 2.960364 172 46 B (0.73255814 0.26744186)
## 16) symmetry_worst>=-1.749307 99 10 B (0.89898990 0.10101010)
## 32) texture_worst< 4.692158 95 6 B (0.93684211 0.06315789)
## 64) texture_mean>=2.518783 93 4 B (0.95698925 0.04301075) *
## 65) texture_mean< 2.518783 2 0 M (0.00000000 1.00000000) *
## 33) texture_worst>=4.692158 4 0 M (0.00000000 1.00000000) *
## 17) symmetry_worst< -1.749307 73 36 B (0.50684932 0.49315068)
## 34) symmetry_worst< -1.786753 54 19 B (0.64814815 0.35185185)
## 68) texture_worst< 4.608983 46 12 B (0.73913043 0.26086957) *
## 69) texture_worst>=4.608983 8 1 M (0.12500000 0.87500000) *
## 35) symmetry_worst>=-1.786753 19 2 M (0.10526316 0.89473684)
## 70) texture_mean>=2.946722 2 0 B (1.00000000 0.00000000) *
## 71) texture_mean< 2.946722 17 0 M (0.00000000 1.00000000) *
## 9) texture_mean>=2.960364 295 130 B (0.55932203 0.44067797)
## 18) symmetry_worst< -2.052205 49 9 B (0.81632653 0.18367347)
## 36) smoothness_worst< -1.514694 43 3 B (0.93023256 0.06976744)
## 72) smoothness_worst>=-1.668282 40 0 B (1.00000000 0.00000000) *
## 73) smoothness_worst< -1.668282 3 0 M (0.00000000 1.00000000) *
## 37) smoothness_worst>=-1.514694 6 0 M (0.00000000 1.00000000) *
## 19) symmetry_worst>=-2.052205 246 121 B (0.50813008 0.49186992)
## 38) compactness_se>=-3.781676 47 10 B (0.78723404 0.21276596)
## 76) texture_worst>=4.59283 41 4 B (0.90243902 0.09756098) *
## 77) texture_worst< 4.59283 6 0 M (0.00000000 1.00000000) *
## 39) compactness_se< -3.781676 199 88 M (0.44221106 0.55778894)
## 78) texture_worst< 5.194184 165 80 B (0.51515152 0.48484848) *
## 79) texture_worst>=5.194184 34 3 M (0.08823529 0.91176471) *
## 5) compactness_se< -4.676462 63 18 M (0.28571429 0.71428571)
## 10) compactness_se< -4.779408 10 0 B (1.00000000 0.00000000) *
## 11) compactness_se>=-4.779408 53 8 M (0.15094340 0.84905660)
## 22) smoothness_mean>=-2.441817 7 0 B (1.00000000 0.00000000) *
## 23) smoothness_mean< -2.441817 46 1 M (0.02173913 0.97826087)
## 46) texture_worst< 4.52395 1 0 B (1.00000000 0.00000000) *
## 47) texture_worst>=4.52395 45 0 M (0.00000000 1.00000000) *
## 3) compactness_se>=-3.648711 382 143 M (0.37434555 0.62565445)
## 6) compactness_se>=-2.809774 27 5 B (0.81481481 0.18518519)
## 12) texture_mean< 3.003947 20 0 B (1.00000000 0.00000000) *
## 13) texture_mean>=3.003947 7 2 M (0.28571429 0.71428571)
## 26) smoothness_mean< -2.364398 2 0 B (1.00000000 0.00000000) *
## 27) smoothness_mean>=-2.364398 5 0 M (0.00000000 1.00000000) *
## 7) compactness_se< -2.809774 355 121 M (0.34084507 0.65915493)
## 14) smoothness_mean< -2.503795 15 3 B (0.80000000 0.20000000)
## 28) compactness_se< -2.979429 12 0 B (1.00000000 0.00000000) *
## 29) compactness_se>=-2.979429 3 0 M (0.00000000 1.00000000) *
## 15) smoothness_mean>=-2.503795 340 109 M (0.32058824 0.67941176)
## 30) compactness_se< -2.910852 316 109 M (0.34493671 0.65506329)
## 60) compactness_se>=-2.924003 9 0 B (1.00000000 0.00000000) *
## 61) compactness_se< -2.924003 307 100 M (0.32573290 0.67426710)
## 122) smoothness_worst< -1.482896 167 72 M (0.43113772 0.56886228) *
## 123) smoothness_worst>=-1.482896 140 28 M (0.20000000 0.80000000) *
## 31) compactness_se>=-2.910852 24 0 M (0.00000000 1.00000000) *
##
## $trees[[55]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 433 B (0.52521930 0.47478070)
## 2) symmetry_worst< -1.424186 819 366 B (0.55311355 0.44688645)
## 4) texture_worst< 4.260219 66 10 B (0.84848485 0.15151515)
## 8) smoothness_mean< -2.318766 26 0 B (1.00000000 0.00000000) *
## 9) smoothness_mean>=-2.318766 40 10 B (0.75000000 0.25000000)
## 18) smoothness_mean>=-2.280931 27 2 B (0.92592593 0.07407407)
## 36) texture_mean< 2.878198 26 1 B (0.96153846 0.03846154)
## 72) compactness_se>=-3.757389 23 0 B (1.00000000 0.00000000) *
## 73) compactness_se< -3.757389 3 1 B (0.66666667 0.33333333) *
## 37) texture_mean>=2.878198 1 0 M (0.00000000 1.00000000) *
## 19) smoothness_mean< -2.280931 13 5 M (0.38461538 0.61538462)
## 38) texture_worst< 4.056844 5 0 B (1.00000000 0.00000000) *
## 39) texture_worst>=4.056844 8 0 M (0.00000000 1.00000000) *
## 5) texture_worst>=4.260219 753 356 B (0.52722444 0.47277556)
## 10) symmetry_worst>=-1.471051 30 2 B (0.93333333 0.06666667)
## 20) smoothness_mean< -2.29943 28 0 B (1.00000000 0.00000000) *
## 21) smoothness_mean>=-2.29943 2 0 M (0.00000000 1.00000000) *
## 11) symmetry_worst< -1.471051 723 354 B (0.51037344 0.48962656)
## 22) smoothness_mean< -2.392182 304 115 B (0.62171053 0.37828947)
## 44) symmetry_worst>=-1.860457 201 60 B (0.70149254 0.29850746)
## 88) symmetry_worst< -1.541072 169 38 B (0.77514793 0.22485207) *
## 89) symmetry_worst>=-1.541072 32 10 M (0.31250000 0.68750000) *
## 45) symmetry_worst< -1.860457 103 48 M (0.46601942 0.53398058)
## 90) symmetry_worst< -2.01934 40 10 B (0.75000000 0.25000000) *
## 91) symmetry_worst>=-2.01934 63 18 M (0.28571429 0.71428571) *
## 23) smoothness_mean>=-2.392182 419 180 M (0.42959427 0.57040573)
## 46) smoothness_mean>=-2.38347 375 180 M (0.48000000 0.52000000)
## 92) compactness_se< -3.445472 255 111 B (0.56470588 0.43529412) *
## 93) compactness_se>=-3.445472 120 36 M (0.30000000 0.70000000) *
## 47) smoothness_mean< -2.38347 44 0 M (0.00000000 1.00000000) *
## 3) symmetry_worst>=-1.424186 93 26 M (0.27956989 0.72043011)
## 6) smoothness_worst< -1.49848 23 8 B (0.65217391 0.34782609)
## 12) smoothness_worst>=-1.553939 13 0 B (1.00000000 0.00000000) *
## 13) smoothness_worst< -1.553939 10 2 M (0.20000000 0.80000000)
## 26) texture_mean< 2.973222 2 0 B (1.00000000 0.00000000) *
## 27) texture_mean>=2.973222 8 0 M (0.00000000 1.00000000) *
## 7) smoothness_worst>=-1.49848 70 11 M (0.15714286 0.84285714)
## 14) compactness_se>=-2.638423 6 0 B (1.00000000 0.00000000) *
## 15) compactness_se< -2.638423 64 5 M (0.07812500 0.92187500)
## 30) texture_mean< 2.77286 10 5 B (0.50000000 0.50000000)
## 60) symmetry_worst< -1.232339 5 0 B (1.00000000 0.00000000) *
## 61) symmetry_worst>=-1.232339 5 0 M (0.00000000 1.00000000) *
## 31) texture_mean>=2.77286 54 0 M (0.00000000 1.00000000) *
##
## $trees[[56]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 452 B (0.50438596 0.49561404)
## 2) smoothness_mean< -2.408446 299 107 B (0.64214047 0.35785953)
## 4) compactness_se>=-4.658767 260 78 B (0.70000000 0.30000000)
## 8) texture_worst>=4.498003 208 47 B (0.77403846 0.22596154)
## 16) texture_mean< 3.388429 199 38 B (0.80904523 0.19095477)
## 32) symmetry_worst>=-2.218277 190 30 B (0.84210526 0.15789474)
## 64) symmetry_worst< -1.496954 184 25 B (0.86413043 0.13586957) *
## 65) symmetry_worst>=-1.496954 6 1 M (0.16666667 0.83333333) *
## 33) symmetry_worst< -2.218277 9 1 M (0.11111111 0.88888889)
## 66) smoothness_mean< -2.57545 1 0 B (1.00000000 0.00000000) *
## 67) smoothness_mean>=-2.57545 8 0 M (0.00000000 1.00000000) *
## 17) texture_mean>=3.388429 9 0 M (0.00000000 1.00000000) *
## 9) texture_worst< 4.498003 52 21 M (0.40384615 0.59615385)
## 18) compactness_se>=-3.483667 11 1 B (0.90909091 0.09090909)
## 36) texture_mean< 3.038737 10 0 B (1.00000000 0.00000000) *
## 37) texture_mean>=3.038737 1 0 M (0.00000000 1.00000000) *
## 19) compactness_se< -3.483667 41 11 M (0.26829268 0.73170732)
## 38) smoothness_mean< -2.469112 10 4 B (0.60000000 0.40000000)
## 76) texture_mean< 2.94329 6 0 B (1.00000000 0.00000000) *
## 77) texture_mean>=2.94329 4 0 M (0.00000000 1.00000000) *
## 39) smoothness_mean>=-2.469112 31 5 M (0.16129032 0.83870968)
## 78) texture_worst>=4.418221 8 3 B (0.62500000 0.37500000) *
## 79) texture_worst< 4.418221 23 0 M (0.00000000 1.00000000) *
## 5) compactness_se< -4.658767 39 10 M (0.25641026 0.74358974)
## 10) compactness_se< -4.706178 9 2 B (0.77777778 0.22222222)
## 20) symmetry_worst< -1.284644 7 0 B (1.00000000 0.00000000) *
## 21) symmetry_worst>=-1.284644 2 0 M (0.00000000 1.00000000) *
## 11) compactness_se>=-4.706178 30 3 M (0.10000000 0.90000000)
## 22) smoothness_mean>=-2.441817 3 0 B (1.00000000 0.00000000) *
## 23) smoothness_mean< -2.441817 27 0 M (0.00000000 1.00000000) *
## 3) smoothness_mean>=-2.408446 613 268 M (0.43719413 0.56280587)
## 6) texture_mean< 3.007414 361 175 B (0.51523546 0.48476454)
## 12) texture_mean>=3.003452 27 2 B (0.92592593 0.07407407)
## 24) smoothness_mean< -2.060282 25 0 B (1.00000000 0.00000000) *
## 25) smoothness_mean>=-2.060282 2 0 M (0.00000000 1.00000000) *
## 13) texture_mean< 3.003452 334 161 M (0.48203593 0.51796407)
## 26) compactness_se>=-2.834229 16 0 B (1.00000000 0.00000000) *
## 27) compactness_se< -2.834229 318 145 M (0.45597484 0.54402516)
## 54) symmetry_worst>=-2.400126 302 145 M (0.48013245 0.51986755)
## 108) smoothness_worst< -1.549191 39 8 B (0.79487179 0.20512821) *
## 109) smoothness_worst>=-1.549191 263 114 M (0.43346008 0.56653992) *
## 55) symmetry_worst< -2.400126 16 0 M (0.00000000 1.00000000) *
## 7) texture_mean>=3.007414 252 82 M (0.32539683 0.67460317)
## 14) symmetry_worst< -2.207988 37 9 B (0.75675676 0.24324324)
## 28) smoothness_mean< -2.282028 30 2 B (0.93333333 0.06666667)
## 56) compactness_se>=-4.450281 28 0 B (1.00000000 0.00000000) *
## 57) compactness_se< -4.450281 2 0 M (0.00000000 1.00000000) *
## 29) smoothness_mean>=-2.282028 7 0 M (0.00000000 1.00000000) *
## 15) symmetry_worst>=-2.207988 215 54 M (0.25116279 0.74883721)
## 30) smoothness_mean>=-2.383798 164 54 M (0.32926829 0.67073171)
## 60) symmetry_worst>=-1.477364 25 7 B (0.72000000 0.28000000)
## 120) symmetry_worst< -1.41845 14 0 B (1.00000000 0.00000000) *
## 121) symmetry_worst>=-1.41845 11 4 M (0.36363636 0.63636364) *
## 61) symmetry_worst< -1.477364 139 36 M (0.25899281 0.74100719)
## 122) texture_worst< 4.682677 36 16 B (0.55555556 0.44444444) *
## 123) texture_worst>=4.682677 103 16 M (0.15533981 0.84466019) *
## 31) smoothness_mean< -2.383798 51 0 M (0.00000000 1.00000000) *
##
## $trees[[57]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 443 B (0.51425439 0.48574561)
## 2) texture_mean< 2.960364 375 133 B (0.64533333 0.35466667)
## 4) compactness_se>=-3.344528 86 9 B (0.89534884 0.10465116)
## 8) compactness_se< -3.086764 55 0 B (1.00000000 0.00000000) *
## 9) compactness_se>=-3.086764 31 9 B (0.70967742 0.29032258)
## 18) texture_mean>=2.850705 16 0 B (1.00000000 0.00000000) *
## 19) texture_mean< 2.850705 15 6 M (0.40000000 0.60000000)
## 38) symmetry_worst< -1.62579 6 0 B (1.00000000 0.00000000) *
## 39) symmetry_worst>=-1.62579 9 0 M (0.00000000 1.00000000) *
## 5) compactness_se< -3.344528 289 124 B (0.57093426 0.42906574)
## 10) compactness_se< -3.668499 188 62 B (0.67021277 0.32978723)
## 20) symmetry_worst>=-1.749307 92 14 B (0.84782609 0.15217391)
## 40) symmetry_worst< -1.533879 43 0 B (1.00000000 0.00000000) *
## 41) symmetry_worst>=-1.533879 49 14 B (0.71428571 0.28571429)
## 82) texture_worst< 4.61159 41 7 B (0.82926829 0.17073171) *
## 83) texture_worst>=4.61159 8 1 M (0.12500000 0.87500000) *
## 21) symmetry_worst< -1.749307 96 48 B (0.50000000 0.50000000)
## 42) symmetry_worst< -1.814891 56 17 B (0.69642857 0.30357143)
## 84) smoothness_mean< -2.311491 38 7 B (0.81578947 0.18421053) *
## 85) smoothness_mean>=-2.311491 18 8 M (0.44444444 0.55555556) *
## 43) symmetry_worst>=-1.814891 40 9 M (0.22500000 0.77500000)
## 86) compactness_se>=-3.93685 8 1 B (0.87500000 0.12500000) *
## 87) compactness_se< -3.93685 32 2 M (0.06250000 0.93750000) *
## 11) compactness_se>=-3.668499 101 39 M (0.38613861 0.61386139)
## 22) symmetry_worst< -1.834844 26 2 B (0.92307692 0.07692308)
## 44) symmetry_worst>=-1.982941 19 0 B (1.00000000 0.00000000) *
## 45) symmetry_worst< -1.982941 7 2 B (0.71428571 0.28571429)
## 90) texture_mean>=2.866189 5 0 B (1.00000000 0.00000000) *
## 91) texture_mean< 2.866189 2 0 M (0.00000000 1.00000000) *
## 23) symmetry_worst>=-1.834844 75 15 M (0.20000000 0.80000000)
## 46) texture_mean>=2.945474 8 0 B (1.00000000 0.00000000) *
## 47) texture_mean< 2.945474 67 7 M (0.10447761 0.89552239)
## 94) compactness_se>=-3.394391 12 5 M (0.41666667 0.58333333) *
## 95) compactness_se< -3.394391 55 2 M (0.03636364 0.96363636) *
## 3) texture_mean>=2.960364 537 227 M (0.42271881 0.57728119)
## 6) smoothness_mean< -2.425205 162 66 B (0.59259259 0.40740741)
## 12) symmetry_worst< -1.541072 143 48 B (0.66433566 0.33566434)
## 24) symmetry_worst>=-2.218277 131 37 B (0.71755725 0.28244275)
## 48) compactness_se>=-4.496793 98 17 B (0.82653061 0.17346939)
## 96) smoothness_worst< -1.522574 66 4 B (0.93939394 0.06060606) *
## 97) smoothness_worst>=-1.522574 32 13 B (0.59375000 0.40625000) *
## 49) compactness_se< -4.496793 33 13 M (0.39393939 0.60606061)
## 98) compactness_se< -4.568856 19 6 B (0.68421053 0.31578947) *
## 99) compactness_se>=-4.568856 14 0 M (0.00000000 1.00000000) *
## 25) symmetry_worst< -2.218277 12 1 M (0.08333333 0.91666667)
## 50) smoothness_mean< -2.57545 1 0 B (1.00000000 0.00000000) *
## 51) smoothness_mean>=-2.57545 11 0 M (0.00000000 1.00000000) *
## 13) symmetry_worst>=-1.541072 19 1 M (0.05263158 0.94736842)
## 26) smoothness_mean< -2.540124 1 0 B (1.00000000 0.00000000) *
## 27) smoothness_mean>=-2.540124 18 0 M (0.00000000 1.00000000) *
## 7) smoothness_mean>=-2.425205 375 131 M (0.34933333 0.65066667)
## 14) smoothness_mean>=-2.383798 294 120 M (0.40816327 0.59183673)
## 28) smoothness_worst< -1.500061 118 50 B (0.57627119 0.42372881)
## 56) compactness_se< -3.723892 39 5 B (0.87179487 0.12820513)
## 112) texture_mean< 3.216271 32 0 B (1.00000000 0.00000000) *
## 113) texture_mean>=3.216271 7 2 M (0.28571429 0.71428571) *
## 57) compactness_se>=-3.723892 79 34 M (0.43037975 0.56962025)
## 114) symmetry_worst>=-1.407053 14 0 B (1.00000000 0.00000000) *
## 115) symmetry_worst< -1.407053 65 20 M (0.30769231 0.69230769) *
## 29) smoothness_worst>=-1.500061 176 52 M (0.29545455 0.70454545)
## 58) smoothness_worst>=-1.447374 90 44 M (0.48888889 0.51111111)
## 116) texture_worst>=4.599485 68 24 B (0.64705882 0.35294118) *
## 117) texture_worst< 4.599485 22 0 M (0.00000000 1.00000000) *
## 59) smoothness_worst< -1.447374 86 8 M (0.09302326 0.90697674)
## 118) smoothness_mean< -2.36516 3 1 B (0.66666667 0.33333333) *
## 119) smoothness_mean>=-2.36516 83 6 M (0.07228916 0.92771084) *
## 15) smoothness_mean< -2.383798 81 11 M (0.13580247 0.86419753)
## 30) symmetry_worst< -1.966444 15 7 M (0.46666667 0.53333333)
## 60) texture_mean< 3.127107 7 0 B (1.00000000 0.00000000) *
## 61) texture_mean>=3.127107 8 0 M (0.00000000 1.00000000) *
## 31) symmetry_worst>=-1.966444 66 4 M (0.06060606 0.93939394)
## 62) texture_mean< 2.974761 8 4 B (0.50000000 0.50000000)
## 124) texture_mean>=2.966802 4 0 B (1.00000000 0.00000000) *
## 125) texture_mean< 2.966802 4 0 M (0.00000000 1.00000000) *
## 63) texture_mean>=2.974761 58 0 M (0.00000000 1.00000000) *
##
## $trees[[58]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 433 M (0.47478070 0.52521930)
## 2) texture_mean< 2.960364 368 141 B (0.61684783 0.38315217)
## 4) smoothness_worst< -1.451541 309 104 B (0.66343042 0.33656958)
## 8) texture_worst< 4.517889 178 43 B (0.75842697 0.24157303)
## 16) symmetry_worst< -1.001713 174 39 B (0.77586207 0.22413793)
## 32) compactness_se>=-3.343833 47 3 B (0.93617021 0.06382979)
## 64) smoothness_worst< -1.464806 42 0 B (1.00000000 0.00000000) *
## 65) smoothness_worst>=-1.464806 5 2 M (0.40000000 0.60000000) *
## 33) compactness_se< -3.343833 127 36 B (0.71653543 0.28346457)
## 66) texture_worst< 4.261496 59 8 B (0.86440678 0.13559322) *
## 67) texture_worst>=4.261496 68 28 B (0.58823529 0.41176471) *
## 17) symmetry_worst>=-1.001713 4 0 M (0.00000000 1.00000000) *
## 9) texture_worst>=4.517889 131 61 B (0.53435115 0.46564885)
## 18) texture_mean>=2.851854 115 45 B (0.60869565 0.39130435)
## 36) texture_worst>=4.545516 101 32 B (0.68316832 0.31683168)
## 72) smoothness_worst>=-1.548117 49 5 B (0.89795918 0.10204082) *
## 73) smoothness_worst< -1.548117 52 25 M (0.48076923 0.51923077) *
## 37) texture_worst< 4.545516 14 1 M (0.07142857 0.92857143)
## 74) texture_mean< 2.890089 1 0 B (1.00000000 0.00000000) *
## 75) texture_mean>=2.890089 13 0 M (0.00000000 1.00000000) *
## 19) texture_mean< 2.851854 16 0 M (0.00000000 1.00000000) *
## 5) smoothness_worst>=-1.451541 59 22 M (0.37288136 0.62711864)
## 10) texture_mean< 2.780541 20 5 B (0.75000000 0.25000000)
## 20) smoothness_worst>=-1.434076 15 0 B (1.00000000 0.00000000) *
## 21) smoothness_worst< -1.434076 5 0 M (0.00000000 1.00000000) *
## 11) texture_mean>=2.780541 39 7 M (0.17948718 0.82051282)
## 22) texture_mean>=2.879842 15 7 M (0.46666667 0.53333333)
## 44) texture_mean< 2.933323 7 0 B (1.00000000 0.00000000) *
## 45) texture_mean>=2.933323 8 0 M (0.00000000 1.00000000) *
## 23) texture_mean< 2.879842 24 0 M (0.00000000 1.00000000) *
## 3) texture_mean>=2.960364 544 206 M (0.37867647 0.62132353)
## 6) texture_mean>=3.336125 28 4 B (0.85714286 0.14285714)
## 12) compactness_se< -3.643388 25 1 B (0.96000000 0.04000000)
## 24) smoothness_mean< -2.346938 24 0 B (1.00000000 0.00000000) *
## 25) smoothness_mean>=-2.346938 1 0 M (0.00000000 1.00000000) *
## 13) compactness_se>=-3.643388 3 0 M (0.00000000 1.00000000) *
## 7) texture_mean< 3.336125 516 182 M (0.35271318 0.64728682)
## 14) texture_worst< 4.357182 11 0 B (1.00000000 0.00000000) *
## 15) texture_worst>=4.357182 505 171 M (0.33861386 0.66138614)
## 30) texture_worst< 5.073596 447 166 M (0.37136465 0.62863535)
## 60) texture_worst>=4.982438 42 11 B (0.73809524 0.26190476)
## 120) symmetry_worst< -1.541072 36 5 B (0.86111111 0.13888889) *
## 121) symmetry_worst>=-1.541072 6 0 M (0.00000000 1.00000000) *
## 61) texture_worst< 4.982438 405 135 M (0.33333333 0.66666667)
## 122) texture_worst< 4.911888 320 129 M (0.40312500 0.59687500) *
## 123) texture_worst>=4.911888 85 6 M (0.07058824 0.92941176) *
## 31) texture_worst>=5.073596 58 5 M (0.08620690 0.91379310)
## 62) smoothness_worst< -1.609702 2 0 B (1.00000000 0.00000000) *
## 63) smoothness_worst>=-1.609702 56 3 M (0.05357143 0.94642857)
## 126) symmetry_worst< -2.299309 1 0 B (1.00000000 0.00000000) *
## 127) symmetry_worst>=-2.299309 55 2 M (0.03636364 0.96363636) *
##
## $trees[[59]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 416 B (0.54385965 0.45614035)
## 2) texture_worst< 4.820212 652 257 B (0.60582822 0.39417178)
## 4) texture_worst>=4.751723 68 2 B (0.97058824 0.02941176)
## 8) symmetry_worst< -0.9904278 66 0 B (1.00000000 0.00000000) *
## 9) symmetry_worst>=-0.9904278 2 0 M (0.00000000 1.00000000) *
## 5) texture_worst< 4.751723 584 255 B (0.56335616 0.43664384)
## 10) compactness_se>=-3.426516 167 48 B (0.71257485 0.28742515)
## 20) smoothness_mean< -2.155475 150 34 B (0.77333333 0.22666667)
## 40) compactness_se< -3.02233 110 10 B (0.90909091 0.09090909)
## 80) texture_worst< 4.68552 106 6 B (0.94339623 0.05660377) *
## 81) texture_worst>=4.68552 4 0 M (0.00000000 1.00000000) *
## 41) compactness_se>=-3.02233 40 16 M (0.40000000 0.60000000)
## 82) compactness_se>=-2.86687 19 4 B (0.78947368 0.21052632) *
## 83) compactness_se< -2.86687 21 1 M (0.04761905 0.95238095) *
## 21) smoothness_mean>=-2.155475 17 3 M (0.17647059 0.82352941)
## 42) texture_mean< 2.664661 3 0 B (1.00000000 0.00000000) *
## 43) texture_mean>=2.664661 14 0 M (0.00000000 1.00000000) *
## 11) compactness_se< -3.426516 417 207 B (0.50359712 0.49640288)
## 22) symmetry_worst>=-1.52618 66 14 B (0.78787879 0.21212121)
## 44) texture_mean< 2.998678 59 7 B (0.88135593 0.11864407)
## 88) compactness_se< -3.964431 42 0 B (1.00000000 0.00000000) *
## 89) compactness_se>=-3.964431 17 7 B (0.58823529 0.41176471) *
## 45) texture_mean>=2.998678 7 0 M (0.00000000 1.00000000) *
## 23) symmetry_worst< -1.52618 351 158 M (0.45014245 0.54985755)
## 46) smoothness_mean< -2.525043 14 0 B (1.00000000 0.00000000) *
## 47) smoothness_mean>=-2.525043 337 144 M (0.42729970 0.57270030)
## 94) texture_worst< 4.254671 37 9 B (0.75675676 0.24324324) *
## 95) texture_worst>=4.254671 300 116 M (0.38666667 0.61333333) *
## 3) texture_worst>=4.820212 260 101 M (0.38846154 0.61153846)
## 6) symmetry_worst< -2.207988 26 4 B (0.84615385 0.15384615)
## 12) compactness_se< -3.413706 22 0 B (1.00000000 0.00000000) *
## 13) compactness_se>=-3.413706 4 0 M (0.00000000 1.00000000) *
## 7) symmetry_worst>=-2.207988 234 79 M (0.33760684 0.66239316)
## 14) symmetry_worst>=-1.857231 144 63 M (0.43750000 0.56250000)
## 28) symmetry_worst< -1.661584 72 28 B (0.61111111 0.38888889)
## 56) texture_worst< 5.052263 40 6 B (0.85000000 0.15000000)
## 112) texture_worst>=4.940521 29 0 B (1.00000000 0.00000000) *
## 113) texture_worst< 4.940521 11 5 M (0.45454545 0.54545455) *
## 57) texture_worst>=5.052263 32 10 M (0.31250000 0.68750000)
## 114) smoothness_mean< -2.544154 5 0 B (1.00000000 0.00000000) *
## 115) smoothness_mean>=-2.544154 27 5 M (0.18518519 0.81481481) *
## 29) symmetry_worst>=-1.661584 72 19 M (0.26388889 0.73611111)
## 58) compactness_se< -4.539406 7 0 B (1.00000000 0.00000000) *
## 59) compactness_se>=-4.539406 65 12 M (0.18461538 0.81538462)
## 118) compactness_se>=-3.132386 13 4 B (0.69230769 0.30769231) *
## 119) compactness_se< -3.132386 52 3 M (0.05769231 0.94230769) *
## 15) symmetry_worst< -1.857231 90 16 M (0.17777778 0.82222222)
## 30) texture_worst< 4.907333 7 0 B (1.00000000 0.00000000) *
## 31) texture_worst>=4.907333 83 9 M (0.10843373 0.89156627)
## 62) texture_mean>=3.287978 9 1 B (0.88888889 0.11111111)
## 124) smoothness_mean< -2.380359 8 0 B (1.00000000 0.00000000) *
## 125) smoothness_mean>=-2.380359 1 0 M (0.00000000 1.00000000) *
## 63) texture_mean< 3.287978 74 1 M (0.01351351 0.98648649)
## 126) compactness_se< -4.706178 1 0 B (1.00000000 0.00000000) *
## 127) compactness_se>=-4.706178 73 0 M (0.00000000 1.00000000) *
##
## $trees[[60]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 406 B (0.55482456 0.44517544)
## 2) texture_worst< 4.858219 714 281 B (0.60644258 0.39355742)
## 4) symmetry_worst< -1.828847 263 67 B (0.74524715 0.25475285)
## 8) symmetry_worst>=-2.400126 243 52 B (0.78600823 0.21399177)
## 16) compactness_se>=-4.098964 195 32 B (0.83589744 0.16410256)
## 32) texture_worst>=4.530419 124 8 B (0.93548387 0.06451613)
## 64) symmetry_worst>=-2.221546 119 4 B (0.96638655 0.03361345) *
## 65) symmetry_worst< -2.221546 5 1 M (0.20000000 0.80000000) *
## 33) texture_worst< 4.530419 71 24 B (0.66197183 0.33802817)
## 66) smoothness_worst< -1.482701 35 4 B (0.88571429 0.11428571) *
## 67) smoothness_worst>=-1.482701 36 16 M (0.44444444 0.55555556) *
## 17) compactness_se< -4.098964 48 20 B (0.58333333 0.41666667)
## 34) smoothness_mean< -2.426727 20 0 B (1.00000000 0.00000000) *
## 35) smoothness_mean>=-2.426727 28 8 M (0.28571429 0.71428571)
## 70) smoothness_mean>=-2.355463 8 0 B (1.00000000 0.00000000) *
## 71) smoothness_mean< -2.355463 20 0 M (0.00000000 1.00000000) *
## 9) symmetry_worst< -2.400126 20 5 M (0.25000000 0.75000000)
## 18) texture_mean< 2.855865 4 0 B (1.00000000 0.00000000) *
## 19) texture_mean>=2.855865 16 1 M (0.06250000 0.93750000)
## 38) texture_worst>=4.573991 1 0 B (1.00000000 0.00000000) *
## 39) texture_worst< 4.573991 15 0 M (0.00000000 1.00000000) *
## 5) symmetry_worst>=-1.828847 451 214 B (0.52549889 0.47450111)
## 10) smoothness_worst>=-1.623935 431 194 B (0.54988399 0.45011601)
## 20) texture_worst>=4.287261 344 136 B (0.60465116 0.39534884)
## 40) smoothness_mean< -2.216408 310 108 B (0.65161290 0.34838710)
## 80) texture_mean< 2.876103 49 0 B (1.00000000 0.00000000) *
## 81) texture_mean>=2.876103 261 108 B (0.58620690 0.41379310) *
## 41) smoothness_mean>=-2.216408 34 6 M (0.17647059 0.82352941)
## 82) texture_mean>=3.039982 7 2 B (0.71428571 0.28571429) *
## 83) texture_mean< 3.039982 27 1 M (0.03703704 0.96296296) *
## 21) texture_worst< 4.287261 87 29 M (0.33333333 0.66666667)
## 42) smoothness_mean< -2.437614 8 0 B (1.00000000 0.00000000) *
## 43) smoothness_mean>=-2.437614 79 21 M (0.26582278 0.73417722)
## 86) smoothness_worst>=-1.35291 5 0 B (1.00000000 0.00000000) *
## 87) smoothness_worst< -1.35291 74 16 M (0.21621622 0.78378378) *
## 11) smoothness_worst< -1.623935 20 0 M (0.00000000 1.00000000) *
## 3) texture_worst>=4.858219 198 73 M (0.36868687 0.63131313)
## 6) symmetry_worst< -2.063958 27 3 B (0.88888889 0.11111111)
## 12) texture_mean>=3.038878 25 1 B (0.96000000 0.04000000)
## 24) compactness_se< -3.400535 24 0 B (1.00000000 0.00000000) *
## 25) compactness_se>=-3.400535 1 0 M (0.00000000 1.00000000) *
## 13) texture_mean< 3.038878 2 0 M (0.00000000 1.00000000) *
## 7) symmetry_worst>=-2.063958 171 49 M (0.28654971 0.71345029)
## 14) smoothness_worst< -1.623453 14 2 B (0.85714286 0.14285714)
## 28) smoothness_mean< -2.396135 12 0 B (1.00000000 0.00000000) *
## 29) smoothness_mean>=-2.396135 2 0 M (0.00000000 1.00000000) *
## 15) smoothness_worst>=-1.623453 157 37 M (0.23566879 0.76433121)
## 30) compactness_se>=-3.902076 75 30 M (0.40000000 0.60000000)
## 60) texture_worst< 5.032208 38 13 B (0.65789474 0.34210526)
## 120) symmetry_worst< -1.541072 29 4 B (0.86206897 0.13793103) *
## 121) symmetry_worst>=-1.541072 9 0 M (0.00000000 1.00000000) *
## 61) texture_worst>=5.032208 37 5 M (0.13513514 0.86486486)
## 122) texture_worst>=5.329405 13 5 M (0.38461538 0.61538462) *
## 123) texture_worst< 5.329405 24 0 M (0.00000000 1.00000000) *
## 31) compactness_se< -3.902076 82 7 M (0.08536585 0.91463415)
## 62) compactness_se< -4.899363 2 0 B (1.00000000 0.00000000) *
## 63) compactness_se>=-4.899363 80 5 M (0.06250000 0.93750000)
## 126) texture_mean< 2.915217 1 0 B (1.00000000 0.00000000) *
## 127) texture_mean>=2.915217 79 4 M (0.05063291 0.94936709) *
##
## $trees[[61]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 450 M (0.49342105 0.50657895)
## 2) symmetry_worst< -1.424186 827 396 B (0.52116082 0.47883918)
## 4) compactness_se< -4.705732 17 0 B (1.00000000 0.00000000) *
## 5) compactness_se>=-4.705732 810 396 B (0.51111111 0.48888889)
## 10) compactness_se>=-4.663537 792 378 B (0.52272727 0.47727273)
## 20) symmetry_worst>=-1.440359 20 1 B (0.95000000 0.05000000)
## 40) smoothness_mean< -2.257921 19 0 B (1.00000000 0.00000000) *
## 41) smoothness_mean>=-2.257921 1 0 M (0.00000000 1.00000000) *
## 21) symmetry_worst< -1.440359 772 377 B (0.51165803 0.48834197)
## 42) symmetry_worst< -1.828847 319 126 B (0.60501567 0.39498433)
## 84) texture_worst< 4.897936 246 78 B (0.68292683 0.31707317) *
## 85) texture_worst>=4.897936 73 25 M (0.34246575 0.65753425) *
## 43) symmetry_worst>=-1.828847 453 202 M (0.44591611 0.55408389)
## 86) symmetry_worst>=-1.749635 322 155 B (0.51863354 0.48136646) *
## 87) symmetry_worst< -1.749635 131 35 M (0.26717557 0.73282443) *
## 11) compactness_se< -4.663537 18 0 M (0.00000000 1.00000000) *
## 3) symmetry_worst>=-1.424186 85 19 M (0.22352941 0.77647059)
## 6) texture_mean< 2.77286 7 1 B (0.85714286 0.14285714)
## 12) compactness_se< -3.1317 6 0 B (1.00000000 0.00000000) *
## 13) compactness_se>=-3.1317 1 0 M (0.00000000 1.00000000) *
## 7) texture_mean>=2.77286 78 13 M (0.16666667 0.83333333)
## 14) smoothness_worst< -1.49848 19 9 B (0.52631579 0.47368421)
## 28) smoothness_mean>=-2.45794 12 2 B (0.83333333 0.16666667)
## 56) texture_worst< 4.857215 10 0 B (1.00000000 0.00000000) *
## 57) texture_worst>=4.857215 2 0 M (0.00000000 1.00000000) *
## 29) smoothness_mean< -2.45794 7 0 M (0.00000000 1.00000000) *
## 15) smoothness_worst>=-1.49848 59 3 M (0.05084746 0.94915254)
## 30) compactness_se>=-2.567912 3 0 B (1.00000000 0.00000000) *
## 31) compactness_se< -2.567912 56 0 M (0.00000000 1.00000000) *
##
## $trees[[62]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 429 M (0.47039474 0.52960526)
## 2) symmetry_worst< -1.816281 361 155 B (0.57063712 0.42936288)
## 4) compactness_se>=-4.44774 311 119 B (0.61736334 0.38263666)
## 8) compactness_se< -4.244326 32 0 B (1.00000000 0.00000000) *
## 9) compactness_se>=-4.244326 279 119 B (0.57347670 0.42652330)
## 18) texture_mean< 3.044457 165 49 B (0.70303030 0.29696970)
## 36) texture_worst>=4.530419 95 13 B (0.86315789 0.13684211)
## 72) texture_worst< 4.669081 69 0 B (1.00000000 0.00000000) *
## 73) texture_worst>=4.669081 26 13 B (0.50000000 0.50000000) *
## 37) texture_worst< 4.530419 70 34 M (0.48571429 0.51428571)
## 74) smoothness_worst>=-1.477976 27 4 B (0.85185185 0.14814815) *
## 75) smoothness_worst< -1.477976 43 11 M (0.25581395 0.74418605) *
## 19) texture_mean>=3.044457 114 44 M (0.38596491 0.61403509)
## 38) symmetry_worst< -2.188127 41 15 B (0.63414634 0.36585366)
## 76) texture_worst>=4.762323 26 3 B (0.88461538 0.11538462) *
## 77) texture_worst< 4.762323 15 3 M (0.20000000 0.80000000) *
## 39) symmetry_worst>=-2.188127 73 18 M (0.24657534 0.75342466)
## 78) smoothness_worst< -1.589834 6 1 B (0.83333333 0.16666667) *
## 79) smoothness_worst>=-1.589834 67 13 M (0.19402985 0.80597015) *
## 5) compactness_se< -4.44774 50 14 M (0.28000000 0.72000000)
## 10) smoothness_worst< -1.626559 9 1 B (0.88888889 0.11111111)
## 20) texture_mean< 3.146983 8 0 B (1.00000000 0.00000000) *
## 21) texture_mean>=3.146983 1 0 M (0.00000000 1.00000000) *
## 11) smoothness_worst>=-1.626559 41 6 M (0.14634146 0.85365854)
## 22) compactness_se< -4.737326 2 0 B (1.00000000 0.00000000) *
## 23) compactness_se>=-4.737326 39 4 M (0.10256410 0.89743590)
## 46) texture_mean< 2.846651 1 0 B (1.00000000 0.00000000) *
## 47) texture_mean>=2.846651 38 3 M (0.07894737 0.92105263)
## 94) smoothness_mean>=-2.271294 1 0 B (1.00000000 0.00000000) *
## 95) smoothness_mean< -2.271294 37 2 M (0.05405405 0.94594595) *
## 3) symmetry_worst>=-1.816281 551 223 M (0.40471869 0.59528131)
## 6) texture_mean< 2.960364 248 118 B (0.52419355 0.47580645)
## 12) symmetry_worst>=-1.769229 196 75 B (0.61734694 0.38265306)
## 24) smoothness_worst< -1.492248 83 12 B (0.85542169 0.14457831)
## 48) compactness_se>=-4.681232 74 6 B (0.91891892 0.08108108)
## 96) symmetry_worst>=-1.748321 67 1 B (0.98507463 0.01492537) *
## 97) symmetry_worst< -1.748321 7 2 M (0.28571429 0.71428571) *
## 49) compactness_se< -4.681232 9 3 M (0.33333333 0.66666667)
## 98) texture_mean>=2.936149 3 0 B (1.00000000 0.00000000) *
## 99) texture_mean< 2.936149 6 0 M (0.00000000 1.00000000) *
## 25) smoothness_worst>=-1.492248 113 50 M (0.44247788 0.55752212)
## 50) symmetry_worst< -1.641484 30 1 B (0.96666667 0.03333333)
## 100) texture_mean< 2.933308 29 0 B (1.00000000 0.00000000) *
## 101) texture_mean>=2.933308 1 0 M (0.00000000 1.00000000) *
## 51) symmetry_worst>=-1.641484 83 21 M (0.25301205 0.74698795)
## 102) compactness_se< -4.218076 3 0 B (1.00000000 0.00000000) *
## 103) compactness_se>=-4.218076 80 18 M (0.22500000 0.77500000) *
## 13) symmetry_worst< -1.769229 52 9 M (0.17307692 0.82692308)
## 26) texture_mean< 2.862952 11 5 B (0.54545455 0.45454545)
## 52) texture_mean>=2.739163 6 0 B (1.00000000 0.00000000) *
## 53) texture_mean< 2.739163 5 0 M (0.00000000 1.00000000) *
## 27) texture_mean>=2.862952 41 3 M (0.07317073 0.92682927)
## 54) smoothness_mean< -2.480896 1 0 B (1.00000000 0.00000000) *
## 55) smoothness_mean>=-2.480896 40 2 M (0.05000000 0.95000000)
## 110) smoothness_mean>=-2.31876 9 2 M (0.22222222 0.77777778) *
## 111) smoothness_mean< -2.31876 31 0 M (0.00000000 1.00000000) *
## 7) texture_mean>=2.960364 303 93 M (0.30693069 0.69306931)
## 14) texture_worst>=4.900538 68 25 B (0.63235294 0.36764706)
## 28) smoothness_mean< -2.333105 45 8 B (0.82222222 0.17777778)
## 56) compactness_se< -3.435038 39 4 B (0.89743590 0.10256410)
## 112) texture_mean>=3.088538 32 1 B (0.96875000 0.03125000) *
## 113) texture_mean< 3.088538 7 3 B (0.57142857 0.42857143) *
## 57) compactness_se>=-3.435038 6 2 M (0.33333333 0.66666667)
## 114) texture_mean>=3.165773 2 0 B (1.00000000 0.00000000) *
## 115) texture_mean< 3.165773 4 0 M (0.00000000 1.00000000) *
## 29) smoothness_mean>=-2.333105 23 6 M (0.26086957 0.73913043)
## 58) symmetry_worst< -1.700833 6 0 B (1.00000000 0.00000000) *
## 59) symmetry_worst>=-1.700833 17 0 M (0.00000000 1.00000000) *
## 15) texture_worst< 4.900538 235 50 M (0.21276596 0.78723404)
## 30) symmetry_worst>=-1.606972 109 40 M (0.36697248 0.63302752)
## 60) compactness_se< -3.483184 67 31 B (0.53731343 0.46268657)
## 120) compactness_se>=-3.494961 17 0 B (1.00000000 0.00000000) *
## 121) compactness_se< -3.494961 50 19 M (0.38000000 0.62000000) *
## 61) compactness_se>=-3.483184 42 4 M (0.09523810 0.90476190)
## 122) symmetry_worst>=-1.471051 4 0 B (1.00000000 0.00000000) *
## 123) symmetry_worst< -1.471051 38 0 M (0.00000000 1.00000000) *
## 31) symmetry_worst< -1.606972 126 10 M (0.07936508 0.92063492)
## 62) smoothness_mean>=-2.137157 4 0 B (1.00000000 0.00000000) *
## 63) smoothness_mean< -2.137157 122 6 M (0.04918033 0.95081967)
## 126) compactness_se>=-2.744014 10 4 M (0.40000000 0.60000000) *
## 127) compactness_se< -2.744014 112 2 M (0.01785714 0.98214286) *
##
## $trees[[63]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 412 B (0.54824561 0.45175439)
## 2) symmetry_worst< -1.758563 452 166 B (0.63274336 0.36725664)
## 4) texture_worst< 4.605004 253 69 B (0.72727273 0.27272727)
## 8) smoothness_mean>=-2.603563 246 62 B (0.74796748 0.25203252)
## 16) symmetry_worst< -1.948993 96 10 B (0.89583333 0.10416667)
## 32) symmetry_worst>=-2.49184 92 6 B (0.93478261 0.06521739)
## 64) texture_worst>=3.98381 89 4 B (0.95505618 0.04494382) *
## 65) texture_worst< 3.98381 3 1 M (0.33333333 0.66666667) *
## 33) symmetry_worst< -2.49184 4 0 M (0.00000000 1.00000000) *
## 17) symmetry_worst>=-1.948993 150 52 B (0.65333333 0.34666667)
## 34) smoothness_worst< -1.455007 127 34 B (0.73228346 0.26771654)
## 68) texture_mean>=2.766607 96 17 B (0.82291667 0.17708333) *
## 69) texture_mean< 2.766607 31 14 M (0.45161290 0.54838710) *
## 35) smoothness_worst>=-1.455007 23 5 M (0.21739130 0.78260870)
## 70) smoothness_mean>=-2.212285 4 0 B (1.00000000 0.00000000) *
## 71) smoothness_mean< -2.212285 19 1 M (0.05263158 0.94736842) *
## 9) smoothness_mean< -2.603563 7 0 M (0.00000000 1.00000000) *
## 5) texture_worst>=4.605004 199 97 B (0.51256281 0.48743719)
## 10) texture_mean>=3.021644 135 47 B (0.65185185 0.34814815)
## 20) texture_worst>=4.651138 125 37 B (0.70400000 0.29600000)
## 40) smoothness_worst< -1.52112 81 13 B (0.83950617 0.16049383)
## 80) compactness_se< -2.810352 79 11 B (0.86075949 0.13924051) *
## 81) compactness_se>=-2.810352 2 0 M (0.00000000 1.00000000) *
## 41) smoothness_worst>=-1.52112 44 20 M (0.45454545 0.54545455)
## 82) smoothness_worst>=-1.49243 30 10 B (0.66666667 0.33333333) *
## 83) smoothness_worst< -1.49243 14 0 M (0.00000000 1.00000000) *
## 21) texture_worst< 4.651138 10 0 M (0.00000000 1.00000000) *
## 11) texture_mean< 3.021644 64 14 M (0.21875000 0.78125000)
## 22) smoothness_mean>=-2.33454 8 1 B (0.87500000 0.12500000)
## 44) texture_worst< 4.831396 7 0 B (1.00000000 0.00000000) *
## 45) texture_worst>=4.831396 1 0 M (0.00000000 1.00000000) *
## 23) smoothness_mean< -2.33454 56 7 M (0.12500000 0.87500000)
## 46) compactness_se< -4.737326 2 0 B (1.00000000 0.00000000) *
## 47) compactness_se>=-4.737326 54 5 M (0.09259259 0.90740741)
## 94) smoothness_mean< -2.58747 1 0 B (1.00000000 0.00000000) *
## 95) smoothness_mean>=-2.58747 53 4 M (0.07547170 0.92452830) *
## 3) symmetry_worst>=-1.758563 460 214 M (0.46521739 0.53478261)
## 6) texture_mean< 2.955938 175 67 B (0.61714286 0.38285714)
## 12) smoothness_mean< -2.22055 131 35 B (0.73282443 0.26717557)
## 24) symmetry_worst>=-1.749307 123 27 B (0.78048780 0.21951220)
## 48) symmetry_worst< -1.426958 76 8 B (0.89473684 0.10526316)
## 96) compactness_se>=-4.650552 70 4 B (0.94285714 0.05714286) *
## 97) compactness_se< -4.650552 6 2 M (0.33333333 0.66666667) *
## 49) symmetry_worst>=-1.426958 47 19 B (0.59574468 0.40425532)
## 98) texture_worst>=4.136225 35 7 B (0.80000000 0.20000000) *
## 99) texture_worst< 4.136225 12 0 M (0.00000000 1.00000000) *
## 25) symmetry_worst< -1.749307 8 0 M (0.00000000 1.00000000) *
## 13) smoothness_mean>=-2.22055 44 12 M (0.27272727 0.72727273)
## 26) smoothness_worst>=-1.333822 6 0 B (1.00000000 0.00000000) *
## 27) smoothness_worst< -1.333822 38 6 M (0.15789474 0.84210526)
## 54) smoothness_worst< -1.534923 4 0 B (1.00000000 0.00000000) *
## 55) smoothness_worst>=-1.534923 34 2 M (0.05882353 0.94117647)
## 110) compactness_se< -3.796566 3 1 B (0.66666667 0.33333333) *
## 111) compactness_se>=-3.796566 31 0 M (0.00000000 1.00000000) *
## 7) texture_mean>=2.955938 285 106 M (0.37192982 0.62807018)
## 14) compactness_se< -3.446121 218 102 M (0.46788991 0.53211009)
## 28) texture_worst>=4.608306 153 64 B (0.58169935 0.41830065)
## 56) smoothness_mean>=-2.306712 59 9 B (0.84745763 0.15254237)
## 112) smoothness_worst< -1.394193 53 3 B (0.94339623 0.05660377) *
## 113) smoothness_worst>=-1.394193 6 0 M (0.00000000 1.00000000) *
## 57) smoothness_mean< -2.306712 94 39 M (0.41489362 0.58510638)
## 114) compactness_se>=-3.765171 40 13 B (0.67500000 0.32500000) *
## 115) compactness_se< -3.765171 54 12 M (0.22222222 0.77777778) *
## 29) texture_worst< 4.608306 65 13 M (0.20000000 0.80000000)
## 58) compactness_se< -4.291103 13 0 B (1.00000000 0.00000000) *
## 59) compactness_se>=-4.291103 52 0 M (0.00000000 1.00000000) *
## 15) compactness_se>=-3.446121 67 4 M (0.05970149 0.94029851)
## 30) smoothness_mean< -2.476623 2 0 B (1.00000000 0.00000000) *
## 31) smoothness_mean>=-2.476623 65 2 M (0.03076923 0.96923077)
## 62) symmetry_worst>=-1.471051 10 2 M (0.20000000 0.80000000)
## 124) symmetry_worst< -1.448226 2 0 B (1.00000000 0.00000000) *
## 125) symmetry_worst>=-1.448226 8 0 M (0.00000000 1.00000000) *
## 63) symmetry_worst< -1.471051 55 0 M (0.00000000 1.00000000) *
##
## $trees[[64]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 452 B (0.50438596 0.49561404)
## 2) texture_mean< 3.007414 529 228 B (0.56899811 0.43100189)
## 4) texture_mean>=2.996482 45 3 B (0.93333333 0.06666667)
## 8) compactness_se< -3.239931 41 0 B (1.00000000 0.00000000) *
## 9) compactness_se>=-3.239931 4 1 M (0.25000000 0.75000000)
## 18) texture_mean>=3.003945 1 0 B (1.00000000 0.00000000) *
## 19) texture_mean< 3.003945 3 0 M (0.00000000 1.00000000) *
## 5) texture_mean< 2.996482 484 225 B (0.53512397 0.46487603)
## 10) texture_mean< 2.737601 67 16 B (0.76119403 0.23880597)
## 20) compactness_se< -3.768369 33 0 B (1.00000000 0.00000000) *
## 21) compactness_se>=-3.768369 34 16 B (0.52941176 0.47058824)
## 42) texture_worst< 4.057309 24 6 B (0.75000000 0.25000000)
## 84) compactness_se>=-3.734904 18 0 B (1.00000000 0.00000000) *
## 85) compactness_se< -3.734904 6 0 M (0.00000000 1.00000000) *
## 43) texture_worst>=4.057309 10 0 M (0.00000000 1.00000000) *
## 11) texture_mean>=2.737601 417 208 M (0.49880096 0.50119904)
## 22) texture_worst>=4.543638 176 65 B (0.63068182 0.36931818)
## 44) symmetry_worst< -1.362443 159 48 B (0.69811321 0.30188679)
## 88) symmetry_worst>=-2.1835 146 37 B (0.74657534 0.25342466) *
## 89) symmetry_worst< -2.1835 13 2 M (0.15384615 0.84615385) *
## 45) symmetry_worst>=-1.362443 17 0 M (0.00000000 1.00000000) *
## 23) texture_worst< 4.543638 241 97 M (0.40248963 0.59751037)
## 46) texture_worst< 4.389172 125 59 B (0.52800000 0.47200000)
## 92) texture_worst>=4.365735 23 3 B (0.86956522 0.13043478) *
## 93) texture_worst< 4.365735 102 46 M (0.45098039 0.54901961) *
## 47) texture_worst>=4.389172 116 31 M (0.26724138 0.73275862)
## 94) texture_worst>=4.465917 71 30 M (0.42253521 0.57746479) *
## 95) texture_worst< 4.465917 45 1 M (0.02222222 0.97777778) *
## 3) texture_mean>=3.007414 383 159 M (0.41514360 0.58485640)
## 6) texture_mean>=3.029409 327 151 M (0.46177370 0.53822630)
## 12) compactness_se< -3.05924 303 149 M (0.49174917 0.50825083)
## 24) smoothness_worst< -1.618721 21 0 B (1.00000000 0.00000000) *
## 25) smoothness_worst>=-1.618721 282 128 M (0.45390071 0.54609929)
## 50) compactness_se< -4.539406 21 3 B (0.85714286 0.14285714)
## 100) symmetry_worst>=-1.674863 18 0 B (1.00000000 0.00000000) *
## 101) symmetry_worst< -1.674863 3 0 M (0.00000000 1.00000000) *
## 51) compactness_se>=-4.539406 261 110 M (0.42145594 0.57854406)
## 102) symmetry_worst< -1.661892 185 91 B (0.50810811 0.49189189) *
## 103) symmetry_worst>=-1.661892 76 16 M (0.21052632 0.78947368) *
## 13) compactness_se>=-3.05924 24 2 M (0.08333333 0.91666667)
## 26) texture_mean< 3.031099 2 0 B (1.00000000 0.00000000) *
## 27) texture_mean>=3.031099 22 0 M (0.00000000 1.00000000) *
## 7) texture_mean< 3.029409 56 8 M (0.14285714 0.85714286)
## 14) compactness_se>=-3.446107 8 0 B (1.00000000 0.00000000) *
## 15) compactness_se< -3.446107 48 0 M (0.00000000 1.00000000) *
##
## $trees[[65]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 433 M (0.47478070 0.52521930)
## 2) texture_worst< 4.820212 669 300 B (0.55156951 0.44843049)
## 4) symmetry_worst< -1.828847 249 77 B (0.69076305 0.30923695)
## 8) smoothness_mean>=-2.358733 115 16 B (0.86086957 0.13913043)
## 16) symmetry_worst>=-2.354921 104 9 B (0.91346154 0.08653846)
## 32) compactness_se< -3.02233 97 5 B (0.94845361 0.05154639)
## 64) compactness_se< -3.4389 75 1 B (0.98666667 0.01333333) *
## 65) compactness_se>=-3.4389 22 4 B (0.81818182 0.18181818) *
## 33) compactness_se>=-3.02233 7 3 M (0.42857143 0.57142857)
## 66) texture_mean< 2.962882 3 0 B (1.00000000 0.00000000) *
## 67) texture_mean>=2.962882 4 0 M (0.00000000 1.00000000) *
## 17) symmetry_worst< -2.354921 11 4 M (0.36363636 0.63636364)
## 34) texture_mean< 2.855865 3 0 B (1.00000000 0.00000000) *
## 35) texture_mean>=2.855865 8 1 M (0.12500000 0.87500000)
## 70) compactness_se< -3.905141 3 1 M (0.33333333 0.66666667) *
## 71) compactness_se>=-3.905141 5 0 M (0.00000000 1.00000000) *
## 9) smoothness_mean< -2.358733 134 61 B (0.54477612 0.45522388)
## 18) smoothness_worst< -1.559148 68 10 B (0.85294118 0.14705882)
## 36) texture_worst>=4.018768 62 5 B (0.91935484 0.08064516)
## 72) smoothness_mean< -2.378659 60 3 B (0.95000000 0.05000000) *
## 73) smoothness_mean>=-2.378659 2 0 M (0.00000000 1.00000000) *
## 37) texture_worst< 4.018768 6 1 M (0.16666667 0.83333333)
## 74) texture_mean< 2.764104 1 0 B (1.00000000 0.00000000) *
## 75) texture_mean>=2.764104 5 0 M (0.00000000 1.00000000) *
## 19) smoothness_worst>=-1.559148 66 15 M (0.22727273 0.77272727)
## 38) smoothness_mean< -2.419122 17 2 B (0.88235294 0.11764706)
## 76) texture_mean< 3.049127 15 0 B (1.00000000 0.00000000) *
## 77) texture_mean>=3.049127 2 0 M (0.00000000 1.00000000) *
## 39) smoothness_mean>=-2.419122 49 0 M (0.00000000 1.00000000) *
## 5) symmetry_worst>=-1.828847 420 197 M (0.46904762 0.53095238)
## 10) texture_worst< 4.50835 171 65 B (0.61988304 0.38011696)
## 20) symmetry_worst>=-1.809351 145 39 B (0.73103448 0.26896552)
## 40) texture_mean< 2.96681 123 25 B (0.79674797 0.20325203)
## 80) smoothness_worst< -1.451541 91 8 B (0.91208791 0.08791209) *
## 81) smoothness_worst>=-1.451541 32 15 M (0.46875000 0.53125000) *
## 41) texture_mean>=2.96681 22 8 M (0.36363636 0.63636364)
## 82) smoothness_mean< -2.381799 10 2 B (0.80000000 0.20000000) *
## 83) smoothness_mean>=-2.381799 12 0 M (0.00000000 1.00000000) *
## 21) symmetry_worst< -1.809351 26 0 M (0.00000000 1.00000000) *
## 11) texture_worst>=4.50835 249 91 M (0.36546185 0.63453815)
## 22) symmetry_worst>=-1.685469 156 76 M (0.48717949 0.51282051)
## 44) symmetry_worst< -1.620541 21 2 B (0.90476190 0.09523810)
## 88) compactness_se< -3.350492 19 0 B (1.00000000 0.00000000) *
## 89) compactness_se>=-3.350492 2 0 M (0.00000000 1.00000000) *
## 45) symmetry_worst>=-1.620541 135 57 M (0.42222222 0.57777778)
## 90) symmetry_worst>=-1.606972 116 57 M (0.49137931 0.50862069) *
## 91) symmetry_worst< -1.606972 19 0 M (0.00000000 1.00000000) *
## 23) symmetry_worst< -1.685469 93 15 M (0.16129032 0.83870968)
## 46) symmetry_worst< -1.801087 21 10 B (0.52380952 0.47619048)
## 92) texture_mean< 3.018415 11 0 B (1.00000000 0.00000000) *
## 93) texture_mean>=3.018415 10 0 M (0.00000000 1.00000000) *
## 47) symmetry_worst>=-1.801087 72 4 M (0.05555556 0.94444444)
## 94) compactness_se< -4.222363 2 0 B (1.00000000 0.00000000) *
## 95) compactness_se>=-4.222363 70 2 M (0.02857143 0.97142857) *
## 3) texture_worst>=4.820212 243 64 M (0.26337449 0.73662551)
## 6) smoothness_worst< -1.623453 10 2 B (0.80000000 0.20000000)
## 12) smoothness_mean< -2.382409 8 0 B (1.00000000 0.00000000) *
## 13) smoothness_mean>=-2.382409 2 0 M (0.00000000 1.00000000) *
## 7) smoothness_worst>=-1.623453 233 56 M (0.24034335 0.75965665)
## 14) texture_worst>=4.982438 137 46 M (0.33576642 0.66423358)
## 28) texture_worst< 4.998743 20 1 B (0.95000000 0.05000000)
## 56) texture_mean>=3.086552 19 0 B (1.00000000 0.00000000) *
## 57) texture_mean< 3.086552 1 0 M (0.00000000 1.00000000) *
## 29) texture_worst>=4.998743 117 27 M (0.23076923 0.76923077)
## 58) symmetry_worst< -2.257286 7 1 B (0.85714286 0.14285714)
## 116) smoothness_mean< -2.317053 6 0 B (1.00000000 0.00000000) *
## 117) smoothness_mean>=-2.317053 1 0 M (0.00000000 1.00000000) *
## 59) symmetry_worst>=-2.257286 110 21 M (0.19090909 0.80909091)
## 118) smoothness_mean< -2.505388 4 0 B (1.00000000 0.00000000) *
## 119) smoothness_mean>=-2.505388 106 17 M (0.16037736 0.83962264) *
## 15) texture_worst< 4.982438 96 10 M (0.10416667 0.89583333)
## 30) compactness_se>=-2.919705 2 0 B (1.00000000 0.00000000) *
## 31) compactness_se< -2.919705 94 8 M (0.08510638 0.91489362)
## 62) texture_mean>=3.208403 15 6 M (0.40000000 0.60000000)
## 124) smoothness_worst< -1.511906 6 0 B (1.00000000 0.00000000) *
## 125) smoothness_worst>=-1.511906 9 0 M (0.00000000 1.00000000) *
## 63) texture_mean< 3.208403 79 2 M (0.02531646 0.97468354)
## 126) symmetry_worst< -2.139 1 0 B (1.00000000 0.00000000) *
## 127) symmetry_worst>=-2.139 78 1 M (0.01282051 0.98717949) *
##
## $trees[[66]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 432 B (0.52631579 0.47368421)
## 2) texture_mean< 3.007414 527 207 B (0.60721063 0.39278937)
## 4) texture_mean>=2.995732 62 6 B (0.90322581 0.09677419)
## 8) smoothness_mean>=-2.301343 48 0 B (1.00000000 0.00000000) *
## 9) smoothness_mean< -2.301343 14 6 B (0.57142857 0.42857143)
## 18) smoothness_mean< -2.332196 8 0 B (1.00000000 0.00000000) *
## 19) smoothness_mean>=-2.332196 6 0 M (0.00000000 1.00000000) *
## 5) texture_mean< 2.995732 465 201 B (0.56774194 0.43225806)
## 10) texture_mean< 2.993479 453 189 B (0.58278146 0.41721854)
## 20) compactness_se< -3.726279 273 94 B (0.65567766 0.34432234)
## 40) texture_mean< 2.813911 68 10 B (0.85294118 0.14705882)
## 80) compactness_se< -3.88564 51 0 B (1.00000000 0.00000000) *
## 81) compactness_se>=-3.88564 17 7 M (0.41176471 0.58823529) *
## 41) texture_mean>=2.813911 205 84 B (0.59024390 0.40975610)
## 82) smoothness_mean>=-2.31481 82 16 B (0.80487805 0.19512195) *
## 83) smoothness_mean< -2.31481 123 55 M (0.44715447 0.55284553) *
## 21) compactness_se>=-3.726279 180 85 M (0.47222222 0.52777778)
## 42) compactness_se>=-3.344528 59 15 B (0.74576271 0.25423729)
## 84) symmetry_worst< -1.316602 46 6 B (0.86956522 0.13043478) *
## 85) symmetry_worst>=-1.316602 13 4 M (0.30769231 0.69230769) *
## 43) compactness_se< -3.344528 121 41 M (0.33884298 0.66115702)
## 86) symmetry_worst< -1.813857 45 18 B (0.60000000 0.40000000) *
## 87) symmetry_worst>=-1.813857 76 14 M (0.18421053 0.81578947) *
## 11) texture_mean>=2.993479 12 0 M (0.00000000 1.00000000) *
## 3) texture_mean>=3.007414 385 160 M (0.41558442 0.58441558)
## 6) compactness_se< -4.26701 54 12 B (0.77777778 0.22222222)
## 12) texture_mean>=3.227241 23 0 B (1.00000000 0.00000000) *
## 13) texture_mean< 3.227241 31 12 B (0.61290323 0.38709677)
## 26) texture_mean< 3.108384 23 4 B (0.82608696 0.17391304)
## 52) smoothness_worst< -1.508396 21 2 B (0.90476190 0.09523810)
## 104) smoothness_mean>=-2.477398 15 0 B (1.00000000 0.00000000) *
## 105) smoothness_mean< -2.477398 6 2 B (0.66666667 0.33333333) *
## 53) smoothness_worst>=-1.508396 2 0 M (0.00000000 1.00000000) *
## 27) texture_mean>=3.108384 8 0 M (0.00000000 1.00000000) *
## 7) compactness_se>=-4.26701 331 118 M (0.35649547 0.64350453)
## 14) texture_worst>=4.678763 224 97 M (0.43303571 0.56696429)
## 28) texture_worst< 4.681966 18 0 B (1.00000000 0.00000000) *
## 29) texture_worst>=4.681966 206 79 M (0.38349515 0.61650485)
## 58) smoothness_worst< -1.610115 11 0 B (1.00000000 0.00000000) *
## 59) smoothness_worst>=-1.610115 195 68 M (0.34871795 0.65128205)
## 118) texture_worst>=4.80876 163 65 M (0.39877301 0.60122699) *
## 119) texture_worst< 4.80876 32 3 M (0.09375000 0.90625000) *
## 15) texture_worst< 4.678763 107 21 M (0.19626168 0.80373832)
## 30) symmetry_worst< -2.232873 13 2 B (0.84615385 0.15384615)
## 60) smoothness_mean< -2.242961 11 0 B (1.00000000 0.00000000) *
## 61) smoothness_mean>=-2.242961 2 0 M (0.00000000 1.00000000) *
## 31) symmetry_worst>=-2.232873 94 10 M (0.10638298 0.89361702)
## 62) smoothness_worst>=-1.51308 33 10 M (0.30303030 0.69696970)
## 124) symmetry_worst< -1.871076 12 2 B (0.83333333 0.16666667) *
## 125) symmetry_worst>=-1.871076 21 0 M (0.00000000 1.00000000) *
## 63) smoothness_worst< -1.51308 61 0 M (0.00000000 1.00000000) *
##
## $trees[[67]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 387 B (0.57565789 0.42434211)
## 2) compactness_se< -3.721197 515 170 B (0.66990291 0.33009709)
## 4) compactness_se>=-3.867535 106 12 B (0.88679245 0.11320755)
## 8) smoothness_worst< -1.48132 85 0 B (1.00000000 0.00000000) *
## 9) smoothness_worst>=-1.48132 21 9 M (0.42857143 0.57142857)
## 18) texture_mean< 2.971675 13 4 B (0.69230769 0.30769231)
## 36) symmetry_worst< -1.612049 9 0 B (1.00000000 0.00000000) *
## 37) symmetry_worst>=-1.612049 4 0 M (0.00000000 1.00000000) *
## 19) texture_mean>=2.971675 8 0 M (0.00000000 1.00000000) *
## 5) compactness_se< -3.867535 409 158 B (0.61369193 0.38630807)
## 10) texture_worst< 4.822896 292 90 B (0.69178082 0.30821918)
## 20) compactness_se< -4.356557 84 9 B (0.89285714 0.10714286)
## 40) symmetry_worst>=-2.374205 79 6 B (0.92405063 0.07594937)
## 80) compactness_se< -4.50262 66 3 B (0.95454545 0.04545455) *
## 81) compactness_se>=-4.50262 13 3 B (0.76923077 0.23076923) *
## 41) symmetry_worst< -2.374205 5 2 M (0.40000000 0.60000000)
## 82) texture_mean< 2.828748 2 0 B (1.00000000 0.00000000) *
## 83) texture_mean>=2.828748 3 0 M (0.00000000 1.00000000) *
## 21) compactness_se>=-4.356557 208 81 B (0.61057692 0.38942308)
## 42) compactness_se>=-4.316443 195 68 B (0.65128205 0.34871795)
## 84) texture_worst>=4.751011 26 0 B (1.00000000 0.00000000) *
## 85) texture_worst< 4.751011 169 68 B (0.59763314 0.40236686) *
## 43) compactness_se< -4.316443 13 0 M (0.00000000 1.00000000) *
## 11) texture_worst>=4.822896 117 49 M (0.41880342 0.58119658)
## 22) compactness_se< -4.060578 82 36 B (0.56097561 0.43902439)
## 44) smoothness_worst>=-1.453658 26 2 B (0.92307692 0.07692308)
## 88) texture_worst>=4.831607 24 0 B (1.00000000 0.00000000) *
## 89) texture_worst< 4.831607 2 0 M (0.00000000 1.00000000) *
## 45) smoothness_worst< -1.453658 56 22 M (0.39285714 0.60714286)
## 90) texture_worst>=4.984637 30 12 B (0.60000000 0.40000000) *
## 91) texture_worst< 4.984637 26 4 M (0.15384615 0.84615385) *
## 23) compactness_se>=-4.060578 35 3 M (0.08571429 0.91428571)
## 46) texture_worst< 4.895983 2 0 B (1.00000000 0.00000000) *
## 47) texture_worst>=4.895983 33 1 M (0.03030303 0.96969697)
## 94) texture_mean< 3.053025 7 1 M (0.14285714 0.85714286) *
## 95) texture_mean>=3.053025 26 0 M (0.00000000 1.00000000) *
## 3) compactness_se>=-3.721197 397 180 M (0.45340050 0.54659950)
## 6) smoothness_mean< -2.298595 248 115 B (0.53629032 0.46370968)
## 12) symmetry_worst>=-1.609029 74 17 B (0.77027027 0.22972973)
## 24) compactness_se>=-3.592308 68 11 B (0.83823529 0.16176471)
## 48) texture_worst< 4.993407 65 8 B (0.87692308 0.12307692)
## 96) texture_mean>=2.896936 61 5 B (0.91803279 0.08196721) *
## 97) texture_mean< 2.896936 4 1 M (0.25000000 0.75000000) *
## 49) texture_worst>=4.993407 3 0 M (0.00000000 1.00000000) *
## 25) compactness_se< -3.592308 6 0 M (0.00000000 1.00000000) *
## 13) symmetry_worst< -1.609029 174 76 M (0.43678161 0.56321839)
## 26) texture_worst>=4.863167 43 13 B (0.69767442 0.30232558)
## 52) smoothness_mean>=-2.457972 29 1 B (0.96551724 0.03448276)
## 104) texture_mean< 3.329636 28 0 B (1.00000000 0.00000000) *
## 105) texture_mean>=3.329636 1 0 M (0.00000000 1.00000000) *
## 53) smoothness_mean< -2.457972 14 2 M (0.14285714 0.85714286)
## 106) compactness_se< -3.643388 2 0 B (1.00000000 0.00000000) *
## 107) compactness_se>=-3.643388 12 0 M (0.00000000 1.00000000) *
## 27) texture_worst< 4.863167 131 46 M (0.35114504 0.64885496)
## 54) smoothness_mean< -2.443631 42 19 B (0.54761905 0.45238095)
## 108) texture_mean< 3.064089 23 3 B (0.86956522 0.13043478) *
## 109) texture_mean>=3.064089 19 3 M (0.15789474 0.84210526) *
## 55) smoothness_mean>=-2.443631 89 23 M (0.25842697 0.74157303)
## 110) symmetry_worst< -1.813857 41 20 M (0.48780488 0.51219512) *
## 111) symmetry_worst>=-1.813857 48 3 M (0.06250000 0.93750000) *
## 7) smoothness_mean>=-2.298595 149 47 M (0.31543624 0.68456376)
## 14) texture_mean< 2.8622 37 9 B (0.75675676 0.24324324)
## 28) symmetry_worst< -1.53342 17 0 B (1.00000000 0.00000000) *
## 29) symmetry_worst>=-1.53342 20 9 B (0.55000000 0.45000000)
## 58) smoothness_worst>=-1.454202 14 3 B (0.78571429 0.21428571)
## 116) symmetry_worst>=-1.435787 11 0 B (1.00000000 0.00000000) *
## 117) symmetry_worst< -1.435787 3 0 M (0.00000000 1.00000000) *
## 59) smoothness_worst< -1.454202 6 0 M (0.00000000 1.00000000) *
## 15) texture_mean>=2.8622 112 19 M (0.16964286 0.83035714)
## 30) smoothness_mean>=-2.093138 9 1 B (0.88888889 0.11111111)
## 60) smoothness_mean< -2.073133 8 0 B (1.00000000 0.00000000) *
## 61) smoothness_mean>=-2.073133 1 0 M (0.00000000 1.00000000) *
## 31) smoothness_mean< -2.093138 103 11 M (0.10679612 0.89320388)
## 62) texture_worst< 4.345743 8 4 B (0.50000000 0.50000000)
## 124) smoothness_mean< -2.178638 4 0 B (1.00000000 0.00000000) *
## 125) smoothness_mean>=-2.178638 4 0 M (0.00000000 1.00000000) *
## 63) texture_worst>=4.345743 95 7 M (0.07368421 0.92631579)
## 126) texture_mean< 2.925574 11 3 M (0.27272727 0.72727273) *
## 127) texture_mean>=2.925574 84 4 M (0.04761905 0.95238095) *
##
## $trees[[68]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 431 B (0.52741228 0.47258772)
## 2) compactness_se< -4.505325 96 24 B (0.75000000 0.25000000)
## 4) smoothness_worst>=-1.576547 61 3 B (0.95081967 0.04918033)
## 8) smoothness_worst>=-1.550704 49 0 B (1.00000000 0.00000000) *
## 9) smoothness_worst< -1.550704 12 3 B (0.75000000 0.25000000)
## 18) texture_worst>=4.62656 9 0 B (1.00000000 0.00000000) *
## 19) texture_worst< 4.62656 3 0 M (0.00000000 1.00000000) *
## 5) smoothness_worst< -1.576547 35 14 M (0.40000000 0.60000000)
## 10) smoothness_worst< -1.629412 9 0 B (1.00000000 0.00000000) *
## 11) smoothness_worst>=-1.629412 26 5 M (0.19230769 0.80769231)
## 22) compactness_se< -4.860597 2 0 B (1.00000000 0.00000000) *
## 23) compactness_se>=-4.860597 24 3 M (0.12500000 0.87500000)
## 46) compactness_se>=-4.557299 8 3 M (0.37500000 0.62500000)
## 92) texture_mean< 3.023648 3 0 B (1.00000000 0.00000000) *
## 93) texture_mean>=3.023648 5 0 M (0.00000000 1.00000000) *
## 47) compactness_se< -4.557299 16 0 M (0.00000000 1.00000000) *
## 3) compactness_se>=-4.505325 816 407 B (0.50122549 0.49877451)
## 6) compactness_se>=-3.933251 529 219 B (0.58601134 0.41398866)
## 12) smoothness_worst< -1.499656 295 98 B (0.66779661 0.33220339)
## 24) compactness_se< -3.723892 80 4 B (0.95000000 0.05000000)
## 48) texture_worst< 5.269605 76 0 B (1.00000000 0.00000000) *
## 49) texture_worst>=5.269605 4 0 M (0.00000000 1.00000000) *
## 25) compactness_se>=-3.723892 215 94 B (0.56279070 0.43720930)
## 50) smoothness_worst>=-1.555451 98 27 B (0.72448980 0.27551020)
## 100) compactness_se>=-3.494301 68 8 B (0.88235294 0.11764706) *
## 101) compactness_se< -3.494301 30 11 M (0.36666667 0.63333333) *
## 51) smoothness_worst< -1.555451 117 50 M (0.42735043 0.57264957)
## 102) smoothness_mean< -2.400474 85 37 B (0.56470588 0.43529412) *
## 103) smoothness_mean>=-2.400474 32 2 M (0.06250000 0.93750000) *
## 13) smoothness_worst>=-1.499656 234 113 M (0.48290598 0.51709402)
## 26) smoothness_worst>=-1.49223 215 102 B (0.52558140 0.47441860)
## 52) symmetry_worst< -2.037336 22 1 B (0.95454545 0.04545455)
## 104) smoothness_mean< -2.231276 21 0 B (1.00000000 0.00000000) *
## 105) smoothness_mean>=-2.231276 1 0 M (0.00000000 1.00000000) *
## 53) symmetry_worst>=-2.037336 193 92 M (0.47668394 0.52331606)
## 106) texture_worst< 4.683387 148 64 B (0.56756757 0.43243243) *
## 107) texture_worst>=4.683387 45 8 M (0.17777778 0.82222222) *
## 27) smoothness_worst< -1.49223 19 0 M (0.00000000 1.00000000) *
## 7) compactness_se< -3.933251 287 99 M (0.34494774 0.65505226)
## 14) compactness_se< -3.996495 224 95 M (0.42410714 0.57589286)
## 28) compactness_se>=-4.100467 52 16 B (0.69230769 0.30769231)
## 56) texture_worst< 4.977078 42 6 B (0.85714286 0.14285714)
## 112) symmetry_worst< -1.485318 35 0 B (1.00000000 0.00000000) *
## 113) symmetry_worst>=-1.485318 7 1 M (0.14285714 0.85714286) *
## 57) texture_worst>=4.977078 10 0 M (0.00000000 1.00000000) *
## 29) compactness_se< -4.100467 172 59 M (0.34302326 0.65697674)
## 58) compactness_se< -4.219581 106 50 M (0.47169811 0.52830189)
## 116) symmetry_worst>=-1.508268 19 0 B (1.00000000 0.00000000) *
## 117) symmetry_worst< -1.508268 87 31 M (0.35632184 0.64367816) *
## 59) compactness_se>=-4.219581 66 9 M (0.13636364 0.86363636)
## 118) smoothness_mean< -2.434347 7 0 B (1.00000000 0.00000000) *
## 119) smoothness_mean>=-2.434347 59 2 M (0.03389831 0.96610169) *
## 15) compactness_se>=-3.996495 63 4 M (0.06349206 0.93650794)
## 30) texture_worst< 4.514719 20 4 M (0.20000000 0.80000000)
## 60) texture_mean>=2.888377 4 0 B (1.00000000 0.00000000) *
## 61) texture_mean< 2.888377 16 0 M (0.00000000 1.00000000) *
## 31) texture_worst>=4.514719 43 0 M (0.00000000 1.00000000) *
##
## $trees[[69]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 453 B (0.50328947 0.49671053)
## 2) texture_worst>=4.753106 275 90 B (0.67272727 0.32727273)
## 4) compactness_se< -3.321165 247 65 B (0.73684211 0.26315789)
## 8) texture_worst< 4.820212 75 3 B (0.96000000 0.04000000)
## 16) symmetry_worst< -0.9904278 72 0 B (1.00000000 0.00000000) *
## 17) symmetry_worst>=-0.9904278 3 0 M (0.00000000 1.00000000) *
## 9) texture_worst>=4.820212 172 62 B (0.63953488 0.36046512)
## 18) texture_mean>=3.166067 109 26 B (0.76146789 0.23853211)
## 36) texture_worst< 5.194184 64 5 B (0.92187500 0.07812500)
## 72) symmetry_worst< -1.302628 62 3 B (0.95161290 0.04838710) *
## 73) symmetry_worst>=-1.302628 2 0 M (0.00000000 1.00000000) *
## 37) texture_worst>=5.194184 45 21 B (0.53333333 0.46666667)
## 74) smoothness_mean< -2.363096 30 6 B (0.80000000 0.20000000) *
## 75) smoothness_mean>=-2.363096 15 0 M (0.00000000 1.00000000) *
## 19) texture_mean< 3.166067 63 27 M (0.42857143 0.57142857)
## 38) smoothness_worst>=-1.441541 14 2 B (0.85714286 0.14285714)
## 76) compactness_se< -4.032549 12 0 B (1.00000000 0.00000000) *
## 77) compactness_se>=-4.032549 2 0 M (0.00000000 1.00000000) *
## 39) smoothness_worst< -1.441541 49 15 M (0.30612245 0.69387755)
## 78) smoothness_worst< -1.52382 24 10 B (0.58333333 0.41666667) *
## 79) smoothness_worst>=-1.52382 25 1 M (0.04000000 0.96000000) *
## 5) compactness_se>=-3.321165 28 3 M (0.10714286 0.89285714)
## 10) smoothness_mean< -2.576888 1 0 B (1.00000000 0.00000000) *
## 11) smoothness_mean>=-2.576888 27 2 M (0.07407407 0.92592593)
## 22) smoothness_worst>=-1.415354 2 1 B (0.50000000 0.50000000)
## 44) texture_mean>=3.085887 1 0 B (1.00000000 0.00000000) *
## 45) texture_mean< 3.085887 1 0 M (0.00000000 1.00000000) *
## 23) smoothness_worst< -1.415354 25 1 M (0.04000000 0.96000000)
## 46) symmetry_worst>=-1.545802 2 1 B (0.50000000 0.50000000)
## 92) texture_mean>=3.19534 1 0 B (1.00000000 0.00000000) *
## 93) texture_mean< 3.19534 1 0 M (0.00000000 1.00000000) *
## 47) symmetry_worst< -1.545802 23 0 M (0.00000000 1.00000000) *
## 3) texture_worst< 4.753106 637 274 M (0.43014129 0.56985871)
## 6) texture_worst< 4.703562 586 271 M (0.46245734 0.53754266)
## 12) symmetry_worst>=-1.56405 153 54 B (0.64705882 0.35294118)
## 24) smoothness_worst>=-1.607486 138 39 B (0.71739130 0.28260870)
## 48) smoothness_worst< -1.451541 110 23 B (0.79090909 0.20909091)
## 96) compactness_se< -3.026518 92 13 B (0.85869565 0.14130435) *
## 97) compactness_se>=-3.026518 18 8 M (0.44444444 0.55555556) *
## 49) smoothness_worst>=-1.451541 28 12 M (0.42857143 0.57142857)
## 98) smoothness_worst>=-1.434633 17 5 B (0.70588235 0.29411765) *
## 99) smoothness_worst< -1.434633 11 0 M (0.00000000 1.00000000) *
## 25) smoothness_worst< -1.607486 15 0 M (0.00000000 1.00000000) *
## 13) symmetry_worst< -1.56405 433 172 M (0.39722864 0.60277136)
## 26) texture_mean< 2.70704 19 0 B (1.00000000 0.00000000) *
## 27) texture_mean>=2.70704 414 153 M (0.36956522 0.63043478)
## 54) symmetry_worst< -2.048468 57 19 B (0.66666667 0.33333333)
## 108) symmetry_worst>=-2.103063 14 0 B (1.00000000 0.00000000) *
## 109) symmetry_worst< -2.103063 43 19 B (0.55813953 0.44186047) *
## 55) symmetry_worst>=-2.048468 357 115 M (0.32212885 0.67787115)
## 110) texture_worst>=4.644679 34 8 B (0.76470588 0.23529412) *
## 111) texture_worst< 4.644679 323 89 M (0.27554180 0.72445820) *
## 7) texture_worst>=4.703562 51 3 M (0.05882353 0.94117647)
## 14) compactness_se< -4.398027 4 1 B (0.75000000 0.25000000)
## 28) texture_mean< 2.985881 3 0 B (1.00000000 0.00000000) *
## 29) texture_mean>=2.985881 1 0 M (0.00000000 1.00000000) *
## 15) compactness_se>=-4.398027 47 0 M (0.00000000 1.00000000) *
##
## $trees[[70]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 434 B (0.52412281 0.47587719)
## 2) smoothness_worst< -1.482502 585 240 B (0.58974359 0.41025641)
## 4) smoothness_worst>=-1.598251 463 164 B (0.64578834 0.35421166)
## 8) compactness_se>=-4.49319 399 122 B (0.69423559 0.30576441)
## 16) texture_worst< 4.595069 205 45 B (0.78048780 0.21951220)
## 32) symmetry_worst< -1.815934 92 5 B (0.94565217 0.05434783)
## 64) texture_mean< 3.059757 91 4 B (0.95604396 0.04395604) *
## 65) texture_mean>=3.059757 1 0 M (0.00000000 1.00000000) *
## 33) symmetry_worst>=-1.815934 113 40 B (0.64601770 0.35398230)
## 66) symmetry_worst>=-1.797319 100 27 B (0.73000000 0.27000000) *
## 67) symmetry_worst< -1.797319 13 0 M (0.00000000 1.00000000) *
## 17) texture_worst>=4.595069 194 77 B (0.60309278 0.39690722)
## 34) compactness_se>=-3.902076 135 35 B (0.74074074 0.25925926)
## 68) texture_worst>=4.606472 126 26 B (0.79365079 0.20634921) *
## 69) texture_worst< 4.606472 9 0 M (0.00000000 1.00000000) *
## 35) compactness_se< -3.902076 59 17 M (0.28813559 0.71186441)
## 70) compactness_se< -4.276595 17 1 B (0.94117647 0.05882353) *
## 71) compactness_se>=-4.276595 42 1 M (0.02380952 0.97619048) *
## 9) compactness_se< -4.49319 64 22 M (0.34375000 0.65625000)
## 18) compactness_se< -4.694501 20 0 B (1.00000000 0.00000000) *
## 19) compactness_se>=-4.694501 44 2 M (0.04545455 0.95454545)
## 38) texture_mean>=3.192081 2 0 B (1.00000000 0.00000000) *
## 39) texture_mean< 3.192081 42 0 M (0.00000000 1.00000000) *
## 5) smoothness_worst< -1.598251 122 46 M (0.37704918 0.62295082)
## 10) symmetry_worst< -1.868413 42 15 B (0.64285714 0.35714286)
## 20) texture_worst>=4.498003 22 0 B (1.00000000 0.00000000) *
## 21) texture_worst< 4.498003 20 5 M (0.25000000 0.75000000)
## 42) symmetry_worst< -2.343297 5 0 B (1.00000000 0.00000000) *
## 43) symmetry_worst>=-2.343297 15 0 M (0.00000000 1.00000000) *
## 11) symmetry_worst>=-1.868413 80 19 M (0.23750000 0.76250000)
## 22) texture_mean< 2.939162 11 0 B (1.00000000 0.00000000) *
## 23) texture_mean>=2.939162 69 8 M (0.11594203 0.88405797)
## 46) texture_mean>=3.083898 18 7 M (0.38888889 0.61111111)
## 92) compactness_se< -3.477558 7 0 B (1.00000000 0.00000000) *
## 93) compactness_se>=-3.477558 11 0 M (0.00000000 1.00000000) *
## 47) texture_mean< 3.083898 51 1 M (0.01960784 0.98039216)
## 94) compactness_se>=-3.433938 1 0 B (1.00000000 0.00000000) *
## 95) compactness_se< -3.433938 50 0 M (0.00000000 1.00000000) *
## 3) smoothness_worst>=-1.482502 327 133 M (0.40672783 0.59327217)
## 6) smoothness_worst>=-1.477976 277 130 M (0.46931408 0.53068592)
## 12) smoothness_worst< -1.472307 40 4 B (0.90000000 0.10000000)
## 24) texture_mean< 3.069079 37 1 B (0.97297297 0.02702703)
## 48) texture_worst< 4.844547 36 0 B (1.00000000 0.00000000) *
## 49) texture_worst>=4.844547 1 0 M (0.00000000 1.00000000) *
## 25) texture_mean>=3.069079 3 0 M (0.00000000 1.00000000) *
## 13) smoothness_worst>=-1.472307 237 94 M (0.39662447 0.60337553)
## 26) smoothness_worst>=-1.465904 204 94 M (0.46078431 0.53921569)
## 52) compactness_se< -4.040144 49 13 B (0.73469388 0.26530612)
## 104) texture_mean< 3.082128 36 5 B (0.86111111 0.13888889) *
## 105) texture_mean>=3.082128 13 5 M (0.38461538 0.61538462) *
## 53) compactness_se>=-4.040144 155 58 M (0.37419355 0.62580645)
## 106) compactness_se>=-3.813086 97 42 B (0.56701031 0.43298969) *
## 107) compactness_se< -3.813086 58 3 M (0.05172414 0.94827586) *
## 27) smoothness_worst< -1.465904 33 0 M (0.00000000 1.00000000) *
## 7) smoothness_worst< -1.477976 50 3 M (0.06000000 0.94000000)
## 14) texture_worst< 4.126187 3 0 B (1.00000000 0.00000000) *
## 15) texture_worst>=4.126187 47 0 M (0.00000000 1.00000000) *
##
## $trees[[71]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 430 M (0.47149123 0.52850877)
## 2) smoothness_mean< -2.201842 830 414 M (0.49879518 0.50120482)
## 4) compactness_se< -3.439055 636 292 B (0.54088050 0.45911950)
## 8) texture_mean< 2.756519 34 6 B (0.82352941 0.17647059)
## 16) smoothness_worst>=-1.569234 26 0 B (1.00000000 0.00000000) *
## 17) smoothness_worst< -1.569234 8 2 M (0.25000000 0.75000000)
## 34) texture_mean>=2.724206 2 0 B (1.00000000 0.00000000) *
## 35) texture_mean< 2.724206 6 0 M (0.00000000 1.00000000) *
## 9) texture_mean>=2.756519 602 286 B (0.52491694 0.47508306)
## 18) texture_mean>=2.770085 579 264 B (0.54404145 0.45595855)
## 36) texture_mean< 2.813911 19 0 B (1.00000000 0.00000000) *
## 37) texture_mean>=2.813911 560 264 B (0.52857143 0.47142857)
## 74) compactness_se< -4.505325 83 25 B (0.69879518 0.30120482) *
## 75) compactness_se>=-4.505325 477 238 M (0.49895178 0.50104822) *
## 19) texture_mean< 2.770085 23 1 M (0.04347826 0.95652174)
## 38) smoothness_mean< -2.443516 1 0 B (1.00000000 0.00000000) *
## 39) smoothness_mean>=-2.443516 22 0 M (0.00000000 1.00000000) *
## 5) compactness_se>=-3.439055 194 70 M (0.36082474 0.63917526)
## 10) texture_mean< 3.071302 138 66 M (0.47826087 0.52173913)
## 20) smoothness_worst< -1.502897 41 4 B (0.90243902 0.09756098)
## 40) compactness_se>=-3.392487 28 0 B (1.00000000 0.00000000) *
## 41) compactness_se< -3.392487 13 4 B (0.69230769 0.30769231)
## 82) compactness_se< -3.404656 9 0 B (1.00000000 0.00000000) *
## 83) compactness_se>=-3.404656 4 0 M (0.00000000 1.00000000) *
## 21) smoothness_worst>=-1.502897 97 29 M (0.29896907 0.70103093)
## 42) symmetry_worst>=-1.471051 38 14 B (0.63157895 0.36842105)
## 84) texture_mean>=2.840763 27 4 B (0.85185185 0.14814815) *
## 85) texture_mean< 2.840763 11 1 M (0.09090909 0.90909091) *
## 43) symmetry_worst< -1.471051 59 5 M (0.08474576 0.91525424)
## 86) texture_worst< 4.217 2 0 B (1.00000000 0.00000000) *
## 87) texture_worst>=4.217 57 3 M (0.05263158 0.94736842) *
## 11) texture_mean>=3.071302 56 4 M (0.07142857 0.92857143)
## 22) smoothness_worst>=-1.427204 5 2 M (0.40000000 0.60000000)
## 44) texture_mean>=3.16708 2 0 B (1.00000000 0.00000000) *
## 45) texture_mean< 3.16708 3 0 M (0.00000000 1.00000000) *
## 23) smoothness_worst< -1.427204 51 2 M (0.03921569 0.96078431)
## 46) compactness_se>=-3.107684 22 2 M (0.09090909 0.90909091)
## 92) compactness_se< -3.05924 2 0 B (1.00000000 0.00000000) *
## 93) compactness_se>=-3.05924 20 0 M (0.00000000 1.00000000) *
## 47) compactness_se< -3.107684 29 0 M (0.00000000 1.00000000) *
## 3) smoothness_mean>=-2.201842 82 16 M (0.19512195 0.80487805)
## 6) symmetry_worst< -1.685481 18 8 B (0.55555556 0.44444444)
## 12) smoothness_mean>=-2.176486 10 0 B (1.00000000 0.00000000) *
## 13) smoothness_mean< -2.176486 8 0 M (0.00000000 1.00000000) *
## 7) symmetry_worst>=-1.685481 64 6 M (0.09375000 0.90625000)
## 14) compactness_se< -4.098715 3 0 B (1.00000000 0.00000000) *
## 15) compactness_se>=-4.098715 61 3 M (0.04918033 0.95081967)
## 30) texture_mean>=3.037093 7 2 M (0.28571429 0.71428571)
## 60) texture_mean< 3.044522 2 0 B (1.00000000 0.00000000) *
## 61) texture_mean>=3.044522 5 0 M (0.00000000 1.00000000) *
## 31) texture_mean< 3.037093 54 1 M (0.01851852 0.98148148)
## 62) smoothness_mean>=-2.000349 3 1 M (0.33333333 0.66666667)
## 124) texture_mean< 2.688296 1 0 B (1.00000000 0.00000000) *
## 125) texture_mean>=2.688296 2 0 M (0.00000000 1.00000000) *
## 63) smoothness_mean< -2.000349 51 0 M (0.00000000 1.00000000) *
##
## $trees[[72]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 432 M (0.47368421 0.52631579)
## 2) compactness_se< -3.488718 643 306 B (0.52410575 0.47589425)
## 4) smoothness_worst< -1.425207 592 265 B (0.55236486 0.44763514)
## 8) compactness_se>=-3.512073 25 0 B (1.00000000 0.00000000) *
## 9) compactness_se< -3.512073 567 265 B (0.53262787 0.46737213)
## 18) symmetry_worst>=-1.559535 129 42 B (0.67441860 0.32558140)
## 36) smoothness_mean>=-2.454281 109 27 B (0.75229358 0.24770642)
## 72) symmetry_worst< -1.423936 84 14 B (0.83333333 0.16666667) *
## 73) symmetry_worst>=-1.423936 25 12 M (0.48000000 0.52000000) *
## 37) smoothness_mean< -2.454281 20 5 M (0.25000000 0.75000000)
## 74) compactness_se>=-4.161775 6 1 B (0.83333333 0.16666667) *
## 75) compactness_se< -4.161775 14 0 M (0.00000000 1.00000000) *
## 19) symmetry_worst< -1.559535 438 215 M (0.49086758 0.50913242)
## 38) texture_mean>=3.33381 31 6 B (0.80645161 0.19354839)
## 76) texture_mean< 3.431382 26 1 B (0.96153846 0.03846154) *
## 77) texture_mean>=3.431382 5 0 M (0.00000000 1.00000000) *
## 39) texture_mean< 3.33381 407 190 M (0.46683047 0.53316953)
## 78) symmetry_worst< -1.787433 255 120 B (0.52941176 0.47058824) *
## 79) symmetry_worst>=-1.787433 152 55 M (0.36184211 0.63815789) *
## 5) smoothness_worst>=-1.425207 51 10 M (0.19607843 0.80392157)
## 10) texture_worst< 4.269167 6 0 B (1.00000000 0.00000000) *
## 11) texture_worst>=4.269167 45 4 M (0.08888889 0.91111111)
## 22) compactness_se< -4.099998 6 2 B (0.66666667 0.33333333)
## 44) texture_mean< 3.075523 4 0 B (1.00000000 0.00000000) *
## 45) texture_mean>=3.075523 2 0 M (0.00000000 1.00000000) *
## 23) compactness_se>=-4.099998 39 0 M (0.00000000 1.00000000) *
## 3) compactness_se>=-3.488718 269 95 M (0.35315985 0.64684015)
## 6) symmetry_worst< -1.656074 146 72 M (0.49315068 0.50684932)
## 12) symmetry_worst>=-1.982549 90 30 B (0.66666667 0.33333333)
## 24) symmetry_worst< -1.839514 39 1 B (0.97435897 0.02564103)
## 48) texture_mean< 3.088324 38 0 B (1.00000000 0.00000000) *
## 49) texture_mean>=3.088324 1 0 M (0.00000000 1.00000000) *
## 25) symmetry_worst>=-1.839514 51 22 M (0.43137255 0.56862745)
## 50) texture_worst< 4.40102 12 0 B (1.00000000 0.00000000) *
## 51) texture_worst>=4.40102 39 10 M (0.25641026 0.74358974)
## 102) smoothness_mean>=-2.120284 6 0 B (1.00000000 0.00000000) *
## 103) smoothness_mean< -2.120284 33 4 M (0.12121212 0.87878788) *
## 13) symmetry_worst< -1.982549 56 12 M (0.21428571 0.78571429)
## 26) smoothness_worst>=-1.477394 7 2 B (0.71428571 0.28571429)
## 52) texture_mean< 3.158131 5 0 B (1.00000000 0.00000000) *
## 53) texture_mean>=3.158131 2 0 M (0.00000000 1.00000000) *
## 27) smoothness_worst< -1.477394 49 7 M (0.14285714 0.85714286)
## 54) compactness_se>=-3.248462 16 7 M (0.43750000 0.56250000)
## 108) texture_mean< 3.076827 7 0 B (1.00000000 0.00000000) *
## 109) texture_mean>=3.076827 9 0 M (0.00000000 1.00000000) *
## 55) compactness_se< -3.248462 33 0 M (0.00000000 1.00000000) *
## 7) symmetry_worst>=-1.656074 123 23 M (0.18699187 0.81300813)
## 14) compactness_se>=-2.470993 5 0 B (1.00000000 0.00000000) *
## 15) compactness_se< -2.470993 118 18 M (0.15254237 0.84745763)
## 30) texture_worst< 3.969009 5 1 B (0.80000000 0.20000000)
## 60) texture_mean>=2.44739 4 0 B (1.00000000 0.00000000) *
## 61) texture_mean< 2.44739 1 0 M (0.00000000 1.00000000) *
## 31) texture_worst>=3.969009 113 14 M (0.12389381 0.87610619)
## 62) smoothness_mean< -2.338805 36 11 M (0.30555556 0.69444444)
## 124) symmetry_worst>=-1.497271 9 0 B (1.00000000 0.00000000) *
## 125) symmetry_worst< -1.497271 27 2 M (0.07407407 0.92592593) *
## 63) smoothness_mean>=-2.338805 77 3 M (0.03896104 0.96103896)
## 126) symmetry_worst< -1.580867 7 2 M (0.28571429 0.71428571) *
## 127) symmetry_worst>=-1.580867 70 1 M (0.01428571 0.98571429) *
##
## $trees[[73]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 452 M (0.49561404 0.50438596)
## 2) compactness_se>=-4.49319 807 383 B (0.52540273 0.47459727)
## 4) smoothness_mean< -2.2971 525 213 B (0.59428571 0.40571429)
## 8) smoothness_mean>=-2.326878 95 15 B (0.84210526 0.15789474)
## 16) smoothness_worst>=-1.560669 84 4 B (0.95238095 0.04761905)
## 32) texture_worst< 5.091364 82 2 B (0.97560976 0.02439024)
## 64) compactness_se>=-4.101376 81 1 B (0.98765432 0.01234568) *
## 65) compactness_se< -4.101376 1 0 M (0.00000000 1.00000000) *
## 33) texture_worst>=5.091364 2 0 M (0.00000000 1.00000000) *
## 17) smoothness_worst< -1.560669 11 0 M (0.00000000 1.00000000) *
## 9) smoothness_mean< -2.326878 430 198 B (0.53953488 0.46046512)
## 18) symmetry_worst< -1.39888 409 178 B (0.56479218 0.43520782)
## 36) symmetry_worst>=-1.750623 162 46 B (0.71604938 0.28395062)
## 72) smoothness_worst>=-1.568787 119 18 B (0.84873950 0.15126050) *
## 73) smoothness_worst< -1.568787 43 15 M (0.34883721 0.65116279) *
## 37) symmetry_worst< -1.750623 247 115 M (0.46558704 0.53441296)
## 74) symmetry_worst< -1.789477 202 92 B (0.54455446 0.45544554) *
## 75) symmetry_worst>=-1.789477 45 5 M (0.11111111 0.88888889) *
## 19) symmetry_worst>=-1.39888 21 1 M (0.04761905 0.95238095)
## 38) texture_worst< 4.479829 1 0 B (1.00000000 0.00000000) *
## 39) texture_worst>=4.479829 20 0 M (0.00000000 1.00000000) *
## 5) smoothness_mean>=-2.2971 282 112 M (0.39716312 0.60283688)
## 10) smoothness_mean>=-2.292637 245 110 M (0.44897959 0.55102041)
## 20) compactness_se< -4.023814 46 11 B (0.76086957 0.23913043)
## 40) smoothness_mean< -2.222419 27 0 B (1.00000000 0.00000000) *
## 41) smoothness_mean>=-2.222419 19 8 M (0.42105263 0.57894737)
## 82) texture_mean< 2.910336 7 0 B (1.00000000 0.00000000) *
## 83) texture_mean>=2.910336 12 1 M (0.08333333 0.91666667) *
## 21) compactness_se>=-4.023814 199 75 M (0.37688442 0.62311558)
## 42) smoothness_worst< -1.482898 51 17 B (0.66666667 0.33333333)
## 84) compactness_se< -3.656611 25 0 B (1.00000000 0.00000000) *
## 85) compactness_se>=-3.656611 26 9 M (0.34615385 0.65384615) *
## 43) smoothness_worst>=-1.482898 148 41 M (0.27702703 0.72297297)
## 86) texture_worst< 4.398698 57 28 B (0.50877193 0.49122807) *
## 87) texture_worst>=4.398698 91 12 M (0.13186813 0.86813187) *
## 11) smoothness_mean< -2.292637 37 2 M (0.05405405 0.94594595)
## 22) texture_mean< 2.753964 1 0 B (1.00000000 0.00000000) *
## 23) texture_mean>=2.753964 36 1 M (0.02777778 0.97222222)
## 46) compactness_se< -4.216002 1 0 B (1.00000000 0.00000000) *
## 47) compactness_se>=-4.216002 35 0 M (0.00000000 1.00000000) *
## 3) compactness_se< -4.49319 105 28 M (0.26666667 0.73333333)
## 6) smoothness_worst>=-1.547264 30 10 B (0.66666667 0.33333333)
## 12) symmetry_worst>=-1.696111 18 0 B (1.00000000 0.00000000) *
## 13) symmetry_worst< -1.696111 12 2 M (0.16666667 0.83333333)
## 26) compactness_se< -4.631213 2 0 B (1.00000000 0.00000000) *
## 27) compactness_se>=-4.631213 10 0 M (0.00000000 1.00000000) *
## 7) smoothness_worst< -1.547264 75 8 M (0.10666667 0.89333333)
## 14) compactness_se< -4.727869 4 1 B (0.75000000 0.25000000)
## 28) smoothness_worst< -1.634 3 0 B (1.00000000 0.00000000) *
## 29) smoothness_worst>=-1.634 1 0 M (0.00000000 1.00000000) *
## 15) compactness_se>=-4.727869 71 5 M (0.07042254 0.92957746)
## 30) symmetry_worst>=-1.459568 1 0 B (1.00000000 0.00000000) *
## 31) symmetry_worst< -1.459568 70 4 M (0.05714286 0.94285714)
## 62) texture_worst>=4.660194 31 4 M (0.12903226 0.87096774)
## 124) texture_worst< 4.812659 4 0 B (1.00000000 0.00000000) *
## 125) texture_worst>=4.812659 27 0 M (0.00000000 1.00000000) *
## 63) texture_worst< 4.660194 39 0 M (0.00000000 1.00000000) *
##
## $trees[[74]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 425 M (0.46600877 0.53399123)
## 2) symmetry_worst>=-1.9261 650 314 B (0.51692308 0.48307692)
## 4) compactness_se< -3.690481 374 146 B (0.60962567 0.39037433)
## 8) smoothness_worst< -1.451541 283 92 B (0.67491166 0.32508834)
## 16) compactness_se>=-4.160164 144 18 B (0.87500000 0.12500000)
## 32) symmetry_worst>=-1.767566 92 3 B (0.96739130 0.03260870)
## 64) symmetry_worst< -1.201763 91 2 B (0.97802198 0.02197802) *
## 65) symmetry_worst>=-1.201763 1 0 M (0.00000000 1.00000000) *
## 33) symmetry_worst< -1.767566 52 15 B (0.71153846 0.28846154)
## 66) symmetry_worst< -1.786753 39 2 B (0.94871795 0.05128205) *
## 67) symmetry_worst>=-1.786753 13 0 M (0.00000000 1.00000000) *
## 17) compactness_se< -4.160164 139 65 M (0.46762590 0.53237410)
## 34) compactness_se< -4.260936 110 48 B (0.56363636 0.43636364)
## 68) smoothness_mean>=-2.454281 54 10 B (0.81481481 0.18518519) *
## 69) smoothness_mean< -2.454281 56 18 M (0.32142857 0.67857143) *
## 35) compactness_se>=-4.260936 29 3 M (0.10344828 0.89655172)
## 70) symmetry_worst< -1.580909 11 3 M (0.27272727 0.72727273) *
## 71) symmetry_worst>=-1.580909 18 0 M (0.00000000 1.00000000) *
## 9) smoothness_worst>=-1.451541 91 37 M (0.40659341 0.59340659)
## 18) compactness_se< -4.040144 41 9 B (0.78048780 0.21951220)
## 36) smoothness_mean< -2.222419 34 3 B (0.91176471 0.08823529)
## 72) smoothness_worst< -1.426496 29 0 B (1.00000000 0.00000000) *
## 73) smoothness_worst>=-1.426496 5 2 M (0.40000000 0.60000000) *
## 37) smoothness_mean>=-2.222419 7 1 M (0.14285714 0.85714286)
## 74) texture_mean< 2.88089 1 0 B (1.00000000 0.00000000) *
## 75) texture_mean>=2.88089 6 0 M (0.00000000 1.00000000) *
## 19) compactness_se>=-4.040144 50 5 M (0.10000000 0.90000000)
## 38) smoothness_mean>=-2.166752 12 4 M (0.33333333 0.66666667)
## 76) smoothness_mean< -2.111645 3 0 B (1.00000000 0.00000000) *
## 77) smoothness_mean>=-2.111645 9 1 M (0.11111111 0.88888889) *
## 39) smoothness_mean< -2.166752 38 1 M (0.02631579 0.97368421)
## 78) symmetry_worst< -1.895488 1 0 B (1.00000000 0.00000000) *
## 79) symmetry_worst>=-1.895488 37 0 M (0.00000000 1.00000000) *
## 5) compactness_se>=-3.690481 276 108 M (0.39130435 0.60869565)
## 10) compactness_se>=-3.586422 228 106 M (0.46491228 0.53508772)
## 20) symmetry_worst< -1.656986 74 23 B (0.68918919 0.31081081)
## 40) smoothness_mean< -2.389667 24 1 B (0.95833333 0.04166667)
## 80) texture_mean< 3.09132 23 0 B (1.00000000 0.00000000) *
## 81) texture_mean>=3.09132 1 0 M (0.00000000 1.00000000) *
## 41) smoothness_mean>=-2.389667 50 22 B (0.56000000 0.44000000)
## 82) compactness_se>=-3.355844 33 8 B (0.75757576 0.24242424) *
## 83) compactness_se< -3.355844 17 3 M (0.17647059 0.82352941) *
## 21) symmetry_worst>=-1.656986 154 55 M (0.35714286 0.64285714)
## 42) smoothness_mean< -2.219632 121 54 M (0.44628099 0.55371901)
## 84) symmetry_worst>=-1.629511 99 45 B (0.54545455 0.45454545) *
## 85) symmetry_worst< -1.629511 22 0 M (0.00000000 1.00000000) *
## 43) smoothness_mean>=-2.219632 33 1 M (0.03030303 0.96969697)
## 86) smoothness_mean>=-1.889548 1 0 B (1.00000000 0.00000000) *
## 87) smoothness_mean< -1.889548 32 0 M (0.00000000 1.00000000) *
## 11) compactness_se< -3.586422 48 2 M (0.04166667 0.95833333)
## 22) smoothness_mean< -2.509667 1 0 B (1.00000000 0.00000000) *
## 23) smoothness_mean>=-2.509667 47 1 M (0.02127660 0.97872340)
## 46) compactness_se< -3.681134 3 1 M (0.33333333 0.66666667)
## 92) texture_mean< 2.986001 1 0 B (1.00000000 0.00000000) *
## 93) texture_mean>=2.986001 2 0 M (0.00000000 1.00000000) *
## 47) compactness_se>=-3.681134 44 0 M (0.00000000 1.00000000) *
## 3) symmetry_worst< -1.9261 262 89 M (0.33969466 0.66030534)
## 6) smoothness_worst>=-1.477976 30 8 B (0.73333333 0.26666667)
## 12) texture_worst< 4.851322 22 0 B (1.00000000 0.00000000) *
## 13) texture_worst>=4.851322 8 0 M (0.00000000 1.00000000) *
## 7) smoothness_worst< -1.477976 232 67 M (0.28879310 0.71120690)
## 14) texture_mean>=3.336125 13 2 B (0.84615385 0.15384615)
## 28) smoothness_mean< -2.380359 11 0 B (1.00000000 0.00000000) *
## 29) smoothness_mean>=-2.380359 2 0 M (0.00000000 1.00000000) *
## 15) texture_mean< 3.336125 219 56 M (0.25570776 0.74429224)
## 30) compactness_se< -4.614925 6 0 B (1.00000000 0.00000000) *
## 31) compactness_se>=-4.614925 213 50 M (0.23474178 0.76525822)
## 62) symmetry_worst< -2.202388 63 27 M (0.42857143 0.57142857)
## 124) smoothness_worst>=-1.553723 26 4 B (0.84615385 0.15384615) *
## 125) smoothness_worst< -1.553723 37 5 M (0.13513514 0.86486486) *
## 63) symmetry_worst>=-2.202388 150 23 M (0.15333333 0.84666667)
## 126) texture_mean< 2.986405 61 20 M (0.32786885 0.67213115) *
## 127) texture_mean>=2.986405 89 3 M (0.03370787 0.96629213) *
##
## $trees[[75]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 434 M (0.47587719 0.52412281)
## 2) compactness_se< -3.987083 335 134 B (0.60000000 0.40000000)
## 4) smoothness_mean>=-2.291157 69 8 B (0.88405797 0.11594203)
## 8) symmetry_worst< -1.449852 65 4 B (0.93846154 0.06153846)
## 16) texture_worst< 5.040422 63 2 B (0.96825397 0.03174603)
## 32) symmetry_worst>=-1.743442 50 0 B (1.00000000 0.00000000) *
## 33) symmetry_worst< -1.743442 13 2 B (0.84615385 0.15384615)
## 66) symmetry_worst< -1.765932 11 0 B (1.00000000 0.00000000) *
## 67) symmetry_worst>=-1.765932 2 0 M (0.00000000 1.00000000) *
## 17) texture_worst>=5.040422 2 0 M (0.00000000 1.00000000) *
## 9) symmetry_worst>=-1.449852 4 0 M (0.00000000 1.00000000) *
## 5) smoothness_mean< -2.291157 266 126 B (0.52631579 0.47368421)
## 10) texture_mean< 2.947329 109 31 B (0.71559633 0.28440367)
## 20) smoothness_mean< -2.329495 77 14 B (0.81818182 0.18181818)
## 40) smoothness_worst< -1.555669 29 0 B (1.00000000 0.00000000) *
## 41) smoothness_worst>=-1.555669 48 14 B (0.70833333 0.29166667)
## 82) smoothness_worst>=-1.541278 35 1 B (0.97142857 0.02857143) *
## 83) smoothness_worst< -1.541278 13 0 M (0.00000000 1.00000000) *
## 21) smoothness_mean>=-2.329495 32 15 M (0.46875000 0.53125000)
## 42) texture_mean< 2.894246 19 4 B (0.78947368 0.21052632)
## 84) smoothness_mean< -2.295113 15 0 B (1.00000000 0.00000000) *
## 85) smoothness_mean>=-2.295113 4 0 M (0.00000000 1.00000000) *
## 43) texture_mean>=2.894246 13 0 M (0.00000000 1.00000000) *
## 11) texture_mean>=2.947329 157 62 M (0.39490446 0.60509554)
## 22) texture_mean>=3.227241 20 2 B (0.90000000 0.10000000)
## 44) texture_mean< 3.409933 18 0 B (1.00000000 0.00000000) *
## 45) texture_mean>=3.409933 2 0 M (0.00000000 1.00000000) *
## 23) texture_mean< 3.227241 137 44 M (0.32116788 0.67883212)
## 46) texture_mean< 3.107047 100 44 M (0.44000000 0.56000000)
## 92) smoothness_mean< -2.422683 45 15 B (0.66666667 0.33333333) *
## 93) smoothness_mean>=-2.422683 55 14 M (0.25454545 0.74545455) *
## 47) texture_mean>=3.107047 37 0 M (0.00000000 1.00000000) *
## 3) compactness_se>=-3.987083 577 233 M (0.40381282 0.59618718)
## 6) smoothness_worst< -1.499656 283 136 B (0.51943463 0.48056537)
## 12) texture_mean< 3.006671 103 25 B (0.75728155 0.24271845)
## 24) compactness_se>=-3.93685 98 20 B (0.79591837 0.20408163)
## 48) smoothness_worst>=-1.568787 59 2 B (0.96610169 0.03389831)
## 96) smoothness_mean< -2.176018 57 0 B (1.00000000 0.00000000) *
## 97) smoothness_mean>=-2.176018 2 0 M (0.00000000 1.00000000) *
## 49) smoothness_worst< -1.568787 39 18 B (0.53846154 0.46153846)
## 98) texture_worst< 4.56463 30 9 B (0.70000000 0.30000000) *
## 99) texture_worst>=4.56463 9 0 M (0.00000000 1.00000000) *
## 25) compactness_se< -3.93685 5 0 M (0.00000000 1.00000000) *
## 13) texture_mean>=3.006671 180 69 M (0.38333333 0.61666667)
## 26) smoothness_worst>=-1.553723 82 37 B (0.54878049 0.45121951)
## 52) compactness_se< -3.616009 27 3 B (0.88888889 0.11111111)
## 104) texture_mean>=3.059872 24 0 B (1.00000000 0.00000000) *
## 105) texture_mean< 3.059872 3 0 M (0.00000000 1.00000000) *
## 53) compactness_se>=-3.616009 55 21 M (0.38181818 0.61818182)
## 106) texture_worst< 4.566107 11 0 B (1.00000000 0.00000000) *
## 107) texture_worst>=4.566107 44 10 M (0.22727273 0.77272727) *
## 27) smoothness_worst< -1.553723 98 24 M (0.24489796 0.75510204)
## 54) symmetry_worst>=-2.04723 45 19 M (0.42222222 0.57777778)
## 108) symmetry_worst< -1.550826 27 8 B (0.70370370 0.29629630) *
## 109) symmetry_worst>=-1.550826 18 0 M (0.00000000 1.00000000) *
## 55) symmetry_worst< -2.04723 53 5 M (0.09433962 0.90566038)
## 110) compactness_se>=-3.179583 19 5 M (0.26315789 0.73684211) *
## 111) compactness_se< -3.179583 34 0 M (0.00000000 1.00000000) *
## 7) smoothness_worst>=-1.499656 294 86 M (0.29251701 0.70748299)
## 14) symmetry_worst< -1.352813 244 82 M (0.33606557 0.66393443)
## 28) symmetry_worst>=-1.471051 17 4 B (0.76470588 0.23529412)
## 56) texture_mean< 2.794024 10 0 B (1.00000000 0.00000000) *
## 57) texture_mean>=2.794024 7 3 M (0.42857143 0.57142857)
## 114) texture_mean>=2.887911 3 0 B (1.00000000 0.00000000) *
## 115) texture_mean< 2.887911 4 0 M (0.00000000 1.00000000) *
## 29) symmetry_worst< -1.471051 227 69 M (0.30396476 0.69603524)
## 58) symmetry_worst< -1.641484 175 64 M (0.36571429 0.63428571)
## 116) smoothness_mean>=-2.14559 15 0 B (1.00000000 0.00000000) *
## 117) smoothness_mean< -2.14559 160 49 M (0.30625000 0.69375000) *
## 59) symmetry_worst>=-1.641484 52 5 M (0.09615385 0.90384615)
## 118) texture_mean>=3.04903 1 0 B (1.00000000 0.00000000) *
## 119) texture_mean< 3.04903 51 4 M (0.07843137 0.92156863) *
## 15) symmetry_worst>=-1.352813 50 4 M (0.08000000 0.92000000)
## 30) smoothness_mean< -2.365259 2 0 B (1.00000000 0.00000000) *
## 31) smoothness_mean>=-2.365259 48 2 M (0.04166667 0.95833333)
## 62) compactness_se>=-2.588521 2 0 B (1.00000000 0.00000000) *
## 63) compactness_se< -2.588521 46 0 M (0.00000000 1.00000000) *
##
## $trees[[76]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 455 M (0.49890351 0.50109649)
## 2) compactness_se< -3.643388 525 222 B (0.57714286 0.42285714)
## 4) compactness_se>=-3.867535 115 25 B (0.78260870 0.21739130)
## 8) smoothness_worst>=-1.574151 105 16 B (0.84761905 0.15238095)
## 16) symmetry_worst< -1.3705 98 10 B (0.89795918 0.10204082)
## 32) smoothness_worst< -1.417195 91 5 B (0.94505495 0.05494505)
## 64) texture_mean>=2.680808 90 4 B (0.95555556 0.04444444) *
## 65) texture_mean< 2.680808 1 0 M (0.00000000 1.00000000) *
## 33) smoothness_worst>=-1.417195 7 2 M (0.28571429 0.71428571)
## 66) texture_mean< 2.89891 2 0 B (1.00000000 0.00000000) *
## 67) texture_mean>=2.89891 5 0 M (0.00000000 1.00000000) *
## 17) symmetry_worst>=-1.3705 7 1 M (0.14285714 0.85714286)
## 34) texture_mean< 2.76528 1 0 B (1.00000000 0.00000000) *
## 35) texture_mean>=2.76528 6 0 M (0.00000000 1.00000000) *
## 9) smoothness_worst< -1.574151 10 1 M (0.10000000 0.90000000)
## 18) texture_mean< 2.850088 1 0 B (1.00000000 0.00000000) *
## 19) texture_mean>=2.850088 9 0 M (0.00000000 1.00000000) *
## 5) compactness_se< -3.867535 410 197 B (0.51951220 0.48048780)
## 10) compactness_se< -3.885633 387 174 B (0.55038760 0.44961240)
## 20) texture_mean< 2.947329 159 49 B (0.69182390 0.30817610)
## 40) compactness_se>=-4.334002 105 20 B (0.80952381 0.19047619)
## 80) smoothness_worst< -1.451541 85 9 B (0.89411765 0.10588235) *
## 81) smoothness_worst>=-1.451541 20 9 M (0.45000000 0.55000000) *
## 41) compactness_se< -4.334002 54 25 M (0.46296296 0.53703704)
## 82) texture_worst< 4.35485 15 0 B (1.00000000 0.00000000) *
## 83) texture_worst>=4.35485 39 10 M (0.25641026 0.74358974) *
## 21) texture_mean>=2.947329 228 103 M (0.45175439 0.54824561)
## 42) symmetry_worst< -1.724518 152 71 B (0.53289474 0.46710526)
## 84) smoothness_worst< -1.576561 58 11 B (0.81034483 0.18965517) *
## 85) smoothness_worst>=-1.576561 94 34 M (0.36170213 0.63829787) *
## 43) symmetry_worst>=-1.724518 76 22 M (0.28947368 0.71052632)
## 86) symmetry_worst>=-1.490299 17 4 B (0.76470588 0.23529412) *
## 87) symmetry_worst< -1.490299 59 9 M (0.15254237 0.84745763) *
## 11) compactness_se>=-3.885633 23 0 M (0.00000000 1.00000000) *
## 3) compactness_se>=-3.643388 387 152 M (0.39276486 0.60723514)
## 6) symmetry_worst< -1.65431 225 112 B (0.50222222 0.49777778)
## 12) compactness_se>=-3.483667 135 48 B (0.64444444 0.35555556)
## 24) texture_mean< 3.135612 111 30 B (0.72972973 0.27027027)
## 48) smoothness_mean>=-2.563309 104 23 B (0.77884615 0.22115385)
## 96) smoothness_mean< -2.385259 37 0 B (1.00000000 0.00000000) *
## 97) smoothness_mean>=-2.385259 67 23 B (0.65671642 0.34328358) *
## 49) smoothness_mean< -2.563309 7 0 M (0.00000000 1.00000000) *
## 25) texture_mean>=3.135612 24 6 M (0.25000000 0.75000000)
## 50) smoothness_worst>=-1.43594 6 0 B (1.00000000 0.00000000) *
## 51) smoothness_worst< -1.43594 18 0 M (0.00000000 1.00000000) *
## 13) compactness_se< -3.483667 90 26 M (0.28888889 0.71111111)
## 26) texture_mean>=3.138519 18 5 B (0.72222222 0.27777778)
## 52) texture_mean< 3.399247 13 0 B (1.00000000 0.00000000) *
## 53) texture_mean>=3.399247 5 0 M (0.00000000 1.00000000) *
## 27) texture_mean< 3.138519 72 13 M (0.18055556 0.81944444)
## 54) smoothness_mean< -2.380923 23 10 M (0.43478261 0.56521739)
## 108) smoothness_mean>=-2.436819 10 0 B (1.00000000 0.00000000) *
## 109) smoothness_mean< -2.436819 13 0 M (0.00000000 1.00000000) *
## 55) smoothness_mean>=-2.380923 49 3 M (0.06122449 0.93877551)
## 110) smoothness_worst>=-1.459744 2 0 B (1.00000000 0.00000000) *
## 111) smoothness_worst< -1.459744 47 1 M (0.02127660 0.97872340) *
## 7) symmetry_worst>=-1.65431 162 39 M (0.24074074 0.75925926)
## 14) compactness_se< -3.484318 29 11 B (0.62068966 0.37931034)
## 28) compactness_se>=-3.502612 17 0 B (1.00000000 0.00000000) *
## 29) compactness_se< -3.502612 12 1 M (0.08333333 0.91666667)
## 58) texture_mean>=3.08111 1 0 B (1.00000000 0.00000000) *
## 59) texture_mean< 3.08111 11 0 M (0.00000000 1.00000000) *
## 15) compactness_se>=-3.484318 133 21 M (0.15789474 0.84210526)
## 30) compactness_se>=-2.659237 7 2 B (0.71428571 0.28571429)
## 60) texture_mean< 3.031641 5 0 B (1.00000000 0.00000000) *
## 61) texture_mean>=3.031641 2 0 M (0.00000000 1.00000000) *
## 31) compactness_se< -2.659237 126 16 M (0.12698413 0.87301587)
## 62) smoothness_worst>=-1.533662 90 16 M (0.17777778 0.82222222)
## 124) smoothness_worst< -1.482108 39 14 M (0.35897436 0.64102564) *
## 125) smoothness_worst>=-1.482108 51 2 M (0.03921569 0.96078431) *
## 63) smoothness_worst< -1.533662 36 0 M (0.00000000 1.00000000) *
##
## $trees[[77]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 450 B (0.50657895 0.49342105)
## 2) compactness_se< -3.672219 561 230 B (0.59001783 0.40998217)
## 4) smoothness_worst>=-1.6166 507 187 B (0.63116371 0.36883629)
## 8) smoothness_mean< -2.468758 46 2 B (0.95652174 0.04347826)
## 16) texture_mean< 3.388429 44 0 B (1.00000000 0.00000000) *
## 17) texture_mean>=3.388429 2 0 M (0.00000000 1.00000000) *
## 9) smoothness_mean>=-2.468758 461 185 B (0.59869848 0.40130152)
## 18) smoothness_mean>=-2.382983 284 90 B (0.68309859 0.31690141)
## 36) symmetry_worst< -1.293329 270 77 B (0.71481481 0.28518519)
## 72) symmetry_worst< -1.786753 114 20 B (0.82456140 0.17543860) *
## 73) symmetry_worst>=-1.786753 156 57 B (0.63461538 0.36538462) *
## 37) symmetry_worst>=-1.293329 14 1 M (0.07142857 0.92857143)
## 74) texture_mean< 2.824658 1 0 B (1.00000000 0.00000000) *
## 75) texture_mean>=2.824658 13 0 M (0.00000000 1.00000000) *
## 19) smoothness_mean< -2.382983 177 82 M (0.46327684 0.53672316)
## 38) smoothness_mean< -2.394871 137 57 B (0.58394161 0.41605839)
## 76) texture_worst>=4.465917 111 35 B (0.68468468 0.31531532) *
## 77) texture_worst< 4.465917 26 4 M (0.15384615 0.84615385) *
## 39) smoothness_mean>=-2.394871 40 2 M (0.05000000 0.95000000)
## 78) texture_mean< 2.909709 2 0 B (1.00000000 0.00000000) *
## 79) texture_mean>=2.909709 38 0 M (0.00000000 1.00000000) *
## 5) smoothness_worst< -1.6166 54 11 M (0.20370370 0.79629630)
## 10) smoothness_mean< -2.572721 6 0 B (1.00000000 0.00000000) *
## 11) smoothness_mean>=-2.572721 48 5 M (0.10416667 0.89583333)
## 22) texture_mean< 2.936117 2 0 B (1.00000000 0.00000000) *
## 23) texture_mean>=2.936117 46 3 M (0.06521739 0.93478261)
## 46) texture_mean>=3.23119 2 0 B (1.00000000 0.00000000) *
## 47) texture_mean< 3.23119 44 1 M (0.02272727 0.97727273)
## 94) compactness_se< -4.803674 1 0 B (1.00000000 0.00000000) *
## 95) compactness_se>=-4.803674 43 0 M (0.00000000 1.00000000) *
## 3) compactness_se>=-3.672219 351 131 M (0.37321937 0.62678063)
## 6) smoothness_mean< -2.413575 75 27 B (0.64000000 0.36000000)
## 12) compactness_se>=-3.125122 40 6 B (0.85000000 0.15000000)
## 24) smoothness_worst>=-1.708845 33 0 B (1.00000000 0.00000000) *
## 25) smoothness_worst< -1.708845 7 1 M (0.14285714 0.85714286)
## 50) texture_mean>=3.103494 1 0 B (1.00000000 0.00000000) *
## 51) texture_mean< 3.103494 6 0 M (0.00000000 1.00000000) *
## 13) compactness_se< -3.125122 35 14 M (0.40000000 0.60000000)
## 26) texture_mean< 3.049127 20 7 B (0.65000000 0.35000000)
## 52) texture_worst>=3.981964 14 2 B (0.85714286 0.14285714)
## 104) symmetry_worst< -1.851403 11 0 B (1.00000000 0.00000000) *
## 105) symmetry_worst>=-1.851403 3 1 M (0.33333333 0.66666667) *
## 53) texture_worst< 3.981964 6 1 M (0.16666667 0.83333333)
## 106) texture_mean< 2.754513 1 0 B (1.00000000 0.00000000) *
## 107) texture_mean>=2.754513 5 0 M (0.00000000 1.00000000) *
## 27) texture_mean>=3.049127 15 1 M (0.06666667 0.93333333)
## 54) texture_mean>=3.337721 2 1 B (0.50000000 0.50000000)
## 108) texture_mean< 3.410351 1 0 B (1.00000000 0.00000000) *
## 109) texture_mean>=3.410351 1 0 M (0.00000000 1.00000000) *
## 55) texture_mean< 3.337721 13 0 M (0.00000000 1.00000000) *
## 7) smoothness_mean>=-2.413575 276 83 M (0.30072464 0.69927536)
## 14) texture_worst< 3.832298 7 0 B (1.00000000 0.00000000) *
## 15) texture_worst>=3.832298 269 76 M (0.28252788 0.71747212)
## 30) symmetry_worst< -2.156952 15 5 B (0.66666667 0.33333333)
## 60) smoothness_mean< -2.244441 10 0 B (1.00000000 0.00000000) *
## 61) smoothness_mean>=-2.244441 5 0 M (0.00000000 1.00000000) *
## 31) symmetry_worst>=-2.156952 254 66 M (0.25984252 0.74015748)
## 62) texture_worst< 4.55941 139 49 M (0.35251799 0.64748201)
## 124) smoothness_worst< -1.503711 53 20 B (0.62264151 0.37735849) *
## 125) smoothness_worst>=-1.503711 86 16 M (0.18604651 0.81395349) *
## 63) texture_worst>=4.55941 115 17 M (0.14782609 0.85217391)
## 126) smoothness_mean>=-2.093138 4 0 B (1.00000000 0.00000000) *
## 127) smoothness_mean< -2.093138 111 13 M (0.11711712 0.88288288) *
##
## $trees[[78]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 446 M (0.48903509 0.51096491)
## 2) smoothness_mean< -2.413908 240 93 B (0.61250000 0.38750000)
## 4) smoothness_worst>=-1.548762 84 11 B (0.86904762 0.13095238)
## 8) symmetry_worst>=-1.996006 77 5 B (0.93506494 0.06493506)
## 16) texture_mean< 3.359301 75 3 B (0.96000000 0.04000000)
## 32) symmetry_worst< -1.429489 73 1 B (0.98630137 0.01369863)
## 64) texture_worst< 5.003123 70 0 B (1.00000000 0.00000000) *
## 65) texture_worst>=5.003123 3 1 B (0.66666667 0.33333333) *
## 33) symmetry_worst>=-1.429489 2 0 M (0.00000000 1.00000000) *
## 17) texture_mean>=3.359301 2 0 M (0.00000000 1.00000000) *
## 9) symmetry_worst< -1.996006 7 1 M (0.14285714 0.85714286)
## 18) texture_mean< 3.047283 1 0 B (1.00000000 0.00000000) *
## 19) texture_mean>=3.047283 6 0 M (0.00000000 1.00000000) *
## 5) smoothness_worst< -1.548762 156 74 M (0.47435897 0.52564103)
## 10) texture_mean< 2.921008 43 13 B (0.69767442 0.30232558)
## 20) texture_mean>=2.881715 20 0 B (1.00000000 0.00000000) *
## 21) texture_mean< 2.881715 23 10 M (0.43478261 0.56521739)
## 42) smoothness_mean< -2.469112 7 0 B (1.00000000 0.00000000) *
## 43) smoothness_mean>=-2.469112 16 3 M (0.18750000 0.81250000)
## 86) compactness_se< -4.514299 3 0 B (1.00000000 0.00000000) *
## 87) compactness_se>=-4.514299 13 0 M (0.00000000 1.00000000) *
## 11) texture_mean>=2.921008 113 44 M (0.38938053 0.61061947)
## 22) smoothness_worst< -1.623453 28 9 B (0.67857143 0.32142857)
## 44) texture_worst>=4.576562 17 1 B (0.94117647 0.05882353)
## 88) symmetry_worst< -1.18694 16 0 B (1.00000000 0.00000000) *
## 89) symmetry_worst>=-1.18694 1 0 M (0.00000000 1.00000000) *
## 45) texture_worst< 4.576562 11 3 M (0.27272727 0.72727273)
## 90) texture_worst< 4.457426 4 1 B (0.75000000 0.25000000) *
## 91) texture_worst>=4.457426 7 0 M (0.00000000 1.00000000) *
## 23) smoothness_worst>=-1.623453 85 25 M (0.29411765 0.70588235)
## 46) texture_mean>=3.337367 7 1 B (0.85714286 0.14285714)
## 92) texture_mean< 3.388429 6 0 B (1.00000000 0.00000000) *
## 93) texture_mean>=3.388429 1 0 M (0.00000000 1.00000000) *
## 47) texture_mean< 3.337367 78 19 M (0.24358974 0.75641026)
## 94) texture_worst< 4.828203 40 17 M (0.42500000 0.57500000) *
## 95) texture_worst>=4.828203 38 2 M (0.05263158 0.94736842) *
## 3) smoothness_mean>=-2.413908 672 299 M (0.44494048 0.55505952)
## 6) smoothness_mean>=-2.354774 460 222 B (0.51739130 0.48260870)
## 12) compactness_se< -3.952856 130 34 B (0.73846154 0.26153846)
## 24) symmetry_worst< -1.33108 124 28 B (0.77419355 0.22580645)
## 48) smoothness_mean< -2.329495 28 0 B (1.00000000 0.00000000) *
## 49) smoothness_mean>=-2.329495 96 28 B (0.70833333 0.29166667)
## 98) smoothness_mean>=-2.294121 70 10 B (0.85714286 0.14285714) *
## 99) smoothness_mean< -2.294121 26 8 M (0.30769231 0.69230769) *
## 25) symmetry_worst>=-1.33108 6 0 M (0.00000000 1.00000000) *
## 13) compactness_se>=-3.952856 330 142 M (0.43030303 0.56969697)
## 26) compactness_se>=-3.904303 291 141 M (0.48453608 0.51546392)
## 52) texture_mean>=3.039742 91 27 B (0.70329670 0.29670330)
## 104) compactness_se< -3.352836 78 14 B (0.82051282 0.17948718) *
## 105) compactness_se>=-3.352836 13 0 M (0.00000000 1.00000000) *
## 53) texture_mean< 3.039742 200 77 M (0.38500000 0.61500000)
## 106) smoothness_mean>=-2.262404 107 48 B (0.55140187 0.44859813) *
## 107) smoothness_mean< -2.262404 93 18 M (0.19354839 0.80645161) *
## 27) compactness_se< -3.904303 39 1 M (0.02564103 0.97435897)
## 54) texture_mean< 2.858451 1 0 B (1.00000000 0.00000000) *
## 55) texture_mean>=2.858451 38 0 M (0.00000000 1.00000000) *
## 7) smoothness_mean< -2.354774 212 61 M (0.28773585 0.71226415)
## 14) texture_mean< 2.974761 70 28 B (0.60000000 0.40000000)
## 28) smoothness_worst< -1.549191 22 0 B (1.00000000 0.00000000) *
## 29) smoothness_worst>=-1.549191 48 20 M (0.41666667 0.58333333)
## 58) symmetry_worst>=-1.582804 11 0 B (1.00000000 0.00000000) *
## 59) symmetry_worst< -1.582804 37 9 M (0.24324324 0.75675676)
## 118) texture_worst< 4.228934 4 0 B (1.00000000 0.00000000) *
## 119) texture_worst>=4.228934 33 5 M (0.15151515 0.84848485) *
## 15) texture_mean>=2.974761 142 19 M (0.13380282 0.86619718)
## 30) texture_mean>=3.351321 13 2 B (0.84615385 0.15384615)
## 60) texture_mean< 3.431382 11 0 B (1.00000000 0.00000000) *
## 61) texture_mean>=3.431382 2 0 M (0.00000000 1.00000000) *
## 31) texture_mean< 3.351321 129 8 M (0.06201550 0.93798450)
## 62) symmetry_worst< -2.204211 7 3 B (0.57142857 0.42857143)
## 124) smoothness_mean< -2.372601 4 0 B (1.00000000 0.00000000) *
## 125) smoothness_mean>=-2.372601 3 0 M (0.00000000 1.00000000) *
## 63) symmetry_worst>=-2.204211 122 4 M (0.03278689 0.96721311)
## 126) smoothness_worst>=-1.421107 2 0 B (1.00000000 0.00000000) *
## 127) smoothness_worst< -1.421107 120 2 M (0.01666667 0.98333333) *
##
## $trees[[79]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 423 M (0.46381579 0.53618421)
## 2) smoothness_mean< -2.408446 266 89 B (0.66541353 0.33458647)
## 4) texture_mean< 2.959066 95 17 B (0.82105263 0.17894737)
## 8) compactness_se< -3.734437 72 6 B (0.91666667 0.08333333)
## 16) smoothness_worst< -1.555886 32 0 B (1.00000000 0.00000000) *
## 17) smoothness_worst>=-1.555886 40 6 B (0.85000000 0.15000000)
## 34) smoothness_worst>=-1.551775 35 1 B (0.97142857 0.02857143)
## 68) texture_mean>=2.837818 32 0 B (1.00000000 0.00000000) *
## 69) texture_mean< 2.837818 3 1 B (0.66666667 0.33333333) *
## 35) smoothness_worst< -1.551775 5 0 M (0.00000000 1.00000000) *
## 9) compactness_se>=-3.734437 23 11 B (0.52173913 0.47826087)
## 18) compactness_se>=-3.483667 11 0 B (1.00000000 0.00000000) *
## 19) compactness_se< -3.483667 12 1 M (0.08333333 0.91666667)
## 38) texture_mean< 2.680923 1 0 B (1.00000000 0.00000000) *
## 39) texture_mean>=2.680923 11 0 M (0.00000000 1.00000000) *
## 5) texture_mean>=2.959066 171 72 B (0.57894737 0.42105263)
## 10) symmetry_worst< -1.541072 158 59 B (0.62658228 0.37341772)
## 20) texture_worst>=4.498003 143 45 B (0.68531469 0.31468531)
## 40) symmetry_worst>=-2.218277 132 36 B (0.72727273 0.27272727)
## 80) compactness_se>=-4.658767 115 24 B (0.79130435 0.20869565) *
## 81) compactness_se< -4.658767 17 5 M (0.29411765 0.70588235) *
## 41) symmetry_worst< -2.218277 11 2 M (0.18181818 0.81818182)
## 82) smoothness_mean< -2.490273 2 0 B (1.00000000 0.00000000) *
## 83) smoothness_mean>=-2.490273 9 0 M (0.00000000 1.00000000) *
## 21) texture_worst< 4.498003 15 1 M (0.06666667 0.93333333)
## 42) compactness_se>=-2.715861 1 0 B (1.00000000 0.00000000) *
## 43) compactness_se< -2.715861 14 0 M (0.00000000 1.00000000) *
## 11) symmetry_worst>=-1.541072 13 0 M (0.00000000 1.00000000) *
## 3) smoothness_mean>=-2.408446 646 246 M (0.38080495 0.61919505)
## 6) compactness_se< -3.063476 566 237 M (0.41872792 0.58127208)
## 12) smoothness_mean>=-2.354774 419 201 M (0.47971360 0.52028640)
## 24) smoothness_mean< -2.344241 30 0 B (1.00000000 0.00000000) *
## 25) smoothness_mean>=-2.344241 389 171 M (0.43958869 0.56041131)
## 50) texture_worst< 4.895983 318 157 M (0.49371069 0.50628931)
## 100) texture_worst>=4.775439 37 2 B (0.94594595 0.05405405) *
## 101) texture_worst< 4.775439 281 122 M (0.43416370 0.56583630) *
## 51) texture_worst>=4.895983 71 14 M (0.19718310 0.80281690)
## 102) compactness_se< -4.040144 9 1 B (0.88888889 0.11111111) *
## 103) compactness_se>=-4.040144 62 6 M (0.09677419 0.90322581) *
## 13) smoothness_mean< -2.354774 147 36 M (0.24489796 0.75510204)
## 26) texture_mean>=3.351321 15 3 B (0.80000000 0.20000000)
## 52) texture_mean< 3.407548 12 0 B (1.00000000 0.00000000) *
## 53) texture_mean>=3.407548 3 0 M (0.00000000 1.00000000) *
## 27) texture_mean< 3.351321 132 24 M (0.18181818 0.81818182)
## 54) texture_worst< 4.212101 4 0 B (1.00000000 0.00000000) *
## 55) texture_worst>=4.212101 128 20 M (0.15625000 0.84375000)
## 110) smoothness_mean< -2.367284 82 20 M (0.24390244 0.75609756) *
## 111) smoothness_mean>=-2.367284 46 0 M (0.00000000 1.00000000) *
## 7) compactness_se>=-3.063476 80 9 M (0.11250000 0.88750000)
## 14) compactness_se>=-2.721974 12 6 B (0.50000000 0.50000000)
## 28) texture_worst< 4.41664 6 0 B (1.00000000 0.00000000) *
## 29) texture_worst>=4.41664 6 0 M (0.00000000 1.00000000) *
## 15) compactness_se< -2.721974 68 3 M (0.04411765 0.95588235)
## 30) smoothness_mean>=-2.080253 1 0 B (1.00000000 0.00000000) *
## 31) smoothness_mean< -2.080253 67 2 M (0.02985075 0.97014925)
## 62) texture_worst>=4.801107 5 2 M (0.40000000 0.60000000)
## 124) texture_mean< 3.159827 2 0 B (1.00000000 0.00000000) *
## 125) texture_mean>=3.159827 3 0 M (0.00000000 1.00000000) *
## 63) texture_worst< 4.801107 62 0 M (0.00000000 1.00000000) *
##
## $trees[[80]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 434 B (0.52412281 0.47587719)
## 2) texture_worst< 4.818867 696 288 B (0.58620690 0.41379310)
## 4) texture_worst>=4.753106 59 4 B (0.93220339 0.06779661)
## 8) compactness_se< -3.322755 57 2 B (0.96491228 0.03508772)
## 16) symmetry_worst< -0.9904278 55 0 B (1.00000000 0.00000000) *
## 17) symmetry_worst>=-0.9904278 2 0 M (0.00000000 1.00000000) *
## 9) compactness_se>=-3.322755 2 0 M (0.00000000 1.00000000) *
## 5) texture_worst< 4.753106 637 284 B (0.55416013 0.44583987)
## 10) texture_worst< 4.681966 554 224 B (0.59566787 0.40433213)
## 20) texture_worst>=4.642157 47 3 B (0.93617021 0.06382979)
## 40) texture_mean< 3.062639 45 1 B (0.97777778 0.02222222)
## 80) texture_mean>=2.836998 44 0 B (1.00000000 0.00000000) *
## 81) texture_mean< 2.836998 1 0 M (0.00000000 1.00000000) *
## 41) texture_mean>=3.062639 2 0 M (0.00000000 1.00000000) *
## 21) texture_worst< 4.642157 507 221 B (0.56410256 0.43589744)
## 42) texture_worst< 4.580648 427 164 B (0.61592506 0.38407494)
## 84) smoothness_worst< -1.384694 404 144 B (0.64356436 0.35643564) *
## 85) smoothness_worst>=-1.384694 23 3 M (0.13043478 0.86956522) *
## 43) texture_worst>=4.580648 80 23 M (0.28750000 0.71250000)
## 86) symmetry_worst>=-1.580305 17 6 B (0.64705882 0.35294118) *
## 87) symmetry_worst< -1.580305 63 12 M (0.19047619 0.80952381) *
## 11) texture_worst>=4.681966 83 23 M (0.27710843 0.72289157)
## 22) symmetry_worst< -1.87333 36 13 B (0.63888889 0.36111111)
## 44) smoothness_mean< -2.391331 23 0 B (1.00000000 0.00000000) *
## 45) smoothness_mean>=-2.391331 13 0 M (0.00000000 1.00000000) *
## 23) symmetry_worst>=-1.87333 47 0 M (0.00000000 1.00000000) *
## 3) texture_worst>=4.818867 216 70 M (0.32407407 0.67592593)
## 6) smoothness_worst< -1.52112 105 50 M (0.47619048 0.52380952)
## 12) smoothness_worst>=-1.588911 67 23 B (0.65671642 0.34328358)
## 24) texture_worst< 5.084467 46 8 B (0.82608696 0.17391304)
## 48) compactness_se< -3.56617 44 6 B (0.86363636 0.13636364)
## 96) compactness_se>=-4.393029 30 0 B (1.00000000 0.00000000) *
## 97) compactness_se< -4.393029 14 6 B (0.57142857 0.42857143) *
## 49) compactness_se>=-3.56617 2 0 M (0.00000000 1.00000000) *
## 25) texture_worst>=5.084467 21 6 M (0.28571429 0.71428571)
## 50) symmetry_worst< -2.159635 3 0 B (1.00000000 0.00000000) *
## 51) symmetry_worst>=-2.159635 18 3 M (0.16666667 0.83333333)
## 102) smoothness_mean< -2.431488 4 1 B (0.75000000 0.25000000) *
## 103) smoothness_mean>=-2.431488 14 0 M (0.00000000 1.00000000) *
## 13) smoothness_worst< -1.588911 38 6 M (0.15789474 0.84210526)
## 26) compactness_se< -4.899363 3 0 B (1.00000000 0.00000000) *
## 27) compactness_se>=-4.899363 35 3 M (0.08571429 0.91428571)
## 54) smoothness_worst< -1.62752 6 3 B (0.50000000 0.50000000)
## 108) texture_mean>=3.22319 3 0 B (1.00000000 0.00000000) *
## 109) texture_mean< 3.22319 3 0 M (0.00000000 1.00000000) *
## 55) smoothness_worst>=-1.62752 29 0 M (0.00000000 1.00000000) *
## 7) smoothness_worst>=-1.52112 111 20 M (0.18018018 0.81981982)
## 14) smoothness_mean>=-2.209101 8 1 B (0.87500000 0.12500000)
## 28) smoothness_mean< -2.075957 7 0 B (1.00000000 0.00000000) *
## 29) smoothness_mean>=-2.075957 1 0 M (0.00000000 1.00000000) *
## 15) smoothness_mean< -2.209101 103 13 M (0.12621359 0.87378641)
## 30) compactness_se>=-3.106177 7 2 B (0.71428571 0.28571429)
## 60) smoothness_mean< -2.309338 5 0 B (1.00000000 0.00000000) *
## 61) smoothness_mean>=-2.309338 2 0 M (0.00000000 1.00000000) *
## 31) compactness_se< -3.106177 96 8 M (0.08333333 0.91666667)
## 62) compactness_se< -4.557422 5 1 B (0.80000000 0.20000000)
## 124) texture_worst>=5.08621 4 0 B (1.00000000 0.00000000) *
## 125) texture_worst< 5.08621 1 0 M (0.00000000 1.00000000) *
## 63) compactness_se>=-4.557422 91 4 M (0.04395604 0.95604396)
## 126) texture_mean>=3.309778 12 2 M (0.16666667 0.83333333) *
## 127) texture_mean< 3.309778 79 2 M (0.02531646 0.97468354) *
##
## $trees[[81]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 445 M (0.48793860 0.51206140)
## 2) compactness_se< -4.219581 196 66 B (0.66326531 0.33673469)
## 4) smoothness_mean>=-2.3007 41 0 B (1.00000000 0.00000000) *
## 5) smoothness_mean< -2.3007 155 66 B (0.57419355 0.42580645)
## 10) symmetry_worst>=-1.52618 27 1 B (0.96296296 0.03703704)
## 20) smoothness_mean>=-2.466044 26 0 B (1.00000000 0.00000000) *
## 21) smoothness_mean< -2.466044 1 0 M (0.00000000 1.00000000) *
## 11) symmetry_worst< -1.52618 128 63 M (0.49218750 0.50781250)
## 22) texture_mean< 2.841101 17 0 B (1.00000000 0.00000000) *
## 23) texture_mean>=2.841101 111 46 M (0.41441441 0.58558559)
## 46) texture_mean>=3.232565 12 0 B (1.00000000 0.00000000) *
## 47) texture_mean< 3.232565 99 34 M (0.34343434 0.65656566)
## 94) smoothness_worst< -1.557411 57 27 B (0.52631579 0.47368421) *
## 95) smoothness_worst>=-1.557411 42 4 M (0.09523810 0.90476190) *
## 3) compactness_se>=-4.219581 716 315 M (0.43994413 0.56005587)
## 6) compactness_se>=-4.180058 689 314 M (0.45573295 0.54426705)
## 12) compactness_se< -3.027402 618 298 M (0.48220065 0.51779935)
## 24) compactness_se>=-3.239083 43 6 B (0.86046512 0.13953488)
## 48) smoothness_worst< -1.477215 38 2 B (0.94736842 0.05263158)
## 96) symmetry_worst< -1.339667 36 0 B (1.00000000 0.00000000) *
## 97) symmetry_worst>=-1.339667 2 0 M (0.00000000 1.00000000) *
## 49) smoothness_worst>=-1.477215 5 1 M (0.20000000 0.80000000)
## 98) texture_mean< 2.701935 1 0 B (1.00000000 0.00000000) *
## 99) texture_mean>=2.701935 4 0 M (0.00000000 1.00000000) *
## 25) compactness_se< -3.239083 575 261 M (0.45391304 0.54608696)
## 50) smoothness_worst>=-1.434633 101 36 B (0.64356436 0.35643564)
## 100) texture_mean< 3.05894 81 19 B (0.76543210 0.23456790) *
## 101) texture_mean>=3.05894 20 3 M (0.15000000 0.85000000) *
## 51) smoothness_worst< -1.434633 474 196 M (0.41350211 0.58649789)
## 102) smoothness_worst< -1.452493 400 187 M (0.46750000 0.53250000) *
## 103) smoothness_worst>=-1.452493 74 9 M (0.12162162 0.87837838) *
## 13) compactness_se>=-3.027402 71 16 M (0.22535211 0.77464789)
## 26) smoothness_mean< -2.336585 23 9 B (0.60869565 0.39130435)
## 52) texture_mean< 3.076827 14 1 B (0.92857143 0.07142857)
## 104) compactness_se>=-2.984387 13 0 B (1.00000000 0.00000000) *
## 105) compactness_se< -2.984387 1 0 M (0.00000000 1.00000000) *
## 53) texture_mean>=3.076827 9 1 M (0.11111111 0.88888889)
## 106) texture_mean>=3.166628 1 0 B (1.00000000 0.00000000) *
## 107) texture_mean< 3.166628 8 0 M (0.00000000 1.00000000) *
## 27) smoothness_mean>=-2.336585 48 2 M (0.04166667 0.95833333)
## 54) compactness_se>=-2.470993 3 1 B (0.66666667 0.33333333)
## 108) texture_mean< 2.929061 2 0 B (1.00000000 0.00000000) *
## 109) texture_mean>=2.929061 1 0 M (0.00000000 1.00000000) *
## 55) compactness_se< -2.470993 45 0 M (0.00000000 1.00000000) *
## 7) compactness_se< -4.180058 27 1 M (0.03703704 0.96296296)
## 14) smoothness_mean< -2.456941 1 0 B (1.00000000 0.00000000) *
## 15) smoothness_mean>=-2.456941 26 0 M (0.00000000 1.00000000) *
##
## $trees[[82]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 451 B (0.50548246 0.49451754)
## 2) smoothness_worst< -1.458403 707 311 B (0.56011315 0.43988685)
## 4) smoothness_worst>=-1.477976 115 22 B (0.80869565 0.19130435)
## 8) texture_worst< 4.682677 84 6 B (0.92857143 0.07142857)
## 16) symmetry_worst< -1.35761 82 4 B (0.95121951 0.04878049)
## 32) smoothness_mean>=-2.354774 77 1 B (0.98701299 0.01298701)
## 64) texture_mean< 3.069079 76 0 B (1.00000000 0.00000000) *
## 65) texture_mean>=3.069079 1 0 M (0.00000000 1.00000000) *
## 33) smoothness_mean< -2.354774 5 2 M (0.40000000 0.60000000)
## 66) texture_mean>=2.774841 2 0 B (1.00000000 0.00000000) *
## 67) texture_mean< 2.774841 3 0 M (0.00000000 1.00000000) *
## 17) symmetry_worst>=-1.35761 2 0 M (0.00000000 1.00000000) *
## 9) texture_worst>=4.682677 31 15 M (0.48387097 0.51612903)
## 18) texture_mean< 2.978826 11 1 B (0.90909091 0.09090909)
## 36) smoothness_mean< -2.304115 10 0 B (1.00000000 0.00000000) *
## 37) smoothness_mean>=-2.304115 1 0 M (0.00000000 1.00000000) *
## 19) texture_mean>=2.978826 20 5 M (0.25000000 0.75000000)
## 38) symmetry_worst< -2.052347 5 0 B (1.00000000 0.00000000) *
## 39) symmetry_worst>=-2.052347 15 0 M (0.00000000 1.00000000) *
## 5) smoothness_worst< -1.477976 592 289 B (0.51182432 0.48817568)
## 10) smoothness_worst< -1.482107 549 248 B (0.54826958 0.45173042)
## 20) smoothness_worst>=-1.484675 37 0 B (1.00000000 0.00000000) *
## 21) smoothness_worst< -1.484675 512 248 B (0.51562500 0.48437500)
## 42) texture_worst< 4.961576 417 184 B (0.55875300 0.44124700)
## 84) symmetry_worst>=-2.391709 388 160 B (0.58762887 0.41237113) *
## 85) symmetry_worst< -2.391709 29 5 M (0.17241379 0.82758621) *
## 43) texture_worst>=4.961576 95 31 M (0.32631579 0.67368421)
## 86) symmetry_worst< -2.057752 23 7 B (0.69565217 0.30434783) *
## 87) symmetry_worst>=-2.057752 72 15 M (0.20833333 0.79166667) *
## 11) smoothness_worst>=-1.482107 43 2 M (0.04651163 0.95348837)
## 22) texture_worst< 4.126187 1 0 B (1.00000000 0.00000000) *
## 23) texture_worst>=4.126187 42 1 M (0.02380952 0.97619048)
## 46) texture_worst>=4.635614 2 1 B (0.50000000 0.50000000)
## 92) texture_mean< 2.931199 1 0 B (1.00000000 0.00000000) *
## 93) texture_mean>=2.931199 1 0 M (0.00000000 1.00000000) *
## 47) texture_worst< 4.635614 40 0 M (0.00000000 1.00000000) *
## 3) smoothness_worst>=-1.458403 205 65 M (0.31707317 0.68292683)
## 6) symmetry_worst< -1.895488 19 2 B (0.89473684 0.10526316)
## 12) texture_mean< 3.129344 17 0 B (1.00000000 0.00000000) *
## 13) texture_mean>=3.129344 2 0 M (0.00000000 1.00000000) *
## 7) symmetry_worst>=-1.895488 186 48 M (0.25806452 0.74193548)
## 14) compactness_se< -4.02632 38 13 B (0.65789474 0.34210526)
## 28) texture_worst< 5.196207 30 5 B (0.83333333 0.16666667)
## 56) compactness_se>=-4.186419 22 1 B (0.95454545 0.04545455)
## 112) symmetry_worst< -1.180039 21 0 B (1.00000000 0.00000000) *
## 113) symmetry_worst>=-1.180039 1 0 M (0.00000000 1.00000000) *
## 57) compactness_se< -4.186419 8 4 B (0.50000000 0.50000000)
## 114) texture_mean< 2.950291 3 0 B (1.00000000 0.00000000) *
## 115) texture_mean>=2.950291 5 1 M (0.20000000 0.80000000) *
## 29) texture_worst>=5.196207 8 0 M (0.00000000 1.00000000) *
## 15) compactness_se>=-4.02632 148 23 M (0.15540541 0.84459459)
## 30) smoothness_worst>=-1.351748 17 8 B (0.52941176 0.47058824)
## 60) symmetry_worst< -1.596878 7 0 B (1.00000000 0.00000000) *
## 61) symmetry_worst>=-1.596878 10 2 M (0.20000000 0.80000000)
## 122) texture_mean< 2.688296 2 0 B (1.00000000 0.00000000) *
## 123) texture_mean>=2.688296 8 0 M (0.00000000 1.00000000) *
## 31) smoothness_worst< -1.351748 131 14 M (0.10687023 0.89312977)
## 62) texture_mean< 2.777879 27 9 M (0.33333333 0.66666667)
## 124) texture_mean>=2.603081 9 0 B (1.00000000 0.00000000) *
## 125) texture_mean< 2.603081 18 0 M (0.00000000 1.00000000) *
## 63) texture_mean>=2.777879 104 5 M (0.04807692 0.95192308)
## 126) compactness_se>=-3.702474 50 5 M (0.10000000 0.90000000) *
## 127) compactness_se< -3.702474 54 0 M (0.00000000 1.00000000) *
##
## $trees[[83]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 391 M (0.42872807 0.57127193)
## 2) compactness_se< -4.705732 18 1 B (0.94444444 0.05555556)
## 4) symmetry_worst< -1.19897 17 0 B (1.00000000 0.00000000) *
## 5) symmetry_worst>=-1.19897 1 0 M (0.00000000 1.00000000) *
## 3) compactness_se>=-4.705732 894 374 M (0.41834452 0.58165548)
## 6) smoothness_worst>=-1.477976 241 111 B (0.53941909 0.46058091)
## 12) smoothness_worst< -1.473476 38 2 B (0.94736842 0.05263158)
## 24) texture_mean< 3.069079 36 0 B (1.00000000 0.00000000) *
## 25) texture_mean>=3.069079 2 0 M (0.00000000 1.00000000) *
## 13) smoothness_worst>=-1.473476 203 94 M (0.46305419 0.53694581)
## 26) symmetry_worst< -1.920541 25 3 B (0.88000000 0.12000000)
## 52) texture_mean< 3.159934 22 0 B (1.00000000 0.00000000) *
## 53) texture_mean>=3.159934 3 0 M (0.00000000 1.00000000) *
## 27) symmetry_worst>=-1.920541 178 72 M (0.40449438 0.59550562)
## 54) smoothness_mean< -2.428454 10 0 B (1.00000000 0.00000000) *
## 55) smoothness_mean>=-2.428454 168 62 M (0.36904762 0.63095238)
## 110) smoothness_mean>=-2.239141 78 36 B (0.53846154 0.46153846) *
## 111) smoothness_mean< -2.239141 90 20 M (0.22222222 0.77777778) *
## 7) smoothness_worst< -1.477976 653 244 M (0.37366003 0.62633997)
## 14) compactness_se>=-4.49319 568 231 M (0.40669014 0.59330986)
## 28) symmetry_worst< -2.052205 89 35 B (0.60674157 0.39325843)
## 56) symmetry_worst>=-2.107807 24 0 B (1.00000000 0.00000000) *
## 57) symmetry_worst< -2.107807 65 30 M (0.46153846 0.53846154)
## 114) compactness_se< -4.170636 11 0 B (1.00000000 0.00000000) *
## 115) compactness_se>=-4.170636 54 19 M (0.35185185 0.64814815) *
## 29) symmetry_worst>=-2.052205 479 177 M (0.36951983 0.63048017)
## 58) symmetry_worst>=-1.98727 427 172 M (0.40281030 0.59718970)
## 116) smoothness_mean>=-2.224699 43 12 B (0.72093023 0.27906977) *
## 117) smoothness_mean< -2.224699 384 141 M (0.36718750 0.63281250) *
## 59) symmetry_worst< -1.98727 52 5 M (0.09615385 0.90384615)
## 118) smoothness_mean< -2.458231 4 0 B (1.00000000 0.00000000) *
## 119) smoothness_mean>=-2.458231 48 1 M (0.02083333 0.97916667) *
## 15) compactness_se< -4.49319 85 13 M (0.15294118 0.84705882)
## 30) smoothness_mean>=-2.295268 3 0 B (1.00000000 0.00000000) *
## 31) smoothness_mean< -2.295268 82 10 M (0.12195122 0.87804878)
## 62) texture_mean< 2.960617 33 8 M (0.24242424 0.75757576)
## 124) texture_mean>=2.930624 6 0 B (1.00000000 0.00000000) *
## 125) texture_mean< 2.930624 27 2 M (0.07407407 0.92592593) *
## 63) texture_mean>=2.960617 49 2 M (0.04081633 0.95918367)
## 126) texture_mean>=3.232565 1 0 B (1.00000000 0.00000000) *
## 127) texture_mean< 3.232565 48 1 M (0.02083333 0.97916667) *
##
## $trees[[84]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 453 B (0.50328947 0.49671053)
## 2) smoothness_mean< -2.216408 798 372 B (0.53383459 0.46616541)
## 4) smoothness_worst>=-1.484675 254 88 B (0.65354331 0.34645669)
## 8) smoothness_worst< -1.372876 242 76 B (0.68595041 0.31404959)
## 16) smoothness_worst< -1.482502 33 0 B (1.00000000 0.00000000) *
## 17) smoothness_worst>=-1.482502 209 76 B (0.63636364 0.36363636)
## 34) smoothness_worst>=-1.478565 187 54 B (0.71122995 0.28877005)
## 68) smoothness_worst< -1.472307 54 1 B (0.98148148 0.01851852) *
## 69) smoothness_worst>=-1.472307 133 53 B (0.60150376 0.39849624) *
## 35) smoothness_worst< -1.478565 22 0 M (0.00000000 1.00000000) *
## 9) smoothness_worst>=-1.372876 12 0 M (0.00000000 1.00000000) *
## 5) smoothness_worst< -1.484675 544 260 M (0.47794118 0.52205882)
## 10) compactness_se>=-4.49319 472 229 B (0.51483051 0.48516949)
## 20) compactness_se< -3.488718 333 138 B (0.58558559 0.41441441)
## 40) symmetry_worst< -2.207519 24 0 B (1.00000000 0.00000000) *
## 41) symmetry_worst>=-2.207519 309 138 B (0.55339806 0.44660194)
## 82) smoothness_worst< -1.556752 159 50 B (0.68553459 0.31446541) *
## 83) smoothness_worst>=-1.556752 150 62 M (0.41333333 0.58666667) *
## 21) compactness_se>=-3.488718 139 48 M (0.34532374 0.65467626)
## 42) smoothness_mean>=-2.224699 8 0 B (1.00000000 0.00000000) *
## 43) smoothness_mean< -2.224699 131 40 M (0.30534351 0.69465649)
## 86) texture_mean< 3.038537 77 32 M (0.41558442 0.58441558) *
## 87) texture_mean>=3.038537 54 8 M (0.14814815 0.85185185) *
## 11) compactness_se< -4.49319 72 17 M (0.23611111 0.76388889)
## 22) compactness_se< -4.706178 13 2 B (0.84615385 0.15384615)
## 44) symmetry_worst< -1.19897 11 0 B (1.00000000 0.00000000) *
## 45) symmetry_worst>=-1.19897 2 0 M (0.00000000 1.00000000) *
## 23) compactness_se>=-4.706178 59 6 M (0.10169492 0.89830508)
## 46) smoothness_mean>=-2.295268 2 0 B (1.00000000 0.00000000) *
## 47) smoothness_mean< -2.295268 57 4 M (0.07017544 0.92982456)
## 94) symmetry_worst>=-1.459568 1 0 B (1.00000000 0.00000000) *
## 95) symmetry_worst< -1.459568 56 3 M (0.05357143 0.94642857) *
## 3) smoothness_mean>=-2.216408 114 33 M (0.28947368 0.71052632)
## 6) smoothness_worst>=-1.426679 37 16 B (0.56756757 0.43243243)
## 12) symmetry_worst< -1.710625 11 0 B (1.00000000 0.00000000) *
## 13) symmetry_worst>=-1.710625 26 10 M (0.38461538 0.61538462)
## 26) texture_mean< 2.688296 11 2 B (0.81818182 0.18181818)
## 52) texture_mean>=2.450874 9 0 B (1.00000000 0.00000000) *
## 53) texture_mean< 2.450874 2 0 M (0.00000000 1.00000000) *
## 27) texture_mean>=2.688296 15 1 M (0.06666667 0.93333333)
## 54) smoothness_worst< -1.388752 3 1 M (0.33333333 0.66666667)
## 108) texture_mean>=3.037093 1 0 B (1.00000000 0.00000000) *
## 109) texture_mean< 3.037093 2 0 M (0.00000000 1.00000000) *
## 55) smoothness_worst>=-1.388752 12 0 M (0.00000000 1.00000000) *
## 7) smoothness_worst< -1.426679 77 12 M (0.15584416 0.84415584)
## 14) smoothness_worst< -1.482898 28 10 M (0.35714286 0.64285714)
## 28) smoothness_worst>=-1.530722 10 1 B (0.90000000 0.10000000)
## 56) smoothness_mean>=-2.213204 9 0 B (1.00000000 0.00000000) *
## 57) smoothness_mean< -2.213204 1 0 M (0.00000000 1.00000000) *
## 29) smoothness_worst< -1.530722 18 1 M (0.05555556 0.94444444)
## 58) texture_mean< 2.820036 1 0 B (1.00000000 0.00000000) *
## 59) texture_mean>=2.820036 17 0 M (0.00000000 1.00000000) *
## 15) smoothness_worst>=-1.482898 49 2 M (0.04081633 0.95918367)
## 30) texture_mean< 2.434062 1 0 B (1.00000000 0.00000000) *
## 31) texture_mean>=2.434062 48 1 M (0.02083333 0.97916667)
## 62) compactness_se< -4.089448 6 1 M (0.16666667 0.83333333)
## 124) texture_mean< 2.892399 1 0 B (1.00000000 0.00000000) *
## 125) texture_mean>=2.892399 5 0 M (0.00000000 1.00000000) *
## 63) compactness_se>=-4.089448 42 0 M (0.00000000 1.00000000) *
##
## $trees[[85]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 441 B (0.51644737 0.48355263)
## 2) smoothness_worst< -1.472307 699 306 B (0.56223176 0.43776824)
## 4) smoothness_worst>=-1.477976 50 3 B (0.94000000 0.06000000)
## 8) texture_mean< 3.069079 47 0 B (1.00000000 0.00000000) *
## 9) texture_mean>=3.069079 3 0 M (0.00000000 1.00000000) *
## 5) smoothness_worst< -1.477976 649 303 B (0.53312789 0.46687211)
## 10) smoothness_worst< -1.482502 621 276 B (0.55555556 0.44444444)
## 20) smoothness_worst>=-1.537883 272 90 B (0.66911765 0.33088235)
## 40) compactness_se< -2.985939 256 76 B (0.70312500 0.29687500)
## 80) texture_worst< 4.521311 63 5 B (0.92063492 0.07936508) *
## 81) texture_worst>=4.521311 193 71 B (0.63212435 0.36787565) *
## 41) compactness_se>=-2.985939 16 2 M (0.12500000 0.87500000)
## 82) smoothness_mean< -2.310579 2 0 B (1.00000000 0.00000000) *
## 83) smoothness_mean>=-2.310579 14 0 M (0.00000000 1.00000000) *
## 21) smoothness_worst< -1.537883 349 163 M (0.46704871 0.53295129)
## 42) texture_worst>=4.683744 145 50 B (0.65517241 0.34482759)
## 84) smoothness_worst< -1.647098 20 0 B (1.00000000 0.00000000) *
## 85) smoothness_worst>=-1.647098 125 50 B (0.60000000 0.40000000) *
## 43) texture_worst< 4.683744 204 68 M (0.33333333 0.66666667)
## 86) texture_mean< 2.976803 118 57 M (0.48305085 0.51694915) *
## 87) texture_mean>=2.976803 86 11 M (0.12790698 0.87209302) *
## 11) smoothness_worst>=-1.482502 28 1 M (0.03571429 0.96428571)
## 22) compactness_se< -4.290267 1 0 B (1.00000000 0.00000000) *
## 23) compactness_se>=-4.290267 27 0 M (0.00000000 1.00000000) *
## 3) smoothness_worst>=-1.472307 213 78 M (0.36619718 0.63380282)
## 6) compactness_se< -4.02632 47 16 B (0.65957447 0.34042553)
## 12) smoothness_worst>=-1.456873 34 6 B (0.82352941 0.17647059)
## 24) symmetry_worst< -1.136473 32 4 B (0.87500000 0.12500000)
## 48) compactness_se>=-4.186419 19 0 B (1.00000000 0.00000000) *
## 49) compactness_se< -4.186419 13 4 B (0.69230769 0.30769231)
## 98) compactness_se< -4.224437 11 2 B (0.81818182 0.18181818) *
## 99) compactness_se>=-4.224437 2 0 M (0.00000000 1.00000000) *
## 25) symmetry_worst>=-1.136473 2 0 M (0.00000000 1.00000000) *
## 13) smoothness_worst< -1.456873 13 3 M (0.23076923 0.76923077)
## 26) texture_mean< 2.901883 3 0 B (1.00000000 0.00000000) *
## 27) texture_mean>=2.901883 10 0 M (0.00000000 1.00000000) *
## 7) compactness_se>=-4.02632 166 47 M (0.28313253 0.71686747)
## 14) symmetry_worst< -1.905461 22 5 B (0.77272727 0.22727273)
## 28) texture_worst< 4.851322 17 0 B (1.00000000 0.00000000) *
## 29) texture_worst>=4.851322 5 0 M (0.00000000 1.00000000) *
## 15) symmetry_worst>=-1.905461 144 30 M (0.20833333 0.79166667)
## 30) compactness_se>=-3.479267 59 23 M (0.38983051 0.61016949)
## 60) smoothness_mean< -2.359377 8 0 B (1.00000000 0.00000000) *
## 61) smoothness_mean>=-2.359377 51 15 M (0.29411765 0.70588235)
## 122) smoothness_mean>=-2.239141 29 14 B (0.51724138 0.48275862) *
## 123) smoothness_mean< -2.239141 22 0 M (0.00000000 1.00000000) *
## 31) compactness_se< -3.479267 85 7 M (0.08235294 0.91764706)
## 62) texture_worst< 4.110502 18 6 M (0.33333333 0.66666667)
## 124) texture_mean>=2.618802 5 0 B (1.00000000 0.00000000) *
## 125) texture_mean< 2.618802 13 1 M (0.07692308 0.92307692) *
## 63) texture_worst>=4.110502 67 1 M (0.01492537 0.98507463)
## 126) texture_worst< 4.30106 7 1 M (0.14285714 0.85714286) *
## 127) texture_worst>=4.30106 60 0 M (0.00000000 1.00000000) *
##
## $trees[[86]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 442 M (0.48464912 0.51535088)
## 2) smoothness_mean< -2.209551 812 393 B (0.51600985 0.48399015)
## 4) smoothness_mean< -2.413908 255 99 B (0.61176471 0.38823529)
## 8) texture_mean< 2.963209 111 25 B (0.77477477 0.22522523)
## 16) compactness_se>=-4.688717 103 19 B (0.81553398 0.18446602)
## 32) symmetry_worst>=-1.748651 51 2 B (0.96078431 0.03921569)
## 64) smoothness_worst< -1.470029 49 0 B (1.00000000 0.00000000) *
## 65) smoothness_worst>=-1.470029 2 0 M (0.00000000 1.00000000) *
## 33) symmetry_worst< -1.748651 52 17 B (0.67307692 0.32692308)
## 66) symmetry_worst< -1.818723 40 5 B (0.87500000 0.12500000) *
## 67) symmetry_worst>=-1.818723 12 0 M (0.00000000 1.00000000) *
## 17) compactness_se< -4.688717 8 2 M (0.25000000 0.75000000)
## 34) compactness_se< -4.694501 2 0 B (1.00000000 0.00000000) *
## 35) compactness_se>=-4.694501 6 0 M (0.00000000 1.00000000) *
## 9) texture_mean>=2.963209 144 70 M (0.48611111 0.51388889)
## 18) texture_mean>=3.015024 107 40 B (0.62616822 0.37383178)
## 36) texture_mean< 3.071535 27 2 B (0.92592593 0.07407407)
## 72) symmetry_worst>=-2.196711 25 0 B (1.00000000 0.00000000) *
## 73) symmetry_worst< -2.196711 2 0 M (0.00000000 1.00000000) *
## 37) texture_mean>=3.071535 80 38 B (0.52500000 0.47500000)
## 74) texture_mean>=3.198061 29 5 B (0.82758621 0.17241379) *
## 75) texture_mean< 3.198061 51 18 M (0.35294118 0.64705882) *
## 19) texture_mean< 3.015024 37 3 M (0.08108108 0.91891892)
## 38) compactness_se>=-3.794131 2 0 B (1.00000000 0.00000000) *
## 39) compactness_se< -3.794131 35 1 M (0.02857143 0.97142857)
## 78) compactness_se< -4.803674 1 0 B (1.00000000 0.00000000) *
## 79) compactness_se>=-4.803674 34 0 M (0.00000000 1.00000000) *
## 5) smoothness_mean>=-2.413908 557 263 M (0.47217235 0.52782765)
## 10) smoothness_mean>=-2.404673 502 246 B (0.50996016 0.49003984)
## 20) texture_worst< 4.818867 411 184 B (0.55231144 0.44768856)
## 40) texture_worst>=4.751723 42 1 B (0.97619048 0.02380952)
## 80) texture_mean>=2.936904 41 0 B (1.00000000 0.00000000) *
## 81) texture_mean< 2.936904 1 0 M (0.00000000 1.00000000) *
## 41) texture_worst< 4.751723 369 183 B (0.50406504 0.49593496)
## 82) texture_worst< 4.682677 325 145 B (0.55384615 0.44615385) *
## 83) texture_worst>=4.682677 44 6 M (0.13636364 0.86363636) *
## 21) texture_worst>=4.818867 91 29 M (0.31868132 0.68131868)
## 42) symmetry_worst< -2.207988 10 0 B (1.00000000 0.00000000) *
## 43) symmetry_worst>=-2.207988 81 19 M (0.23456790 0.76543210)
## 86) symmetry_worst>=-1.733268 39 18 M (0.46153846 0.53846154) *
## 87) symmetry_worst< -1.733268 42 1 M (0.02380952 0.97619048) *
## 11) smoothness_mean< -2.404673 55 7 M (0.12727273 0.87272727)
## 22) smoothness_worst< -1.602866 3 0 B (1.00000000 0.00000000) *
## 23) smoothness_worst>=-1.602866 52 4 M (0.07692308 0.92307692)
## 46) symmetry_worst>=-1.685469 11 4 M (0.36363636 0.63636364)
## 92) texture_mean< 3.023311 4 0 B (1.00000000 0.00000000) *
## 93) texture_mean>=3.023311 7 0 M (0.00000000 1.00000000) *
## 47) symmetry_worst< -1.685469 41 0 M (0.00000000 1.00000000) *
## 3) smoothness_mean>=-2.209551 100 23 M (0.23000000 0.77000000)
## 6) texture_worst< 3.781157 11 0 B (1.00000000 0.00000000) *
## 7) texture_worst>=3.781157 89 12 M (0.13483146 0.86516854)
## 14) compactness_se< -4.024648 5 1 B (0.80000000 0.20000000)
## 28) texture_mean< 3.047521 4 0 B (1.00000000 0.00000000) *
## 29) texture_mean>=3.047521 1 0 M (0.00000000 1.00000000) *
## 15) compactness_se>=-4.024648 84 8 M (0.09523810 0.90476190)
## 30) symmetry_worst< -1.653707 21 6 M (0.28571429 0.71428571)
## 60) texture_mean< 2.909334 4 0 B (1.00000000 0.00000000) *
## 61) texture_mean>=2.909334 17 2 M (0.11764706 0.88235294)
## 122) smoothness_mean>=-2.120284 2 0 B (1.00000000 0.00000000) *
## 123) smoothness_mean< -2.120284 15 0 M (0.00000000 1.00000000) *
## 31) symmetry_worst>=-1.653707 63 2 M (0.03174603 0.96825397)
## 62) smoothness_worst< -1.534923 1 0 B (1.00000000 0.00000000) *
## 63) smoothness_worst>=-1.534923 62 1 M (0.01612903 0.98387097)
## 126) smoothness_worst>=-1.333822 3 1 M (0.33333333 0.66666667) *
## 127) smoothness_worst< -1.333822 59 0 M (0.00000000 1.00000000) *
##
## $trees[[87]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 449 M (0.49232456 0.50767544)
## 2) smoothness_worst< -1.647098 37 3 B (0.91891892 0.08108108)
## 4) texture_worst>=4.595702 22 0 B (1.00000000 0.00000000) *
## 5) texture_worst< 4.595702 15 3 B (0.80000000 0.20000000)
## 10) texture_worst< 4.563505 13 1 B (0.92307692 0.07692308)
## 20) texture_mean< 3.075433 12 0 B (1.00000000 0.00000000) *
## 21) texture_mean>=3.075433 1 0 M (0.00000000 1.00000000) *
## 11) texture_worst>=4.563505 2 0 M (0.00000000 1.00000000) *
## 3) smoothness_worst>=-1.647098 875 415 M (0.47428571 0.52571429)
## 6) smoothness_mean>=-2.496541 837 411 M (0.49103943 0.50896057)
## 12) smoothness_mean< -2.332581 414 178 B (0.57004831 0.42995169)
## 24) texture_mean< 2.976294 192 54 B (0.71875000 0.28125000)
## 48) smoothness_worst< -1.452493 184 46 B (0.75000000 0.25000000)
## 96) smoothness_mean>=-2.354774 35 0 B (1.00000000 0.00000000) *
## 97) smoothness_mean< -2.354774 149 46 B (0.69127517 0.30872483) *
## 49) smoothness_worst>=-1.452493 8 0 M (0.00000000 1.00000000) *
## 25) texture_mean>=2.976294 222 98 M (0.44144144 0.55855856)
## 50) texture_worst>=4.753106 129 47 B (0.63565891 0.36434109)
## 100) symmetry_worst>=-1.74642 75 10 B (0.86666667 0.13333333) *
## 101) symmetry_worst< -1.74642 54 17 M (0.31481481 0.68518519) *
## 51) texture_worst< 4.753106 93 16 M (0.17204301 0.82795699)
## 102) smoothness_worst< -1.610979 12 3 B (0.75000000 0.25000000) *
## 103) smoothness_worst>=-1.610979 81 7 M (0.08641975 0.91358025) *
## 13) smoothness_mean>=-2.332581 423 175 M (0.41371158 0.58628842)
## 26) symmetry_worst< -1.839065 105 34 B (0.67619048 0.32380952)
## 52) smoothness_worst>=-1.567424 89 21 B (0.76404494 0.23595506)
## 104) compactness_se< -3.02233 84 16 B (0.80952381 0.19047619) *
## 105) compactness_se>=-3.02233 5 0 M (0.00000000 1.00000000) *
## 53) smoothness_worst< -1.567424 16 3 M (0.18750000 0.81250000)
## 106) compactness_se< -3.863524 7 3 M (0.42857143 0.57142857) *
## 107) compactness_se>=-3.863524 9 0 M (0.00000000 1.00000000) *
## 27) symmetry_worst>=-1.839065 318 104 M (0.32704403 0.67295597)
## 54) symmetry_worst>=-1.781339 257 101 M (0.39299611 0.60700389)
## 108) compactness_se< -3.294139 198 92 M (0.46464646 0.53535354) *
## 109) compactness_se>=-3.294139 59 9 M (0.15254237 0.84745763) *
## 55) symmetry_worst< -1.781339 61 3 M (0.04918033 0.95081967)
## 110) texture_worst< 4.216838 3 0 B (1.00000000 0.00000000) *
## 111) texture_worst>=4.216838 58 0 M (0.00000000 1.00000000) *
## 7) smoothness_mean< -2.496541 38 4 M (0.10526316 0.89473684)
## 14) smoothness_worst>=-1.570144 3 0 B (1.00000000 0.00000000) *
## 15) smoothness_worst< -1.570144 35 1 M (0.02857143 0.97142857)
## 30) compactness_se< -4.899363 1 0 B (1.00000000 0.00000000) *
## 31) compactness_se>=-4.899363 34 0 M (0.00000000 1.00000000) *
##
## $trees[[88]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 436 M (0.47807018 0.52192982)
## 2) smoothness_mean< -2.413908 234 91 B (0.61111111 0.38888889)
## 4) texture_worst< 4.961576 162 50 B (0.69135802 0.30864198)
## 8) texture_worst>=4.528519 101 18 B (0.82178218 0.17821782)
## 16) compactness_se>=-4.663537 94 12 B (0.87234043 0.12765957)
## 32) texture_mean< 3.172196 85 8 B (0.90588235 0.09411765)
## 64) symmetry_worst< -1.3705 84 7 B (0.91666667 0.08333333) *
## 65) symmetry_worst>=-1.3705 1 0 M (0.00000000 1.00000000) *
## 33) texture_mean>=3.172196 9 4 B (0.55555556 0.44444444)
## 66) texture_mean>=3.176386 5 0 B (1.00000000 0.00000000) *
## 67) texture_mean< 3.176386 4 0 M (0.00000000 1.00000000) *
## 17) compactness_se< -4.663537 7 1 M (0.14285714 0.85714286)
## 34) compactness_se< -4.803674 1 0 B (1.00000000 0.00000000) *
## 35) compactness_se>=-4.803674 6 0 M (0.00000000 1.00000000) *
## 9) texture_worst< 4.528519 61 29 M (0.47540984 0.52459016)
## 18) texture_mean< 2.963351 39 11 B (0.71794872 0.28205128)
## 36) texture_worst>=3.981964 31 4 B (0.87096774 0.12903226)
## 72) texture_mean< 2.869285 20 0 B (1.00000000 0.00000000) *
## 73) texture_mean>=2.869285 11 4 B (0.63636364 0.36363636) *
## 37) texture_worst< 3.981964 8 1 M (0.12500000 0.87500000)
## 74) texture_mean< 2.764104 1 0 B (1.00000000 0.00000000) *
## 75) texture_mean>=2.764104 7 0 M (0.00000000 1.00000000) *
## 19) texture_mean>=2.963351 22 1 M (0.04545455 0.95454545)
## 38) compactness_se< -4.501722 1 0 B (1.00000000 0.00000000) *
## 39) compactness_se>=-4.501722 21 0 M (0.00000000 1.00000000) *
## 5) texture_worst>=4.961576 72 31 M (0.43055556 0.56944444)
## 10) symmetry_worst>=-1.857231 39 12 B (0.69230769 0.30769231)
## 20) symmetry_worst< -1.541072 27 1 B (0.96296296 0.03703704)
## 40) compactness_se>=-4.645782 25 0 B (1.00000000 0.00000000) *
## 41) compactness_se< -4.645782 2 1 B (0.50000000 0.50000000)
## 82) texture_mean< 3.083637 1 0 B (1.00000000 0.00000000) *
## 83) texture_mean>=3.083637 1 0 M (0.00000000 1.00000000) *
## 21) symmetry_worst>=-1.541072 12 1 M (0.08333333 0.91666667)
## 42) smoothness_mean< -2.540124 1 0 B (1.00000000 0.00000000) *
## 43) smoothness_mean>=-2.540124 11 0 M (0.00000000 1.00000000) *
## 11) symmetry_worst< -1.857231 33 4 M (0.12121212 0.87878788)
## 22) texture_worst>=5.222912 4 0 B (1.00000000 0.00000000) *
## 23) texture_worst< 5.222912 29 0 M (0.00000000 1.00000000) *
## 3) smoothness_mean>=-2.413908 678 293 M (0.43215339 0.56784661)
## 6) symmetry_worst< -2.193154 42 12 B (0.71428571 0.28571429)
## 12) smoothness_mean< -2.266808 35 6 B (0.82857143 0.17142857)
## 24) compactness_se>=-4.398122 31 2 B (0.93548387 0.06451613)
## 48) compactness_se< -2.576401 30 1 B (0.96666667 0.03333333)
## 96) symmetry_worst< -2.202388 27 0 B (1.00000000 0.00000000) *
## 97) symmetry_worst>=-2.202388 3 1 B (0.66666667 0.33333333) *
## 49) compactness_se>=-2.576401 1 0 M (0.00000000 1.00000000) *
## 25) compactness_se< -4.398122 4 0 M (0.00000000 1.00000000) *
## 13) smoothness_mean>=-2.266808 7 1 M (0.14285714 0.85714286)
## 26) texture_mean< 2.864879 1 0 B (1.00000000 0.00000000) *
## 27) texture_mean>=2.864879 6 0 M (0.00000000 1.00000000) *
## 7) symmetry_worst>=-2.193154 636 263 M (0.41352201 0.58647799)
## 14) symmetry_worst< -1.072749 618 263 M (0.42556634 0.57443366)
## 28) compactness_se>=-2.622717 10 0 B (1.00000000 0.00000000) *
## 29) compactness_se< -2.622717 608 253 M (0.41611842 0.58388158)
## 58) texture_worst< 4.168738 51 17 B (0.66666667 0.33333333)
## 116) texture_mean>=2.515298 40 7 B (0.82500000 0.17500000) *
## 117) texture_mean< 2.515298 11 1 M (0.09090909 0.90909091) *
## 59) texture_worst>=4.168738 557 219 M (0.39317774 0.60682226)
## 118) texture_mean>=2.899221 373 174 M (0.46648794 0.53351206) *
## 119) texture_mean< 2.899221 184 45 M (0.24456522 0.75543478) *
## 15) symmetry_worst>=-1.072749 18 0 M (0.00000000 1.00000000) *
##
## $trees[[89]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 399 B (0.56250000 0.43750000)
## 2) compactness_se< -3.488718 678 269 B (0.60324484 0.39675516)
## 4) compactness_se>=-3.494301 38 0 B (1.00000000 0.00000000) *
## 5) compactness_se< -3.494301 640 269 B (0.57968750 0.42031250)
## 10) smoothness_mean>=-2.311929 259 79 B (0.69498069 0.30501931)
## 20) compactness_se< -3.658265 213 44 B (0.79342723 0.20657277)
## 40) texture_worst< 5.031275 205 36 B (0.82439024 0.17560976)
## 80) symmetry_worst>=-1.926862 180 23 B (0.87222222 0.12777778) *
## 81) symmetry_worst< -1.926862 25 12 M (0.48000000 0.52000000) *
## 41) texture_worst>=5.031275 8 0 M (0.00000000 1.00000000) *
## 21) compactness_se>=-3.658265 46 11 M (0.23913043 0.76086957)
## 42) symmetry_worst< -1.834844 16 5 B (0.68750000 0.31250000)
## 84) symmetry_worst>=-2.311448 11 0 B (1.00000000 0.00000000) *
## 85) symmetry_worst< -2.311448 5 0 M (0.00000000 1.00000000) *
## 43) symmetry_worst>=-1.834844 30 0 M (0.00000000 1.00000000) *
## 11) smoothness_mean< -2.311929 381 190 B (0.50131234 0.49868766)
## 22) smoothness_mean< -2.366751 278 111 B (0.60071942 0.39928058)
## 44) compactness_se>=-4.098964 143 35 B (0.75524476 0.24475524)
## 88) smoothness_worst< -1.51761 104 14 B (0.86538462 0.13461538) *
## 89) smoothness_worst>=-1.51761 39 18 M (0.46153846 0.53846154) *
## 45) compactness_se< -4.098964 135 59 M (0.43703704 0.56296296)
## 90) smoothness_worst< -1.556321 80 35 B (0.56250000 0.43750000) *
## 91) smoothness_worst>=-1.556321 55 14 M (0.25454545 0.74545455) *
## 23) smoothness_mean>=-2.366751 103 24 M (0.23300971 0.76699029)
## 46) symmetry_worst< -1.995212 25 12 B (0.52000000 0.48000000)
## 92) symmetry_worst>=-2.121358 10 0 B (1.00000000 0.00000000) *
## 93) symmetry_worst< -2.121358 15 3 M (0.20000000 0.80000000) *
## 47) symmetry_worst>=-1.995212 78 11 M (0.14102564 0.85897436)
## 94) compactness_se< -4.534889 3 0 B (1.00000000 0.00000000) *
## 95) compactness_se>=-4.534889 75 8 M (0.10666667 0.89333333) *
## 3) compactness_se>=-3.488718 234 104 M (0.44444444 0.55555556)
## 6) symmetry_worst< -1.317839 203 101 M (0.49753695 0.50246305)
## 12) compactness_se>=-3.476676 188 87 B (0.53723404 0.46276596)
## 24) texture_worst< 5.016194 176 75 B (0.57386364 0.42613636)
## 48) smoothness_mean< -2.385259 47 9 B (0.80851064 0.19148936)
## 96) symmetry_worst< -1.636934 40 3 B (0.92500000 0.07500000) *
## 97) symmetry_worst>=-1.636934 7 1 M (0.14285714 0.85714286) *
## 49) smoothness_mean>=-2.385259 129 63 M (0.48837209 0.51162791)
## 98) symmetry_worst>=-1.471051 20 2 B (0.90000000 0.10000000) *
## 99) symmetry_worst< -1.471051 109 45 M (0.41284404 0.58715596) *
## 25) texture_worst>=5.016194 12 0 M (0.00000000 1.00000000) *
## 13) compactness_se< -3.476676 15 0 M (0.00000000 1.00000000) *
## 7) symmetry_worst>=-1.317839 31 3 M (0.09677419 0.90322581)
## 14) compactness_se>=-2.646661 6 3 B (0.50000000 0.50000000)
## 28) texture_mean< 2.915767 3 0 B (1.00000000 0.00000000) *
## 29) texture_mean>=2.915767 3 0 M (0.00000000 1.00000000) *
## 15) compactness_se< -2.646661 25 0 M (0.00000000 1.00000000) *
##
## $trees[[90]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 424 B (0.53508772 0.46491228)
## 2) symmetry_worst< -1.619058 630 261 B (0.58571429 0.41428571)
## 4) smoothness_worst>=-1.480334 155 41 B (0.73548387 0.26451613)
## 8) texture_worst< 5.032208 142 30 B (0.78873239 0.21126761)
## 16) smoothness_mean>=-2.354774 130 21 B (0.83846154 0.16153846)
## 32) compactness_se< -3.294139 119 13 B (0.89075630 0.10924370)
## 64) texture_mean< 2.932513 61 0 B (1.00000000 0.00000000) *
## 65) texture_mean>=2.932513 58 13 B (0.77586207 0.22413793) *
## 33) compactness_se>=-3.294139 11 3 M (0.27272727 0.72727273)
## 66) texture_mean< 2.781176 3 0 B (1.00000000 0.00000000) *
## 67) texture_mean>=2.781176 8 0 M (0.00000000 1.00000000) *
## 17) smoothness_mean< -2.354774 12 3 M (0.25000000 0.75000000)
## 34) smoothness_mean< -2.396647 3 0 B (1.00000000 0.00000000) *
## 35) smoothness_mean>=-2.396647 9 0 M (0.00000000 1.00000000) *
## 9) texture_worst>=5.032208 13 2 M (0.15384615 0.84615385)
## 18) texture_mean< 2.955358 2 0 B (1.00000000 0.00000000) *
## 19) texture_mean>=2.955358 11 0 M (0.00000000 1.00000000) *
## 5) smoothness_worst< -1.480334 475 220 B (0.53684211 0.46315789)
## 10) smoothness_worst< -1.482107 459 204 B (0.55555556 0.44444444)
## 20) smoothness_worst>=-1.49223 32 3 B (0.90625000 0.09375000)
## 40) compactness_se>=-4.133152 29 0 B (1.00000000 0.00000000) *
## 41) compactness_se< -4.133152 3 0 M (0.00000000 1.00000000) *
## 21) smoothness_worst< -1.49223 427 201 B (0.52927400 0.47072600)
## 42) smoothness_worst< -1.501879 404 179 B (0.55693069 0.44306931)
## 84) texture_mean>=2.717337 389 165 B (0.57583548 0.42416452) *
## 85) texture_mean< 2.717337 15 1 M (0.06666667 0.93333333) *
## 43) smoothness_worst>=-1.501879 23 1 M (0.04347826 0.95652174)
## 86) texture_mean< 2.835488 1 0 B (1.00000000 0.00000000) *
## 87) texture_mean>=2.835488 22 0 M (0.00000000 1.00000000) *
## 11) smoothness_worst>=-1.482107 16 0 M (0.00000000 1.00000000) *
## 3) symmetry_worst>=-1.619058 282 119 M (0.42198582 0.57801418)
## 6) compactness_se>=-3.759737 149 64 B (0.57046980 0.42953020)
## 12) smoothness_mean< -2.216408 121 39 B (0.67768595 0.32231405)
## 24) smoothness_mean>=-2.230731 29 0 B (1.00000000 0.00000000) *
## 25) smoothness_mean< -2.230731 92 39 B (0.57608696 0.42391304)
## 50) smoothness_mean< -2.298098 70 18 B (0.74285714 0.25714286)
## 100) smoothness_worst>=-1.513087 46 5 B (0.89130435 0.10869565) *
## 101) smoothness_worst< -1.513087 24 11 M (0.45833333 0.54166667) *
## 51) smoothness_mean>=-2.298098 22 1 M (0.04545455 0.95454545)
## 102) texture_mean< 2.622235 1 0 B (1.00000000 0.00000000) *
## 103) texture_mean>=2.622235 21 0 M (0.00000000 1.00000000) *
## 13) smoothness_mean>=-2.216408 28 3 M (0.10714286 0.89285714)
## 26) compactness_se< -3.646366 3 0 B (1.00000000 0.00000000) *
## 27) compactness_se>=-3.646366 25 0 M (0.00000000 1.00000000) *
## 7) compactness_se< -3.759737 133 34 M (0.25563910 0.74436090)
## 14) texture_mean< 2.956197 54 25 M (0.46296296 0.53703704)
## 28) smoothness_mean< -2.295113 23 6 B (0.73913043 0.26086957)
## 56) texture_mean< 2.919658 16 0 B (1.00000000 0.00000000) *
## 57) texture_mean>=2.919658 7 1 M (0.14285714 0.85714286)
## 114) texture_mean>=2.92664 1 0 B (1.00000000 0.00000000) *
## 115) texture_mean< 2.92664 6 0 M (0.00000000 1.00000000) *
## 29) smoothness_mean>=-2.295113 31 8 M (0.25806452 0.74193548)
## 58) texture_mean>=2.912011 7 2 B (0.71428571 0.28571429)
## 116) smoothness_mean< -2.200472 5 0 B (1.00000000 0.00000000) *
## 117) smoothness_mean>=-2.200472 2 0 M (0.00000000 1.00000000) *
## 59) texture_mean< 2.912011 24 3 M (0.12500000 0.87500000)
## 118) smoothness_worst>=-1.425992 2 0 B (1.00000000 0.00000000) *
## 119) smoothness_worst< -1.425992 22 1 M (0.04545455 0.95454545) *
## 15) texture_mean>=2.956197 79 9 M (0.11392405 0.88607595)
## 30) compactness_se< -4.291103 15 7 B (0.53333333 0.46666667)
## 60) symmetry_worst< -1.41032 9 1 B (0.88888889 0.11111111)
## 120) smoothness_worst< -1.43601 8 0 B (1.00000000 0.00000000) *
## 121) smoothness_worst>=-1.43601 1 0 M (0.00000000 1.00000000) *
## 61) symmetry_worst>=-1.41032 6 0 M (0.00000000 1.00000000) *
## 31) compactness_se>=-4.291103 64 1 M (0.01562500 0.98437500)
## 62) smoothness_worst>=-1.433164 7 1 M (0.14285714 0.85714286)
## 124) smoothness_mean< -2.265514 1 0 B (1.00000000 0.00000000) *
## 125) smoothness_mean>=-2.265514 6 0 M (0.00000000 1.00000000) *
## 63) smoothness_worst< -1.433164 57 0 M (0.00000000 1.00000000) *
##
## $trees[[91]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 404 M (0.44298246 0.55701754)
## 2) compactness_se>=-4.49319 817 389 M (0.47613219 0.52386781)
## 4) symmetry_worst< -1.427209 746 370 M (0.49597855 0.50402145)
## 8) symmetry_worst>=-1.749963 317 126 B (0.60252366 0.39747634)
## 16) symmetry_worst< -1.713275 57 5 B (0.91228070 0.08771930)
## 32) texture_mean< 3.407548 54 2 B (0.96296296 0.03703704)
## 64) smoothness_mean< -2.223835 51 0 B (1.00000000 0.00000000) *
## 65) smoothness_mean>=-2.223835 3 1 M (0.33333333 0.66666667) *
## 33) texture_mean>=3.407548 3 0 M (0.00000000 1.00000000) *
## 17) symmetry_worst>=-1.713275 260 121 B (0.53461538 0.46538462)
## 34) texture_mean< 2.925843 87 19 B (0.78160920 0.21839080)
## 68) smoothness_mean< -2.081877 80 12 B (0.85000000 0.15000000) *
## 69) smoothness_mean>=-2.081877 7 0 M (0.00000000 1.00000000) *
## 35) texture_mean>=2.925843 173 71 M (0.41040462 0.58959538)
## 70) compactness_se>=-3.863738 107 50 B (0.53271028 0.46728972) *
## 71) compactness_se< -3.863738 66 14 M (0.21212121 0.78787879) *
## 9) symmetry_worst< -1.749963 429 179 M (0.41724942 0.58275058)
## 18) smoothness_mean>=-2.317597 159 67 B (0.57861635 0.42138365)
## 36) symmetry_worst< -1.759228 144 52 B (0.63888889 0.36111111)
## 72) compactness_se< -3.734237 70 12 B (0.82857143 0.17142857) *
## 73) compactness_se>=-3.734237 74 34 M (0.45945946 0.54054054) *
## 37) symmetry_worst>=-1.759228 15 0 M (0.00000000 1.00000000) *
## 19) smoothness_mean< -2.317597 270 87 M (0.32222222 0.67777778)
## 38) symmetry_worst< -1.815934 181 83 M (0.45856354 0.54143646)
## 76) symmetry_worst>=-1.88003 30 2 B (0.93333333 0.06666667) *
## 77) symmetry_worst< -1.88003 151 55 M (0.36423841 0.63576159) *
## 39) symmetry_worst>=-1.815934 89 4 M (0.04494382 0.95505618)
## 78) smoothness_mean< -2.518446 1 0 B (1.00000000 0.00000000) *
## 79) smoothness_mean>=-2.518446 88 3 M (0.03409091 0.96590909) *
## 5) symmetry_worst>=-1.427209 71 19 M (0.26760563 0.73239437)
## 10) smoothness_worst< -1.497484 15 4 B (0.73333333 0.26666667)
## 20) smoothness_mean>=-2.372291 11 0 B (1.00000000 0.00000000) *
## 21) smoothness_mean< -2.372291 4 0 M (0.00000000 1.00000000) *
## 11) smoothness_worst>=-1.497484 56 8 M (0.14285714 0.85714286)
## 22) smoothness_mean>=-2.231196 19 8 M (0.42105263 0.57894737)
## 44) smoothness_mean< -2.217831 7 0 B (1.00000000 0.00000000) *
## 45) smoothness_mean>=-2.217831 12 1 M (0.08333333 0.91666667)
## 90) texture_mean< 2.745901 2 1 B (0.50000000 0.50000000) *
## 91) texture_mean>=2.745901 10 0 M (0.00000000 1.00000000) *
## 23) smoothness_mean< -2.231196 37 0 M (0.00000000 1.00000000) *
## 3) compactness_se< -4.49319 95 15 M (0.15789474 0.84210526)
## 6) compactness_se< -4.705732 7 1 B (0.85714286 0.14285714)
## 12) texture_mean< 2.952113 6 0 B (1.00000000 0.00000000) *
## 13) texture_mean>=2.952113 1 0 M (0.00000000 1.00000000) *
## 7) compactness_se>=-4.705732 88 9 M (0.10227273 0.89772727)
## 14) texture_worst>=5.185666 2 0 B (1.00000000 0.00000000) *
## 15) texture_worst< 5.185666 86 7 M (0.08139535 0.91860465)
## 30) texture_mean>=3.232565 1 0 B (1.00000000 0.00000000) *
## 31) texture_mean< 3.232565 85 6 M (0.07058824 0.92941176)
## 62) smoothness_mean< -2.572721 1 0 B (1.00000000 0.00000000) *
## 63) smoothness_mean>=-2.572721 84 5 M (0.05952381 0.94047619)
## 126) texture_worst< 4.800175 31 5 M (0.16129032 0.83870968) *
## 127) texture_worst>=4.800175 53 0 M (0.00000000 1.00000000) *
##
## $trees[[92]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 441 M (0.48355263 0.51644737)
## 2) symmetry_worst< -2.052205 118 38 B (0.67796610 0.32203390)
## 4) symmetry_worst>=-2.107807 24 0 B (1.00000000 0.00000000) *
## 5) symmetry_worst< -2.107807 94 38 B (0.59574468 0.40425532)
## 10) texture_mean< 2.883577 11 0 B (1.00000000 0.00000000) *
## 11) texture_mean>=2.883577 83 38 B (0.54216867 0.45783133)
## 22) texture_worst>=4.7448 37 10 B (0.72972973 0.27027027)
## 44) smoothness_mean< -2.280871 33 6 B (0.81818182 0.18181818)
## 88) texture_mean< 3.326618 27 2 B (0.92592593 0.07407407) *
## 89) texture_mean>=3.326618 6 2 M (0.33333333 0.66666667) *
## 45) smoothness_mean>=-2.280871 4 0 M (0.00000000 1.00000000) *
## 23) texture_worst< 4.7448 46 18 M (0.39130435 0.60869565)
## 46) smoothness_mean>=-2.30269 7 0 B (1.00000000 0.00000000) *
## 47) smoothness_mean< -2.30269 39 11 M (0.28205128 0.71794872)
## 94) smoothness_worst< -1.614216 17 7 B (0.58823529 0.41176471) *
## 95) smoothness_worst>=-1.614216 22 1 M (0.04545455 0.95454545) *
## 3) symmetry_worst>=-2.052205 794 361 M (0.45465995 0.54534005)
## 6) smoothness_worst>=-1.568787 608 299 B (0.50822368 0.49177632)
## 12) smoothness_worst< -1.559144 32 0 B (1.00000000 0.00000000) *
## 13) smoothness_worst>=-1.559144 576 277 M (0.48090278 0.51909722)
## 26) symmetry_worst>=-1.982941 515 248 B (0.51844660 0.48155340)
## 52) smoothness_mean< -2.413908 68 16 B (0.76470588 0.23529412)
## 104) texture_worst< 5.003123 56 7 B (0.87500000 0.12500000) *
## 105) texture_worst>=5.003123 12 3 M (0.25000000 0.75000000) *
## 53) smoothness_mean>=-2.413908 447 215 M (0.48098434 0.51901566)
## 106) smoothness_worst>=-1.533868 406 196 B (0.51724138 0.48275862) *
## 107) smoothness_worst< -1.533868 41 5 M (0.12195122 0.87804878) *
## 27) symmetry_worst< -1.982941 61 10 M (0.16393443 0.83606557)
## 54) smoothness_worst>=-1.453469 5 0 B (1.00000000 0.00000000) *
## 55) smoothness_worst< -1.453469 56 5 M (0.08928571 0.91071429)
## 110) texture_mean< 2.841101 3 0 B (1.00000000 0.00000000) *
## 111) texture_mean>=2.841101 53 2 M (0.03773585 0.96226415) *
## 7) smoothness_worst< -1.568787 186 52 M (0.27956989 0.72043011)
## 14) smoothness_worst< -1.658238 9 0 B (1.00000000 0.00000000) *
## 15) smoothness_worst>=-1.658238 177 43 M (0.24293785 0.75706215)
## 30) texture_worst>=4.683744 73 30 M (0.41095890 0.58904110)
## 60) smoothness_mean>=-2.472257 28 4 B (0.85714286 0.14285714)
## 120) texture_mean< 3.194865 24 0 B (1.00000000 0.00000000) *
## 121) texture_mean>=3.194865 4 0 M (0.00000000 1.00000000) *
## 61) smoothness_mean< -2.472257 45 6 M (0.13333333 0.86666667)
## 122) compactness_se< -4.938351 3 0 B (1.00000000 0.00000000) *
## 123) compactness_se>=-4.938351 42 3 M (0.07142857 0.92857143) *
## 31) texture_worst< 4.683744 104 13 M (0.12500000 0.87500000)
## 62) compactness_se< -4.387578 4 0 B (1.00000000 0.00000000) *
## 63) compactness_se>=-4.387578 100 9 M (0.09000000 0.91000000)
## 126) texture_mean< 2.67759 2 0 B (1.00000000 0.00000000) *
## 127) texture_mean>=2.67759 98 7 M (0.07142857 0.92857143) *
##
## $trees[[93]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 394 M (0.43201754 0.56798246)
## 2) symmetry_worst< -1.8637 273 117 B (0.57142857 0.42857143)
## 4) texture_worst< 4.907333 228 82 B (0.64035088 0.35964912)
## 8) smoothness_mean>=-2.357468 97 21 B (0.78350515 0.21649485)
## 16) texture_worst>=4.425254 53 1 B (0.98113208 0.01886792)
## 32) texture_mean< 3.104804 50 0 B (1.00000000 0.00000000) *
## 33) texture_mean>=3.104804 3 1 B (0.66666667 0.33333333)
## 66) texture_mean>=3.124007 2 0 B (1.00000000 0.00000000) *
## 67) texture_mean< 3.124007 1 0 M (0.00000000 1.00000000) *
## 17) texture_worst< 4.425254 44 20 B (0.54545455 0.45454545)
## 34) texture_mean< 2.755881 15 0 B (1.00000000 0.00000000) *
## 35) texture_mean>=2.755881 29 9 M (0.31034483 0.68965517)
## 70) smoothness_mean>=-2.278455 10 2 B (0.80000000 0.20000000) *
## 71) smoothness_mean< -2.278455 19 1 M (0.05263158 0.94736842) *
## 9) smoothness_mean< -2.357468 131 61 B (0.53435115 0.46564885)
## 18) smoothness_worst< -1.557839 91 28 B (0.69230769 0.30769231)
## 36) texture_mean>=2.786702 81 19 B (0.76543210 0.23456790)
## 72) smoothness_worst>=-1.694089 68 9 B (0.86764706 0.13235294) *
## 73) smoothness_worst< -1.694089 13 3 M (0.23076923 0.76923077) *
## 37) texture_mean< 2.786702 10 1 M (0.10000000 0.90000000)
## 74) texture_mean< 2.764104 1 0 B (1.00000000 0.00000000) *
## 75) texture_mean>=2.764104 9 0 M (0.00000000 1.00000000) *
## 19) smoothness_worst>=-1.557839 40 7 M (0.17500000 0.82500000)
## 38) compactness_se>=-3.455891 6 0 B (1.00000000 0.00000000) *
## 39) compactness_se< -3.455891 34 1 M (0.02941176 0.97058824)
## 78) texture_worst>=4.815376 1 0 B (1.00000000 0.00000000) *
## 79) texture_worst< 4.815376 33 0 M (0.00000000 1.00000000) *
## 5) texture_worst>=4.907333 45 10 M (0.22222222 0.77777778)
## 10) symmetry_worst< -2.207988 7 0 B (1.00000000 0.00000000) *
## 11) symmetry_worst>=-2.207988 38 3 M (0.07894737 0.92105263)
## 22) texture_mean>=3.361554 3 0 B (1.00000000 0.00000000) *
## 23) texture_mean< 3.361554 35 0 M (0.00000000 1.00000000) *
## 3) symmetry_worst>=-1.8637 639 238 M (0.37245696 0.62754304)
## 6) texture_mean>=3.176386 51 15 B (0.70588235 0.29411765)
## 12) symmetry_worst< -1.453337 46 10 B (0.78260870 0.21739130)
## 24) texture_mean< 3.386045 40 5 B (0.87500000 0.12500000)
## 48) smoothness_mean< -2.340941 33 1 B (0.96969697 0.03030303)
## 96) compactness_se< -3.055765 28 0 B (1.00000000 0.00000000) *
## 97) compactness_se>=-3.055765 5 1 B (0.80000000 0.20000000) *
## 49) smoothness_mean>=-2.340941 7 3 M (0.42857143 0.57142857)
## 98) texture_mean< 3.247139 3 0 B (1.00000000 0.00000000) *
## 99) texture_mean>=3.247139 4 0 M (0.00000000 1.00000000) *
## 25) texture_mean>=3.386045 6 1 M (0.16666667 0.83333333)
## 50) texture_mean>=3.500537 1 0 B (1.00000000 0.00000000) *
## 51) texture_mean< 3.500537 5 0 M (0.00000000 1.00000000) *
## 13) symmetry_worst>=-1.453337 5 0 M (0.00000000 1.00000000) *
## 7) texture_mean< 3.176386 588 202 M (0.34353741 0.65646259)
## 14) texture_mean< 2.960364 319 138 M (0.43260188 0.56739812)
## 28) smoothness_mean< -2.22055 272 135 B (0.50367647 0.49632353)
## 56) symmetry_worst>=-1.769229 162 49 B (0.69753086 0.30246914)
## 112) symmetry_worst< -1.36527 141 31 B (0.78014184 0.21985816) *
## 113) symmetry_worst>=-1.36527 21 3 M (0.14285714 0.85714286) *
## 57) symmetry_worst< -1.769229 110 24 M (0.21818182 0.78181818)
## 114) texture_mean>=2.93492 16 4 B (0.75000000 0.25000000) *
## 115) texture_mean< 2.93492 94 12 M (0.12765957 0.87234043) *
## 29) smoothness_mean>=-2.22055 47 1 M (0.02127660 0.97872340)
## 58) smoothness_worst< -1.534923 1 0 B (1.00000000 0.00000000) *
## 59) smoothness_worst>=-1.534923 46 0 M (0.00000000 1.00000000) *
## 15) texture_mean>=2.960364 269 64 M (0.23791822 0.76208178)
## 30) texture_mean>=2.987952 154 51 M (0.33116883 0.66883117)
## 60) texture_mean< 3.005682 20 6 B (0.70000000 0.30000000)
## 120) compactness_se>=-4.280193 14 1 B (0.92857143 0.07142857) *
## 121) compactness_se< -4.280193 6 1 M (0.16666667 0.83333333) *
## 61) texture_mean>=3.005682 134 37 M (0.27611940 0.72388060)
## 122) texture_worst>=4.667341 95 36 M (0.37894737 0.62105263) *
## 123) texture_worst< 4.667341 39 1 M (0.02564103 0.97435897) *
## 31) texture_mean< 2.987952 115 13 M (0.11304348 0.88695652)
## 62) texture_worst< 4.31854 5 0 B (1.00000000 0.00000000) *
## 63) texture_worst>=4.31854 110 8 M (0.07272727 0.92727273)
## 126) compactness_se< -4.291103 20 8 M (0.40000000 0.60000000) *
## 127) compactness_se>=-4.291103 90 0 M (0.00000000 1.00000000) *
##
## $trees[[94]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 438 M (0.48026316 0.51973684)
## 2) symmetry_worst>=-1.234283 45 10 B (0.77777778 0.22222222)
## 4) symmetry_worst< -1.069325 37 2 B (0.94594595 0.05405405)
## 8) smoothness_mean< -2.185335 35 0 B (1.00000000 0.00000000) *
## 9) smoothness_mean>=-2.185335 2 0 M (0.00000000 1.00000000) *
## 5) symmetry_worst>=-1.069325 8 0 M (0.00000000 1.00000000) *
## 3) symmetry_worst< -1.234283 867 403 M (0.46482122 0.53517878)
## 6) texture_worst< 4.517889 307 135 B (0.56026059 0.43973941)
## 12) compactness_se< -4.020169 86 20 B (0.76744186 0.23255814)
## 24) texture_worst>=4.3976 48 1 B (0.97916667 0.02083333)
## 48) compactness_se>=-4.484801 42 0 B (1.00000000 0.00000000) *
## 49) compactness_se< -4.484801 6 1 B (0.83333333 0.16666667)
## 98) compactness_se< -4.501722 5 0 B (1.00000000 0.00000000) *
## 99) compactness_se>=-4.501722 1 0 M (0.00000000 1.00000000) *
## 25) texture_worst< 4.3976 38 19 B (0.50000000 0.50000000)
## 50) texture_worst< 4.271231 17 0 B (1.00000000 0.00000000) *
## 51) texture_worst>=4.271231 21 2 M (0.09523810 0.90476190)
## 102) smoothness_worst< -1.654625 1 0 B (1.00000000 0.00000000) *
## 103) smoothness_worst>=-1.654625 20 1 M (0.05000000 0.95000000) *
## 13) compactness_se>=-4.020169 221 106 M (0.47963801 0.52036199)
## 26) smoothness_worst< -1.473088 139 58 B (0.58273381 0.41726619)
## 52) smoothness_worst>=-1.479941 19 0 B (1.00000000 0.00000000) *
## 53) smoothness_worst< -1.479941 120 58 B (0.51666667 0.48333333)
## 106) smoothness_worst< -1.482701 103 41 B (0.60194175 0.39805825) *
## 107) smoothness_worst>=-1.482701 17 0 M (0.00000000 1.00000000) *
## 27) smoothness_worst>=-1.473088 82 25 M (0.30487805 0.69512195)
## 54) smoothness_mean>=-2.267218 45 21 B (0.53333333 0.46666667)
## 108) symmetry_worst< -1.619683 18 2 B (0.88888889 0.11111111) *
## 109) symmetry_worst>=-1.619683 27 8 M (0.29629630 0.70370370) *
## 55) smoothness_mean< -2.267218 37 1 M (0.02702703 0.97297297)
## 110) smoothness_mean< -2.405579 6 1 M (0.16666667 0.83333333) *
## 111) smoothness_mean>=-2.405579 31 0 M (0.00000000 1.00000000) *
## 7) texture_worst>=4.517889 560 231 M (0.41250000 0.58750000)
## 14) texture_worst>=4.543638 476 218 M (0.45798319 0.54201681)
## 28) texture_worst< 4.577679 38 6 B (0.84210526 0.15789474)
## 56) smoothness_mean< -2.246212 34 3 B (0.91176471 0.08823529)
## 112) smoothness_mean>=-2.494905 31 1 B (0.96774194 0.03225806) *
## 113) smoothness_mean< -2.494905 3 1 M (0.33333333 0.66666667) *
## 57) smoothness_mean>=-2.246212 4 1 M (0.25000000 0.75000000)
## 114) texture_mean< 2.943507 1 0 B (1.00000000 0.00000000) *
## 115) texture_mean>=2.943507 3 0 M (0.00000000 1.00000000) *
## 29) texture_worst>=4.577679 438 186 M (0.42465753 0.57534247)
## 58) texture_worst>=4.642157 328 163 M (0.49695122 0.50304878)
## 116) symmetry_worst< -1.39888 312 149 B (0.52243590 0.47756410) *
## 117) symmetry_worst>=-1.39888 16 0 M (0.00000000 1.00000000) *
## 59) texture_worst< 4.642157 110 23 M (0.20909091 0.79090909)
## 118) symmetry_worst>=-1.685469 34 16 M (0.47058824 0.52941176) *
## 119) symmetry_worst< -1.685469 76 7 M (0.09210526 0.90789474) *
## 15) texture_worst< 4.543638 84 13 M (0.15476190 0.84523810)
## 30) symmetry_worst< -1.859307 13 3 B (0.76923077 0.23076923)
## 60) texture_mean< 3.157578 10 0 B (1.00000000 0.00000000) *
## 61) texture_mean>=3.157578 3 0 M (0.00000000 1.00000000) *
## 31) symmetry_worst>=-1.859307 71 3 M (0.04225352 0.95774648)
## 62) smoothness_mean>=-2.234468 3 0 B (1.00000000 0.00000000) *
## 63) smoothness_mean< -2.234468 68 0 M (0.00000000 1.00000000) *
##
## $trees[[95]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 436 M (0.47807018 0.52192982)
## 2) symmetry_worst>=-1.412496 61 18 B (0.70491803 0.29508197)
## 4) symmetry_worst< -1.072749 56 13 B (0.76785714 0.23214286)
## 8) symmetry_worst>=-1.126811 22 0 B (1.00000000 0.00000000) *
## 9) symmetry_worst< -1.126811 34 13 B (0.61764706 0.38235294)
## 18) symmetry_worst< -1.293329 24 4 B (0.83333333 0.16666667)
## 36) texture_worst< 4.689831 18 0 B (1.00000000 0.00000000) *
## 37) texture_worst>=4.689831 6 2 M (0.33333333 0.66666667)
## 74) texture_mean< 2.89312 2 0 B (1.00000000 0.00000000) *
## 75) texture_mean>=2.89312 4 0 M (0.00000000 1.00000000) *
## 19) symmetry_worst>=-1.293329 10 1 M (0.10000000 0.90000000)
## 38) texture_mean< 2.756192 1 0 B (1.00000000 0.00000000) *
## 39) texture_mean>=2.756192 9 0 M (0.00000000 1.00000000) *
## 5) symmetry_worst>=-1.072749 5 0 M (0.00000000 1.00000000) *
## 3) symmetry_worst< -1.412496 851 393 M (0.46180964 0.53819036)
## 6) texture_worst< 3.788077 11 0 B (1.00000000 0.00000000) *
## 7) texture_worst>=3.788077 840 382 M (0.45476190 0.54523810)
## 14) compactness_se< -3.721197 453 220 B (0.51434879 0.48565121)
## 28) compactness_se>=-3.867535 65 12 B (0.81538462 0.18461538)
## 56) smoothness_worst< -1.461024 52 0 B (1.00000000 0.00000000) *
## 57) smoothness_worst>=-1.461024 13 1 M (0.07692308 0.92307692)
## 114) compactness_se< -3.816941 1 0 B (1.00000000 0.00000000) *
## 115) compactness_se>=-3.816941 12 0 M (0.00000000 1.00000000) *
## 29) compactness_se< -3.867535 388 180 M (0.46391753 0.53608247)
## 58) compactness_se< -3.987083 315 149 B (0.52698413 0.47301587)
## 116) smoothness_mean>=-2.356093 125 42 B (0.66400000 0.33600000) *
## 117) smoothness_mean< -2.356093 190 83 M (0.43684211 0.56315789) *
## 59) compactness_se>=-3.987083 73 14 M (0.19178082 0.80821918)
## 118) smoothness_mean< -2.394871 14 5 B (0.64285714 0.35714286) *
## 119) smoothness_mean>=-2.394871 59 5 M (0.08474576 0.91525424) *
## 15) compactness_se>=-3.721197 387 149 M (0.38501292 0.61498708)
## 30) symmetry_worst< -1.849754 141 65 B (0.53900709 0.46099291)
## 60) smoothness_worst>=-1.565486 74 21 B (0.71621622 0.28378378)
## 120) texture_worst< 4.605004 43 6 B (0.86046512 0.13953488) *
## 121) texture_worst>=4.605004 31 15 B (0.51612903 0.48387097) *
## 61) smoothness_worst< -1.565486 67 23 M (0.34328358 0.65671642)
## 122) symmetry_worst>=-1.934101 9 0 B (1.00000000 0.00000000) *
## 123) symmetry_worst< -1.934101 58 14 M (0.24137931 0.75862069) *
## 31) symmetry_worst>=-1.849754 246 73 M (0.29674797 0.70325203)
## 62) symmetry_worst>=-1.608735 88 41 B (0.53409091 0.46590909)
## 124) texture_mean< 2.955045 31 1 B (0.96774194 0.03225806) *
## 125) texture_mean>=2.955045 57 17 M (0.29824561 0.70175439) *
## 63) symmetry_worst< -1.608735 158 26 M (0.16455696 0.83544304)
## 126) smoothness_mean>=-2.120284 4 0 B (1.00000000 0.00000000) *
## 127) smoothness_mean< -2.120284 154 22 M (0.14285714 0.85714286) *
##
## $trees[[96]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 424 M (0.46491228 0.53508772)
## 2) smoothness_mean< -2.392182 322 125 B (0.61180124 0.38819876)
## 4) smoothness_mean>=-2.441446 159 37 B (0.76729560 0.23270440)
## 8) symmetry_worst< -1.448573 146 27 B (0.81506849 0.18493151)
## 16) smoothness_mean< -2.424301 44 0 B (1.00000000 0.00000000) *
## 17) smoothness_mean>=-2.424301 102 27 B (0.73529412 0.26470588)
## 34) smoothness_mean>=-2.405782 50 2 B (0.96000000 0.04000000)
## 68) texture_mean< 3.082932 42 0 B (1.00000000 0.00000000) *
## 69) texture_mean>=3.082932 8 2 B (0.75000000 0.25000000) *
## 35) smoothness_mean< -2.405782 52 25 B (0.51923077 0.48076923)
## 70) smoothness_mean< -2.408446 43 16 B (0.62790698 0.37209302) *
## 71) smoothness_mean>=-2.408446 9 0 M (0.00000000 1.00000000) *
## 9) symmetry_worst>=-1.448573 13 3 M (0.23076923 0.76923077)
## 18) smoothness_mean< -2.425324 3 0 B (1.00000000 0.00000000) *
## 19) smoothness_mean>=-2.425324 10 0 M (0.00000000 1.00000000) *
## 5) smoothness_mean< -2.441446 163 75 M (0.46012270 0.53987730)
## 10) texture_mean< 2.921008 23 3 B (0.86956522 0.13043478)
## 20) smoothness_mean< -2.444843 21 1 B (0.95238095 0.04761905)
## 40) symmetry_worst>=-1.811428 17 0 B (1.00000000 0.00000000) *
## 41) symmetry_worst< -1.811428 4 1 B (0.75000000 0.25000000)
## 82) texture_mean< 2.85595 3 0 B (1.00000000 0.00000000) *
## 83) texture_mean>=2.85595 1 0 M (0.00000000 1.00000000) *
## 21) smoothness_mean>=-2.444843 2 0 M (0.00000000 1.00000000) *
## 11) texture_mean>=2.921008 140 55 M (0.39285714 0.60714286)
## 22) symmetry_worst< -1.868413 70 31 B (0.55714286 0.44285714)
## 44) compactness_se< -4.169518 24 2 B (0.91666667 0.08333333)
## 88) smoothness_mean< -2.44767 20 0 B (1.00000000 0.00000000) *
## 89) smoothness_mean>=-2.44767 4 2 B (0.50000000 0.50000000) *
## 45) compactness_se>=-4.169518 46 17 M (0.36956522 0.63043478)
## 90) smoothness_worst< -1.601489 20 6 B (0.70000000 0.30000000) *
## 91) smoothness_worst>=-1.601489 26 3 M (0.11538462 0.88461538) *
## 23) symmetry_worst>=-1.868413 70 16 M (0.22857143 0.77142857)
## 46) smoothness_worst< -1.657635 7 0 B (1.00000000 0.00000000) *
## 47) smoothness_worst>=-1.657635 63 9 M (0.14285714 0.85714286)
## 94) smoothness_worst>=-1.549205 5 1 B (0.80000000 0.20000000) *
## 95) smoothness_worst< -1.549205 58 5 M (0.08620690 0.91379310) *
## 3) smoothness_mean>=-2.392182 590 227 M (0.38474576 0.61525424)
## 6) symmetry_worst>=-1.234283 29 6 B (0.79310345 0.20689655)
## 12) smoothness_worst< -1.440335 24 1 B (0.95833333 0.04166667)
## 24) texture_mean>=2.693961 23 0 B (1.00000000 0.00000000) *
## 25) texture_mean< 2.693961 1 0 M (0.00000000 1.00000000) *
## 13) smoothness_worst>=-1.440335 5 0 M (0.00000000 1.00000000) *
## 7) symmetry_worst< -1.234283 561 204 M (0.36363636 0.63636364)
## 14) smoothness_worst< -1.472307 372 156 M (0.41935484 0.58064516)
## 28) smoothness_worst>=-1.477976 38 0 B (1.00000000 0.00000000) *
## 29) smoothness_worst< -1.477976 334 118 M (0.35329341 0.64670659)
## 58) smoothness_worst< -1.482699 286 115 M (0.40209790 0.59790210)
## 116) compactness_se< -3.965703 62 21 B (0.66129032 0.33870968) *
## 117) compactness_se>=-3.965703 224 74 M (0.33035714 0.66964286) *
## 59) smoothness_worst>=-1.482699 48 3 M (0.06250000 0.93750000)
## 118) texture_worst< 4.136746 2 0 B (1.00000000 0.00000000) *
## 119) texture_worst>=4.136746 46 1 M (0.02173913 0.97826087) *
## 15) smoothness_worst>=-1.472307 189 48 M (0.25396825 0.74603175)
## 30) symmetry_worst< -1.941776 14 5 B (0.64285714 0.35714286)
## 60) texture_worst< 4.85229 9 0 B (1.00000000 0.00000000) *
## 61) texture_worst>=4.85229 5 0 M (0.00000000 1.00000000) *
## 31) symmetry_worst>=-1.941776 175 39 M (0.22285714 0.77714286)
## 62) smoothness_mean>=-2.094359 22 10 B (0.54545455 0.45454545)
## 124) symmetry_worst< -1.596878 9 0 B (1.00000000 0.00000000) *
## 125) symmetry_worst>=-1.596878 13 3 M (0.23076923 0.76923077) *
## 63) smoothness_mean< -2.094359 153 27 M (0.17647059 0.82352941)
## 126) compactness_se< -4.040144 49 20 M (0.40816327 0.59183673) *
## 127) compactness_se>=-4.040144 104 7 M (0.06730769 0.93269231) *
##
## $trees[[97]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 447 B (0.50986842 0.49013158)
## 2) compactness_se< -4.705732 17 1 B (0.94117647 0.05882353)
## 4) symmetry_worst< -1.179946 16 0 B (1.00000000 0.00000000) *
## 5) symmetry_worst>=-1.179946 1 0 M (0.00000000 1.00000000) *
## 3) compactness_se>=-4.705732 895 446 B (0.50167598 0.49832402)
## 6) symmetry_worst>=-2.923662 883 434 B (0.50849377 0.49150623)
## 12) symmetry_worst< -2.202388 65 17 B (0.73846154 0.26153846)
## 24) smoothness_mean>=-2.469349 52 6 B (0.88461538 0.11538462)
## 48) compactness_se>=-4.492707 49 3 B (0.93877551 0.06122449)
## 96) smoothness_mean< -2.266808 45 0 B (1.00000000 0.00000000) *
## 97) smoothness_mean>=-2.266808 4 1 M (0.25000000 0.75000000) *
## 49) compactness_se< -4.492707 3 0 M (0.00000000 1.00000000) *
## 25) smoothness_mean< -2.469349 13 2 M (0.15384615 0.84615385)
## 50) smoothness_mean< -2.57545 2 0 B (1.00000000 0.00000000) *
## 51) smoothness_mean>=-2.57545 11 0 M (0.00000000 1.00000000) *
## 13) symmetry_worst>=-2.202388 818 401 M (0.49022005 0.50977995)
## 26) smoothness_mean>=-2.262885 196 77 B (0.60714286 0.39285714)
## 52) smoothness_mean< -2.21595 114 28 B (0.75438596 0.24561404)
## 104) texture_mean< 3.081899 106 20 B (0.81132075 0.18867925) *
## 105) texture_mean>=3.081899 8 0 M (0.00000000 1.00000000) *
## 53) smoothness_mean>=-2.21595 82 33 M (0.40243902 0.59756098)
## 106) smoothness_mean>=-2.188811 61 30 B (0.50819672 0.49180328) *
## 107) smoothness_mean< -2.188811 21 2 M (0.09523810 0.90476190) *
## 27) smoothness_mean< -2.262885 622 282 M (0.45337621 0.54662379)
## 54) smoothness_mean< -2.295113 557 271 M (0.48653501 0.51346499)
## 108) smoothness_mean>=-2.311929 60 13 B (0.78333333 0.21666667) *
## 109) smoothness_mean< -2.311929 497 224 M (0.45070423 0.54929577) *
## 55) smoothness_mean>=-2.295113 65 11 M (0.16923077 0.83076923)
## 110) smoothness_worst< -1.514953 8 2 B (0.75000000 0.25000000) *
## 111) smoothness_worst>=-1.514953 57 5 M (0.08771930 0.91228070) *
## 7) symmetry_worst< -2.923662 12 0 M (0.00000000 1.00000000) *
##
## $trees[[98]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 448 B (0.50877193 0.49122807)
## 2) smoothness_mean< -2.258569 749 337 B (0.55006676 0.44993324)
## 4) smoothness_mean>=-2.267218 31 1 B (0.96774194 0.03225806)
## 8) compactness_se< -3.294139 30 0 B (1.00000000 0.00000000) *
## 9) compactness_se>=-3.294139 1 0 M (0.00000000 1.00000000) *
## 5) smoothness_mean< -2.267218 718 336 B (0.53203343 0.46796657)
## 10) compactness_se>=-4.406791 616 270 B (0.56168831 0.43831169)
## 20) texture_worst>=4.726366 205 60 B (0.70731707 0.29268293)
## 40) texture_worst< 5.003123 139 23 B (0.83453237 0.16546763)
## 80) compactness_se< -2.614641 135 19 B (0.85925926 0.14074074) *
## 81) compactness_se>=-2.614641 4 0 M (0.00000000 1.00000000) *
## 41) texture_worst>=5.003123 66 29 M (0.43939394 0.56060606)
## 82) smoothness_mean< -2.363096 46 19 B (0.58695652 0.41304348) *
## 83) smoothness_mean>=-2.363096 20 2 M (0.10000000 0.90000000) *
## 21) texture_worst< 4.726366 411 201 M (0.48905109 0.51094891)
## 42) smoothness_mean< -2.296106 353 164 B (0.53541076 0.46458924)
## 84) texture_mean< 2.976548 248 94 B (0.62096774 0.37903226) *
## 85) texture_mean>=2.976548 105 35 M (0.33333333 0.66666667) *
## 43) smoothness_mean>=-2.296106 58 12 M (0.20689655 0.79310345)
## 86) symmetry_worst< -1.93369 10 2 B (0.80000000 0.20000000) *
## 87) symmetry_worst>=-1.93369 48 4 M (0.08333333 0.91666667) *
## 11) compactness_se< -4.406791 102 36 M (0.35294118 0.64705882)
## 22) compactness_se< -4.520844 55 25 B (0.54545455 0.45454545)
## 44) smoothness_mean>=-2.536393 41 12 B (0.70731707 0.29268293)
## 88) symmetry_worst>=-2.330898 33 4 B (0.87878788 0.12121212) *
## 89) symmetry_worst< -2.330898 8 0 M (0.00000000 1.00000000) *
## 45) smoothness_mean< -2.536393 14 1 M (0.07142857 0.92857143)
## 90) texture_mean< 2.933381 1 0 B (1.00000000 0.00000000) *
## 91) texture_mean>=2.933381 13 0 M (0.00000000 1.00000000) *
## 23) compactness_se>=-4.520844 47 6 M (0.12765957 0.87234043)
## 46) texture_mean< 2.841101 3 0 B (1.00000000 0.00000000) *
## 47) texture_mean>=2.841101 44 3 M (0.06818182 0.93181818)
## 94) texture_mean>=3.28326 1 0 B (1.00000000 0.00000000) *
## 95) texture_mean< 3.28326 43 2 M (0.04651163 0.95348837) *
## 3) smoothness_mean>=-2.258569 163 52 M (0.31901840 0.68098160)
## 6) symmetry_worst< -1.765932 51 25 M (0.49019608 0.50980392)
## 12) texture_mean< 2.909334 13 0 B (1.00000000 0.00000000) *
## 13) texture_mean>=2.909334 38 12 M (0.31578947 0.68421053)
## 26) smoothness_worst>=-1.433185 9 2 B (0.77777778 0.22222222)
## 52) texture_mean>=3.014892 7 0 B (1.00000000 0.00000000) *
## 53) texture_mean< 3.014892 2 0 M (0.00000000 1.00000000) *
## 27) smoothness_worst< -1.433185 29 5 M (0.17241379 0.82758621)
## 54) texture_worst< 4.489662 7 3 B (0.57142857 0.42857143)
## 108) texture_mean>=2.956939 4 0 B (1.00000000 0.00000000) *
## 109) texture_mean< 2.956939 3 0 M (0.00000000 1.00000000) *
## 55) texture_worst>=4.489662 22 1 M (0.04545455 0.95454545)
## 110) smoothness_worst< -1.506961 2 1 B (0.50000000 0.50000000) *
## 111) smoothness_worst>=-1.506961 20 0 M (0.00000000 1.00000000) *
## 7) symmetry_worst>=-1.765932 112 27 M (0.24107143 0.75892857)
## 14) texture_worst< 4.680515 91 27 M (0.29670330 0.70329670)
## 28) texture_worst>=4.623656 4 0 B (1.00000000 0.00000000) *
## 29) texture_worst< 4.623656 87 23 M (0.26436782 0.73563218)
## 58) smoothness_mean< -2.216408 31 14 M (0.45161290 0.54838710)
## 116) smoothness_mean>=-2.231196 12 0 B (1.00000000 0.00000000) *
## 117) smoothness_mean< -2.231196 19 2 M (0.10526316 0.89473684) *
## 59) smoothness_mean>=-2.216408 56 9 M (0.16071429 0.83928571)
## 118) texture_worst< 4.185244 20 8 M (0.40000000 0.60000000) *
## 119) texture_worst>=4.185244 36 1 M (0.02777778 0.97222222) *
## 15) texture_worst>=4.680515 21 0 M (0.00000000 1.00000000) *
##
## $trees[[99]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 456 B (0.50000000 0.50000000)
## 2) compactness_se>=-2.809774 31 3 B (0.90322581 0.09677419)
## 4) smoothness_mean< -2.085882 29 1 B (0.96551724 0.03448276)
## 8) symmetry_worst>=-2.167572 28 0 B (1.00000000 0.00000000) *
## 9) symmetry_worst< -2.167572 1 0 M (0.00000000 1.00000000) *
## 5) smoothness_mean>=-2.085882 2 0 M (0.00000000 1.00000000) *
## 3) compactness_se< -2.809774 881 428 M (0.48581158 0.51418842)
## 6) compactness_se< -4.274791 137 46 B (0.66423358 0.33576642)
## 12) compactness_se>=-4.49319 57 7 B (0.87719298 0.12280702)
## 24) smoothness_mean>=-2.467165 47 3 B (0.93617021 0.06382979)
## 48) texture_mean< 3.151222 46 2 B (0.95652174 0.04347826)
## 96) smoothness_worst< -1.483426 38 0 B (1.00000000 0.00000000) *
## 97) smoothness_worst>=-1.483426 8 2 B (0.75000000 0.25000000) *
## 49) texture_mean>=3.151222 1 0 M (0.00000000 1.00000000) *
## 25) smoothness_mean< -2.467165 10 4 B (0.60000000 0.40000000)
## 50) compactness_se< -4.341665 6 0 B (1.00000000 0.00000000) *
## 51) compactness_se>=-4.341665 4 0 M (0.00000000 1.00000000) *
## 13) compactness_se< -4.49319 80 39 B (0.51250000 0.48750000)
## 26) compactness_se< -4.704842 12 0 B (1.00000000 0.00000000) *
## 27) compactness_se>=-4.704842 68 29 M (0.42647059 0.57352941)
## 54) smoothness_worst>=-1.547264 23 6 B (0.73913043 0.26086957)
## 108) symmetry_worst>=-1.809609 16 1 B (0.93750000 0.06250000) *
## 109) symmetry_worst< -1.809609 7 2 M (0.28571429 0.71428571) *
## 55) smoothness_worst< -1.547264 45 12 M (0.26666667 0.73333333)
## 110) texture_worst< 4.812659 29 12 M (0.41379310 0.58620690) *
## 111) texture_worst>=4.812659 16 0 M (0.00000000 1.00000000) *
## 7) compactness_se>=-4.274791 744 337 M (0.45295699 0.54704301)
## 14) smoothness_worst>=-1.40309 48 10 B (0.79166667 0.20833333)
## 28) texture_mean< 3.05894 45 7 B (0.84444444 0.15555556)
## 56) texture_mean>=3.008413 19 0 B (1.00000000 0.00000000) *
## 57) texture_mean< 3.008413 26 7 B (0.73076923 0.26923077)
## 114) compactness_se< -3.086764 23 4 B (0.82608696 0.17391304) *
## 115) compactness_se>=-3.086764 3 0 M (0.00000000 1.00000000) *
## 29) texture_mean>=3.05894 3 0 M (0.00000000 1.00000000) *
## 15) smoothness_worst< -1.40309 696 299 M (0.42959770 0.57040230)
## 30) smoothness_worst< -1.603778 81 25 B (0.69135802 0.30864198)
## 60) texture_mean>=3.086027 40 0 B (1.00000000 0.00000000) *
## 61) texture_mean< 3.086027 41 16 M (0.39024390 0.60975610)
## 122) texture_mean< 2.939162 11 0 B (1.00000000 0.00000000) *
## 123) texture_mean>=2.939162 30 5 M (0.16666667 0.83333333) *
## 31) smoothness_worst>=-1.603778 615 243 M (0.39512195 0.60487805)
## 62) smoothness_worst>=-1.567247 530 230 M (0.43396226 0.56603774)
## 124) smoothness_worst< -1.55958 30 3 B (0.90000000 0.10000000) *
## 125) smoothness_worst>=-1.55958 500 203 M (0.40600000 0.59400000) *
## 63) smoothness_worst< -1.567247 85 13 M (0.15294118 0.84705882)
## 126) smoothness_mean>=-2.419351 42 12 M (0.28571429 0.71428571) *
## 127) smoothness_mean< -2.419351 43 1 M (0.02325581 0.97674419) *
##
## $trees[[100]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 427 B (0.53179825 0.46820175)
## 2) smoothness_worst< -1.603315 108 29 B (0.73148148 0.26851852)
## 4) texture_mean>=3.086027 40 2 B (0.95000000 0.05000000)
## 8) compactness_se>=-4.480894 38 0 B (1.00000000 0.00000000) *
## 9) compactness_se< -4.480894 2 0 M (0.00000000 1.00000000) *
## 5) texture_mean< 3.086027 68 27 B (0.60294118 0.39705882)
## 10) smoothness_worst>=-1.60795 17 0 B (1.00000000 0.00000000) *
## 11) smoothness_worst< -1.60795 51 24 M (0.47058824 0.52941176)
## 22) symmetry_worst< -1.667161 36 13 B (0.63888889 0.36111111)
## 44) texture_mean< 3.080067 29 6 B (0.79310345 0.20689655)
## 88) texture_worst< 4.864124 24 1 B (0.95833333 0.04166667) *
## 89) texture_worst>=4.864124 5 0 M (0.00000000 1.00000000) *
## 45) texture_mean>=3.080067 7 0 M (0.00000000 1.00000000) *
## 23) symmetry_worst>=-1.667161 15 1 M (0.06666667 0.93333333)
## 46) texture_mean< 2.885795 1 0 B (1.00000000 0.00000000) *
## 47) texture_mean>=2.885795 14 0 M (0.00000000 1.00000000) *
## 3) smoothness_worst>=-1.603315 804 398 B (0.50497512 0.49502488)
## 6) smoothness_worst>=-1.59459 778 374 B (0.51928021 0.48071979)
## 12) smoothness_worst< -1.584388 33 1 B (0.96969697 0.03030303)
## 24) texture_mean< 3.267122 32 0 B (1.00000000 0.00000000) *
## 25) texture_mean>=3.267122 1 0 M (0.00000000 1.00000000) *
## 13) smoothness_worst>=-1.584388 745 372 M (0.49932886 0.50067114)
## 26) compactness_se>=-3.494301 226 87 B (0.61504425 0.38495575)
## 52) compactness_se< -3.444843 49 3 B (0.93877551 0.06122449)
## 104) smoothness_mean>=-2.463517 46 0 B (1.00000000 0.00000000) *
## 105) smoothness_mean< -2.463517 3 0 M (0.00000000 1.00000000) *
## 53) compactness_se>=-3.444843 177 84 B (0.52542373 0.47457627)
## 106) compactness_se>=-3.426516 154 63 B (0.59090909 0.40909091) *
## 107) compactness_se< -3.426516 23 2 M (0.08695652 0.91304348) *
## 27) compactness_se< -3.494301 519 233 M (0.44894027 0.55105973)
## 54) compactness_se< -3.66733 444 219 M (0.49324324 0.50675676)
## 108) texture_mean>=3.348904 20 1 B (0.95000000 0.05000000) *
## 109) texture_mean< 3.348904 424 200 M (0.47169811 0.52830189) *
## 55) compactness_se>=-3.66733 75 14 M (0.18666667 0.81333333)
## 110) texture_mean>=3.145434 10 1 B (0.90000000 0.10000000) *
## 111) texture_mean< 3.145434 65 5 M (0.07692308 0.92307692) *
## 7) smoothness_worst< -1.59459 26 2 M (0.07692308 0.92307692)
## 14) texture_mean< 2.755158 2 0 B (1.00000000 0.00000000) *
## 15) texture_mean>=2.755158 24 0 M (0.00000000 1.00000000) *
##
##
## $weights
## [1] 1.0642386 0.9517970 0.7684041 0.5793833 0.6323994 0.7337124 0.5493768
## [8] 0.7347059 0.8042379 0.5636014 0.5367516 0.4876431 0.3206582 0.6539157
## [15] 0.5093635 0.6648857 0.5026250 0.2927844 0.5666438 0.6402904 0.6201142
## [22] 0.3569723 0.5418616 0.4304642 0.5381729 0.9289972 0.6696281 0.6369112
## [29] 0.7936017 0.2413852 0.5679277 0.5423372 0.4333344 0.4961133 0.7167576
## [36] 0.5443922 0.4028488 0.4175805 0.4115830 0.5246761 0.6065379 0.5421187
## [43] 0.4854263 0.6947258 0.6230596 0.6219552 0.5126567 0.4969624 0.3586968
## [50] 0.4692455 0.4498236 0.6898165 0.7332071 0.5226157 0.3772735 0.5096765
## [57] 0.7364397 0.4606224 0.6473455 0.6227635 0.3472248 0.7561317 0.7708424
## [64] 0.4784138 0.7103869 0.5683997 0.6992354 0.6129001 0.5718152 0.7558681
## [71] 0.4084518 0.5597012 0.5215857 0.7652235 0.8515905 0.6346368 0.5966106
## [78] 0.6937558 0.5497377 0.5101590 0.3734845 0.5873386 0.4147868 0.4774993
## [85] 0.5152268 0.4816840 0.5237209 0.3983270 0.5446059 0.5659719 0.5241156
## [92] 0.4338659 0.7521560 0.5336086 0.5130153 0.6569338 0.2870437 0.4795788
## [99] 0.4234391 0.3298559
##
## $votes
## [,1] [,2]
## [1,] 15.770631 40.942345
## [2,] 17.213111 39.499864
## [3,] 13.287915 43.425060
## [4,] 15.945589 40.767387
## [5,] 16.166797 40.546178
## [6,] 14.949651 41.763325
## [7,] 12.289696 44.423280
## [8,] 9.831883 46.881093
## [9,] 16.486741 40.226235
## [10,] 17.584448 39.128527
## [11,] 17.015885 39.697091
## [12,] 11.398470 45.314506
## [13,] 8.843713 47.869262
## [14,] 16.359905 40.353071
## [15,] 10.331381 46.381594
## [16,] 44.274543 12.438432
## [17,] 44.179938 12.533037
## [18,] 44.653472 12.059503
## [19,] 16.029224 40.683752
## [20,] 8.258340 48.454635
## [21,] 15.460261 41.252714
## [22,] 13.423686 43.289289
## [23,] 17.922414 38.790561
## [24,] 18.012245 38.700730
## [25,] 9.799023 46.913952
## [26,] 13.934914 42.778062
## [27,] 43.495625 13.217351
## [28,] 16.615958 40.097017
## [29,] 16.588248 40.124728
## [30,] 16.274650 40.438325
## [31,] 14.644228 42.068747
## [32,] 12.793774 43.919202
## [33,] 15.603049 41.109927
## [34,] 41.356636 15.356340
## [35,] 12.885000 43.827976
## [36,] 42.718814 13.994161
## [37,] 45.950137 10.762838
## [38,] 41.568239 15.144736
## [39,] 17.790713 38.922262
## [40,] 15.448864 41.264111
## [41,] 43.478189 13.234787
## [42,] 16.601468 40.111507
## [43,] 11.966156 44.746819
## [44,] 44.323688 12.389288
## [45,] 45.682097 11.030878
## [46,] 40.440032 16.272944
## [47,] 44.870215 11.842760
## [48,] 12.696284 44.016691
## [49,] 38.915553 17.797423
## [50,] 40.186094 16.526882
## [51,] 39.774979 16.937997
## [52,] 17.520022 39.192953
## [53,] 14.362500 42.350475
## [54,] 17.269488 39.443487
## [55,] 41.030742 15.682233
## [56,] 17.444534 39.268441
## [57,] 10.804987 45.907989
## [58,] 39.109539 17.603436
## [59,] 38.949864 17.763112
## [60,] 39.733432 16.979543
## [61,] 13.527358 43.185618
## [62,] 14.516071 42.196904
## [63,] 41.248066 15.464910
## [64,] 16.986440 39.726535
## [65,] 17.105598 39.607377
## [66,] 14.781496 41.931480
## [67,] 39.092940 17.620036
## [68,] 39.754907 16.958069
## [69,] 40.833049 15.879927
## [70,] 16.198770 40.514205
## [71,] 46.788657 9.924319
## [72,] 41.490335 15.222641
## [73,] 16.688301 40.024675
## [74,] 14.605614 42.107362
## [75,] 42.474587 14.238388
## [76,] 42.544582 14.168393
## [77,] 16.613916 40.099059
## [78,] 48.799685 7.913290
## [79,] 40.697132 16.015844
## [80,] 39.470015 17.242960
## [81,] 39.525214 17.187761
## [82,] 16.072117 40.640858
## [83,] 39.695935 17.017041
## [84,] 14.623544 42.089431
## [85,] 39.044648 17.668328
## [86,] 39.928659 16.784317
## [87,] 39.714371 16.998604
## [88,] 42.245444 14.467532
## [89,] 39.984787 16.728189
## [90,] 39.038393 17.674582
## [91,] 38.672522 18.040454
## [92,] 40.654793 16.058183
## [93,] 14.571006 42.141970
## [94,] 13.159873 43.553102
## [95,] 15.743529 40.969446
## [96,] 42.805768 13.907208
## [97,] 15.613982 41.098993
## [98,] 42.686093 14.026883
## [99,] 40.676317 16.036658
## [100,] 16.278184 40.434791
## [101,] 17.444143 39.268833
## [102,] 41.168457 15.544518
## [103,] 15.511556 41.201419
## [104,] 41.302831 15.410145
## [105,] 17.320774 39.392202
## [106,] 13.628987 43.083989
## [107,] 41.673650 15.039326
## [108,] 13.692129 43.020846
## [109,] 42.497151 14.215825
## [110,] 40.543216 16.169760
## [111,] 16.352812 40.360164
## [112,] 41.984485 14.728491
## [113,] 44.732094 11.980881
## [114,] 18.544930 38.168045
## [115,] 41.676366 15.036610
## [116,] 43.822652 12.890324
## [117,] 15.868165 40.844811
## [118,] 40.818102 15.894873
## [119,] 40.375090 16.337886
## [120,] 44.526715 12.186261
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## [621,] 39.032284 17.680692
## [622,] 17.435224 39.277751
## [623,] 41.716372 14.996604
## [624,] 16.615957 40.097018
## [625,] 38.825920 17.887055
## [626,] 47.571713 9.141263
## [627,] 16.345455 40.367521
## [628,] 39.259423 17.453553
## [629,] 17.306208 39.406767
## [630,] 15.867135 40.845840
## [631,] 10.553852 46.159124
## [632,] 15.673557 41.039418
## [633,] 40.344147 16.368828
## [634,] 41.973868 14.739107
## [635,] 16.468344 40.244631
## [636,] 40.388609 16.324366
## [637,] 44.874083 11.838893
## [638,] 49.197464 7.515512
## [639,] 41.131513 15.581463
## [640,] 40.597623 16.115353
## [641,] 15.145747 41.567228
## [642,] 16.428731 40.284245
## [643,] 41.938170 14.774805
## [644,] 41.146846 15.566129
## [645,] 40.751791 15.961184
## [646,] 16.956111 39.756864
## [647,] 40.932340 15.780636
## [648,] 12.908527 43.804448
## [649,] 45.438347 11.274628
## [650,] 15.585494 41.127481
## [651,] 39.733489 16.979486
## [652,] 41.117037 15.595938
## [653,] 42.426717 14.286258
## [654,] 39.525587 17.187389
## [655,] 44.512441 12.200535
## [656,] 17.263380 39.449595
## [657,] 16.539118 40.173857
## [658,] 18.234634 38.478342
## [659,] 17.519738 39.193237
## [660,] 15.848679 40.864296
## [661,] 17.020160 39.692816
## [662,] 16.282094 40.430882
## [663,] 12.278382 44.434593
## [664,] 9.364056 47.348919
## [665,] 16.193422 40.519554
## [666,] 17.856578 38.856398
## [667,] 16.010525 40.702450
## [668,] 17.557445 39.155531
## [669,] 17.337711 39.375264
## [670,] 15.722596 40.990379
## [671,] 44.151227 12.561749
## [672,] 41.383506 15.329469
## [673,] 40.441111 16.271865
## [674,] 48.505728 8.207247
## [675,] 42.890948 13.822027
## [676,] 16.519004 40.193971
## [677,] 44.935116 11.777859
## [678,] 43.694015 13.018960
## [679,] 15.470179 41.242797
## [680,] 41.721868 14.991108
## [681,] 14.069065 42.643911
## [682,] 45.662435 11.050541
## [683,] 14.230133 42.482843
## [684,] 47.317972 9.395003
## [685,] 47.180112 9.532864
## [686,] 41.217196 15.495780
## [687,] 47.957359 8.755617
## [688,] 41.085638 15.627337
## [689,] 39.475226 17.237750
## [690,] 45.515224 11.197751
## [691,] 39.063678 17.649297
## [692,] 40.136644 16.576331
## [693,] 47.321821 9.391155
## [694,] 48.511766 8.201209
## [695,] 17.202376 39.510599
## [696,] 43.623547 13.089428
## [697,] 41.235680 15.477296
## [698,] 16.587225 40.125750
## [699,] 41.071650 15.641326
## [700,] 15.931765 40.781210
## [701,] 41.648704 15.064271
## [702,] 40.690834 16.022141
## [703,] 41.423976 15.289000
## [704,] 44.800090 11.912886
## [705,] 46.542954 10.170021
## [706,] 51.478066 5.234909
## [707,] 39.985224 16.727751
## [708,] 51.816131 4.896844
## [709,] 42.156969 14.556006
## [710,] 46.789302 9.923673
## [711,] 44.785147 11.927828
## [712,] 47.033954 9.679021
## [713,] 51.991081 4.721894
## [714,] 17.939534 38.773442
## [715,] 40.388572 16.324403
## [716,] 43.852414 12.860562
## [717,] 42.986149 13.726827
## [718,] 12.582705 44.130271
## [719,] 40.602830 16.110146
## [720,] 41.618293 15.094682
## [721,] 46.949851 9.763124
## [722,] 15.995097 40.717878
## [723,] 16.525116 40.187860
## [724,] 18.088077 38.624899
## [725,] 39.847651 16.865324
## [726,] 43.909733 12.803243
## [727,] 14.420848 42.292128
## [728,] 39.678887 17.034088
## [729,] 15.803214 40.909762
## [730,] 39.073850 17.639125
## [731,] 40.497825 16.215150
## [732,] 40.625114 16.087861
## [733,] 15.386031 41.326945
## [734,] 45.839495 10.873480
## [735,] 41.734782 14.978194
## [736,] 45.550802 11.162173
## [737,] 43.880849 12.832127
## [738,] 15.180581 41.532395
## [739,] 14.204322 42.508653
## [740,] 15.885888 40.827087
## [741,] 46.325575 10.387400
## [742,] 42.856689 13.856287
## [743,] 38.975272 17.737704
## [744,] 46.857229 9.855747
## [745,] 43.221246 13.491729
## [746,] 41.586639 15.126336
## [747,] 40.953283 15.759693
## [748,] 38.964295 17.748681
## [749,] 40.455755 16.257220
## [750,] 42.035816 14.677159
## [751,] 16.199566 40.513409
## [752,] 12.404138 44.308838
## [753,] 41.026138 15.686838
## [754,] 16.911264 39.801711
## [755,] 16.573321 40.139654
## [756,] 45.691797 11.021178
## [757,] 18.327690 38.385286
## [758,] 16.801802 39.911173
## [759,] 45.585617 11.127359
## [760,] 39.847554 16.865421
## [761,] 12.408280 44.304696
## [762,] 39.040940 17.672035
## [763,] 40.427360 16.285615
## [764,] 38.808080 17.904895
## [765,] 41.597075 15.115900
## [766,] 17.046261 39.666715
## [767,] 45.442685 11.270290
## [768,] 45.413882 11.299094
## [769,] 46.519439 10.193537
## [770,] 16.077227 40.635749
## [771,] 45.905112 10.807864
## [772,] 15.892853 40.820123
## [773,] 11.487710 45.225265
## [774,] 38.982141 17.730834
## [775,] 44.170004 12.542972
## [776,] 42.992476 13.720499
## [777,] 42.800140 13.912836
## [778,] 14.918980 41.793995
## [779,] 43.296495 13.416481
## [780,] 43.493131 13.219844
## [781,] 43.447472 13.265504
## [782,] 49.647363 7.065613
## [783,] 40.933615 15.779360
## [784,] 40.979971 15.733004
## [785,] 43.223226 13.489750
## [786,] 14.037143 42.675832
## [787,] 40.253440 16.459536
## [788,] 41.609674 15.103302
## [789,] 40.019841 16.693134
## [790,] 39.021293 17.691682
## [791,] 16.943069 39.769907
## [792,] 38.519345 18.193631
## [793,] 14.188733 42.524242
## [794,] 41.726306 14.986670
## [795,] 40.789262 15.923713
## [796,] 41.968979 14.743996
## [797,] 40.958076 15.754899
## [798,] 38.937311 17.775664
## [799,] 40.583640 16.129336
## [800,] 46.425265 10.287711
## [801,] 39.386938 17.326038
## [802,] 15.254605 41.458371
## [803,] 40.276430 16.436546
## [804,] 13.590664 43.122312
## [805,] 14.138133 42.574842
## [806,] 41.238877 15.474098
## [807,] 39.820370 16.892605
## [808,] 41.018459 15.694516
## [809,] 16.135632 40.577343
## [810,] 42.188299 14.524677
## [811,] 40.576433 16.136542
## [812,] 18.109353 38.603622
## [813,] 15.688920 41.024056
## [814,] 42.645322 14.067653
## [815,] 44.151560 12.561415
## [816,] 16.199682 40.513293
## [817,] 41.125843 15.587133
## [818,] 40.566917 16.146058
## [819,] 49.289136 7.423839
## [820,] 42.936683 13.776292
## [821,] 41.360363 15.352612
## [822,] 38.264808 18.448168
## [823,] 40.270093 16.442882
## [824,] 45.213239 11.499736
## [825,] 15.610357 41.102619
## [826,] 14.175765 42.537211
## [827,] 44.983785 11.729191
## [828,] 44.233821 12.479154
## [829,] 39.689827 17.023148
## [830,] 39.011788 17.701187
## [831,] 40.836809 15.876167
## [832,] 16.677779 40.035196
## [833,] 39.333316 17.379660
## [834,] 41.515173 15.197802
## [835,] 43.290172 13.422803
## [836,] 49.360465 7.352510
## [837,] 43.578182 13.134794
## [838,] 39.200069 17.512907
## [839,] 44.116239 12.596737
## [840,] 42.501050 14.211925
## [841,] 40.092031 16.620945
## [842,] 39.900827 16.812148
## [843,] 45.618491 11.094484
## [844,] 41.857680 14.855295
## [845,] 43.052873 13.660102
## [846,] 40.090241 16.622734
## [847,] 42.678594 14.034381
## [848,] 13.100300 43.612676
## [849,] 41.410645 15.302331
## [850,] 49.399813 7.313162
## [851,] 15.531682 41.181294
## [852,] 41.292767 15.420208
## [853,] 40.655632 16.057343
## [854,] 39.200737 17.512238
## [855,] 41.025748 15.687228
## [856,] 17.769347 38.943629
## [857,] 16.922252 39.790724
## [858,] 40.138020 16.574956
## [859,] 8.462537 48.250438
## [860,] 39.517066 17.195909
## [861,] 18.100017 38.612958
## [862,] 40.485095 16.227880
## [863,] 39.695010 17.017965
## [864,] 41.060965 15.652011
## [865,] 40.893101 15.819874
## [866,] 11.507211 45.205764
## [867,] 45.611610 11.101366
## [868,] 45.326365 11.386611
## [869,] 11.342092 45.370883
## [870,] 41.215841 15.497134
## [871,] 17.798164 38.914812
## [872,] 40.642342 16.070633
## [873,] 16.700959 40.012017
## [874,] 40.998006 15.714970
## [875,] 39.324534 17.388441
## [876,] 16.502283 40.210693
## [877,] 42.486641 14.226335
## [878,] 40.052772 16.660203
## [879,] 39.781287 16.931688
## [880,] 40.167569 16.545406
## [881,] 44.141764 12.571211
## [882,] 40.087520 16.625456
## [883,] 39.345624 17.367352
## [884,] 17.531309 39.181666
## [885,] 39.584891 17.128084
## [886,] 17.076299 39.636676
## [887,] 40.232852 16.480123
## [888,] 41.231814 15.481162
## [889,] 38.916149 17.796827
## [890,] 40.518414 16.194561
## [891,] 39.219541 17.493435
## [892,] 43.455687 13.257288
## [893,] 40.894248 15.818727
## [894,] 38.452624 18.260351
## [895,] 40.549726 16.163249
## [896,] 39.073660 17.639316
## [897,] 40.724560 15.988415
## [898,] 39.075012 17.637963
## [899,] 42.758726 13.954249
## [900,] 39.917893 16.795082
## [901,] 39.396710 17.316266
## [902,] 41.247325 15.465651
## [903,] 40.138234 16.574741
## [904,] 40.360527 16.352449
## [905,] 38.858543 17.854432
## [906,] 46.528963 10.184012
## [907,] 7.507534 49.205442
## [908,] 15.046920 41.666056
## [909,] 16.947548 39.765427
## [910,] 17.815924 38.897051
## [911,] 9.742806 46.970169
## [912,] 41.443213 15.269763
##
## $prob
## [,1] [,2]
## [1,] 0.2780780 0.72192200
## [2,] 0.3035127 0.69648725
## [3,] 0.2343011 0.76569885
## [4,] 0.2811630 0.71883703
## [5,] 0.2850635 0.71493654
## [6,] 0.2636019 0.73639805
## [7,] 0.2166999 0.78330010
## [8,] 0.1733621 0.82663786
## [9,] 0.2907049 0.70929508
## [10,] 0.3100604 0.68993960
## [11,] 0.3000351 0.69996487
## [12,] 0.2009852 0.79901479
## [13,] 0.1559381 0.84406191
## [14,] 0.2884685 0.71153154
## [15,] 0.1821696 0.81783038
## [16,] 0.7806775 0.21932251
## [17,] 0.7790094 0.22099065
## [18,] 0.7873590 0.21264099
## [19,] 0.2826377 0.71736233
## [20,] 0.1456164 0.85438359
## [21,] 0.2726054 0.72739464
## [22,] 0.2366951 0.76330485
## [23,] 0.3160196 0.68398036
## [24,] 0.3176036 0.68239639
## [25,] 0.1727827 0.82721726
## [26,] 0.2457094 0.75429056
## [27,] 0.7669431 0.23305691
## [28,] 0.2929834 0.70701664
## [29,] 0.2924948 0.70750524
## [30,] 0.2869652 0.71303480
## [31,] 0.2582165 0.74178346
## [32,] 0.2255881 0.77441187
## [33,] 0.2751231 0.72487692
## [34,] 0.7292270 0.27077295
## [35,] 0.2271967 0.77280332
## [36,] 0.7532459 0.24675413
## [37,] 0.8102226 0.18977735
## [38,] 0.7329582 0.26704182
## [39,] 0.3136974 0.68630259
## [40,] 0.2724044 0.72759560
## [41,] 0.7666356 0.23336435
## [42,] 0.2927279 0.70727214
## [43,] 0.2109950 0.78900497
## [44,] 0.7815440 0.21845596
## [45,] 0.8054964 0.19450361
## [46,] 0.7130649 0.28693511
## [47,] 0.7911808 0.20881924
## [48,] 0.2238691 0.77613088
## [49,] 0.6861843 0.31381571
## [50,] 0.7085873 0.29141271
## [51,] 0.7013382 0.29866175
## [52,] 0.3089244 0.69107559
## [53,] 0.2532489 0.74675108
## [54,] 0.3045068 0.69549317
## [55,] 0.7234807 0.27651932
## [56,] 0.3075934 0.69240665
## [57,] 0.1905205 0.80947946
## [58,] 0.6896048 0.31039522
## [59,] 0.6867893 0.31321072
## [60,] 0.7006057 0.29939433
## [61,] 0.2385232 0.76147685
## [62,] 0.2559568 0.74404321
## [63,] 0.7273127 0.27268733
## [64,] 0.2995159 0.70048406
## [65,] 0.3016170 0.69838299
## [66,] 0.2606369 0.73936307
## [67,] 0.6893121 0.31068790
## [68,] 0.7009843 0.29901567
## [69,] 0.7199948 0.28000518
## [70,] 0.2856272 0.71437277
## [71,] 0.8250080 0.17499203
## [72,] 0.7315845 0.26841548
## [73,] 0.2942590 0.70574105
## [74,] 0.2575357 0.74246434
## [75,] 0.7489395 0.25106051
## [76,] 0.7501737 0.24982631
## [77,] 0.2929474 0.70705265
## [78,] 0.8604677 0.13953227
## [79,] 0.7175983 0.28240175
## [80,] 0.6959609 0.30403907
## [81,] 0.6969342 0.30306576
## [82,] 0.2833940 0.71660599
## [83,] 0.6999445 0.30005551
## [84,] 0.2578518 0.74214818
## [85,] 0.6884606 0.31153943
## [86,] 0.7040480 0.29595197
## [87,] 0.7002696 0.29973043
## [88,] 0.7448991 0.25510091
## [89,] 0.7050377 0.29496229
## [90,] 0.6883503 0.31164971
## [91,] 0.6818990 0.31810099
## [92,] 0.7168517 0.28314831
## [93,] 0.2569254 0.74307457
## [94,] 0.2320434 0.76795657
## [95,] 0.2776001 0.72239987
## [96,] 0.7547791 0.24522091
## [97,] 0.2753159 0.72468413
## [98,] 0.7526689 0.24733110
## [99,] 0.7172312 0.28276877
## [100,] 0.2870275 0.71297249
## [101,] 0.3075865 0.69241355
## [102,] 0.7259090 0.27409104
## [103,] 0.2735098 0.72649017
## [104,] 0.7282783 0.27172167
## [105,] 0.3054111 0.69458887
## [106,] 0.2403151 0.75968486
## [107,] 0.7348168 0.26518316
## [108,] 0.2414285 0.75857149
## [109,] 0.7493374 0.25066265
## [110,] 0.7148843 0.28511570
## [111,] 0.2883434 0.71165661
## [112,] 0.7402977 0.25970231
## [113,] 0.7887453 0.21125468
## [114,] 0.3269963 0.67300375
## [115,] 0.7348647 0.26513526
## [116,] 0.7727094 0.22729056
## [117,] 0.2797978 0.72020222
## [118,] 0.7197313 0.28026872
## [119,] 0.7119198 0.28808020
## [120,] 0.7851239 0.21487606
## [121,] 0.6865326 0.31346738
## [122,] 0.8578811 0.14211895
## [123,] 0.6997941 0.30020594
## [124,] 0.7301520 0.26984798
## [125,] 0.7158878 0.28411223
## [126,] 0.7639070 0.23609299
## [127,] 0.8083766 0.19162341
## [128,] 0.6708804 0.32911956
## [129,] 0.3101894 0.68981058
## [130,] 0.2802322 0.71976781
## [131,] 0.7135891 0.28641092
## [132,] 0.2685956 0.73140440
## [133,] 0.7492713 0.25072868
## [134,] 0.3206334 0.67936660
## [135,] 0.2925193 0.70748072
## [136,] 0.7678237 0.23217631
## [137,] 0.3174764 0.68252356
## [138,] 0.7771459 0.22285414
## [139,] 0.8409872 0.15901279
## [140,] 0.7227022 0.27729781
## [141,] 0.2939985 0.70600153
## [142,] 0.8147592 0.18524076
## [143,] 0.8495166 0.15048335
## [144,] 0.1365329 0.86346711
## [145,] 0.2570483 0.74295169
## [146,] 0.7430558 0.25694422
## [147,] 0.3022627 0.69773725
## [148,] 0.7642221 0.23577789
## [149,] 0.7232284 0.27677155
## [150,] 0.7481993 0.25180068
## [151,] 0.2370037 0.76299631
## [152,] 0.7023619 0.29763808
## [153,] 0.7547330 0.24526697
## [154,] 0.1920534 0.80794659
## [155,] 0.7571676 0.24283237
## [156,] 0.2273305 0.77266953
## [157,] 0.3105389 0.68946111
## [158,] 0.2777195 0.72228054
## [159,] 0.7036250 0.29637499
## [160,] 0.3203600 0.67964005
## [161,] 0.2197928 0.78020719
## [162,] 0.2152383 0.78476173
## [163,] 0.6882426 0.31175743
## [164,] 0.3074292 0.69257081
## [165,] 0.7355701 0.26442986
## [166,] 0.2929833 0.70701665
## [167,] 0.6846038 0.31539617
## [168,] 0.8388153 0.16118468
## [169,] 0.2882137 0.71178633
## [170,] 0.6922476 0.30775237
## [171,] 0.2797796 0.72022037
## [172,] 0.2763663 0.72363367
## [173,] 0.7113742 0.28862580
## [174,] 0.7401105 0.25988951
## [175,] 0.2903805 0.70961946
## [176,] 0.7172925 0.28270751
## [177,] 0.7152328 0.28476723
## [178,] 0.2094237 0.79057629
## [179,] 0.7121582 0.28784183
## [180,] 0.7912490 0.20875104
## [181,] 0.7252575 0.27474247
## [182,] 0.7158436 0.28415636
## [183,] 0.2670597 0.73294035
## [184,] 0.7762962 0.22370376
## [185,] 0.7394810 0.26051896
## [186,] 0.3193720 0.68062802
## [187,] 0.7255279 0.27447210
## [188,] 0.7185620 0.28143796
## [189,] 0.7217456 0.27825441
## [190,] 0.2276115 0.77238847
## [191,] 0.7320989 0.26790115
## [192,] 0.8011984 0.19880156
## [193,] 0.6780866 0.32191339
## [194,] 0.6842307 0.31576927
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## [661,] 0.3001105 0.69988949
## [662,] 0.2870964 0.71290355
## [663,] 0.2165004 0.78349959
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## [665,] 0.2855329 0.71446707
## [666,] 0.3148588 0.68514123
## [667,] 0.2823080 0.71769203
## [668,] 0.3095843 0.69041574
## [669,] 0.3057098 0.69429022
## [670,] 0.2772310 0.72276898
## [671,] 0.7785031 0.22149691
## [672,] 0.7297008 0.27029915
## [673,] 0.7130839 0.28691608
## [674,] 0.8552845 0.14471551
## [675,] 0.7562810 0.24371895
## [676,] 0.2912738 0.70872620
## [677,] 0.7923251 0.20767486
## [678,] 0.7704412 0.22955876
## [679,] 0.2727802 0.72721976
## [680,] 0.7356671 0.26433294
## [681,] 0.2480749 0.75192512
## [682,] 0.8051497 0.19485031
## [683,] 0.2509149 0.74908506
## [684,] 0.8343412 0.16565880
## [685,] 0.8319104 0.16808964
## [686,] 0.7267684 0.27323165
## [687,] 0.8456153 0.15438472
## [688,] 0.7244486 0.27555136
## [689,] 0.6960528 0.30394720
## [690,] 0.8025540 0.19744602
## [691,] 0.6887961 0.31120387
## [692,] 0.7077154 0.29228464
## [693,] 0.8344091 0.16559093
## [694,] 0.8553910 0.14460905
## [695,] 0.3033235 0.69667653
## [696,] 0.7691987 0.23080129
## [697,] 0.7270943 0.27290573
## [698,] 0.2924767 0.70752327
## [699,] 0.7242020 0.27579801
## [700,] 0.2809192 0.71908078
## [701,] 0.7343770 0.26562301
## [702,] 0.7174872 0.28251279
## [703,] 0.7304144 0.26958557
## [704,] 0.7899443 0.21005573
## [705,] 0.8206756 0.17932442
## [706,] 0.9076947 0.09230532
## [707,] 0.7050454 0.29495457
## [708,] 0.9136557 0.08634434
## [709,] 0.7433390 0.25666095
## [710,] 0.8250193 0.17498065
## [711,] 0.7896808 0.21031921
## [712,] 0.8293332 0.17066679
## [713,] 0.9167405 0.08325950
## [714,] 0.3163215 0.68367850
## [715,] 0.7121575 0.28784248
## [716,] 0.7732342 0.22676577
## [717,] 0.7579597 0.24204032
## [718,] 0.2218664 0.77813358
## [719,] 0.7159355 0.28406455
## [720,] 0.7338408 0.26615924
## [721,] 0.8278503 0.17214975
## [722,] 0.2820359 0.71796406
## [723,] 0.2913816 0.70861843
## [724,] 0.3189407 0.68105929
## [725,] 0.7026197 0.29738035
## [726,] 0.7742449 0.22575509
## [727,] 0.2542778 0.74572225
## [728,] 0.6996439 0.30035610
## [729,] 0.2786525 0.72134748
## [730,] 0.6889755 0.31102451
## [731,] 0.7140839 0.28591606
## [732,] 0.7163284 0.28367161
## [733,] 0.2712965 0.72870351
## [734,] 0.8082717 0.19172827
## [735,] 0.7358948 0.26410523
## [736,] 0.8031813 0.19681869
## [737,] 0.7737356 0.22626439
## [738,] 0.2676739 0.73232615
## [739,] 0.2504598 0.74954017
## [740,] 0.2801103 0.71988970
## [741,] 0.8168426 0.18315739
## [742,] 0.7556770 0.24432305
## [743,] 0.6872373 0.31276271
## [744,] 0.8262171 0.17378293
## [745,] 0.7621051 0.23789493
## [746,] 0.7332826 0.26671738
## [747,] 0.7221149 0.27788513
## [748,] 0.6870437 0.31295626
## [749,] 0.7133421 0.28665786
## [750,] 0.7412028 0.25879720
## [751,] 0.2856413 0.71435873
## [752,] 0.2187178 0.78128219
## [753,] 0.7233995 0.27660051
## [754,] 0.2981904 0.70180961
## [755,] 0.2922316 0.70776844
## [756,] 0.8056674 0.19433257
## [757,] 0.3231657 0.67683428
## [758,] 0.2962603 0.70373972
## [759,] 0.8037952 0.19620482
## [760,] 0.7026179 0.29738206
## [761,] 0.2187908 0.78120916
## [762,] 0.6883952 0.31160479
## [763,] 0.7128415 0.28715854
## [764,] 0.6842893 0.31571074
## [765,] 0.7334666 0.26653336
## [766,] 0.3005707 0.69942926
## [767,] 0.8012749 0.19872507
## [768,] 0.8007670 0.19923296
## [769,] 0.8202609 0.17973906
## [770,] 0.2834841 0.71651590
## [771,] 0.8094287 0.19057127
## [772,] 0.2802331 0.71976690
## [773,] 0.2025588 0.79744124
## [774,] 0.6873584 0.31264158
## [775,] 0.7788342 0.22116582
## [776,] 0.7580712 0.24192875
## [777,] 0.7546798 0.24532015
## [778,] 0.2630611 0.73693886
## [779,] 0.7634319 0.23656810
## [780,] 0.7668991 0.23310088
## [781,] 0.7660940 0.23390597
## [782,] 0.8754145 0.12458547
## [783,] 0.7217681 0.27823193
## [784,] 0.7225855 0.27741454
## [785,] 0.7621400 0.23786002
## [786,] 0.2475120 0.75248798
## [787,] 0.7097748 0.29022522
## [788,] 0.7336888 0.26631122
## [789,] 0.7056558 0.29434418
## [790,] 0.6880488 0.31195122
## [791,] 0.2987512 0.70124881
## [792,] 0.6791981 0.32080191
## [793,] 0.2501850 0.74981504
## [794,] 0.7357453 0.26425469
## [795,] 0.7192227 0.28077725
## [796,] 0.7400243 0.25997572
## [797,] 0.7221994 0.27780061
## [798,] 0.6865679 0.31343206
## [799,] 0.7155971 0.28440291
## [800,] 0.8186004 0.18139959
## [801,] 0.6944961 0.30550394
## [802,] 0.2689791 0.73102091
## [803,] 0.7101802 0.28981984
## [804,] 0.2396394 0.76036059
## [805,] 0.2492927 0.75070726
## [806,] 0.7271507 0.27284935
## [807,] 0.7021386 0.29786138
## [808,] 0.7232641 0.27673589
## [809,] 0.2845139 0.71548606
## [810,] 0.7438915 0.25610853
## [811,] 0.7154700 0.28452999
## [812,] 0.3193159 0.68068413
## [813,] 0.2766372 0.72336278
## [814,] 0.7519500 0.24804999
## [815,] 0.7785090 0.22149103
## [816,] 0.2856433 0.71435669
## [817,] 0.7251576 0.27484245
## [818,] 0.7153022 0.28469778
## [819,] 0.8690980 0.13090196
## [820,] 0.7570875 0.24291253
## [821,] 0.7292928 0.27070722
## [822,] 0.6747099 0.32529007
## [823,] 0.7100684 0.28993157
## [824,] 0.7972292 0.20277081
## [825,] 0.2752519 0.72474806
## [826,] 0.2499563 0.75004371
## [827,] 0.7931833 0.20681671
## [828,] 0.7799595 0.22004055
## [829,] 0.6998368 0.30016320
## [830,] 0.6878812 0.31211882
## [831,] 0.7200611 0.27993888
## [832,] 0.2940734 0.70592657
## [833,] 0.6935506 0.30644944
## [834,] 0.7320225 0.26797752
## [835,] 0.7633204 0.23667958
## [836,] 0.8703558 0.12964423
## [837,] 0.7683988 0.23160121
## [838,] 0.6912011 0.30879894
## [839,] 0.7778862 0.22211384
## [840,] 0.7494061 0.25059389
## [841,] 0.7069287 0.29307129
## [842,] 0.7035573 0.29644272
## [843,] 0.8043749 0.19562515
## [844,] 0.7380618 0.26193821
## [845,] 0.7591362 0.24086379
## [846,] 0.7068972 0.29310284
## [847,] 0.7525367 0.24746332
## [848,] 0.2309930 0.76900701
## [849,] 0.7301794 0.26982063
## [850,] 0.8710496 0.12895043
## [851,] 0.2738647 0.72613530
## [852,] 0.7281009 0.27189912
## [853,] 0.7168665 0.28313350
## [854,] 0.6912128 0.30878715
## [855,] 0.7233926 0.27660738
## [856,] 0.3133207 0.68667934
## [857,] 0.2983841 0.70161588
## [858,] 0.7077396 0.29226038
## [859,] 0.1492170 0.85078305
## [860,] 0.6967906 0.30320944
## [861,] 0.3191513 0.68084875
## [862,] 0.7138595 0.28614052
## [863,] 0.6999282 0.30007181
## [864,] 0.7240136 0.27598642
## [865,] 0.7210537 0.27894629
## [866,] 0.2029026 0.79709738
## [867,] 0.8042535 0.19574648
## [868,] 0.7992239 0.20077612
## [869,] 0.1999911 0.80000887
## [870,] 0.7267445 0.27325554
## [871,] 0.3138288 0.68617123
## [872,] 0.7166322 0.28336784
## [873,] 0.2944821 0.70551785
## [874,] 0.7229035 0.27709655
## [875,] 0.6933957 0.30660428
## [876,] 0.2909790 0.70902104
## [877,] 0.7491520 0.25084797
## [878,] 0.7062365 0.29376352
## [879,] 0.7014495 0.29855052
## [880,] 0.7082607 0.29173934
## [881,] 0.7783362 0.22166375
## [882,] 0.7068492 0.29315083
## [883,] 0.6937676 0.30623242
## [884,] 0.3091234 0.69087658
## [885,] 0.6979865 0.30201350
## [886,] 0.3011004 0.69889960
## [887,] 0.7094118 0.29058824
## [888,] 0.7270261 0.27297390
## [889,] 0.6861948 0.31380520
## [890,] 0.7144470 0.28555302
## [891,] 0.6915444 0.30845560
## [892,] 0.7662389 0.23376111
## [893,] 0.7210739 0.27892607
## [894,] 0.6780216 0.32197837
## [895,] 0.7149991 0.28500091
## [896,] 0.6889721 0.31102786
## [897,] 0.7180819 0.28191811
## [898,] 0.6889960 0.31100402
## [899,] 0.7539496 0.24605038
## [900,] 0.7038582 0.29614179
## [901,] 0.6946684 0.30533164
## [902,] 0.7272996 0.27270039
## [903,] 0.7077434 0.29225660
## [904,] 0.7116630 0.28833699
## [905,] 0.6851791 0.31482094
## [906,] 0.8204289 0.17957111
## [907,] 0.1323777 0.86762229
## [908,] 0.2653171 0.73468294
## [909,] 0.2988302 0.70116983
## [910,] 0.3141419 0.68585806
## [911,] 0.1717915 0.82820851
## [912,] 0.7307536 0.26924637
##
## $class
## [1] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "B" "B" "B"
## [19] "M" "M" "M" "M" "M" "M" "M" "M" "B" "M" "M" "M" "M" "M" "M" "B" "M" "B"
## [37] "B" "B" "M" "M" "B" "M" "M" "B" "B" "B" "B" "M" "B" "B" "B" "M" "M" "M"
## [55] "B" "M" "M" "B" "B" "B" "M" "M" "B" "M" "M" "M" "B" "B" "B" "M" "B" "B"
## [73] "M" "M" "B" "B" "M" "B" "B" "B" "B" "M" "B" "M" "B" "B" "B" "B" "B" "B"
## [91] "B" "B" "M" "M" "M" "B" "M" "B" "B" "M" "M" "B" "M" "B" "M" "M" "B" "M"
## [109] "B" "B" "M" "B" "B" "M" "B" "B" "M" "B" "B" "B" "B" "B" "B" "B" "B" "B"
## [127] "B" "B" "M" "M" "B" "M" "B" "M" "M" "B" "M" "B" "B" "B" "M" "B" "B" "M"
## [145] "M" "B" "M" "B" "B" "B" "M" "B" "B" "M" "B" "M" "M" "M" "B" "M" "M" "M"
## [163] "B" "M" "B" "M" "B" "B" "M" "B" "M" "M" "B" "B" "M" "B" "B" "M" "B" "B"
## [181] "B" "B" "M" "B" "B" "M" "B" "B" "B" "M" "B" "B" "B" "B" "M" "B" "B" "B"
## [199] "B" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "B" "B" "B" "B" "B" "M"
## [217] "B" "M" "B" "B" "M" "B" "M" "B" "M" "M" "B" "B" "B" "B" "B" "B" "B" "B"
## [235] "B" "B" "B" "B" "B" "M" "B" "B" "M" "B" "M" "B" "B" "B" "B" "B" "B" "B"
## [253] "B" "B" "B" "M" "B" "B" "B" "M" "B" "M" "B" "B" "M" "M" "B" "B" "B" "M"
## [271] "B" "B" "M" "B" "B" "B" "M" "B" "B" "B" "B" "B" "B" "B" "M" "M" "M" "B"
## [289] "B" "B" "B" "B" "B" "B" "B" "M" "M" "M" "M" "M" "B" "M" "B" "B" "B" "B"
## [307] "B" "M" "B" "B" "B" "B" "B" "M" "B" "M" "B" "M" "B" "B" "B" "B" "M" "B"
## [325] "B" "B" "B" "B" "B" "M" "B" "B" "B" "B" "B" "M" "B" "B" "M" "B" "B" "B"
## [343] "B" "B" "B" "B" "M" "B" "M" "M" "M" "B" "B" "B" "B" "B" "B" "B" "M" "B"
## [361] "M" "B" "B" "M" "B" "M" "B" "B" "B" "B" "B" "B" "M" "M" "B" "B" "B" "B"
## [379] "B" "M" "B" "B" "B" "B" "B" "B" "M" "B" "B" "B" "B" "B" "M" "M" "B" "B"
## [397] "B" "B" "B" "M" "M" "M" "B" "B" "B" "M" "B" "B" "M" "M" "B" "M" "B" "B"
## [415] "B" "M" "B" "B" "B" "B" "B" "B" "B" "B" "B" "B" "M" "B" "B" "B" "B" "B"
## [433] "B" "B" "B" "B" "B" "B" "B" "B" "B" "B" "B" "B" "B" "B" "B" "M" "M" "M"
## [451] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "B"
## [469] "B" "B" "M" "M" "M" "M" "M" "M" "M" "M" "M" "B" "M" "M" "M" "M" "M" "M"
## [487] "M" "B" "B" "B" "B" "B" "M" "M" "B" "M" "B" "B" "B" "B" "M" "B" "M" "B"
## [505] "B" "B" "M" "M" "M" "B" "M" "B" "M" "M" "B" "B" "M" "M" "B" "M" "M" "M"
## [523] "B" "B" "B" "M" "B" "B" "M" "B" "B" "M" "M" "B" "B" "B" "M" "B" "M" "B"
## [541] "B" "B" "B" "B" "B" "B" "M" "M" "B" "M" "B" "B" "B" "M" "B" "M" "B" "M"
## [559] "M" "B" "M" "M" "B" "B" "M" "M" "B" "B" "B" "M" "B" "B" "B" "B" "B" "B"
## [577] "B" "B" "B" "M" "B" "B" "M" "B" "M" "B" "B" "M" "M" "B" "M" "B" "B" "B"
## [595] "B" "M" "B" "B" "M" "M" "M" "B" "M" "B" "M" "B" "B" "B" "M" "B" "B" "M"
## [613] "B" "M" "M" "M" "B" "M" "M" "M" "B" "M" "B" "M" "B" "B" "M" "B" "M" "M"
## [631] "M" "M" "B" "B" "M" "B" "B" "B" "B" "B" "M" "M" "B" "B" "B" "M" "B" "M"
## [649] "B" "M" "B" "B" "B" "B" "B" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [667] "M" "M" "M" "M" "B" "B" "B" "B" "B" "M" "B" "B" "M" "B" "M" "B" "M" "B"
## [685] "B" "B" "B" "B" "B" "B" "B" "B" "B" "B" "M" "B" "B" "M" "B" "M" "B" "B"
## [703] "B" "B" "B" "B" "B" "B" "B" "B" "B" "B" "B" "M" "B" "B" "B" "M" "B" "B"
## [721] "B" "M" "M" "M" "B" "B" "M" "B" "M" "B" "B" "B" "M" "B" "B" "B" "B" "M"
## [739] "M" "M" "B" "B" "B" "B" "B" "B" "B" "B" "B" "B" "M" "M" "B" "M" "M" "B"
## [757] "M" "M" "B" "B" "M" "B" "B" "B" "B" "M" "B" "B" "B" "M" "B" "M" "M" "B"
## [775] "B" "B" "B" "M" "B" "B" "B" "B" "B" "B" "B" "M" "B" "B" "B" "B" "M" "B"
## [793] "M" "B" "B" "B" "B" "B" "B" "B" "B" "M" "B" "M" "M" "B" "B" "B" "M" "B"
## [811] "B" "M" "M" "B" "B" "M" "B" "B" "B" "B" "B" "B" "B" "B" "M" "M" "B" "B"
## [829] "B" "B" "B" "M" "B" "B" "B" "B" "B" "B" "B" "B" "B" "B" "B" "B" "B" "B"
## [847] "B" "M" "B" "B" "M" "B" "B" "B" "B" "M" "M" "B" "M" "B" "M" "B" "B" "B"
## [865] "B" "M" "B" "B" "M" "B" "M" "B" "M" "B" "B" "M" "B" "B" "B" "B" "B" "B"
## [883] "B" "M" "B" "M" "B" "B" "B" "B" "B" "B" "B" "B" "B" "B" "B" "B" "B" "B"
## [901] "B" "B" "B" "B" "B" "B" "M" "M" "M" "M" "M" "B"
##
## $importance
## compactness_se smoothness_mean smoothness_worst symmetry_worst
## 17.98418 16.04764 16.93026 18.55361
## texture_mean texture_worst
## 16.81249 13.67182
##
## $terms
## .outcome ~ texture_mean + smoothness_mean + compactness_se +
## texture_worst + smoothness_worst + symmetry_worst
## attr(,"variables")
## list(.outcome, texture_mean, smoothness_mean, compactness_se,
## texture_worst, smoothness_worst, symmetry_worst)
## attr(,"factors")
## texture_mean smoothness_mean compactness_se texture_worst
## .outcome 0 0 0 0
## texture_mean 1 0 0 0
## smoothness_mean 0 1 0 0
## compactness_se 0 0 1 0
## texture_worst 0 0 0 1
## smoothness_worst 0 0 0 0
## symmetry_worst 0 0 0 0
## smoothness_worst symmetry_worst
## .outcome 0 0
## texture_mean 0 0
## smoothness_mean 0 0
## compactness_se 0 0
## texture_worst 0 0
## smoothness_worst 1 0
## symmetry_worst 0 1
## attr(,"term.labels")
## [1] "texture_mean" "smoothness_mean" "compactness_se" "texture_worst"
## [5] "smoothness_worst" "symmetry_worst"
## attr(,"order")
## [1] 1 1 1 1 1 1
## attr(,"intercept")
## [1] 1
## attr(,"response")
## [1] 1
## attr(,".Environment")
## <environment: 0x0000024d60f9b4c0>
## attr(,"predvars")
## list(.outcome, texture_mean, smoothness_mean, compactness_se,
## texture_worst, smoothness_worst, symmetry_worst)
## attr(,"dataClasses")
## .outcome texture_mean smoothness_mean compactness_se
## "factor" "numeric" "numeric" "numeric"
## texture_worst smoothness_worst symmetry_worst
## "numeric" "numeric" "numeric"
##
## $call
## (function (formula, data, boos = TRUE, mfinal = 100, coeflearn = "Breiman",
## control, ...)
## {
## if (!(as.character(coeflearn) %in% c("Freund", "Breiman",
## "Zhu"))) {
## stop("coeflearn must be 'Freund', 'Breiman' or 'Zhu' ")
## }
## formula <- as.formula(formula)
## vardep <- data[, as.character(formula[[2]])]
## n <- length(data[, 1])
## nclases <- nlevels(vardep)
## pesos <- rep(1/n, n)
## guardarpesos <- array(0, c(n, mfinal))
## w <- rep(1/n, n)
## data <- cbind(pesos, data)
## arboles <- list()
## pond <- rep(0, mfinal)
## pred <- data.frame(rep(0, n))
## arboles[[1]] <- rpart(formula, data = data[, -1], control = rpart.control(minsplit = 1,
## cp = -1, maxdepth = 30))
## nvar <- dim(varImp(arboles[[1]], surrogates = FALSE, competes = FALSE))[1]
## imp <- array(0, c(mfinal, nvar))
## for (m in 1:mfinal) {
## if (boos == TRUE) {
## k <- 1
## while (k == 1) {
## boostrap <- sample(1:n, replace = TRUE, prob = pesos)
## fit <- rpart(formula, data = data[boostrap, -1],
## control = control)
## k <- length(fit$frame$var)
## }
## flearn <- predict(fit, newdata = data[, -1], type = "class")
## ind <- as.numeric(vardep != flearn)
## err <- sum(pesos * ind)
## }
## if (boos == FALSE) {
## w <<- pesos
## fit <- rpart(formula = formula, data = data[, -1],
## weights = w, control = control)
## flearn <- predict(fit, data = data[, -1], type = "class")
## ind <- as.numeric(vardep != flearn)
## err <- sum(pesos * ind)
## }
## c <- log((1 - err)/err)
## if (coeflearn == "Breiman") {
## c <- (1/2) * c
## }
## if (coeflearn == "Zhu") {
## c <- c + log(nclases - 1)
## }
## guardarpesos[, m] <- pesos
## pesos <- pesos * exp(c * ind)
## pesos <- pesos/sum(pesos)
## maxerror <- 0.5
## eac <- 0.001
## if (coeflearn == "Zhu") {
## maxerror <- 1 - 1/nclases
## }
## if (err >= maxerror) {
## pesos <- rep(1/n, n)
## maxerror <- maxerror - eac
## c <- log((1 - maxerror)/maxerror)
## if (coeflearn == "Breiman") {
## c <- (1/2) * c
## }
## if (coeflearn == "Zhu") {
## c <- c + log(nclases - 1)
## }
## }
## if (err == 0) {
## pesos <- rep(1/n, n)
## c <- log((1 - eac)/eac)
## if (coeflearn == "Breiman") {
## c <- (1/2) * c
## }
## if (coeflearn == "Zhu") {
## c <- c + log(nclases - 1)
## }
## }
## arboles[[m]] <- fit
## pond[m] <- c
## if (m == 1) {
## pred <- flearn
## }
## else {
## pred <- data.frame(pred, flearn)
## }
## if (length(fit$frame$var) > 1) {
## k <- varImp(fit, surrogates = FALSE, competes = FALSE)
## imp[m, ] <- k[sort(row.names(k)), ]
## }
## else {
## imp[m, ] <- rep(0, nvar)
## }
## }
## classfinal <- array(0, c(n, nlevels(vardep)))
## for (i in 1:nlevels(vardep)) {
## classfinal[, i] <- matrix(as.numeric(pred == levels(vardep)[i]),
## nrow = n) %*% as.vector(pond)
## }
## predclass <- rep("O", n)
## predclass[] <- apply(classfinal, 1, FUN = select, vardep.summary = summary(vardep))
## imppond <- as.vector(as.vector(pond) %*% imp)
## imppond <- imppond/sum(imppond) * 100
## names(imppond) <- sort(row.names(k))
## votosporc <- classfinal/apply(classfinal, 1, sum)
## ans <- list(formula = formula, trees = arboles, weights = pond,
## votes = classfinal, prob = votosporc, class = predclass,
## importance = imppond)
## attr(ans, "vardep.summary") <- summary(vardep, maxsum = 700)
## mf <- model.frame(formula = formula, data = data[, -1])
## terms <- attr(mf, "terms")
## ans$terms <- terms
## ans$call <- match.call()
## class(ans) <- "boosting"
## ans
## })(formula = .outcome ~ ., data = list(texture_mean = c(2.33988087773774,
## 2.87751164216656, 3.05635689537043, 2.75366071235426, 2.99473177322041,
## 3.03639425527288, 3.08282698040492, 3.17971910966701, 3.14587493198371,
## 2.88424189752063, 3.21084365317094, 3.11839228628988, 3.31563949330051,
## 3.0022112396517, 3.02916704964023, 2.66444656362008, 2.75429745226753,
## 2.52091708731103, 2.65745841498615, 3.0624559055969, 2.79728133483015,
## 3.06944731137627, 3.00815479355255, 2.71137799119488, 2.92852352386054,
## 2.88368276974537, 2.91343703082716, 3.22684399451738, 3.03591406318682,
## 3.06105173967463, 3.21124679770371, 3.08236858021354, 2.86789890204411,
## 2.82375700881418, 2.9263821954192, 2.68307421503203, 2.79361608943186,
## 2.90361698464619, 2.92852352386054, 3.09195113129453, 2.93119375241642,
## 2.92154737536461, 3.07223024452672, 2.96062309644042, 2.70001802940495,
## 3.04356960296815, 2.62900699376176, 3.17136484219715, 3.04499851485691,
## 2.94654202936322, 2.85243910372751, 3.05917644611053, 3.19948911106801,
## 2.75937682826755, 2.80457176809283, 2.78192004966867, 3.17680304844629,
## 2.89037175789616, 3.04309284491383, 2.7638002162067, 3.21526932927409,
## 3.26918863874179, 2.75047091698616, 2.91885122921803, 3.06619073720255,
## 3.2023398562281, 3.08190996979504, 2.7239235502585, 3.17888681665184,
## 3.12500460925813, 2.69192081917233, 2.90690105984738, 2.9871959425317,
## 3.13679771383259, 2.88144312715186, 2.55256529826182, 2.98416563718253,
## 2.59749101053515, 3.02140002030257, 2.96527306606928, 2.95958682691764,
## 2.74470351875025, 2.91993056013771, 2.97909463240097, 3.0568273729138,
## 2.83262493568384, 3.03302805829769, 2.97807733831527, 3.00518743232475,
## 2.76190687389292, 3.06944731137627, 2.75747508442973, 2.8136106967627,
## 3.1315734964654, 2.99623214859564, 2.38139627341834, 3.00568260440716,
## 2.79667139275574, 2.84549061022345, 3.20639830335709, 2.93969088267037,
## 2.79667139275574, 3.2236643416, 2.58701187272515, 2.96938829821439,
## 3.06991167172824, 2.63404478779171, 3.08694315360738, 2.8136106967627,
## 2.73371794785079, 2.86619290219901, 2.59450815970308, 2.48240351956988,
## 2.89314568477889, 2.76757618041624, 2.68444033546308, 2.80819714970715,
## 2.93225985059842, 2.71997877196748, 2.88535921607262, 3.03013370027132,
## 2.57108434602905, 2.73046379593911, 2.88703285663065, 2.96836107675786,
## 2.54474665014402, 2.56186769092413, 3.00469201492546, 2.76883167336207,
## 2.89867056071086, 3.10099278421148, 3.09285898428471, 2.98365969231972,
## 2.9338568698359, 3.20599319903719, 2.51688969564105, 2.97705900828837,
## 2.71800053195538, 2.67069441455844, 2.89369954798884, 3.00121720378456,
## 3.10099278421148, 2.56955412384829, 3.27978275977172, 3.01111337559229,
## 2.70270259477561, 2.92208573338569, 2.84432781939476, 2.85589532836619,
## 2.76631910922619, 3.14069804380418, 3.06385810260159, 2.90251989183181,
## 3.29063819109509, 2.79300390698237, 3.10413814739778, 3.0837431508767,
## 3.00667221359233, 2.97348666460667, 2.96114082878437, 3.28353933819392,
## 3.16758253048065, 2.92316158071916, 2.8142103969306, 2.84897089215859,
## 3.00864849882054, 3.11529150861163, 2.55800220485855, 3.09738592728049,
## 2.94127608775793, 3.24102862950933, 2.82908719614504, 2.90962957450058,
## 2.86105737022739, 3.48031658611475, 2.63188884013665, 2.86391369893314,
## 3.00815479355255, 2.83438912314523, 2.60046499042227, 2.74148497718845,
## 3.17680304844629, 3.10593106585207, 3.29879544804407, 3.52075661671979,
## 3.32539566824587, 2.7669478423497, 3.05635689537043, 3.32683296637329,
## 3.67071548348627, 2.74727091425549, 2.71071331852169, 2.90087199253003,
## 3.1684242813721, 3.15700042115011, 2.98870765861703, 2.64688376586472,
## 3.22763733053677, 2.7033726115511, 3.15955035878339, 2.98669152890184,
## 2.83790818836042, 2.96165829322024, 2.83615020372953, 3.35933317756346,
## 2.84897089215859, 3.14415227867226, 3.09693415406296, 2.96424160646262,
## 3.43785069931019, 2.94180393152844, 3.0837431508767, 2.78562833574758,
## 3.01504458458636, 2.56802155649851, 3.04404613383254, 2.75174805636793,
## 3.19785645764413, 2.8541687092322, 2.65042108826557, 2.99473177322041,
## 2.88144312715186, 3.28091121578765, 2.64048488160644, 2.90032208874933,
## 2.93225985059842, 2.75366071235426, 2.91235066461494, 3.03302805829769,
## 2.57413778351594, 2.99373027088332, 2.93863268151342, 2.98214032003452,
## 2.94968833505258, 2.77383794164021, 2.85991255041146, 2.62321826558551,
## 2.58550584834412, 2.51365606307399, 2.89811944468699, 2.89977188240808,
## 3.1393996233664, 2.9391619220656, 2.99021709286588, 3.17220341666977,
## 2.92369907065416, 3.19826487096408, 2.76127496233951, 2.66722820658195,
## 2.54238908520136, 2.62756295018952, 2.75302356674494, 2.59301339111385,
## 2.37211115564266, 2.82435065679837, 2.93757335938046, 2.9391619220656,
## 2.83321334405622, 2.78377591163035, 2.97858611471902, 2.58926666511224,
## 3.06851794327964, 2.7219531062712, 2.85070650150373, 3.03061667540749,
## 2.74148497718845, 2.96269241947579, 2.98870765861703, 2.94549105711724,
## 3.04452243772342, 2.65535241210176, 2.86391369893314, 3.18924101973851,
## 2.80578168959555, 2.82375700881418, 2.70537997254633, 3.07639017657145,
## 2.73760900334375, 2.68852753461335, 2.9391619220656, 2.69056488676119,
## 2.77446196662146, 2.70537997254633, 2.83732253680635, 2.95595140354215,
## 2.85991255041146, 3.24804620216798, 2.6440448711263, 2.78562833574758,
## 2.74019465442878, 2.90799335924598, 2.89425310460414, 3.07130346040107,
## 2.93598226914822, 2.83026783382646, 3.08099211750481, 3.28952066443753,
## 2.84781214347737, 3.08648663682246, 3.14802408389625, 2.58097411853423,
## 2.85359250639287, 2.77695417974942, 2.77695417974942, 3.00667221359233,
## 3.33967652501391, 2.71800053195538, 2.93545134266906, 2.56186769092413,
## 2.7033726115511, 3.12324559385295, 2.86105737022739, 2.61885462229774,
## 3.14802408389625, 2.64546532591059, 3.14458322028635, 2.82375700881418,
## 3.10368941505908, 2.87469394517693, 2.85991255041146, 2.69665215614984,
## 2.84839168565528, 3.04547436544881, 2.38967979984498, 2.90635446240277,
## 2.7047112998367, 2.92262380173335, 2.69867303928961, 3.06198806933106,
## 3.02819946369149, 2.88591740754678, 2.86619290219901, 2.82316300820271,
## 3.07639017657145, 3.09602999486936, 3.39484390768998, 3.05258508514677,
## 3.07731226054641, 3.04832472367316, 2.49897390699944, 2.94654202936322,
## 2.77383794164021, 2.95125778345216, 2.95073490762326, 3.05776766447344,
## 3.09013294897548, 3.11484775444415, 2.8724340572095, 2.97246364661464,
## 3.08967788639652, 2.97654945413722, 2.97246364661464, 2.77133794033813,
## 2.97552956623647, 2.75110969056266, 2.84490938381941, 2.75937682826755,
## 2.90799335924598, 2.82435065679837, 3.2144661163795, 3.3332753651767,
## 2.87130219517581, 2.96217549002515, 3.02140002030257, 3.06991167172824,
## 3.2188758248682, 3.3403852422654, 2.84199817361195, 3.42491390827947,
## 3.37724616083964, 3.2240623515555, 3.33932197794407, 3.30137704637994,
## 3.26842760369745, 2.91017438519234, 2.90251989183181, 3.0022112396517,
## 3.03206420280138, 2.89591193827178, 3.14974008603334, 2.91723004539903,
## 3.33719205168624, 2.7033726115511, 2.74855214441154, 2.75556971707019,
## 3.02188723103084, 2.97092715463502, 2.89203703721523, 2.95699144523756,
## 2.64333388638252, 2.42303124606991, 2.82435065679837, 2.93492013415723,
## 3.02334744058696, 2.55178617862755, 3.02237420450041, 3.00617753141553,
## 2.89977188240808, 2.85128436918812, 3.05588619637374, 3.19826487096408,
## 2.9871959425317, 2.55489902160804, 2.57566101305646, 2.8402473707136,
## 3.17596832385692, 2.68716699018579, 2.68784749378469, 3.02140002030257,
## 2.94811641961233, 2.92369907065416, 3.0243197304059, 2.90251989183181,
## 2.81540871942271, 2.63188884013665, 3.07269331469012, 2.9274534328007,
## 2.75238601492226, 2.57261223020711, 2.93119375241642, 2.50715725872282,
## 2.57794151575519, 2.598235335095, 2.86562358820697, 2.99673177388707,
## 2.79300390698237, 3.11573506594869, 3.19622113430339, 3.23828621838802,
## 3.23632273847192, 2.67000213346468, 3.23553626576131, 3.33434507467431,
## 3.14544454678232, 2.79422789734326, 2.80819714970715, 3.06712226964066,
## 3.1108450806545, 3.38201456224538, 3.08831145484708, 3.36453339729056,
## 3.31817802594206, 2.97501923195645, 3.32790958589232, 3.12148347885955,
## 3.17513290192028, 3.37997374521053, 3.22246936037833, 3.10861443061066,
## 3.33505757915761, 3.37861088298936, 2.33988087773774, 2.87751164216656,
## 3.05635689537043, 3.01455402779458, 2.75366071235426, 2.99473177322041,
## 3.03639425527288, 3.08282698040492, 3.17971910966701, 3.14587493198371,
## 3.17596832385692, 3.11839228628988, 3.31563949330051, 3.0022112396517,
## 3.02916704964023, 3.09783749649114, 2.66444656362008, 2.75429745226753,
## 2.52091708731103, 2.65745841498615, 3.13723183582769, 2.79728133483015,
## 2.92852352386054, 3.17722014959937, 3.27601201623901, 2.88368276974537,
## 3.07223024452672, 3.07823349506573, 2.91343703082716, 3.22684399451738,
## 3.03591406318682, 3.07176695982999, 3.06105173967463, 3.00963517872298,
## 3.08236858021354, 2.86789890204411, 2.68307421503203, 3.10458667846607,
## 3.07269331469012, 2.79361608943186, 2.90361698464619, 2.92852352386054,
## 3.09195113129453, 2.93119375241642, 3.07223024452672, 2.96062309644042,
## 2.46725171454928, 2.70001802940495, 3.04356960296815, 3.09783749649114,
## 2.62900699376176, 3.17555070012983, 3.04499851485691, 2.94654202936322,
## 2.85243910372751, 3.05917644611053, 3.19948911106801, 2.75937682826755,
## 2.80457176809283, 2.97807733831527, 2.39242579699384, 2.78192004966867,
## 3.17680304844629, 2.89037175789616, 2.7638002162067, 3.21526932927409,
## 3.26918863874179, 2.75047091698616, 2.91885122921803, 3.06619073720255,
## 3.2023398562281, 3.08190996979504, 2.7239235502585, 3.17888681665184,
## 3.12500460925813, 2.69192081917233, 2.90690105984738, 2.9871959425317,
## 2.99272776453369, 2.55256529826182, 2.98416563718253, 3.21807550469743,
## 2.59749101053515, 3.02140002030257, 2.95958682691764, 2.74470351875025,
## 2.91993056013771, 2.97909463240097, 3.0568273729138, 2.83262493568384,
## 3.03302805829769, 2.97807733831527, 3.00518743232475, 3.06944731137627,
## 2.75747508442973, 2.8136106967627, 2.99623214859564, 2.38139627341834,
## 3.00568260440716, 2.38784493694487, 2.79667139275574, 2.84549061022345,
## 2.93969088267037, 2.79667139275574, 3.2236643416, 2.58701187272515,
## 2.96938829821439, 3.06991167172824, 2.63404478779171, 3.08694315360738,
## 3.11218108619724, 2.8136106967627, 2.73371794785079, 2.86619290219901,
## 2.89314568477889, 2.85128436918812, 2.70604819843154, 2.68444033546308,
## 2.80819714970715, 2.93225985059842, 2.71997877196748, 2.88535921607262,
## 3.03399098567108, 3.03013370027132, 2.73046379593911, 2.57108434602905,
## 2.73046379593911, 2.88703285663065, 3.03206420280138, 2.54474665014402,
## 2.56186769092413, 2.76883167336207, 3.10099278421148, 3.09285898428471,
## 2.98365969231972, 2.27315628230323, 2.9338568698359, 3.20599319903719,
## 2.83026783382646, 2.47569771070269, 2.68852753461335, 2.71800053195538,
## 2.67069441455844, 2.89369954798884, 3.00121720378456, 3.10099278421148,
## 2.56955412384829, 3.08511583468868, 3.27978275977172, 3.01111337559229,
## 2.70270259477561, 3.10950728781284, 2.71535677628465, 2.92208573338569,
## 2.84432781939476, 2.85589532836619, 2.76631910922619, 3.14069804380418,
## 3.06385810260159, 2.90251989183181, 3.14458322028635, 2.79300390698237,
## 3.10413814739778, 3.0837431508767, 3.11307076597122, 2.97348666460667,
## 2.96114082878437, 3.28353933819392, 3.16758253048065, 2.92316158071916,
## 2.8142103969306, 2.84897089215859, 3.00864849882054, 3.11529150861163,
## 2.55800220485855, 3.09738592728049, 2.94127608775793, 2.91614779421115,
## 3.24102862950933, 3.17010566049877, 2.82908719614504, 2.90962957450058,
## 2.86105737022739, 3.48031658611475, 2.83438912314523, 2.60046499042227,
## 2.73825604315928, 2.74148497718845, 3.17680304844629, 3.10593106585207,
## 2.94864066602014, 3.52075661671979, 2.7669478423497, 3.05635689537043,
## 3.06619073720255, 3.32683296637329, 3.67071548348627, 2.71071331852169,
## 3.15700042115011, 2.98870765861703, 2.85819285953193, 2.64688376586472,
## 3.22763733053677, 2.7033726115511, 3.15955035878339, 2.98669152890184,
## 2.83790818836042, 2.96165829322024, 2.83615020372953, 3.35933317756346,
## 2.84897089215859, 3.14415227867226, 3.51333488159901, 3.29805662274264,
## 3.13809951484091, 3.09693415406296, 2.96424160646262, 3.09421922026864,
## 3.43785069931019, 3.0837431508767, 2.78562833574758, 3.01504458458636,
## 2.8225686545448, 2.56802155649851, 3.19785645764413, 2.8541687092322,
## 2.65042108826557, 2.99473177322041, 2.71997877196748, 3.28091121578765,
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## 3.80547291455154, 3.85278371543535, 4.25682080339513, 3.74860430284226,
## 3.33461827035041, 4.07626837901377, 4.51826969357939, 4.39319148665841,
## 4.12185715449991, 4.15768292970039, 4.36329684217773, 4.19907427922337,
## 4.99123464718118, 4.22510981161163, 4.20081917204225, 4.8966351137691,
## 4.27631615777365, 4.27378256776766, 4.57693603923099, 4.76825489106129,
## 5.07086352975503, 3.80235188130695, 4.65442707939607, 4.91933405670256,
## 4.23630118715586, 4.36654714126594, 4.09369993366443, 5.16098279528589,
## 3.92880160458909, 4.05498624312565, 4.67020237121107, 3.90606913806289,
## 4.1736232924066, 3.88510875184718, 4.07902964158111, 4.45421195165986,
## 4.40759828979585, 5.0751130969265, 3.68582950234986, 4.56203022993144,
## 3.88310217785028, 4.51979212962875, 4.19032989622769, 4.81853233316707,
## 4.59248790411271, 4.2525608748516, 4.62037564745409, 5.11064911059594,
## 4.62547775120469, 4.5784215265119, 4.93862626226538, 3.67592419710848,
## 4.37465320891252, 4.12185715449991, 4.02315444464448, 4.34615829494115,
## 5.21722997447023, 4.06981233359486, 5.05073262206906, 4.15056255337101,
## 4.17715059261016, 4.78535628237399, 4.37141414682473, 3.84972892391568,
## 4.98180893733216, 4.03725319225232, 4.90054116752379, 4.34451948048039,
## 4.78740043468991, 4.5784215265119, 4.19732806150465, 4.13446018295962,
## 4.4791143283873, 4.72112332197546, 4.11552943063158, 4.48838562918339,
## 3.79609650233605, 4.56203022993144, 4.0642639124341, 4.66662611195927,
## 4.54928680787707, 4.7969173026623, 5.25649971528638, 4.62547775120469,
## 4.73577624450677, 4.97171544875428, 5.289607608586, 4.68089952389297,
## 4.95204221081654, 4.7120079998809, 3.80339271755236, 4.66447781347784,
## 4.07258146061223, 4.55604179233441, 4.37465320891252, 4.80098466524709,
## 5.00249875093668, 4.70638151598143, 4.35351877174842, 4.52359396475879,
## 4.93221217091729, 4.92384900352362, 4.47058365452776, 4.24230491665047,
## 4.73716716909308, 4.0391263923702, 4.6565843327947, 3.86293601702622,
## 4.38595481282061, 4.27968997796331, 4.89533172440296, 5.3789243482449,
## 4.33959612712433, 4.76344497143527, 5.00437117689173, 4.74895806068259,
## 4.93413813813035, 5.3431303609114, 4.29062114000629, 5.53924614207584,
## 5.39342611034402, 4.99248872462087, 5.29016509938456, 5.15099568604474,
## 4.96031120783185, 4.44244806790354, 4.43220506774196, 4.55604179233441,
## 4.55304193558474, 4.48915660389326, 4.79623875179676, 4.56501883193107,
## 5.0817765373419, 3.93469391170234, 4.04286814109814, 4.04286814109814,
## 4.68445475787559, 4.36492255576514, 4.72741395924518, 4.62402111104245,
## 3.9444800183149, 3.50017120752803, 4.55154059307216, 4.89728655105185,
## 4.59764951947146, 3.74433252136007, 4.76825489106129, 4.76962741533791,
## 4.29062114000629, 4.46747395638393, 4.92127004033648, 5.05807160953597,
## 4.43062526611781, 3.74646945527153, 3.91697011212309, 4.16920652420897,
## 5.25706443832225, 3.87304201366326, 3.87102449594356, 4.84927432396436,
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## 3.63696722222788, 3.6803321374997, 4.73508048403053, 4.7921634933305,
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## 5.17559876883805, 4.35106785374955, 5.13623697910506, 4.68516517856677,
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## 5.12912215905078, 5.42589455648472, 3.84564929607836, 4.39399418633571,
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## 5.08419524488049, 4.55454233489475, 4.27968997796331, 4.22942061247119,
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## 5.12258263348064, 4.49531539967184, 5.00374719065992, 4.76138067250417,
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## 3.69678266390047, 4.72531888820021, 4.51750811014081, 3.95907935613536,
## 4.74133518110845, 5.1720985907114, 4.66089306552781, 4.67305957436053,
## 4.47756557994607, 5.7250741812419, 4.40919360925164, 3.80131055253282,
## 4.08454211051463, 4.0391263923702, 4.95649816088697, 4.88160449712241,
## 4.50987883531407, 5.54784383979588, 4.02503937366727, 4.89011128798296,
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## 4.52511300047923, 4.48529922188488, 4.28390036534887, 5.25367423284224,
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## 4.96665334045321, 4.68303329080159, 4.97928939162397, 4.82725933452894,
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## 4.16123482951012, 3.72500546771211, 5.16274115651175, 4.00896702468464,
## 4.00896702468464, 4.4643601672029, 4.25596944489609, 5.23266824566795,
## 3.93861349816864, 4.78126284597665, 4.07442566880621, 4.36735901153395,
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## 5.3659655010133, 5.59835498011832, 4.62256358846899, 5.36325756817213,
## 5.12912215905078, 5.42589455648472, 4.89598350492003), smoothness_worst = c(-1.40183650096854,
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## mfinal = 100, coeflearn = "Breiman", control = list(minsplit = 0,
## minbucket = 0, cp = -1, maxcompete = 4L, maxsurrogate = 5L,
## usesurrogate = 2L, surrogatestyle = 0L, maxdepth = 6,
## xval = 0))
##
## $xNames
## [1] "texture_mean" "smoothness_mean" "compactness_se" "texture_worst"
## [5] "smoothness_worst" "symmetry_worst"
##
## $problemType
## [1] "Classification"
##
## $tuneValue
## mfinal maxdepth coeflearn
## 6 100 6 Breiman
##
## $obsLevels
## [1] "B" "M"
## attr(,"ordered")
## [1] FALSE
##
## $param
## list()
##
## attr(,"vardep.summary")
## B M
## 572 340
## attr(,"class")
## [1] "boosting"MBS_AB_Tune$results## coeflearn maxdepth mfinal ROC Sens Spec ROCSD SensSD
## 1 Breiman 4 50 0.9539212 0.9454523 0.8682353 0.02278794 0.02792403
## 3 Breiman 5 50 0.9624131 0.9531137 0.9011765 0.01779028 0.02435207
## 5 Breiman 6 50 0.9666688 0.9496323 0.8982353 0.01854771 0.02567639
## 2 Breiman 4 100 0.9600671 0.9506972 0.9017647 0.02131985 0.02653148
## 4 Breiman 5 100 0.9671294 0.9506636 0.9000000 0.01843939 0.02577619
## 6 Breiman 6 100 0.9710985 0.9527750 0.8964706 0.01750303 0.02587865
## SpecSD
## 1 0.04502338
## 3 0.03737725
## 5 0.04744784
## 2 0.04281576
## 4 0.03422608
## 6 0.04561985(MBS_AB_Train_AUROC <- MBS_AB_Tune$results[MBS_AB_Tune$results$mfinal==MBS_AB_Tune$bestTune$mfinal &
MBS_AB_Tune$results$maxdepth==MBS_AB_Tune$bestTune$maxdepth &
MBS_AB_Tune$results$coeflearn==MBS_AB_Tune$bestTune$coeflearn,
c("ROC")])## [1] 0.9710985##################################
# Identifying and plotting the
# best model predictors
##################################
MBS_AB_VarImp <- varImp(MBS_AB_Tune, scale = TRUE)
plot(MBS_AB_VarImp,
top=6,
scales=list(y=list(cex = .95)),
main="Ranked Variable Importance : Adaptive Boosting",
xlab="Scaled Variable Importance Metrics",
ylab="Predictors",
cex=2,
origin=0,
alpha=0.45)
##################################
# Independently evaluating the model
# on the test set
##################################
MBS_AB_Test <- data.frame(MBS_AB_Test_Observed = MA_Test$diagnosis,
MBS_AB_Test_Predicted = predict(MBS_AB_Tune,
MA_Test[,!names(MA_Test) %in% c("diagnosis")],
type = "prob"))
##################################
# Reporting the independent evaluation results
# for the test set
##################################
MBS_AB_Test_ROC <- roc(response = MBS_AB_Test$MBS_AB_Test_Observed,
predictor = MBS_AB_Test$MBS_AB_Test_Predicted.M,
levels = rev(levels(MBS_AB_Test$MBS_AB_Test_Observed)))
(MBS_AB_Test_AUROC <- auc(MBS_AB_Test_ROC)[1])## [1] 0.99564051.5.2 Stochastic Gradient Boosting (MBS_GBM)
Stochastic Gradient Boosting works on the principle of the stage-wise addition method but the statistical framework cast boosting as a numerical optimization problem where the objective is to minimize the loss of the model by adding weak learners using a gradient descent-like procedure. An initial learner is developed based on the overall mean or the log-odds of the target variable. For each boosting iteration, a subset of the training data will be sampled with replacement and pseudo-residuals are calculated. A new base learner is fitted to the subsampled data using the pseudo-residuals as the target variable. Gradient descent optimization is applied to minimize the loss function (mean squared error or cross-entropy). Predicted values are obtained and the ensemble prediction is updated for all training samples. The process is repeated for a defined number of cycles with the final ensemble prediction determined as the sum of the individual predictions from all boosting iterations.
[A] The stochastic gradient boosting model was implemented through the gbm package.
[B] The model contains 1 hyperparameter:
[B.1] n.trees = total number of trees to fit (equivalent to the number of iterations and the number of basis functions in the additive expansion) held constant at a value equal to 500
[B.2] interaction.depth = maximum depth of each tree (equivalent to the highest level of variable interactions allowed) made to vary across a range of values equal to 4 to 6
[B.3] shrinkage = learning rate or step-size reduction applied to each tree in the expansion made to vary across a range of values equal to 0.001 to 0.1
[B.4] n.minobsinnode = minimum number of observations in the terminal nodes of the trees made to vary across a range of values equal to 5 to 15
[C] The 5-cycle repeated 5-fold cross-validated model performance of the final model is summarized as follows:
[C.1] Final model configuration involves n.trees=500, interaction.depth=5, shrinkage=0.1 and n.minobsinnode=5
[C.2] AUROC = 0.96473
[D] The model allows for ranking of predictors in terms of variable importance. The top-performing predictors in the model are as follows:
[D.1] symmetry_worst (numeric)
[D.2] texture_worst (numeric)
[D.3] texture_mean (numeric)
[D.4] smoothness_worst (numeric)
[D.5] smoothness_mean (numeric)
[E] The independent test model performance of the final model is summarized as follows:
[E.1] AUROC = 0.98306
Code Chunk | Output
##################################
# Setting the conditions
# for hyperparameter tuning
##################################
GBM_Grid = expand.grid(n.trees = 500,
interaction.depth = c(4,5,6),
shrinkage = c(0.1,0.01,0.001),
n.minobsinnode = c(5, 10, 15))
##################################
# Running the stochastic gradient boosting model
# by setting the caret method to 'gbm'
##################################
set.seed(12345678)
MBS_GBM_Tune <- train(x = MA_Train[,!names(MA_Train) %in% c("diagnosis")],
y = MA_Train$diagnosis,
method = "gbm",
tuneGrid = GBM_Grid,
metric = "ROC",
trControl = RKFold_Control)## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3196 nan 0.0010 0.0004
## 3 1.3187 nan 0.0010 0.0004
## 4 1.3178 nan 0.0010 0.0004
## 5 1.3170 nan 0.0010 0.0004
## 6 1.3162 nan 0.0010 0.0004
## 7 1.3152 nan 0.0010 0.0004
## 8 1.3144 nan 0.0010 0.0004
## 9 1.3135 nan 0.0010 0.0004
## 10 1.3127 nan 0.0010 0.0004
## 20 1.3043 nan 0.0010 0.0004
## 40 1.2881 nan 0.0010 0.0003
## 60 1.2723 nan 0.0010 0.0003
## 80 1.2570 nan 0.0010 0.0004
## 100 1.2426 nan 0.0010 0.0003
## 120 1.2286 nan 0.0010 0.0003
## 140 1.2148 nan 0.0010 0.0003
## 160 1.2015 nan 0.0010 0.0003
## 180 1.1885 nan 0.0010 0.0003
## 200 1.1760 nan 0.0010 0.0002
## 220 1.1635 nan 0.0010 0.0003
## 240 1.1518 nan 0.0010 0.0002
## 260 1.1404 nan 0.0010 0.0002
## 280 1.1296 nan 0.0010 0.0002
## 300 1.1187 nan 0.0010 0.0003
## 320 1.1085 nan 0.0010 0.0002
## 340 1.0984 nan 0.0010 0.0003
## 360 1.0884 nan 0.0010 0.0002
## 380 1.0787 nan 0.0010 0.0002
## 400 1.0691 nan 0.0010 0.0002
## 420 1.0600 nan 0.0010 0.0002
## 440 1.0514 nan 0.0010 0.0002
## 460 1.0430 nan 0.0010 0.0002
## 480 1.0347 nan 0.0010 0.0002
## 500 1.0265 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3195 nan 0.0010 0.0004
## 3 1.3186 nan 0.0010 0.0004
## 4 1.3177 nan 0.0010 0.0004
## 5 1.3168 nan 0.0010 0.0004
## 6 1.3160 nan 0.0010 0.0004
## 7 1.3152 nan 0.0010 0.0003
## 8 1.3143 nan 0.0010 0.0004
## 9 1.3135 nan 0.0010 0.0004
## 10 1.3127 nan 0.0010 0.0003
## 20 1.3044 nan 0.0010 0.0004
## 40 1.2883 nan 0.0010 0.0004
## 60 1.2725 nan 0.0010 0.0003
## 80 1.2574 nan 0.0010 0.0003
## 100 1.2426 nan 0.0010 0.0003
## 120 1.2288 nan 0.0010 0.0004
## 140 1.2151 nan 0.0010 0.0003
## 160 1.2019 nan 0.0010 0.0003
## 180 1.1892 nan 0.0010 0.0003
## 200 1.1769 nan 0.0010 0.0003
## 220 1.1648 nan 0.0010 0.0003
## 240 1.1532 nan 0.0010 0.0003
## 260 1.1419 nan 0.0010 0.0003
## 280 1.1308 nan 0.0010 0.0002
## 300 1.1201 nan 0.0010 0.0003
## 320 1.1097 nan 0.0010 0.0002
## 340 1.0997 nan 0.0010 0.0002
## 360 1.0899 nan 0.0010 0.0002
## 380 1.0800 nan 0.0010 0.0002
## 400 1.0707 nan 0.0010 0.0002
## 420 1.0617 nan 0.0010 0.0002
## 440 1.0529 nan 0.0010 0.0002
## 460 1.0443 nan 0.0010 0.0002
## 480 1.0359 nan 0.0010 0.0002
## 500 1.0276 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3204 nan 0.0010 0.0004
## 2 1.3196 nan 0.0010 0.0004
## 3 1.3187 nan 0.0010 0.0003
## 4 1.3179 nan 0.0010 0.0004
## 5 1.3170 nan 0.0010 0.0004
## 6 1.3161 nan 0.0010 0.0004
## 7 1.3153 nan 0.0010 0.0004
## 8 1.3145 nan 0.0010 0.0004
## 9 1.3137 nan 0.0010 0.0004
## 10 1.3128 nan 0.0010 0.0004
## 20 1.3044 nan 0.0010 0.0004
## 40 1.2885 nan 0.0010 0.0004
## 60 1.2731 nan 0.0010 0.0004
## 80 1.2581 nan 0.0010 0.0003
## 100 1.2435 nan 0.0010 0.0003
## 120 1.2296 nan 0.0010 0.0003
## 140 1.2161 nan 0.0010 0.0003
## 160 1.2029 nan 0.0010 0.0003
## 180 1.1903 nan 0.0010 0.0003
## 200 1.1781 nan 0.0010 0.0003
## 220 1.1659 nan 0.0010 0.0003
## 240 1.1542 nan 0.0010 0.0003
## 260 1.1428 nan 0.0010 0.0003
## 280 1.1317 nan 0.0010 0.0002
## 300 1.1211 nan 0.0010 0.0002
## 320 1.1107 nan 0.0010 0.0002
## 340 1.1008 nan 0.0010 0.0002
## 360 1.0911 nan 0.0010 0.0002
## 380 1.0815 nan 0.0010 0.0002
## 400 1.0723 nan 0.0010 0.0002
## 420 1.0633 nan 0.0010 0.0002
## 440 1.0543 nan 0.0010 0.0002
## 460 1.0457 nan 0.0010 0.0002
## 480 1.0373 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3193 nan 0.0010 0.0004
## 3 1.3185 nan 0.0010 0.0003
## 4 1.3176 nan 0.0010 0.0004
## 5 1.3167 nan 0.0010 0.0004
## 6 1.3158 nan 0.0010 0.0004
## 7 1.3149 nan 0.0010 0.0004
## 8 1.3140 nan 0.0010 0.0004
## 9 1.3132 nan 0.0010 0.0004
## 10 1.3123 nan 0.0010 0.0004
## 20 1.3035 nan 0.0010 0.0004
## 40 1.2861 nan 0.0010 0.0004
## 60 1.2695 nan 0.0010 0.0004
## 80 1.2535 nan 0.0010 0.0003
## 100 1.2381 nan 0.0010 0.0003
## 120 1.2231 nan 0.0010 0.0003
## 140 1.2084 nan 0.0010 0.0003
## 160 1.1946 nan 0.0010 0.0003
## 180 1.1814 nan 0.0010 0.0003
## 200 1.1680 nan 0.0010 0.0003
## 220 1.1549 nan 0.0010 0.0003
## 240 1.1424 nan 0.0010 0.0002
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## 280 1.1188 nan 0.0010 0.0003
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## 320 1.0965 nan 0.0010 0.0003
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## 360 1.0755 nan 0.0010 0.0002
## 380 1.0654 nan 0.0010 0.0002
## 400 1.0556 nan 0.0010 0.0002
## 420 1.0459 nan 0.0010 0.0002
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## 480 1.0186 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3202 nan 0.0010 0.0004
## 2 1.3194 nan 0.0010 0.0003
## 3 1.3185 nan 0.0010 0.0004
## 4 1.3176 nan 0.0010 0.0004
## 5 1.3167 nan 0.0010 0.0004
## 6 1.3158 nan 0.0010 0.0004
## 7 1.3150 nan 0.0010 0.0004
## 8 1.3141 nan 0.0010 0.0004
## 9 1.3132 nan 0.0010 0.0004
## 10 1.3124 nan 0.0010 0.0004
## 20 1.3035 nan 0.0010 0.0004
## 40 1.2863 nan 0.0010 0.0004
## 60 1.2698 nan 0.0010 0.0004
## 80 1.2541 nan 0.0010 0.0004
## 100 1.2389 nan 0.0010 0.0003
## 120 1.2243 nan 0.0010 0.0003
## 140 1.2096 nan 0.0010 0.0003
## 160 1.1959 nan 0.0010 0.0003
## 180 1.1823 nan 0.0010 0.0003
## 200 1.1690 nan 0.0010 0.0003
## 220 1.1562 nan 0.0010 0.0002
## 240 1.1439 nan 0.0010 0.0003
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## 320 1.0985 nan 0.0010 0.0002
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## 380 1.0671 nan 0.0010 0.0002
## 400 1.0574 nan 0.0010 0.0002
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## 440 1.0381 nan 0.0010 0.0002
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## 480 1.0200 nan 0.0010 0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3195 nan 0.0010 0.0004
## 3 1.3186 nan 0.0010 0.0004
## 4 1.3177 nan 0.0010 0.0004
## 5 1.3168 nan 0.0010 0.0004
## 6 1.3160 nan 0.0010 0.0004
## 7 1.3151 nan 0.0010 0.0004
## 8 1.3142 nan 0.0010 0.0004
## 9 1.3134 nan 0.0010 0.0004
## 10 1.3125 nan 0.0010 0.0004
## 20 1.3038 nan 0.0010 0.0004
## 40 1.2870 nan 0.0010 0.0004
## 60 1.2704 nan 0.0010 0.0004
## 80 1.2549 nan 0.0010 0.0004
## 100 1.2395 nan 0.0010 0.0003
## 120 1.2247 nan 0.0010 0.0004
## 140 1.2106 nan 0.0010 0.0003
## 160 1.1967 nan 0.0010 0.0003
## 180 1.1835 nan 0.0010 0.0002
## 200 1.1703 nan 0.0010 0.0003
## 220 1.1578 nan 0.0010 0.0002
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## 320 1.1006 nan 0.0010 0.0002
## 340 1.0901 nan 0.0010 0.0002
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## 380 1.0698 nan 0.0010 0.0002
## 400 1.0601 nan 0.0010 0.0002
## 420 1.0507 nan 0.0010 0.0002
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## 460 1.0327 nan 0.0010 0.0002
## 480 1.0243 nan 0.0010 0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3202 nan 0.0010 0.0005
## 2 1.3192 nan 0.0010 0.0004
## 3 1.3183 nan 0.0010 0.0004
## 4 1.3173 nan 0.0010 0.0005
## 5 1.3163 nan 0.0010 0.0004
## 6 1.3154 nan 0.0010 0.0004
## 7 1.3145 nan 0.0010 0.0004
## 8 1.3136 nan 0.0010 0.0004
## 9 1.3127 nan 0.0010 0.0004
## 10 1.3118 nan 0.0010 0.0004
## 20 1.3026 nan 0.0010 0.0004
## 40 1.2848 nan 0.0010 0.0004
## 60 1.2675 nan 0.0010 0.0004
## 80 1.2509 nan 0.0010 0.0003
## 100 1.2346 nan 0.0010 0.0003
## 120 1.2190 nan 0.0010 0.0003
## 140 1.2040 nan 0.0010 0.0002
## 160 1.1895 nan 0.0010 0.0003
## 180 1.1753 nan 0.0010 0.0003
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## 280 1.1105 nan 0.0010 0.0002
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## 340 1.0762 nan 0.0010 0.0002
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## 380 1.0547 nan 0.0010 0.0002
## 400 1.0446 nan 0.0010 0.0002
## 420 1.0347 nan 0.0010 0.0002
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## 480 1.0062 nan 0.0010 0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3202 nan 0.0010 0.0004
## 2 1.3193 nan 0.0010 0.0004
## 3 1.3184 nan 0.0010 0.0004
## 4 1.3174 nan 0.0010 0.0004
## 5 1.3165 nan 0.0010 0.0004
## 6 1.3156 nan 0.0010 0.0004
## 7 1.3147 nan 0.0010 0.0004
## 8 1.3138 nan 0.0010 0.0004
## 9 1.3129 nan 0.0010 0.0004
## 10 1.3120 nan 0.0010 0.0004
## 20 1.3028 nan 0.0010 0.0004
## 40 1.2853 nan 0.0010 0.0004
## 60 1.2684 nan 0.0010 0.0004
## 80 1.2519 nan 0.0010 0.0004
## 100 1.2361 nan 0.0010 0.0004
## 120 1.2207 nan 0.0010 0.0003
## 140 1.2057 nan 0.0010 0.0003
## 160 1.1915 nan 0.0010 0.0003
## 180 1.1778 nan 0.0010 0.0003
## 200 1.1639 nan 0.0010 0.0002
## 220 1.1507 nan 0.0010 0.0003
## 240 1.1382 nan 0.0010 0.0002
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## 300 1.1019 nan 0.0010 0.0002
## 320 1.0904 nan 0.0010 0.0002
## 340 1.0794 nan 0.0010 0.0002
## 360 1.0687 nan 0.0010 0.0002
## 380 1.0585 nan 0.0010 0.0002
## 400 1.0482 nan 0.0010 0.0002
## 420 1.0384 nan 0.0010 0.0002
## 440 1.0288 nan 0.0010 0.0002
## 460 1.0194 nan 0.0010 0.0002
## 480 1.0103 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0005
## 2 1.3194 nan 0.0010 0.0004
## 3 1.3184 nan 0.0010 0.0004
## 4 1.3175 nan 0.0010 0.0004
## 5 1.3166 nan 0.0010 0.0004
## 6 1.3157 nan 0.0010 0.0004
## 7 1.3147 nan 0.0010 0.0004
## 8 1.3137 nan 0.0010 0.0004
## 9 1.3128 nan 0.0010 0.0004
## 10 1.3119 nan 0.0010 0.0004
## 20 1.3027 nan 0.0010 0.0004
## 40 1.2853 nan 0.0010 0.0004
## 60 1.2686 nan 0.0010 0.0004
## 80 1.2523 nan 0.0010 0.0004
## 100 1.2366 nan 0.0010 0.0003
## 120 1.2214 nan 0.0010 0.0003
## 140 1.2067 nan 0.0010 0.0003
## 160 1.1923 nan 0.0010 0.0003
## 180 1.1783 nan 0.0010 0.0003
## 200 1.1651 nan 0.0010 0.0003
## 220 1.1522 nan 0.0010 0.0003
## 240 1.1396 nan 0.0010 0.0003
## 260 1.1275 nan 0.0010 0.0003
## 280 1.1156 nan 0.0010 0.0003
## 300 1.1043 nan 0.0010 0.0002
## 320 1.0932 nan 0.0010 0.0002
## 340 1.0822 nan 0.0010 0.0003
## 360 1.0718 nan 0.0010 0.0002
## 380 1.0615 nan 0.0010 0.0002
## 400 1.0514 nan 0.0010 0.0002
## 420 1.0419 nan 0.0010 0.0002
## 440 1.0322 nan 0.0010 0.0002
## 460 1.0229 nan 0.0010 0.0002
## 480 1.0139 nan 0.0010 0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3134 nan 0.0100 0.0032
## 2 1.3061 nan 0.0100 0.0036
## 3 1.2981 nan 0.0100 0.0037
## 4 1.2896 nan 0.0100 0.0039
## 5 1.2823 nan 0.0100 0.0034
## 6 1.2742 nan 0.0100 0.0038
## 7 1.2666 nan 0.0100 0.0038
## 8 1.2592 nan 0.0100 0.0034
## 9 1.2525 nan 0.0100 0.0028
## 10 1.2453 nan 0.0100 0.0035
## 20 1.1779 nan 0.0100 0.0024
## 40 1.0706 nan 0.0100 0.0020
## 60 0.9874 nan 0.0100 0.0017
## 80 0.9243 nan 0.0100 0.0011
## 100 0.8736 nan 0.0100 0.0009
## 120 0.8316 nan 0.0100 0.0006
## 140 0.7966 nan 0.0100 0.0004
## 160 0.7688 nan 0.0100 0.0003
## 180 0.7435 nan 0.0100 0.0004
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## 220 0.7034 nan 0.0100 0.0000
## 240 0.6859 nan 0.0100 0.0002
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## 280 0.6570 nan 0.0100 0.0000
## 300 0.6436 nan 0.0100 -0.0001
## 320 0.6315 nan 0.0100 -0.0000
## 340 0.6205 nan 0.0100 -0.0001
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## 380 0.6008 nan 0.0100 0.0000
## 400 0.5915 nan 0.0100 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3127 nan 0.0100 0.0039
## 2 1.3047 nan 0.0100 0.0033
## 3 1.2961 nan 0.0100 0.0038
## 4 1.2873 nan 0.0100 0.0038
## 5 1.2791 nan 0.0100 0.0036
## 6 1.2715 nan 0.0100 0.0035
## 7 1.2634 nan 0.0100 0.0038
## 8 1.2560 nan 0.0100 0.0031
## 9 1.2494 nan 0.0100 0.0033
## 10 1.2424 nan 0.0100 0.0034
## 20 1.1766 nan 0.0100 0.0031
## 40 1.0713 nan 0.0100 0.0020
## 60 0.9897 nan 0.0100 0.0015
## 80 0.9261 nan 0.0100 0.0013
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## 180 0.7496 nan 0.0100 0.0004
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## 220 0.7106 nan 0.0100 0.0001
## 240 0.6941 nan 0.0100 -0.0001
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## 280 0.6645 nan 0.0100 -0.0000
## 300 0.6519 nan 0.0100 -0.0001
## 320 0.6390 nan 0.0100 -0.0001
## 340 0.6282 nan 0.0100 0.0000
## 360 0.6176 nan 0.0100 0.0000
## 380 0.6072 nan 0.0100 -0.0001
## 400 0.5979 nan 0.0100 -0.0001
## 420 0.5891 nan 0.0100 -0.0002
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## 460 0.5718 nan 0.0100 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3136 nan 0.0100 0.0034
## 2 1.3048 nan 0.0100 0.0040
## 3 1.2971 nan 0.0100 0.0036
## 4 1.2891 nan 0.0100 0.0035
## 5 1.2807 nan 0.0100 0.0036
## 6 1.2730 nan 0.0100 0.0037
## 7 1.2658 nan 0.0100 0.0034
## 8 1.2581 nan 0.0100 0.0033
## 9 1.2513 nan 0.0100 0.0030
## 10 1.2438 nan 0.0100 0.0034
## 20 1.1781 nan 0.0100 0.0028
## 40 1.0717 nan 0.0100 0.0023
## 60 0.9920 nan 0.0100 0.0014
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## 180 0.7518 nan 0.0100 0.0001
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## 220 0.7123 nan 0.0100 0.0002
## 240 0.6969 nan 0.0100 0.0000
## 260 0.6819 nan 0.0100 -0.0001
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## 320 0.6462 nan 0.0100 0.0001
## 340 0.6362 nan 0.0100 -0.0000
## 360 0.6266 nan 0.0100 -0.0001
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## 400 0.6069 nan 0.0100 -0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3121 nan 0.0100 0.0044
## 2 1.3032 nan 0.0100 0.0037
## 3 1.2944 nan 0.0100 0.0044
## 4 1.2855 nan 0.0100 0.0041
## 5 1.2774 nan 0.0100 0.0037
## 6 1.2688 nan 0.0100 0.0037
## 7 1.2606 nan 0.0100 0.0039
## 8 1.2535 nan 0.0100 0.0032
## 9 1.2460 nan 0.0100 0.0035
## 10 1.2380 nan 0.0100 0.0041
## 20 1.1689 nan 0.0100 0.0028
## 40 1.0555 nan 0.0100 0.0022
## 60 0.9721 nan 0.0100 0.0014
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## 100 0.8504 nan 0.0100 0.0010
## 120 0.8079 nan 0.0100 0.0005
## 140 0.7702 nan 0.0100 0.0007
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## 280 0.6195 nan 0.0100 0.0000
## 300 0.6049 nan 0.0100 -0.0001
## 320 0.5915 nan 0.0100 0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3121 nan 0.0100 0.0041
## 2 1.3037 nan 0.0100 0.0040
## 3 1.2952 nan 0.0100 0.0038
## 4 1.2863 nan 0.0100 0.0039
## 5 1.2785 nan 0.0100 0.0037
## 6 1.2698 nan 0.0100 0.0037
## 7 1.2611 nan 0.0100 0.0038
## 8 1.2531 nan 0.0100 0.0034
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## 10 1.2371 nan 0.0100 0.0034
## 20 1.1674 nan 0.0100 0.0025
## 40 1.0547 nan 0.0100 0.0024
## 60 0.9700 nan 0.0100 0.0016
## 80 0.9049 nan 0.0100 0.0010
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## 140 0.7761 nan 0.0100 0.0004
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## 180 0.7212 nan 0.0100 0.0002
## 200 0.6981 nan 0.0100 0.0001
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## 300 0.6123 nan 0.0100 -0.0002
## 320 0.5989 nan 0.0100 0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3124 nan 0.0100 0.0042
## 2 1.3047 nan 0.0100 0.0035
## 3 1.2955 nan 0.0100 0.0038
## 4 1.2870 nan 0.0100 0.0037
## 5 1.2789 nan 0.0100 0.0033
## 6 1.2705 nan 0.0100 0.0039
## 7 1.2626 nan 0.0100 0.0036
## 8 1.2543 nan 0.0100 0.0034
## 9 1.2459 nan 0.0100 0.0035
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## 20 1.1693 nan 0.0100 0.0027
## 40 1.0591 nan 0.0100 0.0021
## 60 0.9756 nan 0.0100 0.0013
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## 100 0.8561 nan 0.0100 0.0008
## 120 0.8139 nan 0.0100 0.0007
## 140 0.7796 nan 0.0100 0.0003
## 160 0.7506 nan 0.0100 0.0002
## 180 0.7266 nan 0.0100 0.0001
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## 220 0.6853 nan 0.0100 0.0001
## 240 0.6680 nan 0.0100 -0.0000
## 260 0.6518 nan 0.0100 0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3117 nan 0.0100 0.0043
## 2 1.3026 nan 0.0100 0.0043
## 3 1.2939 nan 0.0100 0.0042
## 4 1.2854 nan 0.0100 0.0041
## 5 1.2768 nan 0.0100 0.0042
## 6 1.2688 nan 0.0100 0.0038
## 7 1.2603 nan 0.0100 0.0039
## 8 1.2520 nan 0.0100 0.0036
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## 10 1.2356 nan 0.0100 0.0037
## 20 1.1623 nan 0.0100 0.0028
## 40 1.0439 nan 0.0100 0.0023
## 60 0.9554 nan 0.0100 0.0018
## 80 0.8847 nan 0.0100 0.0012
## 100 0.8284 nan 0.0100 0.0009
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## 180 0.6869 nan 0.0100 -0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3119 nan 0.0100 0.0042
## 2 1.3030 nan 0.0100 0.0042
## 3 1.2941 nan 0.0100 0.0040
## 4 1.2848 nan 0.0100 0.0041
## 5 1.2761 nan 0.0100 0.0040
## 6 1.2675 nan 0.0100 0.0039
## 7 1.2588 nan 0.0100 0.0038
## 8 1.2501 nan 0.0100 0.0037
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## 60 0.9600 nan 0.0100 0.0017
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## 180 0.6963 nan 0.0100 0.0003
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3118 nan 0.0100 0.0038
## 2 1.3033 nan 0.0100 0.0041
## 3 1.2947 nan 0.0100 0.0041
## 4 1.2861 nan 0.0100 0.0035
## 5 1.2776 nan 0.0100 0.0044
## 6 1.2691 nan 0.0100 0.0036
## 7 1.2615 nan 0.0100 0.0033
## 8 1.2539 nan 0.0100 0.0037
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## 10 1.2383 nan 0.0100 0.0034
## 20 1.1672 nan 0.0100 0.0029
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## 300 0.5930 nan 0.0100 0.0001
## 320 0.5789 nan 0.0100 -0.0000
## 340 0.5658 nan 0.0100 0.0001
## 360 0.5527 nan 0.0100 -0.0001
## 380 0.5415 nan 0.0100 0.0000
## 400 0.5293 nan 0.0100 0.0001
## 420 0.5185 nan 0.0100 -0.0000
## 440 0.5076 nan 0.0100 0.0000
## 460 0.4968 nan 0.0100 0.0000
## 480 0.4876 nan 0.0100 -0.0001
## 500 0.4776 nan 0.0100 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2438 nan 0.1000 0.0383
## 2 1.1707 nan 0.1000 0.0330
## 3 1.1182 nan 0.1000 0.0240
## 4 1.0655 nan 0.1000 0.0228
## 5 1.0191 nan 0.1000 0.0200
## 6 0.9854 nan 0.1000 0.0136
## 7 0.9504 nan 0.1000 0.0142
## 8 0.9220 nan 0.1000 0.0090
## 9 0.8946 nan 0.1000 0.0103
## 10 0.8712 nan 0.1000 0.0084
## 20 0.7244 nan 0.1000 0.0033
## 40 0.5936 nan 0.1000 0.0002
## 60 0.5104 nan 0.1000 -0.0012
## 80 0.4405 nan 0.1000 0.0006
## 100 0.3898 nan 0.1000 -0.0004
## 120 0.3544 nan 0.1000 -0.0006
## 140 0.3150 nan 0.1000 -0.0018
## 160 0.2823 nan 0.1000 -0.0010
## 180 0.2540 nan 0.1000 0.0001
## 200 0.2296 nan 0.1000 -0.0002
## 220 0.2081 nan 0.1000 -0.0005
## 240 0.1905 nan 0.1000 0.0000
## 260 0.1739 nan 0.1000 -0.0002
## 280 0.1585 nan 0.1000 -0.0003
## 300 0.1463 nan 0.1000 -0.0004
## 320 0.1352 nan 0.1000 -0.0001
## 340 0.1247 nan 0.1000 -0.0006
## 360 0.1144 nan 0.1000 -0.0005
## 380 0.1074 nan 0.1000 -0.0003
## 400 0.0996 nan 0.1000 -0.0002
## 420 0.0910 nan 0.1000 -0.0001
## 440 0.0832 nan 0.1000 -0.0001
## 460 0.0769 nan 0.1000 -0.0002
## 480 0.0710 nan 0.1000 -0.0002
## 500 0.0663 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2379 nan 0.1000 0.0374
## 2 1.1677 nan 0.1000 0.0303
## 3 1.1124 nan 0.1000 0.0259
## 4 1.0638 nan 0.1000 0.0228
## 5 1.0229 nan 0.1000 0.0164
## 6 0.9838 nan 0.1000 0.0154
## 7 0.9534 nan 0.1000 0.0143
## 8 0.9247 nan 0.1000 0.0104
## 9 0.8986 nan 0.1000 0.0084
## 10 0.8740 nan 0.1000 0.0081
## 20 0.7318 nan 0.1000 0.0032
## 40 0.6121 nan 0.1000 -0.0002
## 60 0.5309 nan 0.1000 -0.0001
## 80 0.4740 nan 0.1000 -0.0011
## 100 0.4186 nan 0.1000 -0.0003
## 120 0.3693 nan 0.1000 -0.0011
## 140 0.3328 nan 0.1000 -0.0008
## 160 0.3001 nan 0.1000 -0.0018
## 180 0.2721 nan 0.1000 0.0000
## 200 0.2482 nan 0.1000 -0.0005
## 220 0.2270 nan 0.1000 -0.0007
## 240 0.2053 nan 0.1000 -0.0001
## 260 0.1865 nan 0.1000 -0.0006
## 280 0.1702 nan 0.1000 -0.0004
## 300 0.1572 nan 0.1000 -0.0004
## 320 0.1444 nan 0.1000 -0.0005
## 340 0.1336 nan 0.1000 0.0001
## 360 0.1238 nan 0.1000 -0.0006
## 380 0.1136 nan 0.1000 -0.0003
## 400 0.1048 nan 0.1000 -0.0003
## 420 0.0974 nan 0.1000 -0.0003
## 440 0.0899 nan 0.1000 -0.0004
## 460 0.0828 nan 0.1000 -0.0002
## 480 0.0770 nan 0.1000 -0.0001
## 500 0.0720 nan 0.1000 -0.0004
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2459 nan 0.1000 0.0395
## 2 1.1778 nan 0.1000 0.0291
## 3 1.1190 nan 0.1000 0.0258
## 4 1.0676 nan 0.1000 0.0212
## 5 1.0289 nan 0.1000 0.0170
## 6 0.9952 nan 0.1000 0.0141
## 7 0.9601 nan 0.1000 0.0149
## 8 0.9289 nan 0.1000 0.0136
## 9 0.9034 nan 0.1000 0.0099
## 10 0.8773 nan 0.1000 0.0107
## 20 0.7349 nan 0.1000 0.0003
## 40 0.6162 nan 0.1000 -0.0006
## 60 0.5400 nan 0.1000 -0.0025
## 80 0.4767 nan 0.1000 0.0001
## 100 0.4273 nan 0.1000 -0.0028
## 120 0.3853 nan 0.1000 -0.0009
## 140 0.3566 nan 0.1000 -0.0017
## 160 0.3254 nan 0.1000 -0.0010
## 180 0.2943 nan 0.1000 -0.0010
## 200 0.2693 nan 0.1000 -0.0007
## 220 0.2459 nan 0.1000 -0.0014
## 240 0.2260 nan 0.1000 -0.0007
## 260 0.2093 nan 0.1000 -0.0007
## 280 0.1942 nan 0.1000 -0.0006
## 300 0.1776 nan 0.1000 -0.0003
## 320 0.1642 nan 0.1000 -0.0005
## 340 0.1525 nan 0.1000 -0.0005
## 360 0.1398 nan 0.1000 -0.0004
## 380 0.1311 nan 0.1000 -0.0003
## 400 0.1201 nan 0.1000 -0.0002
## 420 0.1099 nan 0.1000 -0.0001
## 440 0.1024 nan 0.1000 -0.0005
## 460 0.0957 nan 0.1000 -0.0004
## 480 0.0888 nan 0.1000 -0.0002
## 500 0.0836 nan 0.1000 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2284 nan 0.1000 0.0379
## 2 1.1599 nan 0.1000 0.0297
## 3 1.1071 nan 0.1000 0.0195
## 4 1.0564 nan 0.1000 0.0226
## 5 1.0111 nan 0.1000 0.0192
## 6 0.9641 nan 0.1000 0.0170
## 7 0.9317 nan 0.1000 0.0117
## 8 0.9026 nan 0.1000 0.0111
## 9 0.8794 nan 0.1000 0.0071
## 10 0.8517 nan 0.1000 0.0100
## 20 0.6992 nan 0.1000 0.0012
## 40 0.5544 nan 0.1000 -0.0001
## 60 0.4582 nan 0.1000 -0.0014
## 80 0.3939 nan 0.1000 -0.0006
## 100 0.3414 nan 0.1000 -0.0012
## 120 0.2963 nan 0.1000 -0.0006
## 140 0.2623 nan 0.1000 -0.0004
## 160 0.2305 nan 0.1000 -0.0008
## 180 0.2022 nan 0.1000 -0.0003
## 200 0.1801 nan 0.1000 0.0002
## 220 0.1611 nan 0.1000 -0.0008
## 240 0.1442 nan 0.1000 -0.0001
## 260 0.1318 nan 0.1000 -0.0005
## 280 0.1179 nan 0.1000 -0.0003
## 300 0.1061 nan 0.1000 -0.0003
## 320 0.0953 nan 0.1000 -0.0003
## 340 0.0864 nan 0.1000 -0.0001
## 360 0.0783 nan 0.1000 -0.0001
## 380 0.0708 nan 0.1000 -0.0002
## 400 0.0648 nan 0.1000 -0.0002
## 420 0.0588 nan 0.1000 -0.0001
## 440 0.0527 nan 0.1000 -0.0001
## 460 0.0478 nan 0.1000 -0.0001
## 480 0.0439 nan 0.1000 -0.0003
## 500 0.0403 nan 0.1000 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2358 nan 0.1000 0.0378
## 2 1.1608 nan 0.1000 0.0326
## 3 1.0972 nan 0.1000 0.0271
## 4 1.0472 nan 0.1000 0.0215
## 5 1.0044 nan 0.1000 0.0168
## 6 0.9633 nan 0.1000 0.0154
## 7 0.9292 nan 0.1000 0.0143
## 8 0.9005 nan 0.1000 0.0120
## 9 0.8746 nan 0.1000 0.0085
## 10 0.8469 nan 0.1000 0.0090
## 20 0.6939 nan 0.1000 0.0008
## 40 0.5697 nan 0.1000 -0.0018
## 60 0.4851 nan 0.1000 -0.0010
## 80 0.4147 nan 0.1000 0.0004
## 100 0.3594 nan 0.1000 -0.0009
## 120 0.3118 nan 0.1000 -0.0005
## 140 0.2779 nan 0.1000 -0.0009
## 160 0.2470 nan 0.1000 -0.0003
## 180 0.2180 nan 0.1000 -0.0000
## 200 0.1946 nan 0.1000 -0.0012
## 220 0.1708 nan 0.1000 -0.0000
## 240 0.1526 nan 0.1000 -0.0006
## 260 0.1358 nan 0.1000 -0.0004
## 280 0.1219 nan 0.1000 -0.0005
## 300 0.1105 nan 0.1000 -0.0004
## 320 0.1000 nan 0.1000 -0.0001
## 340 0.0907 nan 0.1000 -0.0003
## 360 0.0815 nan 0.1000 -0.0004
## 380 0.0742 nan 0.1000 -0.0002
## 400 0.0663 nan 0.1000 0.0000
## 420 0.0597 nan 0.1000 -0.0001
## 440 0.0542 nan 0.1000 -0.0001
## 460 0.0498 nan 0.1000 -0.0001
## 480 0.0456 nan 0.1000 -0.0001
## 500 0.0413 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2346 nan 0.1000 0.0379
## 2 1.1630 nan 0.1000 0.0327
## 3 1.1014 nan 0.1000 0.0281
## 4 1.0501 nan 0.1000 0.0208
## 5 1.0016 nan 0.1000 0.0191
## 6 0.9664 nan 0.1000 0.0155
## 7 0.9309 nan 0.1000 0.0149
## 8 0.8995 nan 0.1000 0.0124
## 9 0.8754 nan 0.1000 0.0077
## 10 0.8507 nan 0.1000 0.0091
## 20 0.7054 nan 0.1000 0.0023
## 40 0.5701 nan 0.1000 -0.0011
## 60 0.4835 nan 0.1000 -0.0015
## 80 0.4214 nan 0.1000 -0.0020
## 100 0.3666 nan 0.1000 -0.0012
## 120 0.3210 nan 0.1000 -0.0014
## 140 0.2823 nan 0.1000 -0.0003
## 160 0.2505 nan 0.1000 -0.0014
## 180 0.2189 nan 0.1000 -0.0012
## 200 0.1941 nan 0.1000 -0.0013
## 220 0.1723 nan 0.1000 -0.0004
## 240 0.1552 nan 0.1000 -0.0006
## 260 0.1388 nan 0.1000 -0.0005
## 280 0.1245 nan 0.1000 -0.0005
## 300 0.1138 nan 0.1000 -0.0006
## 320 0.1045 nan 0.1000 -0.0004
## 340 0.0934 nan 0.1000 -0.0004
## 360 0.0845 nan 0.1000 -0.0003
## 380 0.0771 nan 0.1000 -0.0002
## 400 0.0711 nan 0.1000 -0.0000
## 420 0.0647 nan 0.1000 -0.0003
## 440 0.0590 nan 0.1000 -0.0002
## 460 0.0536 nan 0.1000 -0.0002
## 480 0.0490 nan 0.1000 -0.0001
## 500 0.0448 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2375 nan 0.1000 0.0387
## 2 1.1569 nan 0.1000 0.0386
## 3 1.0856 nan 0.1000 0.0303
## 4 1.0302 nan 0.1000 0.0217
## 5 0.9813 nan 0.1000 0.0216
## 6 0.9453 nan 0.1000 0.0127
## 7 0.9138 nan 0.1000 0.0118
## 8 0.8829 nan 0.1000 0.0121
## 9 0.8554 nan 0.1000 0.0091
## 10 0.8257 nan 0.1000 0.0118
## 20 0.6587 nan 0.1000 0.0028
## 40 0.5094 nan 0.1000 -0.0010
## 60 0.4041 nan 0.1000 -0.0013
## 80 0.3264 nan 0.1000 -0.0004
## 100 0.2751 nan 0.1000 -0.0005
## 120 0.2340 nan 0.1000 -0.0008
## 140 0.2001 nan 0.1000 -0.0000
## 160 0.1719 nan 0.1000 -0.0004
## 180 0.1478 nan 0.1000 -0.0002
## 200 0.1270 nan 0.1000 -0.0000
## 220 0.1101 nan 0.1000 -0.0005
## 240 0.0973 nan 0.1000 -0.0003
## 260 0.0858 nan 0.1000 -0.0002
## 280 0.0759 nan 0.1000 -0.0003
## 300 0.0682 nan 0.1000 -0.0003
## 320 0.0604 nan 0.1000 -0.0002
## 340 0.0532 nan 0.1000 -0.0001
## 360 0.0469 nan 0.1000 -0.0002
## 380 0.0422 nan 0.1000 -0.0002
## 400 0.0376 nan 0.1000 -0.0001
## 420 0.0336 nan 0.1000 -0.0001
## 440 0.0301 nan 0.1000 -0.0000
## 460 0.0265 nan 0.1000 -0.0001
## 480 0.0237 nan 0.1000 -0.0001
## 500 0.0212 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2296 nan 0.1000 0.0396
## 2 1.1548 nan 0.1000 0.0355
## 3 1.0951 nan 0.1000 0.0256
## 4 1.0417 nan 0.1000 0.0247
## 5 0.9941 nan 0.1000 0.0204
## 6 0.9571 nan 0.1000 0.0135
## 7 0.9221 nan 0.1000 0.0119
## 8 0.8892 nan 0.1000 0.0110
## 9 0.8605 nan 0.1000 0.0115
## 10 0.8356 nan 0.1000 0.0077
## 20 0.6743 nan 0.1000 0.0006
## 40 0.5249 nan 0.1000 0.0010
## 60 0.4186 nan 0.1000 -0.0009
## 80 0.3490 nan 0.1000 0.0001
## 100 0.2951 nan 0.1000 -0.0012
## 120 0.2525 nan 0.1000 -0.0013
## 140 0.2151 nan 0.1000 -0.0007
## 160 0.1876 nan 0.1000 -0.0003
## 180 0.1643 nan 0.1000 -0.0002
## 200 0.1424 nan 0.1000 0.0002
## 220 0.1251 nan 0.1000 -0.0004
## 240 0.1101 nan 0.1000 -0.0006
## 260 0.0961 nan 0.1000 -0.0005
## 280 0.0847 nan 0.1000 -0.0004
## 300 0.0756 nan 0.1000 -0.0001
## 320 0.0677 nan 0.1000 -0.0005
## 340 0.0608 nan 0.1000 -0.0000
## 360 0.0530 nan 0.1000 -0.0002
## 380 0.0472 nan 0.1000 -0.0000
## 400 0.0420 nan 0.1000 -0.0002
## 420 0.0373 nan 0.1000 -0.0001
## 440 0.0334 nan 0.1000 -0.0002
## 460 0.0296 nan 0.1000 -0.0000
## 480 0.0263 nan 0.1000 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2333 nan 0.1000 0.0414
## 2 1.1631 nan 0.1000 0.0274
## 3 1.1019 nan 0.1000 0.0251
## 4 1.0483 nan 0.1000 0.0235
## 5 1.0040 nan 0.1000 0.0178
## 6 0.9620 nan 0.1000 0.0153
## 7 0.9242 nan 0.1000 0.0151
## 8 0.8972 nan 0.1000 0.0092
## 9 0.8674 nan 0.1000 0.0108
## 10 0.8395 nan 0.1000 0.0115
## 20 0.6897 nan 0.1000 0.0021
## 40 0.5415 nan 0.1000 -0.0013
## 60 0.4573 nan 0.1000 -0.0006
## 80 0.3777 nan 0.1000 -0.0005
## 100 0.3217 nan 0.1000 -0.0028
## 120 0.2745 nan 0.1000 -0.0006
## 140 0.2333 nan 0.1000 -0.0007
## 160 0.2006 nan 0.1000 -0.0006
## 180 0.1740 nan 0.1000 -0.0009
## 200 0.1527 nan 0.1000 -0.0007
## 220 0.1325 nan 0.1000 -0.0005
## 240 0.1180 nan 0.1000 -0.0003
## 260 0.1042 nan 0.1000 -0.0004
## 280 0.0937 nan 0.1000 -0.0003
## 300 0.0823 nan 0.1000 -0.0004
## 320 0.0739 nan 0.1000 -0.0002
## 340 0.0654 nan 0.1000 -0.0002
## 360 0.0587 nan 0.1000 -0.0003
## 380 0.0529 nan 0.1000 -0.0001
## 400 0.0476 nan 0.1000 -0.0002
## 420 0.0426 nan 0.1000 -0.0003
## 440 0.0380 nan 0.1000 -0.0001
## 460 0.0342 nan 0.1000 -0.0002
## 480 0.0306 nan 0.1000 0.0000
## 500 0.0276 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3195 nan 0.0010 0.0004
## 3 1.3186 nan 0.0010 0.0004
## 4 1.3177 nan 0.0010 0.0004
## 5 1.3169 nan 0.0010 0.0003
## 6 1.3161 nan 0.0010 0.0004
## 7 1.3152 nan 0.0010 0.0004
## 8 1.3145 nan 0.0010 0.0003
## 9 1.3137 nan 0.0010 0.0004
## 10 1.3129 nan 0.0010 0.0003
## 20 1.3047 nan 0.0010 0.0003
## 40 1.2885 nan 0.0010 0.0004
## 60 1.2736 nan 0.0010 0.0003
## 80 1.2588 nan 0.0010 0.0003
## 100 1.2446 nan 0.0010 0.0003
## 120 1.2307 nan 0.0010 0.0004
## 140 1.2175 nan 0.0010 0.0003
## 160 1.2050 nan 0.0010 0.0003
## 180 1.1925 nan 0.0010 0.0002
## 200 1.1802 nan 0.0010 0.0003
## 220 1.1685 nan 0.0010 0.0002
## 240 1.1573 nan 0.0010 0.0003
## 260 1.1460 nan 0.0010 0.0002
## 280 1.1351 nan 0.0010 0.0003
## 300 1.1245 nan 0.0010 0.0002
## 320 1.1143 nan 0.0010 0.0002
## 340 1.1044 nan 0.0010 0.0002
## 360 1.0947 nan 0.0010 0.0002
## 380 1.0850 nan 0.0010 0.0002
## 400 1.0758 nan 0.0010 0.0002
## 420 1.0669 nan 0.0010 0.0002
## 440 1.0581 nan 0.0010 0.0002
## 460 1.0499 nan 0.0010 0.0002
## 480 1.0417 nan 0.0010 0.0002
## 500 1.0336 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3202 nan 0.0010 0.0004
## 2 1.3194 nan 0.0010 0.0004
## 3 1.3185 nan 0.0010 0.0004
## 4 1.3176 nan 0.0010 0.0004
## 5 1.3167 nan 0.0010 0.0004
## 6 1.3159 nan 0.0010 0.0004
## 7 1.3152 nan 0.0010 0.0003
## 8 1.3144 nan 0.0010 0.0004
## 9 1.3135 nan 0.0010 0.0004
## 10 1.3126 nan 0.0010 0.0004
## 20 1.3046 nan 0.0010 0.0003
## 40 1.2890 nan 0.0010 0.0004
## 60 1.2741 nan 0.0010 0.0004
## 80 1.2595 nan 0.0010 0.0003
## 100 1.2457 nan 0.0010 0.0003
## 120 1.2319 nan 0.0010 0.0003
## 140 1.2190 nan 0.0010 0.0002
## 160 1.2060 nan 0.0010 0.0003
## 180 1.1935 nan 0.0010 0.0003
## 200 1.1815 nan 0.0010 0.0003
## 220 1.1697 nan 0.0010 0.0003
## 240 1.1581 nan 0.0010 0.0002
## 260 1.1469 nan 0.0010 0.0003
## 280 1.1360 nan 0.0010 0.0002
## 300 1.1253 nan 0.0010 0.0002
## 320 1.1154 nan 0.0010 0.0002
## 340 1.1054 nan 0.0010 0.0003
## 360 1.0957 nan 0.0010 0.0002
## 380 1.0863 nan 0.0010 0.0002
## 400 1.0772 nan 0.0010 0.0002
## 420 1.0681 nan 0.0010 0.0002
## 440 1.0592 nan 0.0010 0.0002
## 460 1.0508 nan 0.0010 0.0002
## 480 1.0425 nan 0.0010 0.0002
## 500 1.0343 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3195 nan 0.0010 0.0004
## 3 1.3188 nan 0.0010 0.0003
## 4 1.3179 nan 0.0010 0.0004
## 5 1.3171 nan 0.0010 0.0004
## 6 1.3163 nan 0.0010 0.0004
## 7 1.3154 nan 0.0010 0.0003
## 8 1.3146 nan 0.0010 0.0003
## 9 1.3138 nan 0.0010 0.0004
## 10 1.3130 nan 0.0010 0.0003
## 20 1.3053 nan 0.0010 0.0004
## 40 1.2899 nan 0.0010 0.0003
## 60 1.2748 nan 0.0010 0.0003
## 80 1.2597 nan 0.0010 0.0003
## 100 1.2460 nan 0.0010 0.0003
## 120 1.2325 nan 0.0010 0.0003
## 140 1.2194 nan 0.0010 0.0003
## 160 1.2065 nan 0.0010 0.0003
## 180 1.1941 nan 0.0010 0.0003
## 200 1.1821 nan 0.0010 0.0002
## 220 1.1702 nan 0.0010 0.0003
## 240 1.1591 nan 0.0010 0.0002
## 260 1.1484 nan 0.0010 0.0002
## 280 1.1374 nan 0.0010 0.0002
## 300 1.1270 nan 0.0010 0.0002
## 320 1.1166 nan 0.0010 0.0002
## 340 1.1064 nan 0.0010 0.0002
## 360 1.0968 nan 0.0010 0.0002
## 380 1.0874 nan 0.0010 0.0002
## 400 1.0784 nan 0.0010 0.0002
## 420 1.0692 nan 0.0010 0.0002
## 440 1.0604 nan 0.0010 0.0002
## 460 1.0519 nan 0.0010 0.0001
## 480 1.0436 nan 0.0010 0.0002
## 500 1.0354 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3194 nan 0.0010 0.0004
## 3 1.3185 nan 0.0010 0.0004
## 4 1.3177 nan 0.0010 0.0004
## 5 1.3168 nan 0.0010 0.0004
## 6 1.3160 nan 0.0010 0.0003
## 7 1.3152 nan 0.0010 0.0004
## 8 1.3142 nan 0.0010 0.0004
## 9 1.3133 nan 0.0010 0.0004
## 10 1.3125 nan 0.0010 0.0004
## 20 1.3037 nan 0.0010 0.0004
## 40 1.2875 nan 0.0010 0.0004
## 60 1.2711 nan 0.0010 0.0004
## 80 1.2555 nan 0.0010 0.0004
## 100 1.2400 nan 0.0010 0.0003
## 120 1.2256 nan 0.0010 0.0003
## 140 1.2114 nan 0.0010 0.0003
## 160 1.1976 nan 0.0010 0.0003
## 180 1.1841 nan 0.0010 0.0003
## 200 1.1707 nan 0.0010 0.0003
## 220 1.1582 nan 0.0010 0.0003
## 240 1.1462 nan 0.0010 0.0002
## 260 1.1342 nan 0.0010 0.0003
## 280 1.1229 nan 0.0010 0.0002
## 300 1.1116 nan 0.0010 0.0002
## 320 1.1009 nan 0.0010 0.0002
## 340 1.0903 nan 0.0010 0.0003
## 360 1.0799 nan 0.0010 0.0002
## 380 1.0700 nan 0.0010 0.0002
## 400 1.0603 nan 0.0010 0.0002
## 420 1.0508 nan 0.0010 0.0002
## 440 1.0416 nan 0.0010 0.0002
## 460 1.0328 nan 0.0010 0.0002
## 480 1.0239 nan 0.0010 0.0002
## 500 1.0154 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3202 nan 0.0010 0.0005
## 2 1.3194 nan 0.0010 0.0004
## 3 1.3186 nan 0.0010 0.0004
## 4 1.3177 nan 0.0010 0.0003
## 5 1.3168 nan 0.0010 0.0004
## 6 1.3159 nan 0.0010 0.0004
## 7 1.3151 nan 0.0010 0.0003
## 8 1.3142 nan 0.0010 0.0004
## 9 1.3133 nan 0.0010 0.0004
## 10 1.3124 nan 0.0010 0.0004
## 20 1.3037 nan 0.0010 0.0004
## 40 1.2870 nan 0.0010 0.0003
## 60 1.2710 nan 0.0010 0.0003
## 80 1.2556 nan 0.0010 0.0004
## 100 1.2406 nan 0.0010 0.0003
## 120 1.2265 nan 0.0010 0.0003
## 140 1.2124 nan 0.0010 0.0003
## 160 1.1986 nan 0.0010 0.0003
## 180 1.1857 nan 0.0010 0.0003
## 200 1.1727 nan 0.0010 0.0003
## 220 1.1605 nan 0.0010 0.0003
## 240 1.1483 nan 0.0010 0.0003
## 260 1.1364 nan 0.0010 0.0003
## 280 1.1249 nan 0.0010 0.0003
## 300 1.1136 nan 0.0010 0.0002
## 320 1.1029 nan 0.0010 0.0002
## 340 1.0922 nan 0.0010 0.0002
## 360 1.0818 nan 0.0010 0.0002
## 380 1.0719 nan 0.0010 0.0002
## 400 1.0621 nan 0.0010 0.0002
## 420 1.0526 nan 0.0010 0.0002
## 440 1.0434 nan 0.0010 0.0002
## 460 1.0344 nan 0.0010 0.0002
## 480 1.0256 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3195 nan 0.0010 0.0003
## 3 1.3186 nan 0.0010 0.0004
## 4 1.3178 nan 0.0010 0.0004
## 5 1.3170 nan 0.0010 0.0003
## 6 1.3162 nan 0.0010 0.0004
## 7 1.3153 nan 0.0010 0.0004
## 8 1.3145 nan 0.0010 0.0003
## 9 1.3137 nan 0.0010 0.0004
## 10 1.3129 nan 0.0010 0.0004
## 20 1.3044 nan 0.0010 0.0003
## 40 1.2882 nan 0.0010 0.0003
## 60 1.2722 nan 0.0010 0.0004
## 80 1.2571 nan 0.0010 0.0004
## 100 1.2420 nan 0.0010 0.0003
## 120 1.2275 nan 0.0010 0.0003
## 140 1.2135 nan 0.0010 0.0003
## 160 1.1998 nan 0.0010 0.0003
## 180 1.1868 nan 0.0010 0.0003
## 200 1.1743 nan 0.0010 0.0003
## 220 1.1622 nan 0.0010 0.0002
## 240 1.1500 nan 0.0010 0.0003
## 260 1.1382 nan 0.0010 0.0003
## 280 1.1267 nan 0.0010 0.0002
## 300 1.1157 nan 0.0010 0.0002
## 320 1.1048 nan 0.0010 0.0002
## 340 1.0943 nan 0.0010 0.0002
## 360 1.0842 nan 0.0010 0.0002
## 380 1.0743 nan 0.0010 0.0002
## 400 1.0647 nan 0.0010 0.0002
## 420 1.0555 nan 0.0010 0.0002
## 440 1.0466 nan 0.0010 0.0002
## 460 1.0375 nan 0.0010 0.0002
## 480 1.0287 nan 0.0010 0.0002
## 500 1.0203 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3194 nan 0.0010 0.0003
## 3 1.3185 nan 0.0010 0.0004
## 4 1.3175 nan 0.0010 0.0004
## 5 1.3165 nan 0.0010 0.0004
## 6 1.3155 nan 0.0010 0.0004
## 7 1.3146 nan 0.0010 0.0004
## 8 1.3138 nan 0.0010 0.0004
## 9 1.3129 nan 0.0010 0.0004
## 10 1.3120 nan 0.0010 0.0005
## 20 1.3031 nan 0.0010 0.0004
## 40 1.2854 nan 0.0010 0.0004
## 60 1.2686 nan 0.0010 0.0004
## 80 1.2522 nan 0.0010 0.0004
## 100 1.2366 nan 0.0010 0.0003
## 120 1.2212 nan 0.0010 0.0003
## 140 1.2068 nan 0.0010 0.0003
## 160 1.1925 nan 0.0010 0.0003
## 180 1.1785 nan 0.0010 0.0004
## 200 1.1649 nan 0.0010 0.0003
## 220 1.1522 nan 0.0010 0.0003
## 240 1.1395 nan 0.0010 0.0002
## 260 1.1272 nan 0.0010 0.0002
## 280 1.1153 nan 0.0010 0.0003
## 300 1.1037 nan 0.0010 0.0002
## 320 1.0925 nan 0.0010 0.0002
## 340 1.0814 nan 0.0010 0.0002
## 360 1.0706 nan 0.0010 0.0002
## 380 1.0602 nan 0.0010 0.0002
## 400 1.0503 nan 0.0010 0.0002
## 420 1.0403 nan 0.0010 0.0002
## 440 1.0308 nan 0.0010 0.0002
## 460 1.0211 nan 0.0010 0.0002
## 480 1.0120 nan 0.0010 0.0002
## 500 1.0030 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3193 nan 0.0010 0.0004
## 3 1.3183 nan 0.0010 0.0005
## 4 1.3175 nan 0.0010 0.0004
## 5 1.3167 nan 0.0010 0.0004
## 6 1.3157 nan 0.0010 0.0004
## 7 1.3148 nan 0.0010 0.0004
## 8 1.3140 nan 0.0010 0.0004
## 9 1.3129 nan 0.0010 0.0004
## 10 1.3120 nan 0.0010 0.0004
## 20 1.3031 nan 0.0010 0.0004
## 40 1.2858 nan 0.0010 0.0004
## 60 1.2690 nan 0.0010 0.0004
## 80 1.2529 nan 0.0010 0.0004
## 100 1.2370 nan 0.0010 0.0003
## 120 1.2218 nan 0.0010 0.0003
## 140 1.2074 nan 0.0010 0.0003
## 160 1.1930 nan 0.0010 0.0003
## 180 1.1791 nan 0.0010 0.0003
## 200 1.1655 nan 0.0010 0.0003
## 220 1.1525 nan 0.0010 0.0003
## 240 1.1403 nan 0.0010 0.0003
## 260 1.1283 nan 0.0010 0.0002
## 280 1.1163 nan 0.0010 0.0002
## 300 1.1045 nan 0.0010 0.0002
## 320 1.0931 nan 0.0010 0.0003
## 340 1.0821 nan 0.0010 0.0003
## 360 1.0713 nan 0.0010 0.0002
## 380 1.0610 nan 0.0010 0.0002
## 400 1.0508 nan 0.0010 0.0002
## 420 1.0410 nan 0.0010 0.0002
## 440 1.0315 nan 0.0010 0.0002
## 460 1.0223 nan 0.0010 0.0002
## 480 1.0133 nan 0.0010 0.0002
## 500 1.0043 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3195 nan 0.0010 0.0004
## 3 1.3186 nan 0.0010 0.0004
## 4 1.3178 nan 0.0010 0.0004
## 5 1.3168 nan 0.0010 0.0004
## 6 1.3159 nan 0.0010 0.0004
## 7 1.3151 nan 0.0010 0.0004
## 8 1.3142 nan 0.0010 0.0004
## 9 1.3132 nan 0.0010 0.0004
## 10 1.3124 nan 0.0010 0.0004
## 20 1.3034 nan 0.0010 0.0004
## 40 1.2866 nan 0.0010 0.0003
## 60 1.2701 nan 0.0010 0.0004
## 80 1.2541 nan 0.0010 0.0004
## 100 1.2385 nan 0.0010 0.0003
## 120 1.2236 nan 0.0010 0.0004
## 140 1.2090 nan 0.0010 0.0003
## 160 1.1945 nan 0.0010 0.0003
## 180 1.1805 nan 0.0010 0.0003
## 200 1.1673 nan 0.0010 0.0003
## 220 1.1544 nan 0.0010 0.0003
## 240 1.1418 nan 0.0010 0.0003
## 260 1.1299 nan 0.0010 0.0003
## 280 1.1185 nan 0.0010 0.0002
## 300 1.1071 nan 0.0010 0.0002
## 320 1.0961 nan 0.0010 0.0002
## 340 1.0853 nan 0.0010 0.0002
## 360 1.0748 nan 0.0010 0.0002
## 380 1.0647 nan 0.0010 0.0002
## 400 1.0545 nan 0.0010 0.0002
## 420 1.0452 nan 0.0010 0.0002
## 440 1.0358 nan 0.0010 0.0002
## 460 1.0267 nan 0.0010 0.0002
## 480 1.0177 nan 0.0010 0.0002
## 500 1.0091 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3136 nan 0.0100 0.0034
## 2 1.3047 nan 0.0100 0.0034
## 3 1.2965 nan 0.0100 0.0038
## 4 1.2901 nan 0.0100 0.0030
## 5 1.2829 nan 0.0100 0.0032
## 6 1.2749 nan 0.0100 0.0037
## 7 1.2672 nan 0.0100 0.0036
## 8 1.2599 nan 0.0100 0.0031
## 9 1.2532 nan 0.0100 0.0029
## 10 1.2462 nan 0.0100 0.0030
## 20 1.1827 nan 0.0100 0.0030
## 40 1.0806 nan 0.0100 0.0019
## 60 1.0011 nan 0.0100 0.0015
## 80 0.9360 nan 0.0100 0.0010
## 100 0.8844 nan 0.0100 0.0007
## 120 0.8427 nan 0.0100 0.0006
## 140 0.8076 nan 0.0100 0.0004
## 160 0.7778 nan 0.0100 0.0004
## 180 0.7528 nan 0.0100 0.0003
## 200 0.7307 nan 0.0100 0.0001
## 220 0.7120 nan 0.0100 0.0001
## 240 0.6950 nan 0.0100 0.0000
## 260 0.6793 nan 0.0100 -0.0001
## 280 0.6649 nan 0.0100 0.0001
## 300 0.6508 nan 0.0100 0.0001
## 320 0.6381 nan 0.0100 0.0002
## 340 0.6266 nan 0.0100 -0.0000
## 360 0.6162 nan 0.0100 0.0000
## 380 0.6064 nan 0.0100 0.0001
## 400 0.5970 nan 0.0100 -0.0001
## 420 0.5878 nan 0.0100 0.0002
## 440 0.5784 nan 0.0100 0.0001
## 460 0.5691 nan 0.0100 0.0000
## 480 0.5607 nan 0.0100 0.0000
## 500 0.5530 nan 0.0100 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3126 nan 0.0100 0.0038
## 2 1.3042 nan 0.0100 0.0036
## 3 1.2962 nan 0.0100 0.0039
## 4 1.2882 nan 0.0100 0.0032
## 5 1.2813 nan 0.0100 0.0030
## 6 1.2747 nan 0.0100 0.0032
## 7 1.2681 nan 0.0100 0.0031
## 8 1.2606 nan 0.0100 0.0033
## 9 1.2544 nan 0.0100 0.0028
## 10 1.2461 nan 0.0100 0.0037
## 20 1.1796 nan 0.0100 0.0028
## 40 1.0759 nan 0.0100 0.0020
## 60 0.9970 nan 0.0100 0.0013
## 80 0.9352 nan 0.0100 0.0010
## 100 0.8836 nan 0.0100 0.0007
## 120 0.8416 nan 0.0100 0.0005
## 140 0.8062 nan 0.0100 0.0005
## 160 0.7780 nan 0.0100 0.0003
## 180 0.7532 nan 0.0100 0.0002
## 200 0.7312 nan 0.0100 0.0003
## 220 0.7128 nan 0.0100 0.0002
## 240 0.6959 nan 0.0100 -0.0000
## 260 0.6811 nan 0.0100 0.0002
## 280 0.6669 nan 0.0100 -0.0001
## 300 0.6536 nan 0.0100 0.0003
## 320 0.6425 nan 0.0100 0.0000
## 340 0.6314 nan 0.0100 -0.0001
## 360 0.6207 nan 0.0100 -0.0001
## 380 0.6113 nan 0.0100 -0.0000
## 400 0.6013 nan 0.0100 0.0000
## 420 0.5915 nan 0.0100 -0.0001
## 440 0.5827 nan 0.0100 -0.0000
## 460 0.5743 nan 0.0100 -0.0001
## 480 0.5658 nan 0.0100 -0.0000
## 500 0.5588 nan 0.0100 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3141 nan 0.0100 0.0031
## 2 1.3061 nan 0.0100 0.0036
## 3 1.2980 nan 0.0100 0.0039
## 4 1.2907 nan 0.0100 0.0033
## 5 1.2832 nan 0.0100 0.0031
## 6 1.2756 nan 0.0100 0.0037
## 7 1.2683 nan 0.0100 0.0031
## 8 1.2616 nan 0.0100 0.0030
## 9 1.2548 nan 0.0100 0.0031
## 10 1.2481 nan 0.0100 0.0030
## 20 1.1814 nan 0.0100 0.0031
## 40 1.0765 nan 0.0100 0.0020
## 60 0.9977 nan 0.0100 0.0015
## 80 0.9354 nan 0.0100 0.0012
## 100 0.8862 nan 0.0100 0.0009
## 120 0.8443 nan 0.0100 0.0004
## 140 0.8103 nan 0.0100 0.0005
## 160 0.7818 nan 0.0100 0.0004
## 180 0.7567 nan 0.0100 0.0003
## 200 0.7343 nan 0.0100 0.0001
## 220 0.7158 nan 0.0100 0.0000
## 240 0.6990 nan 0.0100 0.0002
## 260 0.6849 nan 0.0100 0.0002
## 280 0.6708 nan 0.0100 -0.0000
## 300 0.6572 nan 0.0100 0.0000
## 320 0.6456 nan 0.0100 -0.0001
## 340 0.6350 nan 0.0100 -0.0000
## 360 0.6245 nan 0.0100 0.0002
## 380 0.6150 nan 0.0100 0.0000
## 400 0.6057 nan 0.0100 0.0001
## 420 0.5960 nan 0.0100 0.0001
## 440 0.5888 nan 0.0100 -0.0001
## 460 0.5806 nan 0.0100 -0.0000
## 480 0.5719 nan 0.0100 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3127 nan 0.0100 0.0037
## 2 1.3041 nan 0.0100 0.0039
## 3 1.2954 nan 0.0100 0.0038
## 4 1.2873 nan 0.0100 0.0034
## 5 1.2791 nan 0.0100 0.0038
## 6 1.2708 nan 0.0100 0.0037
## 7 1.2631 nan 0.0100 0.0035
## 8 1.2568 nan 0.0100 0.0027
## 9 1.2488 nan 0.0100 0.0036
## 10 1.2414 nan 0.0100 0.0033
## 20 1.1749 nan 0.0100 0.0030
## 40 1.0615 nan 0.0100 0.0021
## 60 0.9765 nan 0.0100 0.0017
## 80 0.9093 nan 0.0100 0.0013
## 100 0.8561 nan 0.0100 0.0008
## 120 0.8124 nan 0.0100 0.0007
## 140 0.7763 nan 0.0100 0.0003
## 160 0.7451 nan 0.0100 0.0003
## 180 0.7179 nan 0.0100 0.0003
## 200 0.6949 nan 0.0100 0.0001
## 220 0.6758 nan 0.0100 0.0002
## 240 0.6571 nan 0.0100 0.0001
## 260 0.6396 nan 0.0100 -0.0002
## 280 0.6238 nan 0.0100 0.0001
## 300 0.6086 nan 0.0100 0.0001
## 320 0.5952 nan 0.0100 0.0000
## 340 0.5811 nan 0.0100 -0.0000
## 360 0.5684 nan 0.0100 -0.0000
## 380 0.5578 nan 0.0100 -0.0002
## 400 0.5477 nan 0.0100 0.0000
## 420 0.5371 nan 0.0100 -0.0000
## 440 0.5271 nan 0.0100 -0.0002
## 460 0.5178 nan 0.0100 -0.0000
## 480 0.5089 nan 0.0100 0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3125 nan 0.0100 0.0040
## 2 1.3039 nan 0.0100 0.0039
## 3 1.2952 nan 0.0100 0.0039
## 4 1.2871 nan 0.0100 0.0036
## 5 1.2790 nan 0.0100 0.0035
## 6 1.2706 nan 0.0100 0.0040
## 7 1.2629 nan 0.0100 0.0039
## 8 1.2557 nan 0.0100 0.0031
## 9 1.2479 nan 0.0100 0.0034
## 10 1.2394 nan 0.0100 0.0037
## 20 1.1715 nan 0.0100 0.0026
## 40 1.0632 nan 0.0100 0.0021
## 60 0.9780 nan 0.0100 0.0018
## 80 0.9127 nan 0.0100 0.0011
## 100 0.8579 nan 0.0100 0.0008
## 120 0.8153 nan 0.0100 0.0007
## 140 0.7789 nan 0.0100 0.0004
## 160 0.7475 nan 0.0100 0.0005
## 180 0.7228 nan 0.0100 0.0000
## 200 0.7005 nan 0.0100 0.0004
## 220 0.6795 nan 0.0100 0.0001
## 240 0.6597 nan 0.0100 0.0001
## 260 0.6430 nan 0.0100 0.0002
## 280 0.6275 nan 0.0100 0.0000
## 300 0.6130 nan 0.0100 0.0000
## 320 0.6014 nan 0.0100 0.0000
## 340 0.5885 nan 0.0100 0.0002
## 360 0.5769 nan 0.0100 0.0000
## 380 0.5661 nan 0.0100 0.0001
## 400 0.5549 nan 0.0100 -0.0001
## 420 0.5444 nan 0.0100 0.0000
## 440 0.5332 nan 0.0100 0.0000
## 460 0.5243 nan 0.0100 0.0001
## 480 0.5155 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3112 nan 0.0100 0.0043
## 2 1.3032 nan 0.0100 0.0037
## 3 1.2943 nan 0.0100 0.0038
## 4 1.2859 nan 0.0100 0.0037
## 5 1.2772 nan 0.0100 0.0039
## 6 1.2697 nan 0.0100 0.0033
## 7 1.2620 nan 0.0100 0.0038
## 8 1.2545 nan 0.0100 0.0034
## 9 1.2469 nan 0.0100 0.0037
## 10 1.2394 nan 0.0100 0.0033
## 20 1.1725 nan 0.0100 0.0028
## 40 1.0631 nan 0.0100 0.0017
## 60 0.9781 nan 0.0100 0.0016
## 80 0.9124 nan 0.0100 0.0013
## 100 0.8603 nan 0.0100 0.0007
## 120 0.8178 nan 0.0100 0.0007
## 140 0.7817 nan 0.0100 0.0006
## 160 0.7523 nan 0.0100 0.0005
## 180 0.7269 nan 0.0100 0.0002
## 200 0.7045 nan 0.0100 0.0001
## 220 0.6860 nan 0.0100 0.0003
## 240 0.6692 nan 0.0100 0.0001
## 260 0.6529 nan 0.0100 0.0001
## 280 0.6376 nan 0.0100 0.0000
## 300 0.6238 nan 0.0100 0.0000
## 320 0.6118 nan 0.0100 -0.0001
## 340 0.6000 nan 0.0100 -0.0000
## 360 0.5880 nan 0.0100 -0.0000
## 380 0.5773 nan 0.0100 -0.0000
## 400 0.5671 nan 0.0100 0.0000
## 420 0.5574 nan 0.0100 -0.0000
## 440 0.5479 nan 0.0100 -0.0001
## 460 0.5383 nan 0.0100 -0.0000
## 480 0.5301 nan 0.0100 -0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3125 nan 0.0100 0.0039
## 2 1.3030 nan 0.0100 0.0045
## 3 1.2939 nan 0.0100 0.0043
## 4 1.2849 nan 0.0100 0.0042
## 5 1.2763 nan 0.0100 0.0034
## 6 1.2683 nan 0.0100 0.0033
## 7 1.2604 nan 0.0100 0.0036
## 8 1.2517 nan 0.0100 0.0037
## 9 1.2437 nan 0.0100 0.0037
## 10 1.2361 nan 0.0100 0.0034
## 20 1.1626 nan 0.0100 0.0032
## 40 1.0481 nan 0.0100 0.0017
## 60 0.9607 nan 0.0100 0.0014
## 80 0.8911 nan 0.0100 0.0009
## 100 0.8375 nan 0.0100 0.0007
## 120 0.7922 nan 0.0100 0.0009
## 140 0.7550 nan 0.0100 0.0005
## 160 0.7230 nan 0.0100 0.0003
## 180 0.6937 nan 0.0100 0.0004
## 200 0.6686 nan 0.0100 0.0004
## 220 0.6469 nan 0.0100 0.0004
## 240 0.6263 nan 0.0100 0.0001
## 260 0.6075 nan 0.0100 0.0000
## 280 0.5902 nan 0.0100 0.0001
## 300 0.5749 nan 0.0100 -0.0000
## 320 0.5615 nan 0.0100 0.0001
## 340 0.5475 nan 0.0100 0.0001
## 360 0.5342 nan 0.0100 -0.0000
## 380 0.5219 nan 0.0100 -0.0000
## 400 0.5105 nan 0.0100 0.0000
## 420 0.4989 nan 0.0100 0.0000
## 440 0.4873 nan 0.0100 -0.0000
## 460 0.4768 nan 0.0100 -0.0001
## 480 0.4659 nan 0.0100 0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3114 nan 0.0100 0.0045
## 2 1.3033 nan 0.0100 0.0036
## 3 1.2941 nan 0.0100 0.0042
## 4 1.2861 nan 0.0100 0.0037
## 5 1.2777 nan 0.0100 0.0036
## 6 1.2684 nan 0.0100 0.0040
## 7 1.2614 nan 0.0100 0.0030
## 8 1.2537 nan 0.0100 0.0036
## 9 1.2454 nan 0.0100 0.0033
## 10 1.2377 nan 0.0100 0.0031
## 20 1.1657 nan 0.0100 0.0033
## 40 1.0509 nan 0.0100 0.0021
## 60 0.9630 nan 0.0100 0.0013
## 80 0.8950 nan 0.0100 0.0012
## 100 0.8401 nan 0.0100 0.0008
## 120 0.7958 nan 0.0100 0.0009
## 140 0.7568 nan 0.0100 0.0005
## 160 0.7248 nan 0.0100 0.0006
## 180 0.6968 nan 0.0100 0.0002
## 200 0.6722 nan 0.0100 0.0002
## 220 0.6508 nan 0.0100 0.0002
## 240 0.6321 nan 0.0100 0.0000
## 260 0.6141 nan 0.0100 0.0000
## 280 0.5973 nan 0.0100 -0.0000
## 300 0.5815 nan 0.0100 0.0001
## 320 0.5670 nan 0.0100 0.0001
## 340 0.5540 nan 0.0100 -0.0001
## 360 0.5416 nan 0.0100 -0.0001
## 380 0.5295 nan 0.0100 0.0000
## 400 0.5174 nan 0.0100 0.0001
## 420 0.5068 nan 0.0100 -0.0000
## 440 0.4967 nan 0.0100 0.0000
## 460 0.4873 nan 0.0100 0.0000
## 480 0.4779 nan 0.0100 -0.0000
## 500 0.4693 nan 0.0100 0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3123 nan 0.0100 0.0039
## 2 1.3034 nan 0.0100 0.0041
## 3 1.2943 nan 0.0100 0.0042
## 4 1.2857 nan 0.0100 0.0036
## 5 1.2774 nan 0.0100 0.0038
## 6 1.2693 nan 0.0100 0.0035
## 7 1.2610 nan 0.0100 0.0036
## 8 1.2536 nan 0.0100 0.0034
## 9 1.2452 nan 0.0100 0.0038
## 10 1.2369 nan 0.0100 0.0037
## 20 1.1664 nan 0.0100 0.0029
## 40 1.0525 nan 0.0100 0.0022
## 60 0.9675 nan 0.0100 0.0013
## 80 0.8987 nan 0.0100 0.0011
## 100 0.8449 nan 0.0100 0.0008
## 120 0.8013 nan 0.0100 0.0005
## 140 0.7625 nan 0.0100 0.0004
## 160 0.7318 nan 0.0100 0.0002
## 180 0.7061 nan 0.0100 0.0002
## 200 0.6831 nan 0.0100 0.0001
## 220 0.6619 nan 0.0100 0.0002
## 240 0.6425 nan 0.0100 0.0000
## 260 0.6248 nan 0.0100 0.0000
## 280 0.6079 nan 0.0100 -0.0000
## 300 0.5938 nan 0.0100 0.0000
## 320 0.5795 nan 0.0100 0.0001
## 340 0.5670 nan 0.0100 -0.0003
## 360 0.5551 nan 0.0100 -0.0000
## 380 0.5438 nan 0.0100 0.0001
## 400 0.5326 nan 0.0100 -0.0001
## 420 0.5208 nan 0.0100 -0.0001
## 440 0.5114 nan 0.0100 -0.0001
## 460 0.5008 nan 0.0100 -0.0000
## 480 0.4912 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2458 nan 0.1000 0.0331
## 2 1.1915 nan 0.1000 0.0239
## 3 1.1326 nan 0.1000 0.0215
## 4 1.0847 nan 0.1000 0.0220
## 5 1.0401 nan 0.1000 0.0200
## 6 1.0028 nan 0.1000 0.0147
## 7 0.9636 nan 0.1000 0.0166
## 8 0.9380 nan 0.1000 0.0081
## 9 0.9121 nan 0.1000 0.0100
## 10 0.8861 nan 0.1000 0.0112
## 20 0.7392 nan 0.1000 0.0011
## 40 0.6112 nan 0.1000 -0.0021
## 60 0.5321 nan 0.1000 -0.0006
## 80 0.4658 nan 0.1000 -0.0008
## 100 0.4114 nan 0.1000 -0.0011
## 120 0.3715 nan 0.1000 -0.0007
## 140 0.3334 nan 0.1000 -0.0009
## 160 0.3005 nan 0.1000 -0.0003
## 180 0.2700 nan 0.1000 -0.0003
## 200 0.2428 nan 0.1000 -0.0006
## 220 0.2190 nan 0.1000 -0.0008
## 240 0.1988 nan 0.1000 -0.0013
## 260 0.1821 nan 0.1000 -0.0004
## 280 0.1647 nan 0.1000 -0.0003
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## 320 0.1360 nan 0.1000 -0.0008
## 340 0.1258 nan 0.1000 -0.0003
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## 380 0.1057 nan 0.1000 -0.0003
## 400 0.0976 nan 0.1000 -0.0001
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## 460 0.0766 nan 0.1000 -0.0003
## 480 0.0710 nan 0.1000 -0.0002
## 500 0.0651 nan 0.1000 -0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2535 nan 0.1000 0.0320
## 2 1.1850 nan 0.1000 0.0282
## 3 1.1256 nan 0.1000 0.0279
## 4 1.0712 nan 0.1000 0.0233
## 5 1.0271 nan 0.1000 0.0156
## 6 0.9910 nan 0.1000 0.0130
## 7 0.9561 nan 0.1000 0.0140
## 8 0.9245 nan 0.1000 0.0119
## 9 0.8980 nan 0.1000 0.0083
## 10 0.8757 nan 0.1000 0.0082
## 20 0.7350 nan 0.1000 0.0021
## 40 0.6115 nan 0.1000 -0.0012
## 60 0.5290 nan 0.1000 -0.0010
## 80 0.4697 nan 0.1000 -0.0000
## 100 0.4256 nan 0.1000 -0.0015
## 120 0.3852 nan 0.1000 -0.0008
## 140 0.3450 nan 0.1000 -0.0006
## 160 0.3136 nan 0.1000 -0.0000
## 180 0.2833 nan 0.1000 -0.0004
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## 220 0.2350 nan 0.1000 -0.0005
## 240 0.2163 nan 0.1000 -0.0005
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## 280 0.1830 nan 0.1000 -0.0006
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## 320 0.1547 nan 0.1000 -0.0008
## 340 0.1418 nan 0.1000 -0.0004
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## 380 0.1194 nan 0.1000 -0.0004
## 400 0.1099 nan 0.1000 -0.0002
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## 460 0.0855 nan 0.1000 -0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2415 nan 0.1000 0.0382
## 2 1.1776 nan 0.1000 0.0283
## 3 1.1214 nan 0.1000 0.0259
## 4 1.0725 nan 0.1000 0.0210
## 5 1.0299 nan 0.1000 0.0177
## 6 0.9931 nan 0.1000 0.0134
## 7 0.9608 nan 0.1000 0.0137
## 8 0.9339 nan 0.1000 0.0102
## 9 0.9052 nan 0.1000 0.0102
## 10 0.8818 nan 0.1000 0.0083
## 20 0.7271 nan 0.1000 0.0031
## 40 0.6087 nan 0.1000 -0.0004
## 60 0.5356 nan 0.1000 0.0000
## 80 0.4652 nan 0.1000 -0.0004
## 100 0.4191 nan 0.1000 -0.0016
## 120 0.3786 nan 0.1000 -0.0015
## 140 0.3416 nan 0.1000 -0.0004
## 160 0.3122 nan 0.1000 -0.0008
## 180 0.2824 nan 0.1000 -0.0010
## 200 0.2584 nan 0.1000 -0.0008
## 220 0.2391 nan 0.1000 -0.0008
## 240 0.2207 nan 0.1000 -0.0011
## 260 0.2030 nan 0.1000 0.0000
## 280 0.1869 nan 0.1000 -0.0005
## 300 0.1727 nan 0.1000 -0.0010
## 320 0.1582 nan 0.1000 -0.0002
## 340 0.1471 nan 0.1000 -0.0005
## 360 0.1369 nan 0.1000 -0.0006
## 380 0.1278 nan 0.1000 -0.0004
## 400 0.1192 nan 0.1000 -0.0005
## 420 0.1105 nan 0.1000 -0.0003
## 440 0.1020 nan 0.1000 -0.0003
## 460 0.0949 nan 0.1000 -0.0002
## 480 0.0881 nan 0.1000 -0.0003
## 500 0.0820 nan 0.1000 -0.0004
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2399 nan 0.1000 0.0318
## 2 1.1655 nan 0.1000 0.0299
## 3 1.1067 nan 0.1000 0.0240
## 4 1.0539 nan 0.1000 0.0217
## 5 1.0117 nan 0.1000 0.0181
## 6 0.9719 nan 0.1000 0.0166
## 7 0.9391 nan 0.1000 0.0122
## 8 0.9043 nan 0.1000 0.0131
## 9 0.8815 nan 0.1000 0.0088
## 10 0.8559 nan 0.1000 0.0098
## 20 0.7031 nan 0.1000 0.0016
## 40 0.5556 nan 0.1000 -0.0005
## 60 0.4734 nan 0.1000 -0.0016
## 80 0.4132 nan 0.1000 -0.0007
## 100 0.3566 nan 0.1000 -0.0014
## 120 0.3107 nan 0.1000 -0.0002
## 140 0.2686 nan 0.1000 -0.0002
## 160 0.2381 nan 0.1000 -0.0005
## 180 0.2076 nan 0.1000 -0.0004
## 200 0.1825 nan 0.1000 0.0003
## 220 0.1632 nan 0.1000 -0.0002
## 240 0.1468 nan 0.1000 -0.0006
## 260 0.1334 nan 0.1000 -0.0004
## 280 0.1201 nan 0.1000 -0.0004
## 300 0.1084 nan 0.1000 -0.0000
## 320 0.0971 nan 0.1000 -0.0003
## 340 0.0881 nan 0.1000 -0.0004
## 360 0.0798 nan 0.1000 -0.0003
## 380 0.0726 nan 0.1000 -0.0003
## 400 0.0657 nan 0.1000 -0.0001
## 420 0.0587 nan 0.1000 -0.0001
## 440 0.0529 nan 0.1000 -0.0002
## 460 0.0478 nan 0.1000 -0.0001
## 480 0.0427 nan 0.1000 -0.0000
## 500 0.0391 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2376 nan 0.1000 0.0343
## 2 1.1673 nan 0.1000 0.0325
## 3 1.1066 nan 0.1000 0.0252
## 4 1.0550 nan 0.1000 0.0191
## 5 1.0080 nan 0.1000 0.0191
## 6 0.9714 nan 0.1000 0.0147
## 7 0.9380 nan 0.1000 0.0138
## 8 0.9039 nan 0.1000 0.0128
## 9 0.8781 nan 0.1000 0.0086
## 10 0.8514 nan 0.1000 0.0101
## 20 0.6975 nan 0.1000 0.0031
## 40 0.5671 nan 0.1000 0.0004
## 60 0.4781 nan 0.1000 -0.0004
## 80 0.4137 nan 0.1000 -0.0002
## 100 0.3559 nan 0.1000 0.0004
## 120 0.3116 nan 0.1000 -0.0008
## 140 0.2702 nan 0.1000 -0.0005
## 160 0.2402 nan 0.1000 -0.0011
## 180 0.2082 nan 0.1000 -0.0008
## 200 0.1842 nan 0.1000 -0.0003
## 220 0.1649 nan 0.1000 -0.0003
## 240 0.1479 nan 0.1000 -0.0002
## 260 0.1323 nan 0.1000 -0.0003
## 280 0.1189 nan 0.1000 -0.0004
## 300 0.1075 nan 0.1000 -0.0002
## 320 0.0973 nan 0.1000 -0.0002
## 340 0.0871 nan 0.1000 -0.0003
## 360 0.0788 nan 0.1000 -0.0002
## 380 0.0726 nan 0.1000 -0.0005
## 400 0.0658 nan 0.1000 -0.0002
## 420 0.0600 nan 0.1000 0.0000
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## 460 0.0495 nan 0.1000 -0.0001
## 480 0.0446 nan 0.1000 0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2380 nan 0.1000 0.0365
## 2 1.1705 nan 0.1000 0.0299
## 3 1.1123 nan 0.1000 0.0228
## 4 1.0602 nan 0.1000 0.0191
## 5 1.0095 nan 0.1000 0.0216
## 6 0.9681 nan 0.1000 0.0152
## 7 0.9358 nan 0.1000 0.0131
## 8 0.9065 nan 0.1000 0.0111
## 9 0.8777 nan 0.1000 0.0125
## 10 0.8551 nan 0.1000 0.0090
## 20 0.7100 nan 0.1000 0.0009
## 40 0.5780 nan 0.1000 0.0002
## 60 0.4880 nan 0.1000 -0.0006
## 80 0.4229 nan 0.1000 -0.0010
## 100 0.3682 nan 0.1000 -0.0016
## 120 0.3230 nan 0.1000 -0.0009
## 140 0.2857 nan 0.1000 -0.0016
## 160 0.2553 nan 0.1000 -0.0010
## 180 0.2271 nan 0.1000 -0.0006
## 200 0.2035 nan 0.1000 -0.0013
## 220 0.1778 nan 0.1000 -0.0001
## 240 0.1612 nan 0.1000 -0.0009
## 260 0.1441 nan 0.1000 -0.0002
## 280 0.1308 nan 0.1000 -0.0006
## 300 0.1192 nan 0.1000 -0.0003
## 320 0.1083 nan 0.1000 -0.0004
## 340 0.0991 nan 0.1000 -0.0001
## 360 0.0898 nan 0.1000 -0.0004
## 380 0.0813 nan 0.1000 -0.0003
## 400 0.0749 nan 0.1000 -0.0002
## 420 0.0679 nan 0.1000 -0.0001
## 440 0.0628 nan 0.1000 -0.0002
## 460 0.0571 nan 0.1000 -0.0002
## 480 0.0520 nan 0.1000 -0.0002
## 500 0.0468 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2388 nan 0.1000 0.0357
## 2 1.1737 nan 0.1000 0.0258
## 3 1.1102 nan 0.1000 0.0226
## 4 1.0559 nan 0.1000 0.0240
## 5 1.0070 nan 0.1000 0.0215
## 6 0.9601 nan 0.1000 0.0199
## 7 0.9237 nan 0.1000 0.0147
## 8 0.8940 nan 0.1000 0.0103
## 9 0.8633 nan 0.1000 0.0120
## 10 0.8355 nan 0.1000 0.0088
## 20 0.6673 nan 0.1000 -0.0001
## 40 0.5067 nan 0.1000 -0.0007
## 60 0.4133 nan 0.1000 -0.0000
## 80 0.3447 nan 0.1000 -0.0013
## 100 0.2920 nan 0.1000 -0.0005
## 120 0.2507 nan 0.1000 -0.0003
## 140 0.2152 nan 0.1000 -0.0001
## 160 0.1868 nan 0.1000 -0.0004
## 180 0.1606 nan 0.1000 -0.0009
## 200 0.1396 nan 0.1000 -0.0005
## 220 0.1223 nan 0.1000 -0.0004
## 240 0.1080 nan 0.1000 0.0000
## 260 0.0949 nan 0.1000 0.0001
## 280 0.0832 nan 0.1000 -0.0001
## 300 0.0739 nan 0.1000 0.0000
## 320 0.0649 nan 0.1000 -0.0001
## 340 0.0571 nan 0.1000 -0.0002
## 360 0.0503 nan 0.1000 -0.0002
## 380 0.0447 nan 0.1000 0.0000
## 400 0.0396 nan 0.1000 -0.0000
## 420 0.0358 nan 0.1000 -0.0001
## 440 0.0315 nan 0.1000 -0.0000
## 460 0.0280 nan 0.1000 -0.0001
## 480 0.0249 nan 0.1000 -0.0000
## 500 0.0221 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2388 nan 0.1000 0.0387
## 2 1.1634 nan 0.1000 0.0341
## 3 1.1048 nan 0.1000 0.0244
## 4 1.0513 nan 0.1000 0.0248
## 5 1.0047 nan 0.1000 0.0189
## 6 0.9565 nan 0.1000 0.0210
## 7 0.9196 nan 0.1000 0.0135
## 8 0.8867 nan 0.1000 0.0131
## 9 0.8601 nan 0.1000 0.0095
## 10 0.8350 nan 0.1000 0.0099
## 20 0.6728 nan 0.1000 0.0012
## 40 0.5167 nan 0.1000 0.0006
## 60 0.4234 nan 0.1000 -0.0020
## 80 0.3590 nan 0.1000 -0.0002
## 100 0.3009 nan 0.1000 -0.0014
## 120 0.2592 nan 0.1000 -0.0005
## 140 0.2240 nan 0.1000 0.0010
## 160 0.1923 nan 0.1000 -0.0005
## 180 0.1654 nan 0.1000 -0.0004
## 200 0.1433 nan 0.1000 -0.0004
## 220 0.1255 nan 0.1000 -0.0001
## 240 0.1114 nan 0.1000 -0.0002
## 260 0.0975 nan 0.1000 -0.0006
## 280 0.0860 nan 0.1000 -0.0003
## 300 0.0758 nan 0.1000 -0.0002
## 320 0.0674 nan 0.1000 -0.0004
## 340 0.0595 nan 0.1000 -0.0002
## 360 0.0524 nan 0.1000 -0.0001
## 380 0.0464 nan 0.1000 -0.0001
## 400 0.0412 nan 0.1000 0.0001
## 420 0.0367 nan 0.1000 -0.0000
## 440 0.0324 nan 0.1000 -0.0001
## 460 0.0293 nan 0.1000 -0.0001
## 480 0.0256 nan 0.1000 -0.0000
## 500 0.0225 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2323 nan 0.1000 0.0387
## 2 1.1627 nan 0.1000 0.0312
## 3 1.1052 nan 0.1000 0.0231
## 4 1.0532 nan 0.1000 0.0191
## 5 1.0070 nan 0.1000 0.0201
## 6 0.9681 nan 0.1000 0.0154
## 7 0.9332 nan 0.1000 0.0155
## 8 0.9038 nan 0.1000 0.0117
## 9 0.8751 nan 0.1000 0.0104
## 10 0.8500 nan 0.1000 0.0080
## 20 0.6781 nan 0.1000 0.0047
## 40 0.5396 nan 0.1000 0.0002
## 60 0.4520 nan 0.1000 -0.0017
## 80 0.3819 nan 0.1000 -0.0006
## 100 0.3242 nan 0.1000 -0.0017
## 120 0.2768 nan 0.1000 -0.0019
## 140 0.2398 nan 0.1000 -0.0009
## 160 0.2088 nan 0.1000 -0.0005
## 180 0.1826 nan 0.1000 -0.0004
## 200 0.1572 nan 0.1000 -0.0008
## 220 0.1384 nan 0.1000 -0.0007
## 240 0.1211 nan 0.1000 -0.0002
## 260 0.1075 nan 0.1000 -0.0007
## 280 0.0962 nan 0.1000 -0.0005
## 300 0.0834 nan 0.1000 -0.0002
## 320 0.0736 nan 0.1000 -0.0003
## 340 0.0656 nan 0.1000 -0.0002
## 360 0.0589 nan 0.1000 -0.0003
## 380 0.0528 nan 0.1000 -0.0002
## 400 0.0472 nan 0.1000 -0.0003
## 420 0.0421 nan 0.1000 -0.0001
## 440 0.0376 nan 0.1000 -0.0001
## 460 0.0336 nan 0.1000 -0.0002
## 480 0.0297 nan 0.1000 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3180 nan 0.0010 0.0004
## 4 1.3172 nan 0.0010 0.0003
## 5 1.3163 nan 0.0010 0.0004
## 6 1.3154 nan 0.0010 0.0003
## 7 1.3147 nan 0.0010 0.0003
## 8 1.3139 nan 0.0010 0.0004
## 9 1.3130 nan 0.0010 0.0004
## 10 1.3122 nan 0.0010 0.0004
## 20 1.3041 nan 0.0010 0.0003
## 40 1.2878 nan 0.0010 0.0003
## 60 1.2725 nan 0.0010 0.0004
## 80 1.2575 nan 0.0010 0.0003
## 100 1.2425 nan 0.0010 0.0003
## 120 1.2282 nan 0.0010 0.0003
## 140 1.2145 nan 0.0010 0.0003
## 160 1.2009 nan 0.0010 0.0003
## 180 1.1880 nan 0.0010 0.0003
## 200 1.1753 nan 0.0010 0.0003
## 220 1.1634 nan 0.0010 0.0003
## 240 1.1514 nan 0.0010 0.0002
## 260 1.1396 nan 0.0010 0.0002
## 280 1.1282 nan 0.0010 0.0003
## 300 1.1172 nan 0.0010 0.0002
## 320 1.1065 nan 0.0010 0.0002
## 340 1.0961 nan 0.0010 0.0002
## 360 1.0860 nan 0.0010 0.0002
## 380 1.0760 nan 0.0010 0.0002
## 400 1.0666 nan 0.0010 0.0001
## 420 1.0571 nan 0.0010 0.0002
## 440 1.0480 nan 0.0010 0.0002
## 460 1.0389 nan 0.0010 0.0002
## 480 1.0305 nan 0.0010 0.0002
## 500 1.0222 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0003
## 2 1.3190 nan 0.0010 0.0004
## 3 1.3182 nan 0.0010 0.0004
## 4 1.3173 nan 0.0010 0.0004
## 5 1.3165 nan 0.0010 0.0004
## 6 1.3157 nan 0.0010 0.0004
## 7 1.3149 nan 0.0010 0.0004
## 8 1.3141 nan 0.0010 0.0004
## 9 1.3132 nan 0.0010 0.0004
## 10 1.3123 nan 0.0010 0.0004
## 20 1.3041 nan 0.0010 0.0004
## 40 1.2878 nan 0.0010 0.0003
## 60 1.2726 nan 0.0010 0.0003
## 80 1.2572 nan 0.0010 0.0003
## 100 1.2427 nan 0.0010 0.0003
## 120 1.2282 nan 0.0010 0.0003
## 140 1.2145 nan 0.0010 0.0003
## 160 1.2012 nan 0.0010 0.0003
## 180 1.1885 nan 0.0010 0.0003
## 200 1.1759 nan 0.0010 0.0003
## 220 1.1639 nan 0.0010 0.0003
## 240 1.1520 nan 0.0010 0.0003
## 260 1.1406 nan 0.0010 0.0002
## 280 1.1293 nan 0.0010 0.0003
## 300 1.1184 nan 0.0010 0.0002
## 320 1.1078 nan 0.0010 0.0002
## 340 1.0973 nan 0.0010 0.0002
## 360 1.0870 nan 0.0010 0.0002
## 380 1.0770 nan 0.0010 0.0002
## 400 1.0672 nan 0.0010 0.0002
## 420 1.0578 nan 0.0010 0.0002
## 440 1.0487 nan 0.0010 0.0002
## 460 1.0400 nan 0.0010 0.0002
## 480 1.0310 nan 0.0010 0.0002
## 500 1.0224 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3181 nan 0.0010 0.0004
## 4 1.3173 nan 0.0010 0.0004
## 5 1.3165 nan 0.0010 0.0004
## 6 1.3157 nan 0.0010 0.0003
## 7 1.3148 nan 0.0010 0.0004
## 8 1.3139 nan 0.0010 0.0004
## 9 1.3131 nan 0.0010 0.0004
## 10 1.3123 nan 0.0010 0.0003
## 20 1.3038 nan 0.0010 0.0004
## 40 1.2880 nan 0.0010 0.0004
## 60 1.2729 nan 0.0010 0.0003
## 80 1.2580 nan 0.0010 0.0003
## 100 1.2435 nan 0.0010 0.0004
## 120 1.2296 nan 0.0010 0.0003
## 140 1.2160 nan 0.0010 0.0003
## 160 1.2028 nan 0.0010 0.0003
## 180 1.1897 nan 0.0010 0.0003
## 200 1.1770 nan 0.0010 0.0003
## 220 1.1647 nan 0.0010 0.0003
## 240 1.1528 nan 0.0010 0.0002
## 260 1.1415 nan 0.0010 0.0003
## 280 1.1303 nan 0.0010 0.0002
## 300 1.1192 nan 0.0010 0.0003
## 320 1.1084 nan 0.0010 0.0002
## 340 1.0978 nan 0.0010 0.0003
## 360 1.0877 nan 0.0010 0.0002
## 380 1.0779 nan 0.0010 0.0002
## 400 1.0681 nan 0.0010 0.0002
## 420 1.0590 nan 0.0010 0.0002
## 440 1.0500 nan 0.0010 0.0002
## 460 1.0411 nan 0.0010 0.0002
## 480 1.0327 nan 0.0010 0.0002
## 500 1.0241 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3179 nan 0.0010 0.0004
## 4 1.3170 nan 0.0010 0.0004
## 5 1.3161 nan 0.0010 0.0004
## 6 1.3152 nan 0.0010 0.0004
## 7 1.3144 nan 0.0010 0.0004
## 8 1.3135 nan 0.0010 0.0004
## 9 1.3126 nan 0.0010 0.0004
## 10 1.3117 nan 0.0010 0.0004
## 20 1.3030 nan 0.0010 0.0004
## 40 1.2860 nan 0.0010 0.0004
## 60 1.2693 nan 0.0010 0.0004
## 80 1.2532 nan 0.0010 0.0004
## 100 1.2372 nan 0.0010 0.0004
## 120 1.2222 nan 0.0010 0.0003
## 140 1.2077 nan 0.0010 0.0003
## 160 1.1934 nan 0.0010 0.0003
## 180 1.1796 nan 0.0010 0.0004
## 200 1.1663 nan 0.0010 0.0003
## 220 1.1532 nan 0.0010 0.0002
## 240 1.1401 nan 0.0010 0.0003
## 260 1.1280 nan 0.0010 0.0003
## 280 1.1160 nan 0.0010 0.0003
## 300 1.1040 nan 0.0010 0.0003
## 320 1.0928 nan 0.0010 0.0003
## 340 1.0817 nan 0.0010 0.0002
## 360 1.0709 nan 0.0010 0.0002
## 380 1.0606 nan 0.0010 0.0002
## 400 1.0506 nan 0.0010 0.0002
## 420 1.0410 nan 0.0010 0.0002
## 440 1.0313 nan 0.0010 0.0002
## 460 1.0219 nan 0.0010 0.0002
## 480 1.0127 nan 0.0010 0.0002
## 500 1.0038 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0003
## 3 1.3181 nan 0.0010 0.0004
## 4 1.3172 nan 0.0010 0.0004
## 5 1.3163 nan 0.0010 0.0004
## 6 1.3154 nan 0.0010 0.0004
## 7 1.3145 nan 0.0010 0.0004
## 8 1.3135 nan 0.0010 0.0004
## 9 1.3126 nan 0.0010 0.0004
## 10 1.3117 nan 0.0010 0.0004
## 20 1.3030 nan 0.0010 0.0004
## 40 1.2862 nan 0.0010 0.0004
## 60 1.2698 nan 0.0010 0.0003
## 80 1.2536 nan 0.0010 0.0004
## 100 1.2380 nan 0.0010 0.0003
## 120 1.2227 nan 0.0010 0.0004
## 140 1.2080 nan 0.0010 0.0003
## 160 1.1938 nan 0.0010 0.0003
## 180 1.1802 nan 0.0010 0.0003
## 200 1.1670 nan 0.0010 0.0003
## 220 1.1537 nan 0.0010 0.0003
## 240 1.1411 nan 0.0010 0.0003
## 260 1.1289 nan 0.0010 0.0003
## 280 1.1170 nan 0.0010 0.0003
## 300 1.1055 nan 0.0010 0.0003
## 320 1.0941 nan 0.0010 0.0002
## 340 1.0831 nan 0.0010 0.0003
## 360 1.0726 nan 0.0010 0.0002
## 380 1.0622 nan 0.0010 0.0002
## 400 1.0518 nan 0.0010 0.0002
## 420 1.0422 nan 0.0010 0.0002
## 440 1.0325 nan 0.0010 0.0002
## 460 1.0232 nan 0.0010 0.0002
## 480 1.0142 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3180 nan 0.0010 0.0004
## 4 1.3171 nan 0.0010 0.0004
## 5 1.3163 nan 0.0010 0.0004
## 6 1.3154 nan 0.0010 0.0004
## 7 1.3145 nan 0.0010 0.0004
## 8 1.3136 nan 0.0010 0.0004
## 9 1.3126 nan 0.0010 0.0004
## 10 1.3118 nan 0.0010 0.0004
## 20 1.3031 nan 0.0010 0.0004
## 40 1.2861 nan 0.0010 0.0003
## 60 1.2694 nan 0.0010 0.0003
## 80 1.2532 nan 0.0010 0.0004
## 100 1.2379 nan 0.0010 0.0004
## 120 1.2232 nan 0.0010 0.0003
## 140 1.2084 nan 0.0010 0.0003
## 160 1.1945 nan 0.0010 0.0003
## 180 1.1808 nan 0.0010 0.0003
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## 220 1.1548 nan 0.0010 0.0003
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## 260 1.1301 nan 0.0010 0.0003
## 280 1.1181 nan 0.0010 0.0002
## 300 1.1064 nan 0.0010 0.0002
## 320 1.0952 nan 0.0010 0.0002
## 340 1.0841 nan 0.0010 0.0003
## 360 1.0737 nan 0.0010 0.0002
## 380 1.0634 nan 0.0010 0.0002
## 400 1.0534 nan 0.0010 0.0002
## 420 1.0435 nan 0.0010 0.0002
## 440 1.0339 nan 0.0010 0.0002
## 460 1.0247 nan 0.0010 0.0002
## 480 1.0157 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3187 nan 0.0010 0.0004
## 3 1.3178 nan 0.0010 0.0004
## 4 1.3168 nan 0.0010 0.0004
## 5 1.3157 nan 0.0010 0.0004
## 6 1.3149 nan 0.0010 0.0003
## 7 1.3139 nan 0.0010 0.0004
## 8 1.3129 nan 0.0010 0.0004
## 9 1.3119 nan 0.0010 0.0004
## 10 1.3110 nan 0.0010 0.0004
## 20 1.3017 nan 0.0010 0.0004
## 40 1.2836 nan 0.0010 0.0004
## 60 1.2662 nan 0.0010 0.0004
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## 100 1.2333 nan 0.0010 0.0003
## 120 1.2174 nan 0.0010 0.0003
## 140 1.2019 nan 0.0010 0.0003
## 160 1.1870 nan 0.0010 0.0004
## 180 1.1725 nan 0.0010 0.0003
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## 320 1.0823 nan 0.0010 0.0002
## 340 1.0710 nan 0.0010 0.0003
## 360 1.0599 nan 0.0010 0.0002
## 380 1.0493 nan 0.0010 0.0002
## 400 1.0390 nan 0.0010 0.0002
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## 460 1.0091 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3179 nan 0.0010 0.0004
## 4 1.3169 nan 0.0010 0.0004
## 5 1.3159 nan 0.0010 0.0004
## 6 1.3150 nan 0.0010 0.0004
## 7 1.3141 nan 0.0010 0.0004
## 8 1.3132 nan 0.0010 0.0004
## 9 1.3122 nan 0.0010 0.0004
## 10 1.3113 nan 0.0010 0.0004
## 20 1.3021 nan 0.0010 0.0004
## 40 1.2841 nan 0.0010 0.0004
## 60 1.2665 nan 0.0010 0.0004
## 80 1.2494 nan 0.0010 0.0003
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## 140 1.2021 nan 0.0010 0.0003
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## 260 1.1200 nan 0.0010 0.0003
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## 320 1.0842 nan 0.0010 0.0002
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## 360 1.0616 nan 0.0010 0.0002
## 380 1.0511 nan 0.0010 0.0002
## 400 1.0406 nan 0.0010 0.0002
## 420 1.0300 nan 0.0010 0.0002
## 440 1.0201 nan 0.0010 0.0002
## 460 1.0103 nan 0.0010 0.0002
## 480 1.0006 nan 0.0010 0.0002
## 500 0.9917 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3188 nan 0.0010 0.0005
## 3 1.3179 nan 0.0010 0.0004
## 4 1.3170 nan 0.0010 0.0004
## 5 1.3161 nan 0.0010 0.0004
## 6 1.3152 nan 0.0010 0.0004
## 7 1.3143 nan 0.0010 0.0004
## 8 1.3134 nan 0.0010 0.0004
## 9 1.3124 nan 0.0010 0.0004
## 10 1.3115 nan 0.0010 0.0003
## 20 1.3023 nan 0.0010 0.0004
## 40 1.2847 nan 0.0010 0.0004
## 60 1.2675 nan 0.0010 0.0004
## 80 1.2509 nan 0.0010 0.0003
## 100 1.2349 nan 0.0010 0.0003
## 120 1.2193 nan 0.0010 0.0004
## 140 1.2042 nan 0.0010 0.0004
## 160 1.1895 nan 0.0010 0.0003
## 180 1.1752 nan 0.0010 0.0003
## 200 1.1615 nan 0.0010 0.0003
## 220 1.1483 nan 0.0010 0.0003
## 240 1.1350 nan 0.0010 0.0003
## 260 1.1224 nan 0.0010 0.0002
## 280 1.1101 nan 0.0010 0.0003
## 300 1.0982 nan 0.0010 0.0002
## 320 1.0866 nan 0.0010 0.0002
## 340 1.0753 nan 0.0010 0.0002
## 360 1.0643 nan 0.0010 0.0003
## 380 1.0536 nan 0.0010 0.0002
## 400 1.0434 nan 0.0010 0.0002
## 420 1.0336 nan 0.0010 0.0002
## 440 1.0239 nan 0.0010 0.0002
## 460 1.0144 nan 0.0010 0.0002
## 480 1.0050 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3127 nan 0.0100 0.0036
## 2 1.3037 nan 0.0100 0.0036
## 3 1.2950 nan 0.0100 0.0047
## 4 1.2871 nan 0.0100 0.0038
## 5 1.2791 nan 0.0100 0.0036
## 6 1.2711 nan 0.0100 0.0037
## 7 1.2638 nan 0.0100 0.0037
## 8 1.2565 nan 0.0100 0.0031
## 9 1.2486 nan 0.0100 0.0035
## 10 1.2414 nan 0.0100 0.0033
## 20 1.1747 nan 0.0100 0.0025
## 40 1.0650 nan 0.0100 0.0020
## 60 0.9803 nan 0.0100 0.0015
## 80 0.9146 nan 0.0100 0.0011
## 100 0.8627 nan 0.0100 0.0005
## 120 0.8193 nan 0.0100 0.0008
## 140 0.7835 nan 0.0100 0.0003
## 160 0.7538 nan 0.0100 0.0003
## 180 0.7285 nan 0.0100 0.0002
## 200 0.7060 nan 0.0100 0.0002
## 220 0.6863 nan 0.0100 0.0003
## 240 0.6683 nan 0.0100 0.0001
## 260 0.6531 nan 0.0100 0.0001
## 280 0.6381 nan 0.0100 0.0003
## 300 0.6245 nan 0.0100 -0.0001
## 320 0.6113 nan 0.0100 -0.0000
## 340 0.5998 nan 0.0100 0.0001
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## 380 0.5787 nan 0.0100 0.0001
## 400 0.5686 nan 0.0100 0.0001
## 420 0.5603 nan 0.0100 -0.0000
## 440 0.5521 nan 0.0100 -0.0001
## 460 0.5429 nan 0.0100 -0.0001
## 480 0.5348 nan 0.0100 -0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3132 nan 0.0100 0.0036
## 2 1.3049 nan 0.0100 0.0032
## 3 1.2969 nan 0.0100 0.0034
## 4 1.2893 nan 0.0100 0.0033
## 5 1.2810 nan 0.0100 0.0041
## 6 1.2729 nan 0.0100 0.0038
## 7 1.2654 nan 0.0100 0.0035
## 8 1.2584 nan 0.0100 0.0029
## 9 1.2512 nan 0.0100 0.0033
## 10 1.2438 nan 0.0100 0.0030
## 20 1.1751 nan 0.0100 0.0028
## 40 1.0659 nan 0.0100 0.0024
## 60 0.9816 nan 0.0100 0.0014
## 80 0.9172 nan 0.0100 0.0013
## 100 0.8641 nan 0.0100 0.0007
## 120 0.8197 nan 0.0100 0.0006
## 140 0.7844 nan 0.0100 0.0006
## 160 0.7541 nan 0.0100 0.0004
## 180 0.7280 nan 0.0100 0.0002
## 200 0.7066 nan 0.0100 0.0002
## 220 0.6881 nan 0.0100 0.0003
## 240 0.6726 nan 0.0100 -0.0001
## 260 0.6572 nan 0.0100 0.0001
## 280 0.6429 nan 0.0100 -0.0000
## 300 0.6313 nan 0.0100 0.0000
## 320 0.6200 nan 0.0100 0.0000
## 340 0.6089 nan 0.0100 -0.0001
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## 380 0.5883 nan 0.0100 -0.0001
## 400 0.5783 nan 0.0100 -0.0001
## 420 0.5692 nan 0.0100 0.0000
## 440 0.5615 nan 0.0100 -0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3116 nan 0.0100 0.0042
## 2 1.3030 nan 0.0100 0.0035
## 3 1.2948 nan 0.0100 0.0037
## 4 1.2870 nan 0.0100 0.0035
## 5 1.2787 nan 0.0100 0.0039
## 6 1.2710 nan 0.0100 0.0037
## 7 1.2634 nan 0.0100 0.0030
## 8 1.2557 nan 0.0100 0.0035
## 9 1.2482 nan 0.0100 0.0036
## 10 1.2406 nan 0.0100 0.0031
## 20 1.1743 nan 0.0100 0.0028
## 40 1.0683 nan 0.0100 0.0018
## 60 0.9859 nan 0.0100 0.0015
## 80 0.9186 nan 0.0100 0.0009
## 100 0.8658 nan 0.0100 0.0008
## 120 0.8226 nan 0.0100 0.0007
## 140 0.7865 nan 0.0100 0.0008
## 160 0.7565 nan 0.0100 0.0003
## 180 0.7313 nan 0.0100 0.0003
## 200 0.7100 nan 0.0100 0.0002
## 220 0.6914 nan 0.0100 0.0003
## 240 0.6746 nan 0.0100 0.0001
## 260 0.6604 nan 0.0100 0.0001
## 280 0.6474 nan 0.0100 0.0001
## 300 0.6358 nan 0.0100 0.0000
## 320 0.6246 nan 0.0100 -0.0001
## 340 0.6142 nan 0.0100 0.0000
## 360 0.6057 nan 0.0100 -0.0001
## 380 0.5960 nan 0.0100 -0.0002
## 400 0.5872 nan 0.0100 -0.0000
## 420 0.5788 nan 0.0100 0.0000
## 440 0.5706 nan 0.0100 -0.0002
## 460 0.5635 nan 0.0100 -0.0001
## 480 0.5561 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3118 nan 0.0100 0.0042
## 2 1.3027 nan 0.0100 0.0041
## 3 1.2939 nan 0.0100 0.0038
## 4 1.2848 nan 0.0100 0.0037
## 5 1.2762 nan 0.0100 0.0034
## 6 1.2692 nan 0.0100 0.0032
## 7 1.2610 nan 0.0100 0.0037
## 8 1.2532 nan 0.0100 0.0035
## 9 1.2455 nan 0.0100 0.0036
## 10 1.2376 nan 0.0100 0.0035
## 20 1.1687 nan 0.0100 0.0027
## 40 1.0519 nan 0.0100 0.0021
## 60 0.9627 nan 0.0100 0.0016
## 80 0.8926 nan 0.0100 0.0010
## 100 0.8357 nan 0.0100 0.0011
## 120 0.7902 nan 0.0100 0.0008
## 140 0.7525 nan 0.0100 0.0005
## 160 0.7217 nan 0.0100 0.0002
## 180 0.6946 nan 0.0100 0.0003
## 200 0.6723 nan 0.0100 0.0001
## 220 0.6517 nan 0.0100 0.0001
## 240 0.6326 nan 0.0100 0.0002
## 260 0.6165 nan 0.0100 0.0001
## 280 0.6007 nan 0.0100 -0.0000
## 300 0.5861 nan 0.0100 0.0001
## 320 0.5726 nan 0.0100 0.0000
## 340 0.5610 nan 0.0100 0.0000
## 360 0.5490 nan 0.0100 -0.0001
## 380 0.5377 nan 0.0100 -0.0000
## 400 0.5269 nan 0.0100 -0.0000
## 420 0.5162 nan 0.0100 -0.0001
## 440 0.5060 nan 0.0100 -0.0000
## 460 0.4965 nan 0.0100 -0.0000
## 480 0.4874 nan 0.0100 -0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3112 nan 0.0100 0.0041
## 2 1.3019 nan 0.0100 0.0040
## 3 1.2936 nan 0.0100 0.0037
## 4 1.2848 nan 0.0100 0.0042
## 5 1.2760 nan 0.0100 0.0036
## 6 1.2673 nan 0.0100 0.0043
## 7 1.2588 nan 0.0100 0.0038
## 8 1.2516 nan 0.0100 0.0034
## 9 1.2441 nan 0.0100 0.0034
## 10 1.2356 nan 0.0100 0.0035
## 20 1.1643 nan 0.0100 0.0026
## 40 1.0514 nan 0.0100 0.0025
## 60 0.9639 nan 0.0100 0.0018
## 80 0.8931 nan 0.0100 0.0012
## 100 0.8405 nan 0.0100 0.0008
## 120 0.7959 nan 0.0100 0.0009
## 140 0.7584 nan 0.0100 0.0006
## 160 0.7291 nan 0.0100 0.0001
## 180 0.7019 nan 0.0100 0.0002
## 200 0.6788 nan 0.0100 0.0003
## 220 0.6579 nan 0.0100 0.0003
## 240 0.6410 nan 0.0100 0.0001
## 260 0.6245 nan 0.0100 0.0001
## 280 0.6095 nan 0.0100 0.0001
## 300 0.5962 nan 0.0100 -0.0001
## 320 0.5832 nan 0.0100 0.0001
## 340 0.5708 nan 0.0100 0.0002
## 360 0.5595 nan 0.0100 0.0001
## 380 0.5487 nan 0.0100 0.0001
## 400 0.5383 nan 0.0100 -0.0000
## 420 0.5300 nan 0.0100 -0.0001
## 440 0.5196 nan 0.0100 -0.0001
## 460 0.5102 nan 0.0100 0.0000
## 480 0.5015 nan 0.0100 -0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3116 nan 0.0100 0.0040
## 2 1.3025 nan 0.0100 0.0038
## 3 1.2941 nan 0.0100 0.0041
## 4 1.2855 nan 0.0100 0.0039
## 5 1.2771 nan 0.0100 0.0029
## 6 1.2694 nan 0.0100 0.0039
## 7 1.2610 nan 0.0100 0.0038
## 8 1.2539 nan 0.0100 0.0030
## 9 1.2460 nan 0.0100 0.0034
## 10 1.2386 nan 0.0100 0.0035
## 20 1.1679 nan 0.0100 0.0031
## 40 1.0532 nan 0.0100 0.0023
## 60 0.9656 nan 0.0100 0.0016
## 80 0.8977 nan 0.0100 0.0010
## 100 0.8423 nan 0.0100 0.0008
## 120 0.7984 nan 0.0100 0.0007
## 140 0.7608 nan 0.0100 0.0006
## 160 0.7311 nan 0.0100 0.0003
## 180 0.7045 nan 0.0100 0.0000
## 200 0.6838 nan 0.0100 0.0003
## 220 0.6636 nan 0.0100 0.0001
## 240 0.6464 nan 0.0100 0.0000
## 260 0.6301 nan 0.0100 0.0000
## 280 0.6150 nan 0.0100 -0.0000
## 300 0.6023 nan 0.0100 0.0002
## 320 0.5905 nan 0.0100 0.0000
## 340 0.5788 nan 0.0100 -0.0001
## 360 0.5686 nan 0.0100 -0.0000
## 380 0.5582 nan 0.0100 0.0001
## 400 0.5483 nan 0.0100 -0.0000
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## 460 0.5221 nan 0.0100 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3112 nan 0.0100 0.0041
## 2 1.3020 nan 0.0100 0.0039
## 3 1.2932 nan 0.0100 0.0039
## 4 1.2838 nan 0.0100 0.0042
## 5 1.2756 nan 0.0100 0.0033
## 6 1.2665 nan 0.0100 0.0042
## 7 1.2574 nan 0.0100 0.0038
## 8 1.2492 nan 0.0100 0.0035
## 9 1.2413 nan 0.0100 0.0031
## 10 1.2331 nan 0.0100 0.0035
## 20 1.1593 nan 0.0100 0.0031
## 40 1.0388 nan 0.0100 0.0020
## 60 0.9485 nan 0.0100 0.0016
## 80 0.8750 nan 0.0100 0.0010
## 100 0.8158 nan 0.0100 0.0008
## 120 0.7692 nan 0.0100 0.0005
## 140 0.7297 nan 0.0100 0.0002
## 160 0.6965 nan 0.0100 0.0004
## 180 0.6671 nan 0.0100 0.0004
## 200 0.6430 nan 0.0100 0.0003
## 220 0.6215 nan 0.0100 0.0003
## 240 0.5998 nan 0.0100 0.0001
## 260 0.5815 nan 0.0100 0.0000
## 280 0.5644 nan 0.0100 0.0001
## 300 0.5486 nan 0.0100 0.0002
## 320 0.5339 nan 0.0100 -0.0001
## 340 0.5202 nan 0.0100 0.0001
## 360 0.5078 nan 0.0100 -0.0001
## 380 0.4959 nan 0.0100 0.0001
## 400 0.4841 nan 0.0100 -0.0001
## 420 0.4738 nan 0.0100 0.0001
## 440 0.4645 nan 0.0100 -0.0001
## 460 0.4550 nan 0.0100 -0.0001
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## 500 0.4347 nan 0.0100 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3109 nan 0.0100 0.0040
## 2 1.3012 nan 0.0100 0.0049
## 3 1.2917 nan 0.0100 0.0043
## 4 1.2831 nan 0.0100 0.0039
## 5 1.2740 nan 0.0100 0.0043
## 6 1.2647 nan 0.0100 0.0038
## 7 1.2565 nan 0.0100 0.0037
## 8 1.2473 nan 0.0100 0.0040
## 9 1.2394 nan 0.0100 0.0032
## 10 1.2313 nan 0.0100 0.0036
## 20 1.1574 nan 0.0100 0.0031
## 40 1.0384 nan 0.0100 0.0023
## 60 0.9492 nan 0.0100 0.0016
## 80 0.8776 nan 0.0100 0.0014
## 100 0.8208 nan 0.0100 0.0010
## 120 0.7748 nan 0.0100 0.0008
## 140 0.7377 nan 0.0100 0.0005
## 160 0.7062 nan 0.0100 0.0004
## 180 0.6780 nan 0.0100 0.0004
## 200 0.6539 nan 0.0100 0.0003
## 220 0.6320 nan 0.0100 0.0002
## 240 0.6120 nan 0.0100 0.0002
## 260 0.5942 nan 0.0100 0.0001
## 280 0.5777 nan 0.0100 -0.0000
## 300 0.5636 nan 0.0100 0.0002
## 320 0.5500 nan 0.0100 -0.0001
## 340 0.5372 nan 0.0100 -0.0001
## 360 0.5248 nan 0.0100 -0.0001
## 380 0.5116 nan 0.0100 0.0000
## 400 0.5000 nan 0.0100 -0.0001
## 420 0.4887 nan 0.0100 -0.0001
## 440 0.4780 nan 0.0100 0.0000
## 460 0.4677 nan 0.0100 -0.0000
## 480 0.4579 nan 0.0100 -0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3113 nan 0.0100 0.0045
## 2 1.3022 nan 0.0100 0.0041
## 3 1.2929 nan 0.0100 0.0038
## 4 1.2845 nan 0.0100 0.0038
## 5 1.2760 nan 0.0100 0.0034
## 6 1.2664 nan 0.0100 0.0040
## 7 1.2579 nan 0.0100 0.0038
## 8 1.2494 nan 0.0100 0.0032
## 9 1.2414 nan 0.0100 0.0033
## 10 1.2325 nan 0.0100 0.0036
## 20 1.1595 nan 0.0100 0.0034
## 40 1.0427 nan 0.0100 0.0021
## 60 0.9541 nan 0.0100 0.0016
## 80 0.8835 nan 0.0100 0.0009
## 100 0.8257 nan 0.0100 0.0009
## 120 0.7794 nan 0.0100 0.0007
## 140 0.7432 nan 0.0100 0.0003
## 160 0.7113 nan 0.0100 0.0002
## 180 0.6845 nan 0.0100 0.0002
## 200 0.6613 nan 0.0100 -0.0002
## 220 0.6413 nan 0.0100 0.0001
## 240 0.6224 nan 0.0100 0.0002
## 260 0.6039 nan 0.0100 0.0001
## 280 0.5885 nan 0.0100 0.0003
## 300 0.5736 nan 0.0100 0.0001
## 320 0.5609 nan 0.0100 -0.0001
## 340 0.5488 nan 0.0100 -0.0000
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## 380 0.5257 nan 0.0100 0.0000
## 400 0.5144 nan 0.0100 0.0000
## 420 0.5041 nan 0.0100 -0.0002
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## 460 0.4854 nan 0.0100 -0.0001
## 480 0.4757 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2317 nan 0.1000 0.0382
## 2 1.1609 nan 0.1000 0.0319
## 3 1.1012 nan 0.1000 0.0230
## 4 1.0546 nan 0.1000 0.0210
## 5 1.0142 nan 0.1000 0.0170
## 6 0.9734 nan 0.1000 0.0182
## 7 0.9376 nan 0.1000 0.0143
## 8 0.9118 nan 0.1000 0.0101
## 9 0.8851 nan 0.1000 0.0104
## 10 0.8641 nan 0.1000 0.0071
## 20 0.7084 nan 0.1000 0.0030
## 40 0.5717 nan 0.1000 -0.0011
## 60 0.4943 nan 0.1000 -0.0011
## 80 0.4376 nan 0.1000 0.0000
## 100 0.3901 nan 0.1000 -0.0008
## 120 0.3501 nan 0.1000 -0.0012
## 140 0.3118 nan 0.1000 0.0001
## 160 0.2833 nan 0.1000 -0.0009
## 180 0.2542 nan 0.1000 -0.0003
## 200 0.2290 nan 0.1000 -0.0002
## 220 0.2093 nan 0.1000 -0.0004
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## 280 0.1572 nan 0.1000 -0.0006
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## 380 0.1016 nan 0.1000 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2424 nan 0.1000 0.0370
## 2 1.1698 nan 0.1000 0.0316
## 3 1.1115 nan 0.1000 0.0271
## 4 1.0587 nan 0.1000 0.0226
## 5 1.0135 nan 0.1000 0.0223
## 6 0.9786 nan 0.1000 0.0128
## 7 0.9431 nan 0.1000 0.0156
## 8 0.9120 nan 0.1000 0.0121
## 9 0.8836 nan 0.1000 0.0115
## 10 0.8566 nan 0.1000 0.0102
## 20 0.7126 nan 0.1000 0.0022
## 40 0.5808 nan 0.1000 -0.0009
## 60 0.5091 nan 0.1000 -0.0011
## 80 0.4531 nan 0.1000 -0.0004
## 100 0.3989 nan 0.1000 -0.0014
## 120 0.3558 nan 0.1000 -0.0004
## 140 0.3250 nan 0.1000 -0.0003
## 160 0.2956 nan 0.1000 -0.0004
## 180 0.2698 nan 0.1000 -0.0007
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## 220 0.2246 nan 0.1000 -0.0002
## 240 0.2073 nan 0.1000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2406 nan 0.1000 0.0359
## 2 1.1777 nan 0.1000 0.0309
## 3 1.1210 nan 0.1000 0.0250
## 4 1.0721 nan 0.1000 0.0212
## 5 1.0292 nan 0.1000 0.0181
## 6 0.9870 nan 0.1000 0.0182
## 7 0.9525 nan 0.1000 0.0141
## 8 0.9197 nan 0.1000 0.0135
## 9 0.8914 nan 0.1000 0.0118
## 10 0.8641 nan 0.1000 0.0101
## 20 0.7186 nan 0.1000 0.0029
## 40 0.5927 nan 0.1000 -0.0012
## 60 0.5180 nan 0.1000 0.0002
## 80 0.4649 nan 0.1000 -0.0010
## 100 0.4102 nan 0.1000 -0.0007
## 120 0.3739 nan 0.1000 -0.0016
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## 180 0.2809 nan 0.1000 -0.0013
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2268 nan 0.1000 0.0457
## 2 1.1601 nan 0.1000 0.0300
## 3 1.0936 nan 0.1000 0.0286
## 4 1.0402 nan 0.1000 0.0235
## 5 0.9945 nan 0.1000 0.0185
## 6 0.9552 nan 0.1000 0.0135
## 7 0.9243 nan 0.1000 0.0111
## 8 0.8939 nan 0.1000 0.0100
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## 10 0.8462 nan 0.1000 0.0060
## 20 0.6707 nan 0.1000 -0.0001
## 40 0.5270 nan 0.1000 0.0014
## 60 0.4319 nan 0.1000 0.0002
## 80 0.3796 nan 0.1000 -0.0016
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2358 nan 0.1000 0.0413
## 2 1.1594 nan 0.1000 0.0339
## 3 1.0990 nan 0.1000 0.0283
## 4 1.0473 nan 0.1000 0.0208
## 5 0.9968 nan 0.1000 0.0204
## 6 0.9544 nan 0.1000 0.0176
## 7 0.9158 nan 0.1000 0.0160
## 8 0.8864 nan 0.1000 0.0091
## 9 0.8577 nan 0.1000 0.0120
## 10 0.8328 nan 0.1000 0.0091
## 20 0.6825 nan 0.1000 0.0028
## 40 0.5438 nan 0.1000 -0.0020
## 60 0.4611 nan 0.1000 -0.0011
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## 120 0.2868 nan 0.1000 -0.0015
## 140 0.2489 nan 0.1000 -0.0004
## 160 0.2187 nan 0.1000 -0.0010
## 180 0.1934 nan 0.1000 -0.0010
## 200 0.1716 nan 0.1000 -0.0004
## 220 0.1516 nan 0.1000 -0.0006
## 240 0.1351 nan 0.1000 -0.0008
## 260 0.1208 nan 0.1000 -0.0001
## 280 0.1083 nan 0.1000 -0.0004
## 300 0.0968 nan 0.1000 -0.0002
## 320 0.0879 nan 0.1000 -0.0006
## 340 0.0797 nan 0.1000 -0.0002
## 360 0.0716 nan 0.1000 -0.0001
## 380 0.0647 nan 0.1000 -0.0002
## 400 0.0590 nan 0.1000 -0.0002
## 420 0.0530 nan 0.1000 -0.0003
## 440 0.0487 nan 0.1000 -0.0001
## 460 0.0447 nan 0.1000 -0.0002
## 480 0.0410 nan 0.1000 -0.0001
## 500 0.0371 nan 0.1000 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2352 nan 0.1000 0.0386
## 2 1.1654 nan 0.1000 0.0314
## 3 1.0990 nan 0.1000 0.0295
## 4 1.0449 nan 0.1000 0.0228
## 5 0.9996 nan 0.1000 0.0200
## 6 0.9577 nan 0.1000 0.0178
## 7 0.9249 nan 0.1000 0.0118
## 8 0.8991 nan 0.1000 0.0091
## 9 0.8657 nan 0.1000 0.0110
## 10 0.8410 nan 0.1000 0.0092
## 20 0.6850 nan 0.1000 0.0001
## 40 0.5577 nan 0.1000 -0.0003
## 60 0.4830 nan 0.1000 -0.0013
## 80 0.4130 nan 0.1000 -0.0021
## 100 0.3626 nan 0.1000 0.0006
## 120 0.3188 nan 0.1000 -0.0015
## 140 0.2815 nan 0.1000 -0.0006
## 160 0.2493 nan 0.1000 -0.0013
## 180 0.2202 nan 0.1000 -0.0013
## 200 0.1964 nan 0.1000 -0.0002
## 220 0.1770 nan 0.1000 -0.0005
## 240 0.1611 nan 0.1000 -0.0007
## 260 0.1443 nan 0.1000 -0.0005
## 280 0.1300 nan 0.1000 -0.0008
## 300 0.1168 nan 0.1000 -0.0003
## 320 0.1069 nan 0.1000 -0.0005
## 340 0.0964 nan 0.1000 -0.0001
## 360 0.0870 nan 0.1000 -0.0003
## 380 0.0800 nan 0.1000 -0.0005
## 400 0.0729 nan 0.1000 -0.0002
## 420 0.0662 nan 0.1000 -0.0004
## 440 0.0608 nan 0.1000 -0.0004
## 460 0.0548 nan 0.1000 -0.0003
## 480 0.0496 nan 0.1000 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2355 nan 0.1000 0.0332
## 2 1.1565 nan 0.1000 0.0372
## 3 1.0930 nan 0.1000 0.0277
## 4 1.0372 nan 0.1000 0.0231
## 5 0.9927 nan 0.1000 0.0176
## 6 0.9480 nan 0.1000 0.0155
## 7 0.9091 nan 0.1000 0.0170
## 8 0.8783 nan 0.1000 0.0124
## 9 0.8468 nan 0.1000 0.0109
## 10 0.8215 nan 0.1000 0.0096
## 20 0.6506 nan 0.1000 0.0017
## 40 0.4879 nan 0.1000 -0.0004
## 60 0.4003 nan 0.1000 -0.0001
## 80 0.3321 nan 0.1000 -0.0004
## 100 0.2853 nan 0.1000 0.0004
## 120 0.2420 nan 0.1000 -0.0002
## 140 0.2057 nan 0.1000 0.0001
## 160 0.1755 nan 0.1000 -0.0002
## 180 0.1505 nan 0.1000 -0.0002
## 200 0.1311 nan 0.1000 -0.0001
## 220 0.1144 nan 0.1000 0.0000
## 240 0.0999 nan 0.1000 -0.0004
## 260 0.0880 nan 0.1000 -0.0001
## 280 0.0772 nan 0.1000 -0.0002
## 300 0.0682 nan 0.1000 -0.0001
## 320 0.0602 nan 0.1000 -0.0002
## 340 0.0527 nan 0.1000 -0.0001
## 360 0.0466 nan 0.1000 -0.0001
## 380 0.0411 nan 0.1000 -0.0001
## 400 0.0369 nan 0.1000 -0.0001
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## 460 0.0255 nan 0.1000 -0.0002
## 480 0.0227 nan 0.1000 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2183 nan 0.1000 0.0402
## 2 1.1477 nan 0.1000 0.0283
## 3 1.0855 nan 0.1000 0.0331
## 4 1.0329 nan 0.1000 0.0230
## 5 0.9858 nan 0.1000 0.0181
## 6 0.9398 nan 0.1000 0.0156
## 7 0.9030 nan 0.1000 0.0142
## 8 0.8684 nan 0.1000 0.0106
## 9 0.8368 nan 0.1000 0.0115
## 10 0.8114 nan 0.1000 0.0090
## 20 0.6492 nan 0.1000 0.0032
## 40 0.5044 nan 0.1000 0.0003
## 60 0.4109 nan 0.1000 -0.0007
## 80 0.3421 nan 0.1000 -0.0004
## 100 0.2889 nan 0.1000 -0.0003
## 120 0.2409 nan 0.1000 -0.0005
## 140 0.2003 nan 0.1000 -0.0002
## 160 0.1719 nan 0.1000 -0.0003
## 180 0.1501 nan 0.1000 -0.0004
## 200 0.1312 nan 0.1000 -0.0004
## 220 0.1140 nan 0.1000 -0.0002
## 240 0.1001 nan 0.1000 -0.0004
## 260 0.0882 nan 0.1000 -0.0003
## 280 0.0783 nan 0.1000 -0.0002
## 300 0.0683 nan 0.1000 -0.0001
## 320 0.0609 nan 0.1000 -0.0003
## 340 0.0541 nan 0.1000 -0.0001
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## 380 0.0429 nan 0.1000 -0.0001
## 400 0.0381 nan 0.1000 -0.0002
## 420 0.0342 nan 0.1000 -0.0001
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## 460 0.0271 nan 0.1000 -0.0001
## 480 0.0246 nan 0.1000 -0.0001
## 500 0.0218 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2368 nan 0.1000 0.0358
## 2 1.1576 nan 0.1000 0.0361
## 3 1.0924 nan 0.1000 0.0305
## 4 1.0330 nan 0.1000 0.0239
## 5 0.9790 nan 0.1000 0.0233
## 6 0.9393 nan 0.1000 0.0156
## 7 0.9022 nan 0.1000 0.0147
## 8 0.8726 nan 0.1000 0.0102
## 9 0.8453 nan 0.1000 0.0084
## 10 0.8206 nan 0.1000 0.0120
## 20 0.6593 nan 0.1000 0.0028
## 40 0.5233 nan 0.1000 0.0006
## 60 0.4412 nan 0.1000 -0.0017
## 80 0.3734 nan 0.1000 -0.0010
## 100 0.3172 nan 0.1000 -0.0006
## 120 0.2729 nan 0.1000 -0.0011
## 140 0.2367 nan 0.1000 -0.0009
## 160 0.2019 nan 0.1000 -0.0012
## 180 0.1769 nan 0.1000 -0.0003
## 200 0.1559 nan 0.1000 -0.0007
## 220 0.1388 nan 0.1000 -0.0005
## 240 0.1220 nan 0.1000 -0.0004
## 260 0.1062 nan 0.1000 -0.0002
## 280 0.0942 nan 0.1000 -0.0004
## 300 0.0836 nan 0.1000 -0.0002
## 320 0.0740 nan 0.1000 -0.0001
## 340 0.0664 nan 0.1000 -0.0003
## 360 0.0597 nan 0.1000 -0.0002
## 380 0.0534 nan 0.1000 -0.0003
## 400 0.0474 nan 0.1000 -0.0002
## 420 0.0423 nan 0.1000 -0.0002
## 440 0.0385 nan 0.1000 -0.0001
## 460 0.0344 nan 0.1000 -0.0002
## 480 0.0305 nan 0.1000 -0.0001
## 500 0.0274 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3199 nan 0.0010 0.0003
## 2 1.3190 nan 0.0010 0.0004
## 3 1.3182 nan 0.0010 0.0004
## 4 1.3174 nan 0.0010 0.0004
## 5 1.3166 nan 0.0010 0.0003
## 6 1.3159 nan 0.0010 0.0003
## 7 1.3151 nan 0.0010 0.0003
## 8 1.3143 nan 0.0010 0.0004
## 9 1.3136 nan 0.0010 0.0003
## 10 1.3128 nan 0.0010 0.0004
## 20 1.3050 nan 0.0010 0.0004
## 40 1.2895 nan 0.0010 0.0003
## 60 1.2745 nan 0.0010 0.0004
## 80 1.2603 nan 0.0010 0.0003
## 100 1.2462 nan 0.0010 0.0003
## 120 1.2325 nan 0.0010 0.0003
## 140 1.2193 nan 0.0010 0.0003
## 160 1.2065 nan 0.0010 0.0003
## 180 1.1942 nan 0.0010 0.0002
## 200 1.1820 nan 0.0010 0.0002
## 220 1.1702 nan 0.0010 0.0002
## 240 1.1586 nan 0.0010 0.0003
## 260 1.1473 nan 0.0010 0.0002
## 280 1.1364 nan 0.0010 0.0003
## 300 1.1263 nan 0.0010 0.0002
## 320 1.1164 nan 0.0010 0.0002
## 340 1.1066 nan 0.0010 0.0002
## 360 1.0969 nan 0.0010 0.0002
## 380 1.0874 nan 0.0010 0.0002
## 400 1.0780 nan 0.0010 0.0002
## 420 1.0690 nan 0.0010 0.0002
## 440 1.0604 nan 0.0010 0.0002
## 460 1.0520 nan 0.0010 0.0002
## 480 1.0439 nan 0.0010 0.0001
## 500 1.0359 nan 0.0010 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3191 nan 0.0010 0.0003
## 3 1.3183 nan 0.0010 0.0003
## 4 1.3175 nan 0.0010 0.0004
## 5 1.3167 nan 0.0010 0.0003
## 6 1.3159 nan 0.0010 0.0004
## 7 1.3152 nan 0.0010 0.0003
## 8 1.3144 nan 0.0010 0.0003
## 9 1.3136 nan 0.0010 0.0004
## 10 1.3127 nan 0.0010 0.0004
## 20 1.3051 nan 0.0010 0.0003
## 40 1.2898 nan 0.0010 0.0004
## 60 1.2748 nan 0.0010 0.0004
## 80 1.2602 nan 0.0010 0.0003
## 100 1.2463 nan 0.0010 0.0003
## 120 1.2327 nan 0.0010 0.0003
## 140 1.2200 nan 0.0010 0.0003
## 160 1.2070 nan 0.0010 0.0003
## 180 1.1945 nan 0.0010 0.0003
## 200 1.1826 nan 0.0010 0.0003
## 220 1.1712 nan 0.0010 0.0002
## 240 1.1602 nan 0.0010 0.0002
## 260 1.1493 nan 0.0010 0.0002
## 280 1.1384 nan 0.0010 0.0003
## 300 1.1277 nan 0.0010 0.0002
## 320 1.1175 nan 0.0010 0.0003
## 340 1.1079 nan 0.0010 0.0002
## 360 1.0983 nan 0.0010 0.0002
## 380 1.0892 nan 0.0010 0.0002
## 400 1.0801 nan 0.0010 0.0002
## 420 1.0710 nan 0.0010 0.0002
## 440 1.0624 nan 0.0010 0.0002
## 460 1.0539 nan 0.0010 0.0002
## 480 1.0456 nan 0.0010 0.0002
## 500 1.0376 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3199 nan 0.0010 0.0004
## 2 1.3191 nan 0.0010 0.0004
## 3 1.3182 nan 0.0010 0.0004
## 4 1.3174 nan 0.0010 0.0004
## 5 1.3166 nan 0.0010 0.0004
## 6 1.3158 nan 0.0010 0.0004
## 7 1.3150 nan 0.0010 0.0004
## 8 1.3142 nan 0.0010 0.0004
## 9 1.3133 nan 0.0010 0.0003
## 10 1.3124 nan 0.0010 0.0004
## 20 1.3044 nan 0.0010 0.0003
## 40 1.2889 nan 0.0010 0.0004
## 60 1.2740 nan 0.0010 0.0003
## 80 1.2595 nan 0.0010 0.0003
## 100 1.2457 nan 0.0010 0.0003
## 120 1.2322 nan 0.0010 0.0003
## 140 1.2190 nan 0.0010 0.0003
## 160 1.2062 nan 0.0010 0.0003
## 180 1.1941 nan 0.0010 0.0003
## 200 1.1822 nan 0.0010 0.0003
## 220 1.1704 nan 0.0010 0.0002
## 240 1.1592 nan 0.0010 0.0002
## 260 1.1486 nan 0.0010 0.0002
## 280 1.1379 nan 0.0010 0.0002
## 300 1.1273 nan 0.0010 0.0002
## 320 1.1173 nan 0.0010 0.0002
## 340 1.1076 nan 0.0010 0.0002
## 360 1.0977 nan 0.0010 0.0002
## 380 1.0885 nan 0.0010 0.0002
## 400 1.0795 nan 0.0010 0.0002
## 420 1.0707 nan 0.0010 0.0002
## 440 1.0623 nan 0.0010 0.0002
## 460 1.0541 nan 0.0010 0.0002
## 480 1.0459 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3180 nan 0.0010 0.0004
## 4 1.3171 nan 0.0010 0.0004
## 5 1.3162 nan 0.0010 0.0004
## 6 1.3154 nan 0.0010 0.0004
## 7 1.3146 nan 0.0010 0.0004
## 8 1.3137 nan 0.0010 0.0004
## 9 1.3129 nan 0.0010 0.0004
## 10 1.3121 nan 0.0010 0.0004
## 20 1.3035 nan 0.0010 0.0004
## 40 1.2869 nan 0.0010 0.0004
## 60 1.2708 nan 0.0010 0.0004
## 80 1.2554 nan 0.0010 0.0003
## 100 1.2401 nan 0.0010 0.0003
## 120 1.2259 nan 0.0010 0.0003
## 140 1.2121 nan 0.0010 0.0002
## 160 1.1985 nan 0.0010 0.0003
## 180 1.1850 nan 0.0010 0.0003
## 200 1.1721 nan 0.0010 0.0003
## 220 1.1596 nan 0.0010 0.0003
## 240 1.1477 nan 0.0010 0.0003
## 260 1.1362 nan 0.0010 0.0002
## 280 1.1251 nan 0.0010 0.0002
## 300 1.1140 nan 0.0010 0.0002
## 320 1.1033 nan 0.0010 0.0002
## 340 1.0930 nan 0.0010 0.0002
## 360 1.0828 nan 0.0010 0.0002
## 380 1.0729 nan 0.0010 0.0002
## 400 1.0631 nan 0.0010 0.0002
## 420 1.0537 nan 0.0010 0.0002
## 440 1.0444 nan 0.0010 0.0002
## 460 1.0355 nan 0.0010 0.0002
## 480 1.0268 nan 0.0010 0.0002
## 500 1.0185 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3190 nan 0.0010 0.0004
## 3 1.3181 nan 0.0010 0.0004
## 4 1.3172 nan 0.0010 0.0004
## 5 1.3163 nan 0.0010 0.0004
## 6 1.3154 nan 0.0010 0.0004
## 7 1.3145 nan 0.0010 0.0004
## 8 1.3136 nan 0.0010 0.0004
## 9 1.3127 nan 0.0010 0.0004
## 10 1.3118 nan 0.0010 0.0004
## 20 1.3032 nan 0.0010 0.0004
## 40 1.2869 nan 0.0010 0.0004
## 60 1.2707 nan 0.0010 0.0004
## 80 1.2554 nan 0.0010 0.0003
## 100 1.2404 nan 0.0010 0.0003
## 120 1.2262 nan 0.0010 0.0003
## 140 1.2121 nan 0.0010 0.0003
## 160 1.1988 nan 0.0010 0.0003
## 180 1.1857 nan 0.0010 0.0002
## 200 1.1726 nan 0.0010 0.0003
## 220 1.1602 nan 0.0010 0.0003
## 240 1.1483 nan 0.0010 0.0003
## 260 1.1366 nan 0.0010 0.0002
## 280 1.1254 nan 0.0010 0.0002
## 300 1.1143 nan 0.0010 0.0002
## 320 1.1036 nan 0.0010 0.0002
## 340 1.0935 nan 0.0010 0.0002
## 360 1.0838 nan 0.0010 0.0002
## 380 1.0739 nan 0.0010 0.0002
## 400 1.0642 nan 0.0010 0.0002
## 420 1.0550 nan 0.0010 0.0002
## 440 1.0459 nan 0.0010 0.0002
## 460 1.0372 nan 0.0010 0.0002
## 480 1.0285 nan 0.0010 0.0002
## 500 1.0201 nan 0.0010 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3180 nan 0.0010 0.0004
## 4 1.3171 nan 0.0010 0.0004
## 5 1.3162 nan 0.0010 0.0004
## 6 1.3154 nan 0.0010 0.0004
## 7 1.3146 nan 0.0010 0.0004
## 8 1.3137 nan 0.0010 0.0004
## 9 1.3128 nan 0.0010 0.0004
## 10 1.3120 nan 0.0010 0.0004
## 20 1.3036 nan 0.0010 0.0003
## 40 1.2873 nan 0.0010 0.0003
## 60 1.2715 nan 0.0010 0.0004
## 80 1.2560 nan 0.0010 0.0003
## 100 1.2417 nan 0.0010 0.0004
## 120 1.2273 nan 0.0010 0.0003
## 140 1.2136 nan 0.0010 0.0003
## 160 1.2003 nan 0.0010 0.0003
## 180 1.1874 nan 0.0010 0.0003
## 200 1.1748 nan 0.0010 0.0003
## 220 1.1624 nan 0.0010 0.0003
## 240 1.1505 nan 0.0010 0.0003
## 260 1.1388 nan 0.0010 0.0002
## 280 1.1274 nan 0.0010 0.0003
## 300 1.1166 nan 0.0010 0.0002
## 320 1.1061 nan 0.0010 0.0002
## 340 1.0960 nan 0.0010 0.0002
## 360 1.0858 nan 0.0010 0.0002
## 380 1.0760 nan 0.0010 0.0002
## 400 1.0665 nan 0.0010 0.0002
## 420 1.0573 nan 0.0010 0.0002
## 440 1.0482 nan 0.0010 0.0002
## 460 1.0395 nan 0.0010 0.0002
## 480 1.0309 nan 0.0010 0.0002
## 500 1.0226 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3180 nan 0.0010 0.0004
## 4 1.3170 nan 0.0010 0.0004
## 5 1.3162 nan 0.0010 0.0004
## 6 1.3153 nan 0.0010 0.0004
## 7 1.3145 nan 0.0010 0.0004
## 8 1.3136 nan 0.0010 0.0004
## 9 1.3127 nan 0.0010 0.0004
## 10 1.3118 nan 0.0010 0.0004
## 20 1.3027 nan 0.0010 0.0004
## 40 1.2856 nan 0.0010 0.0004
## 60 1.2686 nan 0.0010 0.0004
## 80 1.2522 nan 0.0010 0.0003
## 100 1.2366 nan 0.0010 0.0004
## 120 1.2214 nan 0.0010 0.0003
## 140 1.2068 nan 0.0010 0.0003
## 160 1.1928 nan 0.0010 0.0003
## 180 1.1792 nan 0.0010 0.0003
## 200 1.1658 nan 0.0010 0.0003
## 220 1.1529 nan 0.0010 0.0003
## 240 1.1404 nan 0.0010 0.0003
## 260 1.1281 nan 0.0010 0.0002
## 280 1.1162 nan 0.0010 0.0002
## 300 1.1046 nan 0.0010 0.0003
## 320 1.0934 nan 0.0010 0.0002
## 340 1.0825 nan 0.0010 0.0002
## 360 1.0717 nan 0.0010 0.0002
## 380 1.0613 nan 0.0010 0.0001
## 400 1.0516 nan 0.0010 0.0002
## 420 1.0419 nan 0.0010 0.0002
## 440 1.0322 nan 0.0010 0.0002
## 460 1.0229 nan 0.0010 0.0002
## 480 1.0139 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3179 nan 0.0010 0.0004
## 4 1.3170 nan 0.0010 0.0004
## 5 1.3161 nan 0.0010 0.0004
## 6 1.3151 nan 0.0010 0.0004
## 7 1.3143 nan 0.0010 0.0004
## 8 1.3134 nan 0.0010 0.0004
## 9 1.3125 nan 0.0010 0.0005
## 10 1.3116 nan 0.0010 0.0004
## 20 1.3028 nan 0.0010 0.0003
## 40 1.2858 nan 0.0010 0.0004
## 60 1.2697 nan 0.0010 0.0003
## 80 1.2538 nan 0.0010 0.0004
## 100 1.2381 nan 0.0010 0.0003
## 120 1.2230 nan 0.0010 0.0003
## 140 1.2086 nan 0.0010 0.0003
## 160 1.1942 nan 0.0010 0.0003
## 180 1.1806 nan 0.0010 0.0003
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## 220 1.1547 nan 0.0010 0.0003
## 240 1.1425 nan 0.0010 0.0002
## 260 1.1302 nan 0.0010 0.0003
## 280 1.1180 nan 0.0010 0.0002
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## 320 1.0959 nan 0.0010 0.0002
## 340 1.0848 nan 0.0010 0.0002
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## 380 1.0640 nan 0.0010 0.0002
## 400 1.0543 nan 0.0010 0.0002
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## 440 1.0351 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3180 nan 0.0010 0.0004
## 4 1.3171 nan 0.0010 0.0004
## 5 1.3162 nan 0.0010 0.0004
## 6 1.3153 nan 0.0010 0.0004
## 7 1.3145 nan 0.0010 0.0004
## 8 1.3135 nan 0.0010 0.0004
## 9 1.3126 nan 0.0010 0.0004
## 10 1.3117 nan 0.0010 0.0004
## 20 1.3030 nan 0.0010 0.0004
## 40 1.2861 nan 0.0010 0.0004
## 60 1.2697 nan 0.0010 0.0004
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## 100 1.2380 nan 0.0010 0.0003
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## 140 1.2090 nan 0.0010 0.0003
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## 280 1.1200 nan 0.0010 0.0003
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## 380 1.0659 nan 0.0010 0.0002
## 400 1.0561 nan 0.0010 0.0002
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## 460 1.0279 nan 0.0010 0.0002
## 480 1.0190 nan 0.0010 0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3128 nan 0.0100 0.0038
## 2 1.3052 nan 0.0100 0.0036
## 3 1.2974 nan 0.0100 0.0036
## 4 1.2897 nan 0.0100 0.0034
## 5 1.2819 nan 0.0100 0.0037
## 6 1.2739 nan 0.0100 0.0037
## 7 1.2659 nan 0.0100 0.0036
## 8 1.2584 nan 0.0100 0.0035
## 9 1.2515 nan 0.0100 0.0034
## 10 1.2443 nan 0.0100 0.0030
## 20 1.1811 nan 0.0100 0.0025
## 40 1.0754 nan 0.0100 0.0017
## 60 0.9959 nan 0.0100 0.0013
## 80 0.9353 nan 0.0100 0.0008
## 100 0.8837 nan 0.0100 0.0008
## 120 0.8427 nan 0.0100 0.0003
## 140 0.8080 nan 0.0100 0.0003
## 160 0.7799 nan 0.0100 0.0003
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## 300 0.6556 nan 0.0100 0.0002
## 320 0.6433 nan 0.0100 0.0000
## 340 0.6311 nan 0.0100 0.0002
## 360 0.6207 nan 0.0100 0.0001
## 380 0.6096 nan 0.0100 -0.0000
## 400 0.6000 nan 0.0100 0.0000
## 420 0.5906 nan 0.0100 -0.0001
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## 460 0.5723 nan 0.0100 0.0001
## 480 0.5635 nan 0.0100 -0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3135 nan 0.0100 0.0032
## 2 1.3058 nan 0.0100 0.0038
## 3 1.2980 nan 0.0100 0.0035
## 4 1.2902 nan 0.0100 0.0036
## 5 1.2817 nan 0.0100 0.0036
## 6 1.2751 nan 0.0100 0.0031
## 7 1.2677 nan 0.0100 0.0034
## 8 1.2601 nan 0.0100 0.0033
## 9 1.2533 nan 0.0100 0.0029
## 10 1.2463 nan 0.0100 0.0029
## 20 1.1820 nan 0.0100 0.0025
## 40 1.0800 nan 0.0100 0.0019
## 60 0.9970 nan 0.0100 0.0016
## 80 0.9344 nan 0.0100 0.0009
## 100 0.8853 nan 0.0100 0.0007
## 120 0.8445 nan 0.0100 0.0005
## 140 0.8113 nan 0.0100 0.0006
## 160 0.7832 nan 0.0100 0.0002
## 180 0.7594 nan 0.0100 0.0001
## 200 0.7395 nan 0.0100 0.0002
## 220 0.7208 nan 0.0100 0.0003
## 240 0.7033 nan 0.0100 0.0002
## 260 0.6879 nan 0.0100 -0.0000
## 280 0.6730 nan 0.0100 0.0001
## 300 0.6607 nan 0.0100 0.0000
## 320 0.6489 nan 0.0100 0.0001
## 340 0.6386 nan 0.0100 0.0000
## 360 0.6280 nan 0.0100 -0.0002
## 380 0.6186 nan 0.0100 -0.0001
## 400 0.6090 nan 0.0100 0.0001
## 420 0.6000 nan 0.0100 -0.0002
## 440 0.5924 nan 0.0100 -0.0000
## 460 0.5829 nan 0.0100 0.0002
## 480 0.5741 nan 0.0100 -0.0001
## 500 0.5660 nan 0.0100 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3126 nan 0.0100 0.0035
## 2 1.3041 nan 0.0100 0.0037
## 3 1.2970 nan 0.0100 0.0032
## 4 1.2893 nan 0.0100 0.0039
## 5 1.2825 nan 0.0100 0.0031
## 6 1.2747 nan 0.0100 0.0037
## 7 1.2665 nan 0.0100 0.0037
## 8 1.2594 nan 0.0100 0.0035
## 9 1.2533 nan 0.0100 0.0027
## 10 1.2467 nan 0.0100 0.0029
## 20 1.1839 nan 0.0100 0.0025
## 40 1.0796 nan 0.0100 0.0019
## 60 0.9999 nan 0.0100 0.0015
## 80 0.9400 nan 0.0100 0.0009
## 100 0.8907 nan 0.0100 0.0009
## 120 0.8505 nan 0.0100 0.0005
## 140 0.8168 nan 0.0100 0.0007
## 160 0.7882 nan 0.0100 0.0004
## 180 0.7641 nan 0.0100 0.0003
## 200 0.7431 nan 0.0100 0.0002
## 220 0.7259 nan 0.0100 0.0002
## 240 0.7097 nan 0.0100 0.0001
## 260 0.6952 nan 0.0100 0.0002
## 280 0.6821 nan 0.0100 0.0001
## 300 0.6697 nan 0.0100 -0.0001
## 320 0.6581 nan 0.0100 0.0001
## 340 0.6482 nan 0.0100 -0.0001
## 360 0.6377 nan 0.0100 -0.0001
## 380 0.6274 nan 0.0100 0.0001
## 400 0.6185 nan 0.0100 -0.0001
## 420 0.6089 nan 0.0100 -0.0001
## 440 0.6002 nan 0.0100 -0.0000
## 460 0.5922 nan 0.0100 0.0001
## 480 0.5840 nan 0.0100 -0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3114 nan 0.0100 0.0042
## 2 1.3026 nan 0.0100 0.0041
## 3 1.2950 nan 0.0100 0.0034
## 4 1.2868 nan 0.0100 0.0039
## 5 1.2790 nan 0.0100 0.0035
## 6 1.2711 nan 0.0100 0.0034
## 7 1.2635 nan 0.0100 0.0036
## 8 1.2561 nan 0.0100 0.0031
## 9 1.2481 nan 0.0100 0.0031
## 10 1.2400 nan 0.0100 0.0038
## 20 1.1704 nan 0.0100 0.0026
## 40 1.0598 nan 0.0100 0.0022
## 60 0.9768 nan 0.0100 0.0014
## 80 0.9089 nan 0.0100 0.0010
## 100 0.8563 nan 0.0100 0.0010
## 120 0.8150 nan 0.0100 0.0003
## 140 0.7799 nan 0.0100 0.0005
## 160 0.7492 nan 0.0100 0.0001
## 180 0.7242 nan 0.0100 0.0000
## 200 0.7012 nan 0.0100 0.0001
## 220 0.6804 nan 0.0100 0.0001
## 240 0.6607 nan 0.0100 0.0003
## 260 0.6427 nan 0.0100 0.0001
## 280 0.6266 nan 0.0100 0.0001
## 300 0.6126 nan 0.0100 0.0000
## 320 0.5976 nan 0.0100 0.0001
## 340 0.5842 nan 0.0100 0.0000
## 360 0.5708 nan 0.0100 0.0001
## 380 0.5585 nan 0.0100 0.0001
## 400 0.5472 nan 0.0100 -0.0001
## 420 0.5363 nan 0.0100 -0.0001
## 440 0.5272 nan 0.0100 0.0000
## 460 0.5165 nan 0.0100 -0.0001
## 480 0.5074 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3123 nan 0.0100 0.0038
## 2 1.3037 nan 0.0100 0.0034
## 3 1.2949 nan 0.0100 0.0038
## 4 1.2866 nan 0.0100 0.0039
## 5 1.2783 nan 0.0100 0.0039
## 6 1.2699 nan 0.0100 0.0039
## 7 1.2625 nan 0.0100 0.0034
## 8 1.2547 nan 0.0100 0.0035
## 9 1.2470 nan 0.0100 0.0034
## 10 1.2395 nan 0.0100 0.0036
## 20 1.1732 nan 0.0100 0.0031
## 40 1.0655 nan 0.0100 0.0020
## 60 0.9813 nan 0.0100 0.0014
## 80 0.9166 nan 0.0100 0.0010
## 100 0.8645 nan 0.0100 0.0007
## 120 0.8211 nan 0.0100 0.0006
## 140 0.7867 nan 0.0100 0.0005
## 160 0.7567 nan 0.0100 0.0006
## 180 0.7316 nan 0.0100 0.0004
## 200 0.7071 nan 0.0100 -0.0001
## 220 0.6878 nan 0.0100 0.0002
## 240 0.6693 nan 0.0100 0.0001
## 260 0.6520 nan 0.0100 0.0001
## 280 0.6367 nan 0.0100 -0.0001
## 300 0.6226 nan 0.0100 0.0001
## 320 0.6092 nan 0.0100 0.0001
## 340 0.5967 nan 0.0100 -0.0001
## 360 0.5851 nan 0.0100 0.0001
## 380 0.5741 nan 0.0100 0.0000
## 400 0.5629 nan 0.0100 -0.0000
## 420 0.5520 nan 0.0100 -0.0001
## 440 0.5424 nan 0.0100 0.0001
## 460 0.5326 nan 0.0100 -0.0001
## 480 0.5228 nan 0.0100 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3122 nan 0.0100 0.0037
## 2 1.3036 nan 0.0100 0.0036
## 3 1.2943 nan 0.0100 0.0041
## 4 1.2867 nan 0.0100 0.0030
## 5 1.2791 nan 0.0100 0.0036
## 6 1.2714 nan 0.0100 0.0036
## 7 1.2643 nan 0.0100 0.0034
## 8 1.2568 nan 0.0100 0.0032
## 9 1.2489 nan 0.0100 0.0037
## 10 1.2413 nan 0.0100 0.0031
## 20 1.1741 nan 0.0100 0.0027
## 40 1.0649 nan 0.0100 0.0022
## 60 0.9831 nan 0.0100 0.0016
## 80 0.9197 nan 0.0100 0.0010
## 100 0.8690 nan 0.0100 0.0007
## 120 0.8286 nan 0.0100 0.0002
## 140 0.7935 nan 0.0100 0.0004
## 160 0.7653 nan 0.0100 0.0002
## 180 0.7397 nan 0.0100 0.0002
## 200 0.7180 nan 0.0100 0.0001
## 220 0.6974 nan 0.0100 0.0001
## 240 0.6792 nan 0.0100 -0.0001
## 260 0.6639 nan 0.0100 -0.0000
## 280 0.6477 nan 0.0100 0.0001
## 300 0.6351 nan 0.0100 0.0000
## 320 0.6221 nan 0.0100 -0.0000
## 340 0.6099 nan 0.0100 0.0001
## 360 0.5975 nan 0.0100 0.0001
## 380 0.5854 nan 0.0100 0.0000
## 400 0.5751 nan 0.0100 -0.0001
## 420 0.5640 nan 0.0100 0.0001
## 440 0.5553 nan 0.0100 -0.0001
## 460 0.5457 nan 0.0100 -0.0002
## 480 0.5364 nan 0.0100 -0.0000
## 500 0.5272 nan 0.0100 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3107 nan 0.0100 0.0046
## 2 1.3020 nan 0.0100 0.0039
## 3 1.2926 nan 0.0100 0.0039
## 4 1.2833 nan 0.0100 0.0041
## 5 1.2756 nan 0.0100 0.0036
## 6 1.2675 nan 0.0100 0.0036
## 7 1.2595 nan 0.0100 0.0036
## 8 1.2513 nan 0.0100 0.0038
## 9 1.2435 nan 0.0100 0.0031
## 10 1.2362 nan 0.0100 0.0032
## 20 1.1667 nan 0.0100 0.0025
## 40 1.0518 nan 0.0100 0.0024
## 60 0.9638 nan 0.0100 0.0015
## 80 0.8947 nan 0.0100 0.0008
## 100 0.8407 nan 0.0100 0.0009
## 120 0.7938 nan 0.0100 0.0007
## 140 0.7569 nan 0.0100 0.0004
## 160 0.7239 nan 0.0100 0.0003
## 180 0.6948 nan 0.0100 0.0004
## 200 0.6710 nan 0.0100 0.0002
## 220 0.6485 nan 0.0100 0.0002
## 240 0.6293 nan 0.0100 0.0000
## 260 0.6109 nan 0.0100 0.0002
## 280 0.5931 nan 0.0100 0.0002
## 300 0.5770 nan 0.0100 -0.0001
## 320 0.5621 nan 0.0100 0.0001
## 340 0.5481 nan 0.0100 0.0002
## 360 0.5348 nan 0.0100 0.0001
## 380 0.5227 nan 0.0100 0.0000
## 400 0.5113 nan 0.0100 -0.0000
## 420 0.5002 nan 0.0100 0.0000
## 440 0.4897 nan 0.0100 0.0001
## 460 0.4786 nan 0.0100 -0.0001
## 480 0.4687 nan 0.0100 -0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3116 nan 0.0100 0.0043
## 2 1.3029 nan 0.0100 0.0040
## 3 1.2937 nan 0.0100 0.0040
## 4 1.2847 nan 0.0100 0.0044
## 5 1.2762 nan 0.0100 0.0036
## 6 1.2680 nan 0.0100 0.0036
## 7 1.2600 nan 0.0100 0.0035
## 8 1.2523 nan 0.0100 0.0032
## 9 1.2436 nan 0.0100 0.0038
## 10 1.2360 nan 0.0100 0.0032
## 20 1.1692 nan 0.0100 0.0030
## 40 1.0548 nan 0.0100 0.0022
## 60 0.9681 nan 0.0100 0.0017
## 80 0.8999 nan 0.0100 0.0010
## 100 0.8461 nan 0.0100 0.0004
## 120 0.8019 nan 0.0100 0.0007
## 140 0.7644 nan 0.0100 0.0003
## 160 0.7324 nan 0.0100 0.0003
## 180 0.7047 nan 0.0100 0.0003
## 200 0.6793 nan 0.0100 0.0001
## 220 0.6577 nan 0.0100 0.0002
## 240 0.6387 nan 0.0100 0.0001
## 260 0.6206 nan 0.0100 -0.0000
## 280 0.6051 nan 0.0100 -0.0000
## 300 0.5896 nan 0.0100 -0.0001
## 320 0.5743 nan 0.0100 0.0004
## 340 0.5606 nan 0.0100 0.0000
## 360 0.5475 nan 0.0100 0.0000
## 380 0.5341 nan 0.0100 0.0001
## 400 0.5224 nan 0.0100 -0.0000
## 420 0.5111 nan 0.0100 0.0000
## 440 0.5003 nan 0.0100 -0.0001
## 460 0.4901 nan 0.0100 -0.0000
## 480 0.4788 nan 0.0100 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3110 nan 0.0100 0.0043
## 2 1.3022 nan 0.0100 0.0039
## 3 1.2932 nan 0.0100 0.0043
## 4 1.2851 nan 0.0100 0.0032
## 5 1.2771 nan 0.0100 0.0036
## 6 1.2694 nan 0.0100 0.0034
## 7 1.2612 nan 0.0100 0.0036
## 8 1.2531 nan 0.0100 0.0035
## 9 1.2452 nan 0.0100 0.0038
## 10 1.2378 nan 0.0100 0.0032
## 20 1.1686 nan 0.0100 0.0029
## 40 1.0545 nan 0.0100 0.0022
## 60 0.9691 nan 0.0100 0.0015
## 80 0.9042 nan 0.0100 0.0014
## 100 0.8516 nan 0.0100 0.0010
## 120 0.8076 nan 0.0100 0.0006
## 140 0.7730 nan 0.0100 0.0007
## 160 0.7426 nan 0.0100 0.0005
## 180 0.7149 nan 0.0100 0.0004
## 200 0.6899 nan 0.0100 0.0003
## 220 0.6697 nan 0.0100 0.0000
## 240 0.6511 nan 0.0100 -0.0001
## 260 0.6341 nan 0.0100 0.0001
## 280 0.6189 nan 0.0100 0.0002
## 300 0.6031 nan 0.0100 -0.0000
## 320 0.5894 nan 0.0100 -0.0000
## 340 0.5755 nan 0.0100 0.0000
## 360 0.5632 nan 0.0100 0.0001
## 380 0.5513 nan 0.0100 -0.0000
## 400 0.5406 nan 0.0100 0.0001
## 420 0.5299 nan 0.0100 -0.0002
## 440 0.5191 nan 0.0100 -0.0001
## 460 0.5086 nan 0.0100 -0.0000
## 480 0.4989 nan 0.0100 -0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2377 nan 0.1000 0.0394
## 2 1.1647 nan 0.1000 0.0329
## 3 1.1150 nan 0.1000 0.0207
## 4 1.0740 nan 0.1000 0.0184
## 5 1.0333 nan 0.1000 0.0198
## 6 0.9989 nan 0.1000 0.0106
## 7 0.9726 nan 0.1000 0.0074
## 8 0.9407 nan 0.1000 0.0112
## 9 0.9115 nan 0.1000 0.0129
## 10 0.8886 nan 0.1000 0.0096
## 20 0.7392 nan 0.1000 0.0025
## 40 0.6029 nan 0.1000 -0.0002
## 60 0.5196 nan 0.1000 -0.0012
## 80 0.4498 nan 0.1000 0.0003
## 100 0.3879 nan 0.1000 0.0005
## 120 0.3468 nan 0.1000 -0.0006
## 140 0.3144 nan 0.1000 -0.0008
## 160 0.2830 nan 0.1000 -0.0001
## 180 0.2573 nan 0.1000 -0.0002
## 200 0.2325 nan 0.1000 -0.0002
## 220 0.2111 nan 0.1000 -0.0006
## 240 0.1917 nan 0.1000 -0.0000
## 260 0.1740 nan 0.1000 -0.0000
## 280 0.1597 nan 0.1000 -0.0002
## 300 0.1455 nan 0.1000 0.0001
## 320 0.1328 nan 0.1000 -0.0003
## 340 0.1231 nan 0.1000 0.0000
## 360 0.1135 nan 0.1000 -0.0003
## 380 0.1043 nan 0.1000 -0.0002
## 400 0.0957 nan 0.1000 0.0001
## 420 0.0893 nan 0.1000 -0.0002
## 440 0.0822 nan 0.1000 -0.0002
## 460 0.0763 nan 0.1000 -0.0000
## 480 0.0704 nan 0.1000 -0.0000
## 500 0.0649 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2451 nan 0.1000 0.0349
## 2 1.1749 nan 0.1000 0.0335
## 3 1.1207 nan 0.1000 0.0219
## 4 1.0786 nan 0.1000 0.0180
## 5 1.0403 nan 0.1000 0.0138
## 6 1.0012 nan 0.1000 0.0170
## 7 0.9653 nan 0.1000 0.0147
## 8 0.9329 nan 0.1000 0.0127
## 9 0.9056 nan 0.1000 0.0112
## 10 0.8802 nan 0.1000 0.0106
## 20 0.7353 nan 0.1000 0.0017
## 40 0.6077 nan 0.1000 -0.0009
## 60 0.5234 nan 0.1000 -0.0009
## 80 0.4588 nan 0.1000 -0.0001
## 100 0.4046 nan 0.1000 0.0000
## 120 0.3583 nan 0.1000 -0.0009
## 140 0.3236 nan 0.1000 -0.0011
## 160 0.2920 nan 0.1000 -0.0014
## 180 0.2670 nan 0.1000 -0.0008
## 200 0.2430 nan 0.1000 -0.0006
## 220 0.2230 nan 0.1000 -0.0004
## 240 0.2042 nan 0.1000 -0.0005
## 260 0.1858 nan 0.1000 -0.0008
## 280 0.1710 nan 0.1000 -0.0004
## 300 0.1558 nan 0.1000 -0.0003
## 320 0.1418 nan 0.1000 -0.0004
## 340 0.1305 nan 0.1000 -0.0003
## 360 0.1202 nan 0.1000 -0.0006
## 380 0.1099 nan 0.1000 -0.0005
## 400 0.1011 nan 0.1000 -0.0001
## 420 0.0932 nan 0.1000 -0.0002
## 440 0.0862 nan 0.1000 -0.0001
## 460 0.0795 nan 0.1000 -0.0003
## 480 0.0734 nan 0.1000 -0.0003
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2538 nan 0.1000 0.0265
## 2 1.1924 nan 0.1000 0.0268
## 3 1.1294 nan 0.1000 0.0274
## 4 1.0806 nan 0.1000 0.0233
## 5 1.0365 nan 0.1000 0.0205
## 6 1.0025 nan 0.1000 0.0152
## 7 0.9715 nan 0.1000 0.0114
## 8 0.9405 nan 0.1000 0.0142
## 9 0.9168 nan 0.1000 0.0062
## 10 0.8950 nan 0.1000 0.0091
## 20 0.7542 nan 0.1000 0.0015
## 40 0.6220 nan 0.1000 0.0018
## 60 0.5430 nan 0.1000 0.0003
## 80 0.4906 nan 0.1000 -0.0014
## 100 0.4405 nan 0.1000 -0.0008
## 120 0.3928 nan 0.1000 -0.0007
## 140 0.3533 nan 0.1000 -0.0007
## 160 0.3195 nan 0.1000 -0.0010
## 180 0.2912 nan 0.1000 -0.0010
## 200 0.2643 nan 0.1000 -0.0008
## 220 0.2390 nan 0.1000 -0.0012
## 240 0.2166 nan 0.1000 -0.0001
## 260 0.1984 nan 0.1000 -0.0012
## 280 0.1845 nan 0.1000 -0.0008
## 300 0.1683 nan 0.1000 -0.0004
## 320 0.1551 nan 0.1000 -0.0008
## 340 0.1421 nan 0.1000 -0.0003
## 360 0.1317 nan 0.1000 -0.0005
## 380 0.1221 nan 0.1000 -0.0001
## 400 0.1134 nan 0.1000 -0.0006
## 420 0.1050 nan 0.1000 -0.0003
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## 460 0.0917 nan 0.1000 -0.0002
## 480 0.0844 nan 0.1000 -0.0003
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2352 nan 0.1000 0.0369
## 2 1.1665 nan 0.1000 0.0337
## 3 1.1090 nan 0.1000 0.0255
## 4 1.0583 nan 0.1000 0.0245
## 5 1.0133 nan 0.1000 0.0178
## 6 0.9767 nan 0.1000 0.0132
## 7 0.9436 nan 0.1000 0.0124
## 8 0.9075 nan 0.1000 0.0136
## 9 0.8817 nan 0.1000 0.0099
## 10 0.8601 nan 0.1000 0.0087
## 20 0.7040 nan 0.1000 0.0015
## 40 0.5596 nan 0.1000 0.0003
## 60 0.4619 nan 0.1000 0.0008
## 80 0.3900 nan 0.1000 -0.0007
## 100 0.3409 nan 0.1000 -0.0001
## 120 0.2949 nan 0.1000 -0.0011
## 140 0.2570 nan 0.1000 -0.0003
## 160 0.2273 nan 0.1000 -0.0002
## 180 0.2021 nan 0.1000 0.0000
## 200 0.1783 nan 0.1000 -0.0009
## 220 0.1605 nan 0.1000 -0.0005
## 240 0.1420 nan 0.1000 -0.0002
## 260 0.1271 nan 0.1000 -0.0003
## 280 0.1138 nan 0.1000 -0.0003
## 300 0.1008 nan 0.1000 -0.0000
## 320 0.0911 nan 0.1000 -0.0001
## 340 0.0825 nan 0.1000 -0.0003
## 360 0.0745 nan 0.1000 -0.0001
## 380 0.0673 nan 0.1000 -0.0001
## 400 0.0612 nan 0.1000 -0.0001
## 420 0.0555 nan 0.1000 -0.0002
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## 460 0.0457 nan 0.1000 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2408 nan 0.1000 0.0372
## 2 1.1738 nan 0.1000 0.0248
## 3 1.1183 nan 0.1000 0.0245
## 4 1.0663 nan 0.1000 0.0234
## 5 1.0204 nan 0.1000 0.0197
## 6 0.9858 nan 0.1000 0.0140
## 7 0.9552 nan 0.1000 0.0133
## 8 0.9246 nan 0.1000 0.0126
## 9 0.8899 nan 0.1000 0.0136
## 10 0.8644 nan 0.1000 0.0112
## 20 0.7156 nan 0.1000 0.0037
## 40 0.5734 nan 0.1000 -0.0011
## 60 0.4829 nan 0.1000 0.0004
## 80 0.4091 nan 0.1000 0.0004
## 100 0.3564 nan 0.1000 -0.0016
## 120 0.3171 nan 0.1000 -0.0009
## 140 0.2751 nan 0.1000 -0.0004
## 160 0.2426 nan 0.1000 -0.0014
## 180 0.2139 nan 0.1000 -0.0003
## 200 0.1886 nan 0.1000 -0.0011
## 220 0.1678 nan 0.1000 -0.0009
## 240 0.1495 nan 0.1000 -0.0004
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## 280 0.1227 nan 0.1000 -0.0002
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## 320 0.1002 nan 0.1000 -0.0004
## 340 0.0897 nan 0.1000 -0.0002
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## 380 0.0723 nan 0.1000 -0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2358 nan 0.1000 0.0365
## 2 1.1739 nan 0.1000 0.0252
## 3 1.1178 nan 0.1000 0.0265
## 4 1.0743 nan 0.1000 0.0185
## 5 1.0254 nan 0.1000 0.0218
## 6 0.9864 nan 0.1000 0.0149
## 7 0.9571 nan 0.1000 0.0117
## 8 0.9241 nan 0.1000 0.0098
## 9 0.8967 nan 0.1000 0.0099
## 10 0.8720 nan 0.1000 0.0093
## 20 0.7240 nan 0.1000 0.0020
## 40 0.5901 nan 0.1000 -0.0010
## 60 0.4930 nan 0.1000 0.0008
## 80 0.4156 nan 0.1000 -0.0020
## 100 0.3582 nan 0.1000 -0.0008
## 120 0.3154 nan 0.1000 -0.0009
## 140 0.2799 nan 0.1000 -0.0004
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## 180 0.2168 nan 0.1000 -0.0009
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## 320 0.1023 nan 0.1000 -0.0003
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## 380 0.0745 nan 0.1000 -0.0003
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2381 nan 0.1000 0.0364
## 2 1.1613 nan 0.1000 0.0347
## 3 1.1067 nan 0.1000 0.0240
## 4 1.0568 nan 0.1000 0.0194
## 5 1.0098 nan 0.1000 0.0198
## 6 0.9690 nan 0.1000 0.0160
## 7 0.9290 nan 0.1000 0.0127
## 8 0.8984 nan 0.1000 0.0117
## 9 0.8679 nan 0.1000 0.0122
## 10 0.8399 nan 0.1000 0.0101
## 20 0.6780 nan 0.1000 0.0000
## 40 0.5153 nan 0.1000 -0.0009
## 60 0.4154 nan 0.1000 -0.0007
## 80 0.3412 nan 0.1000 -0.0002
## 100 0.2875 nan 0.1000 -0.0011
## 120 0.2410 nan 0.1000 -0.0007
## 140 0.2035 nan 0.1000 -0.0001
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## 180 0.1514 nan 0.1000 -0.0004
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## 220 0.1162 nan 0.1000 -0.0004
## 240 0.1002 nan 0.1000 -0.0001
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## 400 0.0369 nan 0.1000 -0.0000
## 420 0.0327 nan 0.1000 -0.0001
## 440 0.0291 nan 0.1000 -0.0000
## 460 0.0258 nan 0.1000 -0.0000
## 480 0.0230 nan 0.1000 -0.0000
## 500 0.0204 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2278 nan 0.1000 0.0391
## 2 1.1651 nan 0.1000 0.0313
## 3 1.1032 nan 0.1000 0.0215
## 4 1.0494 nan 0.1000 0.0230
## 5 1.0053 nan 0.1000 0.0193
## 6 0.9649 nan 0.1000 0.0147
## 7 0.9271 nan 0.1000 0.0149
## 8 0.8952 nan 0.1000 0.0096
## 9 0.8635 nan 0.1000 0.0123
## 10 0.8401 nan 0.1000 0.0075
## 20 0.6785 nan 0.1000 0.0033
## 40 0.5274 nan 0.1000 -0.0010
## 60 0.4233 nan 0.1000 -0.0004
## 80 0.3541 nan 0.1000 -0.0008
## 100 0.2965 nan 0.1000 0.0000
## 120 0.2500 nan 0.1000 0.0002
## 140 0.2136 nan 0.1000 -0.0006
## 160 0.1833 nan 0.1000 -0.0005
## 180 0.1592 nan 0.1000 -0.0005
## 200 0.1379 nan 0.1000 -0.0001
## 220 0.1208 nan 0.1000 -0.0001
## 240 0.1051 nan 0.1000 -0.0004
## 260 0.0933 nan 0.1000 -0.0003
## 280 0.0809 nan 0.1000 -0.0001
## 300 0.0716 nan 0.1000 -0.0003
## 320 0.0631 nan 0.1000 -0.0002
## 340 0.0562 nan 0.1000 -0.0002
## 360 0.0508 nan 0.1000 -0.0002
## 380 0.0451 nan 0.1000 -0.0002
## 400 0.0401 nan 0.1000 -0.0001
## 420 0.0361 nan 0.1000 -0.0002
## 440 0.0326 nan 0.1000 -0.0001
## 460 0.0291 nan 0.1000 -0.0000
## 480 0.0257 nan 0.1000 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2349 nan 0.1000 0.0392
## 2 1.1618 nan 0.1000 0.0301
## 3 1.1028 nan 0.1000 0.0239
## 4 1.0528 nan 0.1000 0.0225
## 5 1.0038 nan 0.1000 0.0205
## 6 0.9653 nan 0.1000 0.0167
## 7 0.9304 nan 0.1000 0.0145
## 8 0.9082 nan 0.1000 0.0058
## 9 0.8852 nan 0.1000 0.0084
## 10 0.8581 nan 0.1000 0.0095
## 20 0.6884 nan 0.1000 0.0019
## 40 0.5469 nan 0.1000 0.0006
## 60 0.4472 nan 0.1000 -0.0007
## 80 0.3777 nan 0.1000 0.0000
## 100 0.3151 nan 0.1000 -0.0003
## 120 0.2686 nan 0.1000 -0.0010
## 140 0.2271 nan 0.1000 -0.0006
## 160 0.1975 nan 0.1000 -0.0008
## 180 0.1728 nan 0.1000 -0.0005
## 200 0.1500 nan 0.1000 -0.0004
## 220 0.1322 nan 0.1000 -0.0002
## 240 0.1179 nan 0.1000 -0.0003
## 260 0.1036 nan 0.1000 -0.0004
## 280 0.0915 nan 0.1000 -0.0003
## 300 0.0814 nan 0.1000 -0.0003
## 320 0.0716 nan 0.1000 -0.0002
## 340 0.0644 nan 0.1000 -0.0002
## 360 0.0573 nan 0.1000 -0.0002
## 380 0.0514 nan 0.1000 -0.0001
## 400 0.0454 nan 0.1000 -0.0001
## 420 0.0405 nan 0.1000 -0.0002
## 440 0.0363 nan 0.1000 -0.0001
## 460 0.0326 nan 0.1000 -0.0001
## 480 0.0290 nan 0.1000 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3199 nan 0.0010 0.0004
## 2 1.3191 nan 0.0010 0.0003
## 3 1.3182 nan 0.0010 0.0004
## 4 1.3174 nan 0.0010 0.0004
## 5 1.3166 nan 0.0010 0.0003
## 6 1.3159 nan 0.0010 0.0003
## 7 1.3150 nan 0.0010 0.0003
## 8 1.3142 nan 0.0010 0.0004
## 9 1.3134 nan 0.0010 0.0003
## 10 1.3126 nan 0.0010 0.0004
## 20 1.3048 nan 0.0010 0.0003
## 40 1.2896 nan 0.0010 0.0003
## 60 1.2746 nan 0.0010 0.0003
## 80 1.2604 nan 0.0010 0.0003
## 100 1.2463 nan 0.0010 0.0003
## 120 1.2329 nan 0.0010 0.0003
## 140 1.2196 nan 0.0010 0.0003
## 160 1.2067 nan 0.0010 0.0002
## 180 1.1942 nan 0.0010 0.0003
## 200 1.1821 nan 0.0010 0.0003
## 220 1.1701 nan 0.0010 0.0003
## 240 1.1588 nan 0.0010 0.0002
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## 280 1.1371 nan 0.0010 0.0002
## 300 1.1267 nan 0.0010 0.0002
## 320 1.1168 nan 0.0010 0.0002
## 340 1.1071 nan 0.0010 0.0002
## 360 1.0976 nan 0.0010 0.0002
## 380 1.0880 nan 0.0010 0.0002
## 400 1.0788 nan 0.0010 0.0002
## 420 1.0699 nan 0.0010 0.0002
## 440 1.0611 nan 0.0010 0.0002
## 460 1.0526 nan 0.0010 0.0001
## 480 1.0442 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3181 nan 0.0010 0.0003
## 4 1.3173 nan 0.0010 0.0004
## 5 1.3165 nan 0.0010 0.0004
## 6 1.3156 nan 0.0010 0.0004
## 7 1.3147 nan 0.0010 0.0004
## 8 1.3139 nan 0.0010 0.0003
## 9 1.3131 nan 0.0010 0.0004
## 10 1.3123 nan 0.0010 0.0004
## 20 1.3040 nan 0.0010 0.0004
## 40 1.2885 nan 0.0010 0.0003
## 60 1.2735 nan 0.0010 0.0004
## 80 1.2591 nan 0.0010 0.0003
## 100 1.2450 nan 0.0010 0.0003
## 120 1.2314 nan 0.0010 0.0003
## 140 1.2183 nan 0.0010 0.0003
## 160 1.2055 nan 0.0010 0.0003
## 180 1.1934 nan 0.0010 0.0003
## 200 1.1812 nan 0.0010 0.0003
## 220 1.1694 nan 0.0010 0.0002
## 240 1.1580 nan 0.0010 0.0002
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## 280 1.1362 nan 0.0010 0.0002
## 300 1.1257 nan 0.0010 0.0002
## 320 1.1156 nan 0.0010 0.0002
## 340 1.1057 nan 0.0010 0.0002
## 360 1.0963 nan 0.0010 0.0002
## 380 1.0870 nan 0.0010 0.0002
## 400 1.0781 nan 0.0010 0.0002
## 420 1.0691 nan 0.0010 0.0002
## 440 1.0606 nan 0.0010 0.0002
## 460 1.0524 nan 0.0010 0.0002
## 480 1.0442 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3190 nan 0.0010 0.0004
## 3 1.3182 nan 0.0010 0.0004
## 4 1.3174 nan 0.0010 0.0003
## 5 1.3166 nan 0.0010 0.0003
## 6 1.3158 nan 0.0010 0.0003
## 7 1.3150 nan 0.0010 0.0004
## 8 1.3141 nan 0.0010 0.0004
## 9 1.3134 nan 0.0010 0.0004
## 10 1.3126 nan 0.0010 0.0004
## 20 1.3047 nan 0.0010 0.0003
## 40 1.2892 nan 0.0010 0.0003
## 60 1.2741 nan 0.0010 0.0004
## 80 1.2597 nan 0.0010 0.0003
## 100 1.2461 nan 0.0010 0.0003
## 120 1.2325 nan 0.0010 0.0003
## 140 1.2195 nan 0.0010 0.0003
## 160 1.2072 nan 0.0010 0.0003
## 180 1.1949 nan 0.0010 0.0003
## 200 1.1830 nan 0.0010 0.0003
## 220 1.1714 nan 0.0010 0.0002
## 240 1.1601 nan 0.0010 0.0002
## 260 1.1493 nan 0.0010 0.0003
## 280 1.1386 nan 0.0010 0.0003
## 300 1.1285 nan 0.0010 0.0002
## 320 1.1183 nan 0.0010 0.0002
## 340 1.1083 nan 0.0010 0.0002
## 360 1.0986 nan 0.0010 0.0002
## 380 1.0895 nan 0.0010 0.0002
## 400 1.0805 nan 0.0010 0.0002
## 420 1.0715 nan 0.0010 0.0002
## 440 1.0629 nan 0.0010 0.0002
## 460 1.0545 nan 0.0010 0.0002
## 480 1.0463 nan 0.0010 0.0002
## 500 1.0382 nan 0.0010 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3190 nan 0.0010 0.0004
## 3 1.3181 nan 0.0010 0.0003
## 4 1.3173 nan 0.0010 0.0004
## 5 1.3164 nan 0.0010 0.0004
## 6 1.3155 nan 0.0010 0.0004
## 7 1.3146 nan 0.0010 0.0004
## 8 1.3138 nan 0.0010 0.0004
## 9 1.3130 nan 0.0010 0.0004
## 10 1.3122 nan 0.0010 0.0004
## 20 1.3037 nan 0.0010 0.0004
## 40 1.2869 nan 0.0010 0.0004
## 60 1.2707 nan 0.0010 0.0003
## 80 1.2552 nan 0.0010 0.0003
## 100 1.2403 nan 0.0010 0.0003
## 120 1.2255 nan 0.0010 0.0003
## 140 1.2113 nan 0.0010 0.0003
## 160 1.1976 nan 0.0010 0.0003
## 180 1.1847 nan 0.0010 0.0003
## 200 1.1719 nan 0.0010 0.0003
## 220 1.1594 nan 0.0010 0.0003
## 240 1.1473 nan 0.0010 0.0003
## 260 1.1358 nan 0.0010 0.0002
## 280 1.1240 nan 0.0010 0.0002
## 300 1.1128 nan 0.0010 0.0002
## 320 1.1021 nan 0.0010 0.0002
## 340 1.0916 nan 0.0010 0.0002
## 360 1.0818 nan 0.0010 0.0002
## 380 1.0721 nan 0.0010 0.0002
## 400 1.0625 nan 0.0010 0.0002
## 420 1.0531 nan 0.0010 0.0002
## 440 1.0436 nan 0.0010 0.0002
## 460 1.0349 nan 0.0010 0.0002
## 480 1.0262 nan 0.0010 0.0002
## 500 1.0175 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3181 nan 0.0010 0.0004
## 4 1.3173 nan 0.0010 0.0004
## 5 1.3164 nan 0.0010 0.0004
## 6 1.3155 nan 0.0010 0.0004
## 7 1.3147 nan 0.0010 0.0004
## 8 1.3138 nan 0.0010 0.0004
## 9 1.3129 nan 0.0010 0.0004
## 10 1.3121 nan 0.0010 0.0004
## 20 1.3036 nan 0.0010 0.0004
## 40 1.2869 nan 0.0010 0.0004
## 60 1.2708 nan 0.0010 0.0003
## 80 1.2553 nan 0.0010 0.0004
## 100 1.2406 nan 0.0010 0.0004
## 120 1.2261 nan 0.0010 0.0003
## 140 1.2121 nan 0.0010 0.0003
## 160 1.1983 nan 0.0010 0.0003
## 180 1.1853 nan 0.0010 0.0003
## 200 1.1723 nan 0.0010 0.0003
## 220 1.1598 nan 0.0010 0.0003
## 240 1.1477 nan 0.0010 0.0002
## 260 1.1360 nan 0.0010 0.0002
## 280 1.1249 nan 0.0010 0.0002
## 300 1.1138 nan 0.0010 0.0002
## 320 1.1031 nan 0.0010 0.0002
## 340 1.0928 nan 0.0010 0.0002
## 360 1.0827 nan 0.0010 0.0002
## 380 1.0726 nan 0.0010 0.0002
## 400 1.0630 nan 0.0010 0.0002
## 420 1.0537 nan 0.0010 0.0002
## 440 1.0446 nan 0.0010 0.0002
## 460 1.0358 nan 0.0010 0.0002
## 480 1.0270 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3190 nan 0.0010 0.0004
## 3 1.3181 nan 0.0010 0.0004
## 4 1.3173 nan 0.0010 0.0004
## 5 1.3165 nan 0.0010 0.0003
## 6 1.3156 nan 0.0010 0.0004
## 7 1.3148 nan 0.0010 0.0004
## 8 1.3138 nan 0.0010 0.0004
## 9 1.3130 nan 0.0010 0.0004
## 10 1.3121 nan 0.0010 0.0004
## 20 1.3036 nan 0.0010 0.0004
## 40 1.2873 nan 0.0010 0.0003
## 60 1.2715 nan 0.0010 0.0003
## 80 1.2562 nan 0.0010 0.0003
## 100 1.2413 nan 0.0010 0.0003
## 120 1.2268 nan 0.0010 0.0003
## 140 1.2130 nan 0.0010 0.0003
## 160 1.1995 nan 0.0010 0.0003
## 180 1.1866 nan 0.0010 0.0003
## 200 1.1741 nan 0.0010 0.0002
## 220 1.1620 nan 0.0010 0.0003
## 240 1.1503 nan 0.0010 0.0003
## 260 1.1387 nan 0.0010 0.0003
## 280 1.1274 nan 0.0010 0.0003
## 300 1.1162 nan 0.0010 0.0002
## 320 1.1054 nan 0.0010 0.0002
## 340 1.0950 nan 0.0010 0.0002
## 360 1.0849 nan 0.0010 0.0002
## 380 1.0751 nan 0.0010 0.0002
## 400 1.0656 nan 0.0010 0.0002
## 420 1.0562 nan 0.0010 0.0002
## 440 1.0474 nan 0.0010 0.0002
## 460 1.0385 nan 0.0010 0.0002
## 480 1.0297 nan 0.0010 0.0002
## 500 1.0216 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0003
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3179 nan 0.0010 0.0004
## 4 1.3169 nan 0.0010 0.0004
## 5 1.3159 nan 0.0010 0.0004
## 6 1.3151 nan 0.0010 0.0004
## 7 1.3142 nan 0.0010 0.0004
## 8 1.3133 nan 0.0010 0.0004
## 9 1.3123 nan 0.0010 0.0004
## 10 1.3114 nan 0.0010 0.0004
## 20 1.3023 nan 0.0010 0.0004
## 40 1.2849 nan 0.0010 0.0004
## 60 1.2681 nan 0.0010 0.0004
## 80 1.2519 nan 0.0010 0.0003
## 100 1.2361 nan 0.0010 0.0004
## 120 1.2208 nan 0.0010 0.0003
## 140 1.2060 nan 0.0010 0.0003
## 160 1.1916 nan 0.0010 0.0003
## 180 1.1779 nan 0.0010 0.0003
## 200 1.1643 nan 0.0010 0.0002
## 220 1.1512 nan 0.0010 0.0003
## 240 1.1386 nan 0.0010 0.0003
## 260 1.1263 nan 0.0010 0.0002
## 280 1.1144 nan 0.0010 0.0002
## 300 1.1026 nan 0.0010 0.0003
## 320 1.0912 nan 0.0010 0.0002
## 340 1.0804 nan 0.0010 0.0002
## 360 1.0698 nan 0.0010 0.0002
## 380 1.0595 nan 0.0010 0.0002
## 400 1.0494 nan 0.0010 0.0002
## 420 1.0396 nan 0.0010 0.0002
## 440 1.0299 nan 0.0010 0.0002
## 460 1.0206 nan 0.0010 0.0002
## 480 1.0113 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3179 nan 0.0010 0.0004
## 4 1.3171 nan 0.0010 0.0004
## 5 1.3161 nan 0.0010 0.0004
## 6 1.3152 nan 0.0010 0.0004
## 7 1.3143 nan 0.0010 0.0004
## 8 1.3134 nan 0.0010 0.0004
## 9 1.3125 nan 0.0010 0.0004
## 10 1.3116 nan 0.0010 0.0004
## 20 1.3027 nan 0.0010 0.0004
## 40 1.2855 nan 0.0010 0.0004
## 60 1.2685 nan 0.0010 0.0004
## 80 1.2523 nan 0.0010 0.0004
## 100 1.2365 nan 0.0010 0.0003
## 120 1.2215 nan 0.0010 0.0003
## 140 1.2068 nan 0.0010 0.0003
## 160 1.1927 nan 0.0010 0.0003
## 180 1.1789 nan 0.0010 0.0003
## 200 1.1655 nan 0.0010 0.0003
## 220 1.1523 nan 0.0010 0.0003
## 240 1.1398 nan 0.0010 0.0003
## 260 1.1278 nan 0.0010 0.0002
## 280 1.1157 nan 0.0010 0.0002
## 300 1.1040 nan 0.0010 0.0003
## 320 1.0929 nan 0.0010 0.0002
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## 360 1.0714 nan 0.0010 0.0002
## 380 1.0611 nan 0.0010 0.0002
## 400 1.0512 nan 0.0010 0.0002
## 420 1.0415 nan 0.0010 0.0002
## 440 1.0321 nan 0.0010 0.0002
## 460 1.0228 nan 0.0010 0.0002
## 480 1.0138 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3180 nan 0.0010 0.0004
## 4 1.3171 nan 0.0010 0.0004
## 5 1.3162 nan 0.0010 0.0004
## 6 1.3154 nan 0.0010 0.0004
## 7 1.3145 nan 0.0010 0.0004
## 8 1.3135 nan 0.0010 0.0004
## 9 1.3126 nan 0.0010 0.0004
## 10 1.3117 nan 0.0010 0.0004
## 20 1.3031 nan 0.0010 0.0003
## 40 1.2858 nan 0.0010 0.0004
## 60 1.2694 nan 0.0010 0.0004
## 80 1.2532 nan 0.0010 0.0004
## 100 1.2375 nan 0.0010 0.0003
## 120 1.2225 nan 0.0010 0.0003
## 140 1.2081 nan 0.0010 0.0003
## 160 1.1942 nan 0.0010 0.0003
## 180 1.1806 nan 0.0010 0.0003
## 200 1.1675 nan 0.0010 0.0003
## 220 1.1546 nan 0.0010 0.0003
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## 280 1.1180 nan 0.0010 0.0003
## 300 1.1066 nan 0.0010 0.0002
## 320 1.0957 nan 0.0010 0.0002
## 340 1.0852 nan 0.0010 0.0002
## 360 1.0746 nan 0.0010 0.0002
## 380 1.0643 nan 0.0010 0.0002
## 400 1.0543 nan 0.0010 0.0002
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## 460 1.0264 nan 0.0010 0.0002
## 480 1.0171 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3128 nan 0.0100 0.0037
## 2 1.3062 nan 0.0100 0.0028
## 3 1.2990 nan 0.0100 0.0030
## 4 1.2919 nan 0.0100 0.0033
## 5 1.2837 nan 0.0100 0.0036
## 6 1.2764 nan 0.0100 0.0036
## 7 1.2690 nan 0.0100 0.0035
## 8 1.2610 nan 0.0100 0.0032
## 9 1.2542 nan 0.0100 0.0032
## 10 1.2468 nan 0.0100 0.0034
## 20 1.1828 nan 0.0100 0.0026
## 40 1.0788 nan 0.0100 0.0020
## 60 0.9969 nan 0.0100 0.0014
## 80 0.9348 nan 0.0100 0.0012
## 100 0.8837 nan 0.0100 0.0008
## 120 0.8408 nan 0.0100 0.0008
## 140 0.8047 nan 0.0100 0.0004
## 160 0.7742 nan 0.0100 0.0005
## 180 0.7476 nan 0.0100 0.0002
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## 220 0.7050 nan 0.0100 0.0001
## 240 0.6875 nan 0.0100 0.0003
## 260 0.6707 nan 0.0100 0.0002
## 280 0.6562 nan 0.0100 -0.0000
## 300 0.6434 nan 0.0100 0.0001
## 320 0.6315 nan 0.0100 -0.0000
## 340 0.6201 nan 0.0100 -0.0000
## 360 0.6096 nan 0.0100 -0.0000
## 380 0.5991 nan 0.0100 -0.0002
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## 460 0.5594 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3123 nan 0.0100 0.0038
## 2 1.3050 nan 0.0100 0.0033
## 3 1.2978 nan 0.0100 0.0030
## 4 1.2898 nan 0.0100 0.0034
## 5 1.2827 nan 0.0100 0.0033
## 6 1.2755 nan 0.0100 0.0029
## 7 1.2674 nan 0.0100 0.0035
## 8 1.2602 nan 0.0100 0.0027
## 9 1.2535 nan 0.0100 0.0032
## 10 1.2463 nan 0.0100 0.0034
## 20 1.1830 nan 0.0100 0.0029
## 40 1.0780 nan 0.0100 0.0019
## 60 0.9977 nan 0.0100 0.0013
## 80 0.9350 nan 0.0100 0.0009
## 100 0.8841 nan 0.0100 0.0010
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## 140 0.8068 nan 0.0100 0.0006
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3128 nan 0.0100 0.0035
## 2 1.3048 nan 0.0100 0.0035
## 3 1.2964 nan 0.0100 0.0037
## 4 1.2885 nan 0.0100 0.0034
## 5 1.2811 nan 0.0100 0.0035
## 6 1.2728 nan 0.0100 0.0037
## 7 1.2660 nan 0.0100 0.0030
## 8 1.2586 nan 0.0100 0.0035
## 9 1.2512 nan 0.0100 0.0029
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## 20 1.1826 nan 0.0100 0.0025
## 40 1.0800 nan 0.0100 0.0023
## 60 0.9989 nan 0.0100 0.0014
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## 220 0.7135 nan 0.0100 0.0003
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## 380 0.6126 nan 0.0100 -0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3122 nan 0.0100 0.0037
## 2 1.3043 nan 0.0100 0.0036
## 3 1.2965 nan 0.0100 0.0031
## 4 1.2881 nan 0.0100 0.0039
## 5 1.2796 nan 0.0100 0.0034
## 6 1.2714 nan 0.0100 0.0033
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## 20 1.1730 nan 0.0100 0.0028
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## 340 0.5755 nan 0.0100 0.0000
## 360 0.5641 nan 0.0100 -0.0001
## 380 0.5518 nan 0.0100 0.0001
## 400 0.5400 nan 0.0100 0.0001
## 420 0.5292 nan 0.0100 0.0000
## 440 0.5200 nan 0.0100 -0.0000
## 460 0.5100 nan 0.0100 0.0000
## 480 0.5000 nan 0.0100 -0.0001
## 500 0.4904 nan 0.0100 0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3119 nan 0.0100 0.0038
## 2 1.3028 nan 0.0100 0.0038
## 3 1.2946 nan 0.0100 0.0037
## 4 1.2864 nan 0.0100 0.0038
## 5 1.2781 nan 0.0100 0.0035
## 6 1.2706 nan 0.0100 0.0037
## 7 1.2631 nan 0.0100 0.0037
## 8 1.2553 nan 0.0100 0.0034
## 9 1.2475 nan 0.0100 0.0031
## 10 1.2401 nan 0.0100 0.0035
## 20 1.1728 nan 0.0100 0.0027
## 40 1.0619 nan 0.0100 0.0020
## 60 0.9766 nan 0.0100 0.0015
## 80 0.9098 nan 0.0100 0.0012
## 100 0.8556 nan 0.0100 0.0009
## 120 0.8111 nan 0.0100 0.0007
## 140 0.7742 nan 0.0100 0.0005
## 160 0.7435 nan 0.0100 0.0004
## 180 0.7155 nan 0.0100 0.0004
## 200 0.6917 nan 0.0100 0.0004
## 220 0.6707 nan 0.0100 0.0001
## 240 0.6519 nan 0.0100 0.0002
## 260 0.6347 nan 0.0100 0.0000
## 280 0.6179 nan 0.0100 0.0002
## 300 0.6031 nan 0.0100 0.0000
## 320 0.5900 nan 0.0100 0.0001
## 340 0.5776 nan 0.0100 0.0001
## 360 0.5654 nan 0.0100 -0.0000
## 380 0.5541 nan 0.0100 -0.0001
## 400 0.5438 nan 0.0100 -0.0001
## 420 0.5335 nan 0.0100 0.0001
## 440 0.5235 nan 0.0100 -0.0002
## 460 0.5138 nan 0.0100 -0.0001
## 480 0.5040 nan 0.0100 -0.0001
## 500 0.4944 nan 0.0100 0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3117 nan 0.0100 0.0039
## 2 1.3031 nan 0.0100 0.0038
## 3 1.2945 nan 0.0100 0.0041
## 4 1.2866 nan 0.0100 0.0038
## 5 1.2787 nan 0.0100 0.0034
## 6 1.2709 nan 0.0100 0.0034
## 7 1.2634 nan 0.0100 0.0036
## 8 1.2560 nan 0.0100 0.0032
## 9 1.2496 nan 0.0100 0.0028
## 10 1.2417 nan 0.0100 0.0035
## 20 1.1751 nan 0.0100 0.0027
## 40 1.0653 nan 0.0100 0.0021
## 60 0.9821 nan 0.0100 0.0013
## 80 0.9158 nan 0.0100 0.0010
## 100 0.8618 nan 0.0100 0.0007
## 120 0.8177 nan 0.0100 0.0006
## 140 0.7809 nan 0.0100 0.0004
## 160 0.7513 nan 0.0100 0.0005
## 180 0.7255 nan 0.0100 0.0004
## 200 0.7017 nan 0.0100 0.0003
## 220 0.6817 nan 0.0100 0.0001
## 240 0.6638 nan 0.0100 0.0001
## 260 0.6476 nan 0.0100 0.0002
## 280 0.6325 nan 0.0100 0.0001
## 300 0.6182 nan 0.0100 -0.0002
## 320 0.6055 nan 0.0100 -0.0002
## 340 0.5931 nan 0.0100 -0.0001
## 360 0.5815 nan 0.0100 0.0001
## 380 0.5697 nan 0.0100 -0.0000
## 400 0.5589 nan 0.0100 0.0001
## 420 0.5490 nan 0.0100 -0.0001
## 440 0.5380 nan 0.0100 -0.0000
## 460 0.5296 nan 0.0100 -0.0000
## 480 0.5194 nan 0.0100 0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3116 nan 0.0100 0.0042
## 2 1.3030 nan 0.0100 0.0040
## 3 1.2939 nan 0.0100 0.0037
## 4 1.2853 nan 0.0100 0.0041
## 5 1.2767 nan 0.0100 0.0038
## 6 1.2690 nan 0.0100 0.0031
## 7 1.2607 nan 0.0100 0.0037
## 8 1.2522 nan 0.0100 0.0035
## 9 1.2440 nan 0.0100 0.0035
## 10 1.2357 nan 0.0100 0.0034
## 20 1.1640 nan 0.0100 0.0028
## 40 1.0473 nan 0.0100 0.0020
## 60 0.9597 nan 0.0100 0.0018
## 80 0.8901 nan 0.0100 0.0011
## 100 0.8326 nan 0.0100 0.0009
## 120 0.7861 nan 0.0100 0.0008
## 140 0.7464 nan 0.0100 0.0007
## 160 0.7123 nan 0.0100 0.0008
## 180 0.6831 nan 0.0100 0.0006
## 200 0.6573 nan 0.0100 0.0003
## 220 0.6335 nan 0.0100 0.0001
## 240 0.6131 nan 0.0100 0.0003
## 260 0.5942 nan 0.0100 0.0002
## 280 0.5783 nan 0.0100 0.0001
## 300 0.5622 nan 0.0100 0.0001
## 320 0.5477 nan 0.0100 0.0000
## 340 0.5332 nan 0.0100 -0.0000
## 360 0.5196 nan 0.0100 0.0000
## 380 0.5076 nan 0.0100 -0.0001
## 400 0.4959 nan 0.0100 -0.0000
## 420 0.4847 nan 0.0100 -0.0001
## 440 0.4730 nan 0.0100 0.0000
## 460 0.4618 nan 0.0100 0.0000
## 480 0.4515 nan 0.0100 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3124 nan 0.0100 0.0034
## 2 1.3034 nan 0.0100 0.0038
## 3 1.2950 nan 0.0100 0.0039
## 4 1.2863 nan 0.0100 0.0035
## 5 1.2784 nan 0.0100 0.0037
## 6 1.2705 nan 0.0100 0.0033
## 7 1.2624 nan 0.0100 0.0034
## 8 1.2538 nan 0.0100 0.0037
## 9 1.2460 nan 0.0100 0.0035
## 10 1.2385 nan 0.0100 0.0031
## 20 1.1676 nan 0.0100 0.0029
## 40 1.0538 nan 0.0100 0.0021
## 60 0.9651 nan 0.0100 0.0014
## 80 0.8952 nan 0.0100 0.0010
## 100 0.8389 nan 0.0100 0.0010
## 120 0.7937 nan 0.0100 0.0007
## 140 0.7546 nan 0.0100 0.0006
## 160 0.7206 nan 0.0100 0.0005
## 180 0.6925 nan 0.0100 0.0004
## 200 0.6665 nan 0.0100 0.0000
## 220 0.6439 nan 0.0100 0.0003
## 240 0.6243 nan 0.0100 0.0002
## 260 0.6054 nan 0.0100 0.0001
## 280 0.5892 nan 0.0100 0.0001
## 300 0.5733 nan 0.0100 -0.0000
## 320 0.5578 nan 0.0100 0.0003
## 340 0.5431 nan 0.0100 -0.0000
## 360 0.5292 nan 0.0100 0.0001
## 380 0.5164 nan 0.0100 -0.0000
## 400 0.5044 nan 0.0100 -0.0002
## 420 0.4931 nan 0.0100 0.0001
## 440 0.4830 nan 0.0100 -0.0000
## 460 0.4727 nan 0.0100 -0.0001
## 480 0.4619 nan 0.0100 -0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3119 nan 0.0100 0.0036
## 2 1.3031 nan 0.0100 0.0039
## 3 1.2937 nan 0.0100 0.0041
## 4 1.2854 nan 0.0100 0.0038
## 5 1.2764 nan 0.0100 0.0041
## 6 1.2687 nan 0.0100 0.0036
## 7 1.2611 nan 0.0100 0.0036
## 8 1.2531 nan 0.0100 0.0037
## 9 1.2450 nan 0.0100 0.0037
## 10 1.2370 nan 0.0100 0.0033
## 20 1.1674 nan 0.0100 0.0032
## 40 1.0561 nan 0.0100 0.0019
## 60 0.9680 nan 0.0100 0.0013
## 80 0.9002 nan 0.0100 0.0011
## 100 0.8444 nan 0.0100 0.0009
## 120 0.7993 nan 0.0100 0.0008
## 140 0.7628 nan 0.0100 0.0005
## 160 0.7320 nan 0.0100 0.0001
## 180 0.7051 nan 0.0100 0.0001
## 200 0.6792 nan 0.0100 0.0002
## 220 0.6578 nan 0.0100 0.0002
## 240 0.6379 nan 0.0100 0.0002
## 260 0.6197 nan 0.0100 0.0001
## 280 0.6030 nan 0.0100 0.0001
## 300 0.5867 nan 0.0100 0.0000
## 320 0.5723 nan 0.0100 -0.0000
## 340 0.5578 nan 0.0100 -0.0000
## 360 0.5450 nan 0.0100 0.0001
## 380 0.5327 nan 0.0100 -0.0001
## 400 0.5208 nan 0.0100 0.0001
## 420 0.5087 nan 0.0100 -0.0000
## 440 0.4969 nan 0.0100 -0.0000
## 460 0.4862 nan 0.0100 -0.0002
## 480 0.4755 nan 0.0100 -0.0000
## 500 0.4652 nan 0.0100 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2382 nan 0.1000 0.0361
## 2 1.1684 nan 0.1000 0.0311
## 3 1.1128 nan 0.1000 0.0241
## 4 1.0661 nan 0.1000 0.0195
## 5 1.0238 nan 0.1000 0.0180
## 6 0.9878 nan 0.1000 0.0130
## 7 0.9536 nan 0.1000 0.0123
## 8 0.9231 nan 0.1000 0.0105
## 9 0.8985 nan 0.1000 0.0096
## 10 0.8725 nan 0.1000 0.0091
## 20 0.7264 nan 0.1000 0.0026
## 40 0.5872 nan 0.1000 -0.0003
## 60 0.5086 nan 0.1000 -0.0019
## 80 0.4445 nan 0.1000 -0.0009
## 100 0.3904 nan 0.1000 -0.0013
## 120 0.3434 nan 0.1000 -0.0005
## 140 0.3068 nan 0.1000 -0.0010
## 160 0.2757 nan 0.1000 -0.0003
## 180 0.2505 nan 0.1000 -0.0015
## 200 0.2268 nan 0.1000 -0.0003
## 220 0.2060 nan 0.1000 -0.0005
## 240 0.1822 nan 0.1000 -0.0001
## 260 0.1671 nan 0.1000 -0.0006
## 280 0.1519 nan 0.1000 -0.0004
## 300 0.1393 nan 0.1000 -0.0002
## 320 0.1268 nan 0.1000 -0.0001
## 340 0.1162 nan 0.1000 -0.0001
## 360 0.1062 nan 0.1000 -0.0002
## 380 0.0981 nan 0.1000 0.0000
## 400 0.0913 nan 0.1000 -0.0002
## 420 0.0844 nan 0.1000 -0.0002
## 440 0.0780 nan 0.1000 -0.0002
## 460 0.0721 nan 0.1000 -0.0002
## 480 0.0664 nan 0.1000 -0.0003
## 500 0.0613 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2384 nan 0.1000 0.0356
## 2 1.1724 nan 0.1000 0.0302
## 3 1.1189 nan 0.1000 0.0247
## 4 1.0693 nan 0.1000 0.0221
## 5 1.0277 nan 0.1000 0.0175
## 6 0.9871 nan 0.1000 0.0116
## 7 0.9554 nan 0.1000 0.0138
## 8 0.9308 nan 0.1000 0.0091
## 9 0.9060 nan 0.1000 0.0104
## 10 0.8833 nan 0.1000 0.0083
## 20 0.7294 nan 0.1000 0.0030
## 40 0.5873 nan 0.1000 -0.0011
## 60 0.5065 nan 0.1000 -0.0010
## 80 0.4444 nan 0.1000 -0.0008
## 100 0.3967 nan 0.1000 -0.0011
## 120 0.3541 nan 0.1000 -0.0004
## 140 0.3122 nan 0.1000 0.0007
## 160 0.2858 nan 0.1000 -0.0007
## 180 0.2579 nan 0.1000 0.0002
## 200 0.2299 nan 0.1000 -0.0007
## 220 0.2077 nan 0.1000 -0.0003
## 240 0.1919 nan 0.1000 -0.0008
## 260 0.1760 nan 0.1000 -0.0011
## 280 0.1604 nan 0.1000 -0.0005
## 300 0.1482 nan 0.1000 -0.0008
## 320 0.1357 nan 0.1000 -0.0003
## 340 0.1251 nan 0.1000 -0.0004
## 360 0.1142 nan 0.1000 -0.0001
## 380 0.1049 nan 0.1000 -0.0001
## 400 0.0966 nan 0.1000 -0.0003
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## 440 0.0835 nan 0.1000 -0.0004
## 460 0.0769 nan 0.1000 -0.0003
## 480 0.0718 nan 0.1000 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2462 nan 0.1000 0.0343
## 2 1.1869 nan 0.1000 0.0261
## 3 1.1257 nan 0.1000 0.0246
## 4 1.0800 nan 0.1000 0.0209
## 5 1.0396 nan 0.1000 0.0163
## 6 1.0001 nan 0.1000 0.0149
## 7 0.9691 nan 0.1000 0.0127
## 8 0.9380 nan 0.1000 0.0125
## 9 0.9136 nan 0.1000 0.0102
## 10 0.8842 nan 0.1000 0.0106
## 20 0.7323 nan 0.1000 0.0032
## 40 0.6031 nan 0.1000 0.0003
## 60 0.5256 nan 0.1000 -0.0001
## 80 0.4669 nan 0.1000 -0.0012
## 100 0.4190 nan 0.1000 -0.0006
## 120 0.3783 nan 0.1000 -0.0012
## 140 0.3367 nan 0.1000 -0.0003
## 160 0.3080 nan 0.1000 -0.0008
## 180 0.2777 nan 0.1000 -0.0006
## 200 0.2530 nan 0.1000 -0.0006
## 220 0.2291 nan 0.1000 -0.0006
## 240 0.2081 nan 0.1000 -0.0010
## 260 0.1922 nan 0.1000 -0.0004
## 280 0.1775 nan 0.1000 -0.0006
## 300 0.1664 nan 0.1000 -0.0006
## 320 0.1532 nan 0.1000 -0.0008
## 340 0.1405 nan 0.1000 0.0000
## 360 0.1296 nan 0.1000 -0.0004
## 380 0.1186 nan 0.1000 -0.0002
## 400 0.1088 nan 0.1000 -0.0001
## 420 0.1008 nan 0.1000 -0.0003
## 440 0.0924 nan 0.1000 -0.0002
## 460 0.0841 nan 0.1000 -0.0002
## 480 0.0780 nan 0.1000 -0.0004
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2312 nan 0.1000 0.0392
## 2 1.1613 nan 0.1000 0.0294
## 3 1.1001 nan 0.1000 0.0267
## 4 1.0501 nan 0.1000 0.0223
## 5 1.0040 nan 0.1000 0.0191
## 6 0.9613 nan 0.1000 0.0161
## 7 0.9264 nan 0.1000 0.0114
## 8 0.8981 nan 0.1000 0.0096
## 9 0.8706 nan 0.1000 0.0083
## 10 0.8459 nan 0.1000 0.0080
## 20 0.6910 nan 0.1000 0.0008
## 40 0.5411 nan 0.1000 -0.0001
## 60 0.4516 nan 0.1000 -0.0009
## 80 0.3879 nan 0.1000 0.0002
## 100 0.3331 nan 0.1000 -0.0007
## 120 0.2907 nan 0.1000 -0.0006
## 140 0.2504 nan 0.1000 -0.0008
## 160 0.2178 nan 0.1000 -0.0009
## 180 0.1898 nan 0.1000 -0.0008
## 200 0.1685 nan 0.1000 -0.0003
## 220 0.1489 nan 0.1000 -0.0003
## 240 0.1326 nan 0.1000 -0.0000
## 260 0.1177 nan 0.1000 -0.0004
## 280 0.1049 nan 0.1000 -0.0003
## 300 0.0947 nan 0.1000 0.0000
## 320 0.0836 nan 0.1000 -0.0004
## 340 0.0752 nan 0.1000 -0.0002
## 360 0.0685 nan 0.1000 -0.0001
## 380 0.0613 nan 0.1000 -0.0002
## 400 0.0560 nan 0.1000 -0.0002
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## 460 0.0410 nan 0.1000 0.0001
## 480 0.0365 nan 0.1000 -0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2439 nan 0.1000 0.0346
## 2 1.1725 nan 0.1000 0.0301
## 3 1.1044 nan 0.1000 0.0289
## 4 1.0567 nan 0.1000 0.0199
## 5 1.0098 nan 0.1000 0.0170
## 6 0.9697 nan 0.1000 0.0174
## 7 0.9346 nan 0.1000 0.0132
## 8 0.9036 nan 0.1000 0.0118
## 9 0.8711 nan 0.1000 0.0108
## 10 0.8476 nan 0.1000 0.0088
## 20 0.6882 nan 0.1000 0.0024
## 40 0.5450 nan 0.1000 0.0001
## 60 0.4627 nan 0.1000 -0.0005
## 80 0.3992 nan 0.1000 -0.0011
## 100 0.3413 nan 0.1000 -0.0002
## 120 0.2999 nan 0.1000 -0.0005
## 140 0.2598 nan 0.1000 -0.0003
## 160 0.2304 nan 0.1000 -0.0004
## 180 0.1994 nan 0.1000 -0.0003
## 200 0.1758 nan 0.1000 -0.0003
## 220 0.1550 nan 0.1000 -0.0002
## 240 0.1377 nan 0.1000 -0.0003
## 260 0.1220 nan 0.1000 -0.0000
## 280 0.1093 nan 0.1000 -0.0003
## 300 0.0986 nan 0.1000 -0.0001
## 320 0.0895 nan 0.1000 -0.0004
## 340 0.0811 nan 0.1000 -0.0002
## 360 0.0727 nan 0.1000 -0.0002
## 380 0.0648 nan 0.1000 -0.0000
## 400 0.0582 nan 0.1000 -0.0004
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## 460 0.0430 nan 0.1000 -0.0003
## 480 0.0387 nan 0.1000 -0.0001
## 500 0.0351 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2453 nan 0.1000 0.0374
## 2 1.1707 nan 0.1000 0.0321
## 3 1.1157 nan 0.1000 0.0240
## 4 1.0604 nan 0.1000 0.0235
## 5 1.0190 nan 0.1000 0.0173
## 6 0.9792 nan 0.1000 0.0173
## 7 0.9391 nan 0.1000 0.0169
## 8 0.9043 nan 0.1000 0.0146
## 9 0.8778 nan 0.1000 0.0100
## 10 0.8520 nan 0.1000 0.0093
## 20 0.6957 nan 0.1000 0.0003
## 40 0.5610 nan 0.1000 0.0003
## 60 0.4637 nan 0.1000 -0.0008
## 80 0.3987 nan 0.1000 -0.0007
## 100 0.3456 nan 0.1000 -0.0008
## 120 0.3036 nan 0.1000 0.0006
## 140 0.2654 nan 0.1000 -0.0019
## 160 0.2355 nan 0.1000 -0.0005
## 180 0.2096 nan 0.1000 0.0001
## 200 0.1873 nan 0.1000 -0.0007
## 220 0.1662 nan 0.1000 0.0001
## 240 0.1492 nan 0.1000 -0.0013
## 260 0.1349 nan 0.1000 -0.0003
## 280 0.1220 nan 0.1000 -0.0004
## 300 0.1091 nan 0.1000 -0.0008
## 320 0.0974 nan 0.1000 -0.0005
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## 380 0.0707 nan 0.1000 -0.0002
## 400 0.0638 nan 0.1000 -0.0002
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## 460 0.0476 nan 0.1000 -0.0002
## 480 0.0436 nan 0.1000 -0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2334 nan 0.1000 0.0357
## 2 1.1634 nan 0.1000 0.0333
## 3 1.0990 nan 0.1000 0.0266
## 4 1.0466 nan 0.1000 0.0235
## 5 0.9980 nan 0.1000 0.0184
## 6 0.9624 nan 0.1000 0.0128
## 7 0.9263 nan 0.1000 0.0155
## 8 0.8882 nan 0.1000 0.0108
## 9 0.8584 nan 0.1000 0.0092
## 10 0.8294 nan 0.1000 0.0105
## 20 0.6632 nan 0.1000 0.0021
## 40 0.5049 nan 0.1000 -0.0004
## 60 0.4016 nan 0.1000 -0.0003
## 80 0.3368 nan 0.1000 -0.0001
## 100 0.2807 nan 0.1000 0.0007
## 120 0.2411 nan 0.1000 -0.0009
## 140 0.2101 nan 0.1000 -0.0010
## 160 0.1790 nan 0.1000 -0.0011
## 180 0.1535 nan 0.1000 -0.0002
## 200 0.1304 nan 0.1000 -0.0003
## 220 0.1146 nan 0.1000 -0.0005
## 240 0.0986 nan 0.1000 -0.0001
## 260 0.0856 nan 0.1000 0.0000
## 280 0.0750 nan 0.1000 -0.0002
## 300 0.0662 nan 0.1000 -0.0002
## 320 0.0578 nan 0.1000 -0.0001
## 340 0.0511 nan 0.1000 -0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2334 nan 0.1000 0.0404
## 2 1.1633 nan 0.1000 0.0305
## 3 1.1051 nan 0.1000 0.0221
## 4 1.0559 nan 0.1000 0.0218
## 5 1.0143 nan 0.1000 0.0155
## 6 0.9736 nan 0.1000 0.0180
## 7 0.9393 nan 0.1000 0.0125
## 8 0.9097 nan 0.1000 0.0109
## 9 0.8770 nan 0.1000 0.0125
## 10 0.8458 nan 0.1000 0.0093
## 20 0.6754 nan 0.1000 0.0022
## 40 0.5126 nan 0.1000 0.0006
## 60 0.4212 nan 0.1000 -0.0014
## 80 0.3528 nan 0.1000 -0.0023
## 100 0.2925 nan 0.1000 -0.0015
## 120 0.2528 nan 0.1000 -0.0008
## 140 0.2138 nan 0.1000 -0.0000
## 160 0.1849 nan 0.1000 -0.0006
## 180 0.1569 nan 0.1000 -0.0003
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## 280 0.0788 nan 0.1000 -0.0003
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2317 nan 0.1000 0.0425
## 2 1.1614 nan 0.1000 0.0332
## 3 1.0980 nan 0.1000 0.0281
## 4 1.0501 nan 0.1000 0.0224
## 5 1.0042 nan 0.1000 0.0166
## 6 0.9622 nan 0.1000 0.0166
## 7 0.9258 nan 0.1000 0.0145
## 8 0.8920 nan 0.1000 0.0108
## 9 0.8620 nan 0.1000 0.0111
## 10 0.8406 nan 0.1000 0.0068
## 20 0.6752 nan 0.1000 0.0010
## 40 0.5242 nan 0.1000 -0.0005
## 60 0.4385 nan 0.1000 -0.0010
## 80 0.3599 nan 0.1000 -0.0008
## 100 0.3034 nan 0.1000 -0.0004
## 120 0.2601 nan 0.1000 -0.0004
## 140 0.2176 nan 0.1000 -0.0013
## 160 0.1902 nan 0.1000 -0.0009
## 180 0.1639 nan 0.1000 -0.0003
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3191 nan 0.0010 0.0003
## 3 1.3184 nan 0.0010 0.0003
## 4 1.3176 nan 0.0010 0.0004
## 5 1.3167 nan 0.0010 0.0004
## 6 1.3160 nan 0.0010 0.0004
## 7 1.3151 nan 0.0010 0.0004
## 8 1.3144 nan 0.0010 0.0004
## 9 1.3137 nan 0.0010 0.0003
## 10 1.3129 nan 0.0010 0.0003
## 20 1.3048 nan 0.0010 0.0003
## 40 1.2894 nan 0.0010 0.0003
## 60 1.2741 nan 0.0010 0.0004
## 80 1.2597 nan 0.0010 0.0004
## 100 1.2452 nan 0.0010 0.0004
## 120 1.2315 nan 0.0010 0.0003
## 140 1.2181 nan 0.0010 0.0003
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## 480 1.0444 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3199 nan 0.0010 0.0004
## 2 1.3191 nan 0.0010 0.0004
## 3 1.3182 nan 0.0010 0.0004
## 4 1.3174 nan 0.0010 0.0003
## 5 1.3165 nan 0.0010 0.0004
## 6 1.3158 nan 0.0010 0.0003
## 7 1.3150 nan 0.0010 0.0003
## 8 1.3141 nan 0.0010 0.0004
## 9 1.3134 nan 0.0010 0.0003
## 10 1.3125 nan 0.0010 0.0004
## 20 1.3044 nan 0.0010 0.0004
## 40 1.2891 nan 0.0010 0.0003
## 60 1.2739 nan 0.0010 0.0003
## 80 1.2597 nan 0.0010 0.0003
## 100 1.2459 nan 0.0010 0.0003
## 120 1.2321 nan 0.0010 0.0003
## 140 1.2190 nan 0.0010 0.0003
## 160 1.2062 nan 0.0010 0.0003
## 180 1.1937 nan 0.0010 0.0002
## 200 1.1815 nan 0.0010 0.0003
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## 380 1.0871 nan 0.0010 0.0002
## 400 1.0780 nan 0.0010 0.0002
## 420 1.0696 nan 0.0010 0.0001
## 440 1.0610 nan 0.0010 0.0002
## 460 1.0526 nan 0.0010 0.0002
## 480 1.0446 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0003
## 2 1.3191 nan 0.0010 0.0003
## 3 1.3182 nan 0.0010 0.0004
## 4 1.3173 nan 0.0010 0.0004
## 5 1.3166 nan 0.0010 0.0004
## 6 1.3158 nan 0.0010 0.0004
## 7 1.3150 nan 0.0010 0.0004
## 8 1.3142 nan 0.0010 0.0004
## 9 1.3134 nan 0.0010 0.0004
## 10 1.3126 nan 0.0010 0.0004
## 20 1.3046 nan 0.0010 0.0004
## 40 1.2892 nan 0.0010 0.0003
## 60 1.2741 nan 0.0010 0.0003
## 80 1.2598 nan 0.0010 0.0003
## 100 1.2460 nan 0.0010 0.0004
## 120 1.2328 nan 0.0010 0.0003
## 140 1.2199 nan 0.0010 0.0003
## 160 1.2071 nan 0.0010 0.0003
## 180 1.1948 nan 0.0010 0.0002
## 200 1.1829 nan 0.0010 0.0003
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## 380 1.0897 nan 0.0010 0.0002
## 400 1.0804 nan 0.0010 0.0002
## 420 1.0716 nan 0.0010 0.0001
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## 460 1.0547 nan 0.0010 0.0002
## 480 1.0463 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3180 nan 0.0010 0.0004
## 4 1.3172 nan 0.0010 0.0004
## 5 1.3164 nan 0.0010 0.0004
## 6 1.3155 nan 0.0010 0.0004
## 7 1.3146 nan 0.0010 0.0004
## 8 1.3137 nan 0.0010 0.0004
## 9 1.3128 nan 0.0010 0.0004
## 10 1.3119 nan 0.0010 0.0004
## 20 1.3033 nan 0.0010 0.0004
## 40 1.2867 nan 0.0010 0.0003
## 60 1.2710 nan 0.0010 0.0003
## 80 1.2558 nan 0.0010 0.0003
## 100 1.2410 nan 0.0010 0.0004
## 120 1.2265 nan 0.0010 0.0003
## 140 1.2127 nan 0.0010 0.0003
## 160 1.1990 nan 0.0010 0.0003
## 180 1.1856 nan 0.0010 0.0003
## 200 1.1731 nan 0.0010 0.0002
## 220 1.1606 nan 0.0010 0.0002
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## 340 1.0931 nan 0.0010 0.0003
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## 380 1.0732 nan 0.0010 0.0002
## 400 1.0634 nan 0.0010 0.0002
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## 460 1.0362 nan 0.0010 0.0002
## 480 1.0276 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3190 nan 0.0010 0.0004
## 3 1.3181 nan 0.0010 0.0004
## 4 1.3172 nan 0.0010 0.0004
## 5 1.3164 nan 0.0010 0.0003
## 6 1.3155 nan 0.0010 0.0004
## 7 1.3146 nan 0.0010 0.0004
## 8 1.3138 nan 0.0010 0.0004
## 9 1.3129 nan 0.0010 0.0004
## 10 1.3121 nan 0.0010 0.0004
## 20 1.3035 nan 0.0010 0.0004
## 40 1.2869 nan 0.0010 0.0004
## 60 1.2711 nan 0.0010 0.0004
## 80 1.2560 nan 0.0010 0.0003
## 100 1.2412 nan 0.0010 0.0003
## 120 1.2268 nan 0.0010 0.0003
## 140 1.2128 nan 0.0010 0.0003
## 160 1.1993 nan 0.0010 0.0003
## 180 1.1862 nan 0.0010 0.0003
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## 220 1.1615 nan 0.0010 0.0003
## 240 1.1494 nan 0.0010 0.0002
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## 320 1.1048 nan 0.0010 0.0003
## 340 1.0943 nan 0.0010 0.0002
## 360 1.0842 nan 0.0010 0.0002
## 380 1.0742 nan 0.0010 0.0002
## 400 1.0648 nan 0.0010 0.0002
## 420 1.0553 nan 0.0010 0.0002
## 440 1.0460 nan 0.0010 0.0002
## 460 1.0372 nan 0.0010 0.0002
## 480 1.0287 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3181 nan 0.0010 0.0003
## 4 1.3172 nan 0.0010 0.0004
## 5 1.3164 nan 0.0010 0.0004
## 6 1.3156 nan 0.0010 0.0004
## 7 1.3147 nan 0.0010 0.0004
## 8 1.3139 nan 0.0010 0.0004
## 9 1.3131 nan 0.0010 0.0004
## 10 1.3122 nan 0.0010 0.0004
## 20 1.3038 nan 0.0010 0.0004
## 40 1.2875 nan 0.0010 0.0004
## 60 1.2716 nan 0.0010 0.0004
## 80 1.2565 nan 0.0010 0.0004
## 100 1.2420 nan 0.0010 0.0003
## 120 1.2278 nan 0.0010 0.0003
## 140 1.2139 nan 0.0010 0.0003
## 160 1.2005 nan 0.0010 0.0003
## 180 1.1876 nan 0.0010 0.0003
## 200 1.1748 nan 0.0010 0.0003
## 220 1.1625 nan 0.0010 0.0002
## 240 1.1507 nan 0.0010 0.0003
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## 280 1.1282 nan 0.0010 0.0002
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## 320 1.1069 nan 0.0010 0.0002
## 340 1.0964 nan 0.0010 0.0002
## 360 1.0863 nan 0.0010 0.0002
## 380 1.0766 nan 0.0010 0.0002
## 400 1.0671 nan 0.0010 0.0002
## 420 1.0581 nan 0.0010 0.0002
## 440 1.0490 nan 0.0010 0.0002
## 460 1.0402 nan 0.0010 0.0002
## 480 1.0317 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3178 nan 0.0010 0.0004
## 4 1.3169 nan 0.0010 0.0004
## 5 1.3160 nan 0.0010 0.0003
## 6 1.3151 nan 0.0010 0.0004
## 7 1.3142 nan 0.0010 0.0004
## 8 1.3132 nan 0.0010 0.0004
## 9 1.3124 nan 0.0010 0.0004
## 10 1.3115 nan 0.0010 0.0003
## 20 1.3027 nan 0.0010 0.0004
## 40 1.2857 nan 0.0010 0.0003
## 60 1.2694 nan 0.0010 0.0004
## 80 1.2536 nan 0.0010 0.0004
## 100 1.2382 nan 0.0010 0.0004
## 120 1.2227 nan 0.0010 0.0003
## 140 1.2082 nan 0.0010 0.0003
## 160 1.1944 nan 0.0010 0.0003
## 180 1.1809 nan 0.0010 0.0002
## 200 1.1677 nan 0.0010 0.0003
## 220 1.1545 nan 0.0010 0.0003
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## 300 1.1059 nan 0.0010 0.0003
## 320 1.0946 nan 0.0010 0.0003
## 340 1.0838 nan 0.0010 0.0003
## 360 1.0732 nan 0.0010 0.0002
## 380 1.0630 nan 0.0010 0.0002
## 400 1.0532 nan 0.0010 0.0002
## 420 1.0436 nan 0.0010 0.0002
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## 460 1.0252 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3179 nan 0.0010 0.0004
## 4 1.3170 nan 0.0010 0.0005
## 5 1.3161 nan 0.0010 0.0004
## 6 1.3153 nan 0.0010 0.0004
## 7 1.3144 nan 0.0010 0.0004
## 8 1.3136 nan 0.0010 0.0004
## 9 1.3127 nan 0.0010 0.0004
## 10 1.3118 nan 0.0010 0.0004
## 20 1.3030 nan 0.0010 0.0004
## 40 1.2857 nan 0.0010 0.0004
## 60 1.2692 nan 0.0010 0.0003
## 80 1.2532 nan 0.0010 0.0004
## 100 1.2381 nan 0.0010 0.0004
## 120 1.2231 nan 0.0010 0.0003
## 140 1.2085 nan 0.0010 0.0003
## 160 1.1942 nan 0.0010 0.0003
## 180 1.1804 nan 0.0010 0.0003
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## 220 1.1540 nan 0.0010 0.0003
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## 280 1.1181 nan 0.0010 0.0002
## 300 1.1066 nan 0.0010 0.0002
## 320 1.0954 nan 0.0010 0.0002
## 340 1.0846 nan 0.0010 0.0003
## 360 1.0742 nan 0.0010 0.0002
## 380 1.0642 nan 0.0010 0.0002
## 400 1.0543 nan 0.0010 0.0002
## 420 1.0447 nan 0.0010 0.0002
## 440 1.0354 nan 0.0010 0.0002
## 460 1.0261 nan 0.0010 0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3180 nan 0.0010 0.0004
## 4 1.3172 nan 0.0010 0.0004
## 5 1.3163 nan 0.0010 0.0004
## 6 1.3154 nan 0.0010 0.0004
## 7 1.3145 nan 0.0010 0.0004
## 8 1.3136 nan 0.0010 0.0004
## 9 1.3127 nan 0.0010 0.0004
## 10 1.3118 nan 0.0010 0.0004
## 20 1.3033 nan 0.0010 0.0004
## 40 1.2865 nan 0.0010 0.0004
## 60 1.2704 nan 0.0010 0.0004
## 80 1.2544 nan 0.0010 0.0003
## 100 1.2390 nan 0.0010 0.0004
## 120 1.2241 nan 0.0010 0.0003
## 140 1.2096 nan 0.0010 0.0003
## 160 1.1959 nan 0.0010 0.0003
## 180 1.1825 nan 0.0010 0.0003
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## 240 1.1448 nan 0.0010 0.0003
## 260 1.1326 nan 0.0010 0.0003
## 280 1.1210 nan 0.0010 0.0002
## 300 1.1095 nan 0.0010 0.0002
## 320 1.0986 nan 0.0010 0.0002
## 340 1.0880 nan 0.0010 0.0002
## 360 1.0777 nan 0.0010 0.0002
## 380 1.0675 nan 0.0010 0.0002
## 400 1.0576 nan 0.0010 0.0002
## 420 1.0481 nan 0.0010 0.0001
## 440 1.0386 nan 0.0010 0.0002
## 460 1.0295 nan 0.0010 0.0002
## 480 1.0207 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3125 nan 0.0100 0.0041
## 2 1.3044 nan 0.0100 0.0036
## 3 1.2964 nan 0.0100 0.0036
## 4 1.2887 nan 0.0100 0.0034
## 5 1.2813 nan 0.0100 0.0035
## 6 1.2748 nan 0.0100 0.0028
## 7 1.2670 nan 0.0100 0.0030
## 8 1.2603 nan 0.0100 0.0028
## 9 1.2535 nan 0.0100 0.0029
## 10 1.2459 nan 0.0100 0.0032
## 20 1.1812 nan 0.0100 0.0026
## 40 1.0792 nan 0.0100 0.0019
## 60 1.0000 nan 0.0100 0.0013
## 80 0.9375 nan 0.0100 0.0012
## 100 0.8883 nan 0.0100 0.0005
## 120 0.8462 nan 0.0100 0.0004
## 140 0.8108 nan 0.0100 0.0004
## 160 0.7828 nan 0.0100 0.0004
## 180 0.7581 nan 0.0100 0.0001
## 200 0.7366 nan 0.0100 0.0004
## 220 0.7172 nan 0.0100 0.0002
## 240 0.6985 nan 0.0100 0.0003
## 260 0.6828 nan 0.0100 0.0001
## 280 0.6685 nan 0.0100 -0.0001
## 300 0.6559 nan 0.0100 0.0000
## 320 0.6439 nan 0.0100 -0.0001
## 340 0.6323 nan 0.0100 -0.0002
## 360 0.6216 nan 0.0100 0.0000
## 380 0.6109 nan 0.0100 -0.0001
## 400 0.6008 nan 0.0100 0.0001
## 420 0.5915 nan 0.0100 -0.0001
## 440 0.5823 nan 0.0100 0.0000
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## 480 0.5642 nan 0.0100 -0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3127 nan 0.0100 0.0033
## 2 1.3045 nan 0.0100 0.0037
## 3 1.2965 nan 0.0100 0.0035
## 4 1.2886 nan 0.0100 0.0039
## 5 1.2818 nan 0.0100 0.0026
## 6 1.2736 nan 0.0100 0.0038
## 7 1.2661 nan 0.0100 0.0034
## 8 1.2588 nan 0.0100 0.0032
## 9 1.2518 nan 0.0100 0.0031
## 10 1.2446 nan 0.0100 0.0031
## 20 1.1801 nan 0.0100 0.0021
## 40 1.0792 nan 0.0100 0.0020
## 60 1.0022 nan 0.0100 0.0014
## 80 0.9391 nan 0.0100 0.0010
## 100 0.8896 nan 0.0100 0.0005
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## 140 0.8140 nan 0.0100 0.0006
## 160 0.7849 nan 0.0100 0.0004
## 180 0.7586 nan 0.0100 0.0003
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## 220 0.7196 nan 0.0100 0.0001
## 240 0.7037 nan 0.0100 0.0002
## 260 0.6891 nan 0.0100 0.0001
## 280 0.6754 nan 0.0100 0.0002
## 300 0.6636 nan 0.0100 -0.0000
## 320 0.6515 nan 0.0100 0.0001
## 340 0.6405 nan 0.0100 -0.0000
## 360 0.6307 nan 0.0100 0.0000
## 380 0.6207 nan 0.0100 0.0000
## 400 0.6106 nan 0.0100 -0.0002
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## 480 0.5759 nan 0.0100 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3127 nan 0.0100 0.0039
## 2 1.3043 nan 0.0100 0.0038
## 3 1.2968 nan 0.0100 0.0036
## 4 1.2891 nan 0.0100 0.0036
## 5 1.2816 nan 0.0100 0.0036
## 6 1.2744 nan 0.0100 0.0030
## 7 1.2671 nan 0.0100 0.0033
## 8 1.2601 nan 0.0100 0.0030
## 9 1.2527 nan 0.0100 0.0035
## 10 1.2455 nan 0.0100 0.0031
## 20 1.1812 nan 0.0100 0.0025
## 40 1.0788 nan 0.0100 0.0019
## 60 1.0009 nan 0.0100 0.0013
## 80 0.9399 nan 0.0100 0.0010
## 100 0.8913 nan 0.0100 0.0007
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## 160 0.7886 nan 0.0100 0.0004
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## 220 0.7261 nan 0.0100 0.0001
## 240 0.7106 nan 0.0100 0.0000
## 260 0.6956 nan 0.0100 -0.0001
## 280 0.6809 nan 0.0100 0.0001
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## 400 0.6187 nan 0.0100 -0.0002
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## 460 0.5956 nan 0.0100 0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3123 nan 0.0100 0.0034
## 2 1.3040 nan 0.0100 0.0040
## 3 1.2956 nan 0.0100 0.0035
## 4 1.2876 nan 0.0100 0.0033
## 5 1.2786 nan 0.0100 0.0038
## 6 1.2706 nan 0.0100 0.0034
## 7 1.2634 nan 0.0100 0.0031
## 8 1.2557 nan 0.0100 0.0034
## 9 1.2486 nan 0.0100 0.0029
## 10 1.2406 nan 0.0100 0.0035
## 20 1.1723 nan 0.0100 0.0029
## 40 1.0649 nan 0.0100 0.0017
## 60 0.9798 nan 0.0100 0.0017
## 80 0.9136 nan 0.0100 0.0014
## 100 0.8610 nan 0.0100 0.0007
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## 140 0.7817 nan 0.0100 0.0003
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## 300 0.6192 nan 0.0100 -0.0000
## 320 0.6050 nan 0.0100 -0.0000
## 340 0.5908 nan 0.0100 0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3117 nan 0.0100 0.0041
## 2 1.3028 nan 0.0100 0.0038
## 3 1.2935 nan 0.0100 0.0037
## 4 1.2845 nan 0.0100 0.0038
## 5 1.2767 nan 0.0100 0.0033
## 6 1.2686 nan 0.0100 0.0036
## 7 1.2618 nan 0.0100 0.0030
## 8 1.2551 nan 0.0100 0.0032
## 9 1.2479 nan 0.0100 0.0034
## 10 1.2405 nan 0.0100 0.0029
## 20 1.1725 nan 0.0100 0.0030
## 40 1.0641 nan 0.0100 0.0022
## 60 0.9802 nan 0.0100 0.0014
## 80 0.9140 nan 0.0100 0.0011
## 100 0.8620 nan 0.0100 0.0009
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## 140 0.7856 nan 0.0100 0.0003
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3124 nan 0.0100 0.0038
## 2 1.3041 nan 0.0100 0.0038
## 3 1.2963 nan 0.0100 0.0038
## 4 1.2878 nan 0.0100 0.0039
## 5 1.2796 nan 0.0100 0.0037
## 6 1.2718 nan 0.0100 0.0036
## 7 1.2645 nan 0.0100 0.0032
## 8 1.2566 nan 0.0100 0.0034
## 9 1.2494 nan 0.0100 0.0029
## 10 1.2418 nan 0.0100 0.0036
## 20 1.1770 nan 0.0100 0.0024
## 40 1.0683 nan 0.0100 0.0019
## 60 0.9846 nan 0.0100 0.0016
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## 320 0.6198 nan 0.0100 -0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3105 nan 0.0100 0.0044
## 2 1.3022 nan 0.0100 0.0037
## 3 1.2935 nan 0.0100 0.0038
## 4 1.2844 nan 0.0100 0.0041
## 5 1.2761 nan 0.0100 0.0037
## 6 1.2687 nan 0.0100 0.0031
## 7 1.2603 nan 0.0100 0.0035
## 8 1.2520 nan 0.0100 0.0035
## 9 1.2441 nan 0.0100 0.0038
## 10 1.2357 nan 0.0100 0.0039
## 20 1.1630 nan 0.0100 0.0029
## 40 1.0504 nan 0.0100 0.0016
## 60 0.9637 nan 0.0100 0.0013
## 80 0.8949 nan 0.0100 0.0012
## 100 0.8394 nan 0.0100 0.0007
## 120 0.7945 nan 0.0100 0.0004
## 140 0.7555 nan 0.0100 0.0005
## 160 0.7230 nan 0.0100 0.0003
## 180 0.6945 nan 0.0100 0.0004
## 200 0.6703 nan 0.0100 0.0003
## 220 0.6479 nan 0.0100 0.0001
## 240 0.6282 nan 0.0100 0.0002
## 260 0.6104 nan 0.0100 0.0001
## 280 0.5936 nan 0.0100 0.0001
## 300 0.5779 nan 0.0100 0.0002
## 320 0.5639 nan 0.0100 0.0001
## 340 0.5512 nan 0.0100 0.0001
## 360 0.5383 nan 0.0100 -0.0001
## 380 0.5268 nan 0.0100 0.0000
## 400 0.5153 nan 0.0100 -0.0001
## 420 0.5033 nan 0.0100 -0.0001
## 440 0.4925 nan 0.0100 0.0002
## 460 0.4821 nan 0.0100 -0.0000
## 480 0.4721 nan 0.0100 0.0000
## 500 0.4618 nan 0.0100 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3106 nan 0.0100 0.0044
## 2 1.3019 nan 0.0100 0.0042
## 3 1.2929 nan 0.0100 0.0040
## 4 1.2843 nan 0.0100 0.0036
## 5 1.2753 nan 0.0100 0.0037
## 6 1.2671 nan 0.0100 0.0038
## 7 1.2589 nan 0.0100 0.0035
## 8 1.2503 nan 0.0100 0.0037
## 9 1.2429 nan 0.0100 0.0032
## 10 1.2352 nan 0.0100 0.0037
## 20 1.1642 nan 0.0100 0.0031
## 40 1.0505 nan 0.0100 0.0017
## 60 0.9653 nan 0.0100 0.0016
## 80 0.8967 nan 0.0100 0.0011
## 100 0.8434 nan 0.0100 0.0010
## 120 0.7990 nan 0.0100 0.0007
## 140 0.7615 nan 0.0100 0.0005
## 160 0.7302 nan 0.0100 0.0003
## 180 0.7034 nan 0.0100 0.0004
## 200 0.6799 nan 0.0100 0.0001
## 220 0.6590 nan 0.0100 -0.0000
## 240 0.6402 nan 0.0100 -0.0001
## 260 0.6225 nan 0.0100 0.0001
## 280 0.6061 nan 0.0100 -0.0000
## 300 0.5907 nan 0.0100 -0.0001
## 320 0.5765 nan 0.0100 -0.0003
## 340 0.5634 nan 0.0100 -0.0000
## 360 0.5512 nan 0.0100 0.0001
## 380 0.5380 nan 0.0100 -0.0000
## 400 0.5266 nan 0.0100 0.0001
## 420 0.5153 nan 0.0100 -0.0001
## 440 0.5045 nan 0.0100 0.0000
## 460 0.4939 nan 0.0100 -0.0001
## 480 0.4832 nan 0.0100 0.0000
## 500 0.4740 nan 0.0100 0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3113 nan 0.0100 0.0042
## 2 1.3020 nan 0.0100 0.0042
## 3 1.2940 nan 0.0100 0.0037
## 4 1.2857 nan 0.0100 0.0036
## 5 1.2773 nan 0.0100 0.0040
## 6 1.2692 nan 0.0100 0.0038
## 7 1.2616 nan 0.0100 0.0034
## 8 1.2535 nan 0.0100 0.0034
## 9 1.2461 nan 0.0100 0.0032
## 10 1.2390 nan 0.0100 0.0033
## 20 1.1692 nan 0.0100 0.0026
## 40 1.0585 nan 0.0100 0.0020
## 60 0.9745 nan 0.0100 0.0014
## 80 0.9082 nan 0.0100 0.0010
## 100 0.8530 nan 0.0100 0.0007
## 120 0.8104 nan 0.0100 0.0008
## 140 0.7759 nan 0.0100 0.0004
## 160 0.7442 nan 0.0100 0.0002
## 180 0.7164 nan 0.0100 0.0001
## 200 0.6920 nan 0.0100 0.0003
## 220 0.6715 nan 0.0100 0.0002
## 240 0.6533 nan 0.0100 0.0000
## 260 0.6359 nan 0.0100 0.0000
## 280 0.6199 nan 0.0100 0.0002
## 300 0.6052 nan 0.0100 0.0000
## 320 0.5914 nan 0.0100 0.0002
## 340 0.5775 nan 0.0100 0.0002
## 360 0.5645 nan 0.0100 0.0001
## 380 0.5527 nan 0.0100 -0.0000
## 400 0.5423 nan 0.0100 -0.0000
## 420 0.5320 nan 0.0100 0.0000
## 440 0.5214 nan 0.0100 0.0000
## 460 0.5113 nan 0.0100 -0.0001
## 480 0.5013 nan 0.0100 -0.0001
## 500 0.4917 nan 0.0100 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2333 nan 0.1000 0.0384
## 2 1.1751 nan 0.1000 0.0211
## 3 1.1296 nan 0.1000 0.0187
## 4 1.0791 nan 0.1000 0.0237
## 5 1.0393 nan 0.1000 0.0161
## 6 1.0005 nan 0.1000 0.0173
## 7 0.9673 nan 0.1000 0.0102
## 8 0.9372 nan 0.1000 0.0098
## 9 0.9113 nan 0.1000 0.0115
## 10 0.8892 nan 0.1000 0.0077
## 20 0.7379 nan 0.1000 0.0021
## 40 0.5983 nan 0.1000 0.0016
## 60 0.5242 nan 0.1000 -0.0009
## 80 0.4616 nan 0.1000 -0.0004
## 100 0.4131 nan 0.1000 -0.0000
## 120 0.3679 nan 0.1000 -0.0019
## 140 0.3269 nan 0.1000 0.0006
## 160 0.2903 nan 0.1000 -0.0005
## 180 0.2632 nan 0.1000 -0.0008
## 200 0.2368 nan 0.1000 -0.0010
## 220 0.2160 nan 0.1000 -0.0005
## 240 0.1955 nan 0.1000 -0.0006
## 260 0.1765 nan 0.1000 -0.0002
## 280 0.1615 nan 0.1000 -0.0007
## 300 0.1472 nan 0.1000 -0.0000
## 320 0.1369 nan 0.1000 -0.0001
## 340 0.1267 nan 0.1000 -0.0003
## 360 0.1177 nan 0.1000 0.0001
## 380 0.1101 nan 0.1000 -0.0001
## 400 0.1020 nan 0.1000 0.0001
## 420 0.0947 nan 0.1000 -0.0003
## 440 0.0883 nan 0.1000 -0.0002
## 460 0.0818 nan 0.1000 -0.0002
## 480 0.0759 nan 0.1000 -0.0001
## 500 0.0712 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2446 nan 0.1000 0.0345
## 2 1.1704 nan 0.1000 0.0320
## 3 1.1179 nan 0.1000 0.0212
## 4 1.0743 nan 0.1000 0.0183
## 5 1.0333 nan 0.1000 0.0160
## 6 0.9923 nan 0.1000 0.0170
## 7 0.9521 nan 0.1000 0.0161
## 8 0.9194 nan 0.1000 0.0147
## 9 0.8984 nan 0.1000 0.0078
## 10 0.8777 nan 0.1000 0.0065
## 20 0.7314 nan 0.1000 0.0028
## 40 0.6115 nan 0.1000 -0.0014
## 60 0.5357 nan 0.1000 -0.0002
## 80 0.4758 nan 0.1000 -0.0009
## 100 0.4222 nan 0.1000 -0.0004
## 120 0.3784 nan 0.1000 -0.0011
## 140 0.3405 nan 0.1000 -0.0015
## 160 0.3045 nan 0.1000 -0.0014
## 180 0.2780 nan 0.1000 0.0002
## 200 0.2548 nan 0.1000 -0.0007
## 220 0.2317 nan 0.1000 -0.0006
## 240 0.2135 nan 0.1000 -0.0008
## 260 0.1969 nan 0.1000 -0.0007
## 280 0.1808 nan 0.1000 -0.0003
## 300 0.1668 nan 0.1000 -0.0006
## 320 0.1532 nan 0.1000 -0.0008
## 340 0.1424 nan 0.1000 -0.0004
## 360 0.1332 nan 0.1000 -0.0003
## 380 0.1232 nan 0.1000 -0.0003
## 400 0.1147 nan 0.1000 -0.0002
## 420 0.1063 nan 0.1000 -0.0001
## 440 0.0980 nan 0.1000 -0.0006
## 460 0.0915 nan 0.1000 -0.0004
## 480 0.0844 nan 0.1000 -0.0003
## 500 0.0788 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2363 nan 0.1000 0.0388
## 2 1.1681 nan 0.1000 0.0294
## 3 1.1138 nan 0.1000 0.0262
## 4 1.0703 nan 0.1000 0.0196
## 5 1.0317 nan 0.1000 0.0184
## 6 0.9948 nan 0.1000 0.0154
## 7 0.9626 nan 0.1000 0.0124
## 8 0.9387 nan 0.1000 0.0071
## 9 0.9133 nan 0.1000 0.0126
## 10 0.8921 nan 0.1000 0.0065
## 20 0.7389 nan 0.1000 0.0059
## 40 0.6130 nan 0.1000 0.0004
## 60 0.5299 nan 0.1000 -0.0025
## 80 0.4716 nan 0.1000 -0.0004
## 100 0.4235 nan 0.1000 -0.0007
## 120 0.3777 nan 0.1000 -0.0012
## 140 0.3459 nan 0.1000 -0.0016
## 160 0.3119 nan 0.1000 -0.0007
## 180 0.2878 nan 0.1000 -0.0009
## 200 0.2633 nan 0.1000 -0.0004
## 220 0.2387 nan 0.1000 -0.0000
## 240 0.2201 nan 0.1000 -0.0010
## 260 0.2015 nan 0.1000 -0.0013
## 280 0.1856 nan 0.1000 -0.0006
## 300 0.1718 nan 0.1000 -0.0005
## 320 0.1601 nan 0.1000 -0.0005
## 340 0.1482 nan 0.1000 -0.0002
## 360 0.1378 nan 0.1000 -0.0004
## 380 0.1288 nan 0.1000 -0.0005
## 400 0.1199 nan 0.1000 -0.0004
## 420 0.1120 nan 0.1000 -0.0003
## 440 0.1057 nan 0.1000 -0.0003
## 460 0.0990 nan 0.1000 -0.0003
## 480 0.0925 nan 0.1000 -0.0006
## 500 0.0862 nan 0.1000 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2379 nan 0.1000 0.0347
## 2 1.1778 nan 0.1000 0.0266
## 3 1.1257 nan 0.1000 0.0245
## 4 1.0755 nan 0.1000 0.0198
## 5 1.0261 nan 0.1000 0.0223
## 6 0.9773 nan 0.1000 0.0158
## 7 0.9408 nan 0.1000 0.0117
## 8 0.9116 nan 0.1000 0.0132
## 9 0.8840 nan 0.1000 0.0111
## 10 0.8597 nan 0.1000 0.0093
## 20 0.7030 nan 0.1000 0.0047
## 40 0.5579 nan 0.1000 0.0002
## 60 0.4718 nan 0.1000 -0.0012
## 80 0.3988 nan 0.1000 -0.0005
## 100 0.3444 nan 0.1000 -0.0005
## 120 0.3006 nan 0.1000 -0.0008
## 140 0.2657 nan 0.1000 -0.0007
## 160 0.2350 nan 0.1000 0.0002
## 180 0.2103 nan 0.1000 -0.0009
## 200 0.1886 nan 0.1000 0.0004
## 220 0.1681 nan 0.1000 -0.0003
## 240 0.1496 nan 0.1000 -0.0002
## 260 0.1351 nan 0.1000 -0.0002
## 280 0.1229 nan 0.1000 -0.0003
## 300 0.1105 nan 0.1000 -0.0003
## 320 0.1002 nan 0.1000 -0.0001
## 340 0.0905 nan 0.1000 -0.0002
## 360 0.0816 nan 0.1000 -0.0002
## 380 0.0747 nan 0.1000 -0.0003
## 400 0.0683 nan 0.1000 -0.0001
## 420 0.0627 nan 0.1000 -0.0001
## 440 0.0577 nan 0.1000 -0.0001
## 460 0.0521 nan 0.1000 -0.0001
## 480 0.0474 nan 0.1000 -0.0002
## 500 0.0434 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2470 nan 0.1000 0.0314
## 2 1.1746 nan 0.1000 0.0324
## 3 1.1217 nan 0.1000 0.0243
## 4 1.0721 nan 0.1000 0.0212
## 5 1.0330 nan 0.1000 0.0172
## 6 0.9932 nan 0.1000 0.0155
## 7 0.9601 nan 0.1000 0.0127
## 8 0.9262 nan 0.1000 0.0124
## 9 0.8959 nan 0.1000 0.0112
## 10 0.8702 nan 0.1000 0.0100
## 20 0.7257 nan 0.1000 0.0018
## 40 0.5737 nan 0.1000 -0.0010
## 60 0.4821 nan 0.1000 0.0003
## 80 0.4135 nan 0.1000 -0.0008
## 100 0.3577 nan 0.1000 -0.0020
## 120 0.3132 nan 0.1000 -0.0004
## 140 0.2721 nan 0.1000 -0.0006
## 160 0.2387 nan 0.1000 -0.0010
## 180 0.2139 nan 0.1000 -0.0008
## 200 0.1923 nan 0.1000 -0.0012
## 220 0.1704 nan 0.1000 -0.0012
## 240 0.1517 nan 0.1000 -0.0005
## 260 0.1366 nan 0.1000 -0.0002
## 280 0.1242 nan 0.1000 -0.0006
## 300 0.1121 nan 0.1000 -0.0001
## 320 0.1016 nan 0.1000 -0.0003
## 340 0.0913 nan 0.1000 -0.0001
## 360 0.0831 nan 0.1000 -0.0003
## 380 0.0738 nan 0.1000 -0.0003
## 400 0.0674 nan 0.1000 -0.0001
## 420 0.0623 nan 0.1000 -0.0001
## 440 0.0570 nan 0.1000 -0.0001
## 460 0.0523 nan 0.1000 -0.0001
## 480 0.0485 nan 0.1000 -0.0003
## 500 0.0440 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2394 nan 0.1000 0.0396
## 2 1.1733 nan 0.1000 0.0263
## 3 1.1140 nan 0.1000 0.0244
## 4 1.0634 nan 0.1000 0.0192
## 5 1.0189 nan 0.1000 0.0175
## 6 0.9812 nan 0.1000 0.0149
## 7 0.9467 nan 0.1000 0.0137
## 8 0.9169 nan 0.1000 0.0109
## 9 0.8888 nan 0.1000 0.0112
## 10 0.8617 nan 0.1000 0.0104
## 20 0.7167 nan 0.1000 0.0022
## 40 0.5768 nan 0.1000 -0.0004
## 60 0.4887 nan 0.1000 -0.0015
## 80 0.4159 nan 0.1000 -0.0000
## 100 0.3621 nan 0.1000 -0.0006
## 120 0.3158 nan 0.1000 -0.0016
## 140 0.2845 nan 0.1000 -0.0012
## 160 0.2550 nan 0.1000 -0.0014
## 180 0.2276 nan 0.1000 -0.0007
## 200 0.2029 nan 0.1000 -0.0005
## 220 0.1823 nan 0.1000 -0.0005
## 240 0.1634 nan 0.1000 -0.0001
## 260 0.1462 nan 0.1000 -0.0008
## 280 0.1324 nan 0.1000 -0.0003
## 300 0.1203 nan 0.1000 -0.0007
## 320 0.1085 nan 0.1000 -0.0001
## 340 0.0996 nan 0.1000 -0.0007
## 360 0.0907 nan 0.1000 -0.0002
## 380 0.0828 nan 0.1000 -0.0004
## 400 0.0754 nan 0.1000 -0.0002
## 420 0.0691 nan 0.1000 -0.0003
## 440 0.0630 nan 0.1000 -0.0002
## 460 0.0579 nan 0.1000 -0.0003
## 480 0.0533 nan 0.1000 -0.0001
## 500 0.0488 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2374 nan 0.1000 0.0370
## 2 1.1697 nan 0.1000 0.0292
## 3 1.1112 nan 0.1000 0.0258
## 4 1.0585 nan 0.1000 0.0243
## 5 1.0118 nan 0.1000 0.0197
## 6 0.9717 nan 0.1000 0.0146
## 7 0.9368 nan 0.1000 0.0148
## 8 0.9058 nan 0.1000 0.0111
## 9 0.8776 nan 0.1000 0.0103
## 10 0.8560 nan 0.1000 0.0071
## 20 0.6814 nan 0.1000 0.0036
## 40 0.5277 nan 0.1000 -0.0001
## 60 0.4203 nan 0.1000 0.0001
## 80 0.3495 nan 0.1000 -0.0002
## 100 0.2927 nan 0.1000 -0.0004
## 120 0.2474 nan 0.1000 -0.0007
## 140 0.2144 nan 0.1000 -0.0012
## 160 0.1850 nan 0.1000 -0.0003
## 180 0.1610 nan 0.1000 -0.0007
## 200 0.1411 nan 0.1000 -0.0004
## 220 0.1244 nan 0.1000 -0.0004
## 240 0.1088 nan 0.1000 -0.0007
## 260 0.0960 nan 0.1000 -0.0003
## 280 0.0853 nan 0.1000 -0.0000
## 300 0.0751 nan 0.1000 -0.0002
## 320 0.0663 nan 0.1000 -0.0001
## 340 0.0589 nan 0.1000 -0.0001
## 360 0.0524 nan 0.1000 -0.0000
## 380 0.0471 nan 0.1000 -0.0001
## 400 0.0420 nan 0.1000 -0.0002
## 420 0.0376 nan 0.1000 -0.0001
## 440 0.0341 nan 0.1000 -0.0001
## 460 0.0308 nan 0.1000 -0.0001
## 480 0.0277 nan 0.1000 -0.0002
## 500 0.0248 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2349 nan 0.1000 0.0375
## 2 1.1647 nan 0.1000 0.0294
## 3 1.1003 nan 0.1000 0.0290
## 4 1.0551 nan 0.1000 0.0177
## 5 1.0070 nan 0.1000 0.0202
## 6 0.9668 nan 0.1000 0.0169
## 7 0.9333 nan 0.1000 0.0128
## 8 0.8966 nan 0.1000 0.0149
## 9 0.8691 nan 0.1000 0.0108
## 10 0.8430 nan 0.1000 0.0104
## 20 0.6799 nan 0.1000 0.0031
## 40 0.5260 nan 0.1000 -0.0003
## 60 0.4293 nan 0.1000 -0.0006
## 80 0.3606 nan 0.1000 0.0003
## 100 0.3018 nan 0.1000 -0.0011
## 120 0.2588 nan 0.1000 0.0004
## 140 0.2218 nan 0.1000 -0.0003
## 160 0.1936 nan 0.1000 -0.0010
## 180 0.1681 nan 0.1000 -0.0009
## 200 0.1477 nan 0.1000 -0.0005
## 220 0.1278 nan 0.1000 -0.0004
## 240 0.1111 nan 0.1000 -0.0005
## 260 0.0982 nan 0.1000 -0.0003
## 280 0.0870 nan 0.1000 -0.0001
## 300 0.0786 nan 0.1000 -0.0003
## 320 0.0706 nan 0.1000 -0.0001
## 340 0.0627 nan 0.1000 -0.0001
## 360 0.0560 nan 0.1000 -0.0001
## 380 0.0489 nan 0.1000 -0.0001
## 400 0.0439 nan 0.1000 -0.0002
## 420 0.0397 nan 0.1000 -0.0001
## 440 0.0358 nan 0.1000 -0.0001
## 460 0.0323 nan 0.1000 -0.0002
## 480 0.0293 nan 0.1000 -0.0001
## 500 0.0264 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2307 nan 0.1000 0.0350
## 2 1.1581 nan 0.1000 0.0325
## 3 1.0971 nan 0.1000 0.0233
## 4 1.0422 nan 0.1000 0.0222
## 5 1.0060 nan 0.1000 0.0140
## 6 0.9635 nan 0.1000 0.0171
## 7 0.9296 nan 0.1000 0.0145
## 8 0.9000 nan 0.1000 0.0122
## 9 0.8757 nan 0.1000 0.0089
## 10 0.8525 nan 0.1000 0.0067
## 20 0.6910 nan 0.1000 0.0026
## 40 0.5468 nan 0.1000 -0.0010
## 60 0.4605 nan 0.1000 -0.0013
## 80 0.3852 nan 0.1000 -0.0014
## 100 0.3300 nan 0.1000 -0.0007
## 120 0.2837 nan 0.1000 -0.0016
## 140 0.2448 nan 0.1000 -0.0002
## 160 0.2152 nan 0.1000 -0.0005
## 180 0.1892 nan 0.1000 -0.0004
## 200 0.1664 nan 0.1000 -0.0002
## 220 0.1452 nan 0.1000 -0.0005
## 240 0.1278 nan 0.1000 -0.0005
## 260 0.1144 nan 0.1000 -0.0009
## 280 0.1019 nan 0.1000 -0.0004
## 300 0.0899 nan 0.1000 -0.0001
## 320 0.0799 nan 0.1000 -0.0001
## 340 0.0708 nan 0.1000 -0.0005
## 360 0.0629 nan 0.1000 -0.0001
## 380 0.0559 nan 0.1000 -0.0002
## 400 0.0505 nan 0.1000 -0.0002
## 420 0.0452 nan 0.1000 -0.0002
## 440 0.0406 nan 0.1000 -0.0001
## 460 0.0365 nan 0.1000 -0.0001
## 480 0.0329 nan 0.1000 -0.0001
## 500 0.0298 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3199 nan 0.0010 0.0003
## 2 1.3191 nan 0.0010 0.0003
## 3 1.3183 nan 0.0010 0.0003
## 4 1.3174 nan 0.0010 0.0003
## 5 1.3166 nan 0.0010 0.0004
## 6 1.3157 nan 0.0010 0.0004
## 7 1.3149 nan 0.0010 0.0004
## 8 1.3141 nan 0.0010 0.0003
## 9 1.3132 nan 0.0010 0.0004
## 10 1.3123 nan 0.0010 0.0004
## 20 1.3040 nan 0.0010 0.0004
## 40 1.2881 nan 0.0010 0.0004
## 60 1.2724 nan 0.0010 0.0003
## 80 1.2573 nan 0.0010 0.0003
## 100 1.2424 nan 0.0010 0.0003
## 120 1.2283 nan 0.0010 0.0004
## 140 1.2141 nan 0.0010 0.0003
## 160 1.2006 nan 0.0010 0.0003
## 180 1.1879 nan 0.0010 0.0003
## 200 1.1753 nan 0.0010 0.0003
## 220 1.1633 nan 0.0010 0.0002
## 240 1.1514 nan 0.0010 0.0002
## 260 1.1395 nan 0.0010 0.0003
## 280 1.1283 nan 0.0010 0.0002
## 300 1.1177 nan 0.0010 0.0002
## 320 1.1074 nan 0.0010 0.0002
## 340 1.0971 nan 0.0010 0.0002
## 360 1.0872 nan 0.0010 0.0002
## 380 1.0779 nan 0.0010 0.0002
## 400 1.0682 nan 0.0010 0.0002
## 420 1.0589 nan 0.0010 0.0002
## 440 1.0497 nan 0.0010 0.0002
## 460 1.0411 nan 0.0010 0.0002
## 480 1.0327 nan 0.0010 0.0002
## 500 1.0245 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3181 nan 0.0010 0.0004
## 4 1.3173 nan 0.0010 0.0004
## 5 1.3164 nan 0.0010 0.0004
## 6 1.3156 nan 0.0010 0.0004
## 7 1.3149 nan 0.0010 0.0003
## 8 1.3140 nan 0.0010 0.0004
## 9 1.3131 nan 0.0010 0.0004
## 10 1.3123 nan 0.0010 0.0004
## 20 1.3044 nan 0.0010 0.0004
## 40 1.2886 nan 0.0010 0.0003
## 60 1.2733 nan 0.0010 0.0003
## 80 1.2581 nan 0.0010 0.0003
## 100 1.2433 nan 0.0010 0.0003
## 120 1.2301 nan 0.0010 0.0003
## 140 1.2164 nan 0.0010 0.0003
## 160 1.2029 nan 0.0010 0.0003
## 180 1.1900 nan 0.0010 0.0003
## 200 1.1774 nan 0.0010 0.0003
## 220 1.1650 nan 0.0010 0.0003
## 240 1.1531 nan 0.0010 0.0003
## 260 1.1416 nan 0.0010 0.0003
## 280 1.1304 nan 0.0010 0.0002
## 300 1.1195 nan 0.0010 0.0002
## 320 1.1092 nan 0.0010 0.0002
## 340 1.0990 nan 0.0010 0.0002
## 360 1.0889 nan 0.0010 0.0002
## 380 1.0793 nan 0.0010 0.0002
## 400 1.0697 nan 0.0010 0.0002
## 420 1.0606 nan 0.0010 0.0002
## 440 1.0517 nan 0.0010 0.0002
## 460 1.0427 nan 0.0010 0.0002
## 480 1.0343 nan 0.0010 0.0002
## 500 1.0260 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3199 nan 0.0010 0.0003
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3180 nan 0.0010 0.0004
## 4 1.3171 nan 0.0010 0.0004
## 5 1.3163 nan 0.0010 0.0004
## 6 1.3155 nan 0.0010 0.0003
## 7 1.3147 nan 0.0010 0.0004
## 8 1.3138 nan 0.0010 0.0004
## 9 1.3129 nan 0.0010 0.0004
## 10 1.3120 nan 0.0010 0.0004
## 20 1.3038 nan 0.0010 0.0004
## 40 1.2878 nan 0.0010 0.0003
## 60 1.2728 nan 0.0010 0.0003
## 80 1.2577 nan 0.0010 0.0004
## 100 1.2433 nan 0.0010 0.0003
## 120 1.2293 nan 0.0010 0.0003
## 140 1.2155 nan 0.0010 0.0003
## 160 1.2027 nan 0.0010 0.0003
## 180 1.1899 nan 0.0010 0.0003
## 200 1.1775 nan 0.0010 0.0003
## 220 1.1653 nan 0.0010 0.0003
## 240 1.1535 nan 0.0010 0.0003
## 260 1.1419 nan 0.0010 0.0003
## 280 1.1306 nan 0.0010 0.0002
## 300 1.1196 nan 0.0010 0.0002
## 320 1.1091 nan 0.0010 0.0002
## 340 1.0992 nan 0.0010 0.0002
## 360 1.0894 nan 0.0010 0.0002
## 380 1.0796 nan 0.0010 0.0002
## 400 1.0702 nan 0.0010 0.0002
## 420 1.0610 nan 0.0010 0.0002
## 440 1.0520 nan 0.0010 0.0002
## 460 1.0432 nan 0.0010 0.0002
## 480 1.0347 nan 0.0010 0.0002
## 500 1.0264 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3180 nan 0.0010 0.0004
## 4 1.3170 nan 0.0010 0.0004
## 5 1.3160 nan 0.0010 0.0005
## 6 1.3151 nan 0.0010 0.0004
## 7 1.3143 nan 0.0010 0.0004
## 8 1.3135 nan 0.0010 0.0004
## 9 1.3126 nan 0.0010 0.0004
## 10 1.3118 nan 0.0010 0.0004
## 20 1.3029 nan 0.0010 0.0003
## 40 1.2852 nan 0.0010 0.0004
## 60 1.2689 nan 0.0010 0.0003
## 80 1.2529 nan 0.0010 0.0003
## 100 1.2371 nan 0.0010 0.0003
## 120 1.2218 nan 0.0010 0.0004
## 140 1.2071 nan 0.0010 0.0003
## 160 1.1928 nan 0.0010 0.0003
## 180 1.1789 nan 0.0010 0.0004
## 200 1.1655 nan 0.0010 0.0003
## 220 1.1529 nan 0.0010 0.0003
## 240 1.1405 nan 0.0010 0.0003
## 260 1.1281 nan 0.0010 0.0002
## 280 1.1164 nan 0.0010 0.0002
## 300 1.1049 nan 0.0010 0.0002
## 320 1.0937 nan 0.0010 0.0002
## 340 1.0832 nan 0.0010 0.0002
## 360 1.0728 nan 0.0010 0.0002
## 380 1.0624 nan 0.0010 0.0002
## 400 1.0525 nan 0.0010 0.0002
## 420 1.0427 nan 0.0010 0.0002
## 440 1.0331 nan 0.0010 0.0002
## 460 1.0238 nan 0.0010 0.0002
## 480 1.0150 nan 0.0010 0.0002
## 500 1.0065 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0005
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3180 nan 0.0010 0.0004
## 4 1.3171 nan 0.0010 0.0004
## 5 1.3163 nan 0.0010 0.0004
## 6 1.3154 nan 0.0010 0.0004
## 7 1.3145 nan 0.0010 0.0004
## 8 1.3136 nan 0.0010 0.0004
## 9 1.3127 nan 0.0010 0.0004
## 10 1.3118 nan 0.0010 0.0004
## 20 1.3031 nan 0.0010 0.0004
## 40 1.2859 nan 0.0010 0.0004
## 60 1.2694 nan 0.0010 0.0003
## 80 1.2534 nan 0.0010 0.0004
## 100 1.2380 nan 0.0010 0.0003
## 120 1.2230 nan 0.0010 0.0004
## 140 1.2084 nan 0.0010 0.0003
## 160 1.1946 nan 0.0010 0.0003
## 180 1.1809 nan 0.0010 0.0003
## 200 1.1675 nan 0.0010 0.0003
## 220 1.1542 nan 0.0010 0.0003
## 240 1.1417 nan 0.0010 0.0002
## 260 1.1295 nan 0.0010 0.0003
## 280 1.1178 nan 0.0010 0.0003
## 300 1.1064 nan 0.0010 0.0002
## 320 1.0953 nan 0.0010 0.0003
## 340 1.0844 nan 0.0010 0.0002
## 360 1.0741 nan 0.0010 0.0002
## 380 1.0639 nan 0.0010 0.0002
## 400 1.0539 nan 0.0010 0.0002
## 420 1.0442 nan 0.0010 0.0002
## 440 1.0347 nan 0.0010 0.0002
## 460 1.0255 nan 0.0010 0.0002
## 480 1.0168 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3179 nan 0.0010 0.0004
## 4 1.3170 nan 0.0010 0.0005
## 5 1.3161 nan 0.0010 0.0003
## 6 1.3153 nan 0.0010 0.0004
## 7 1.3144 nan 0.0010 0.0004
## 8 1.3136 nan 0.0010 0.0004
## 9 1.3127 nan 0.0010 0.0004
## 10 1.3119 nan 0.0010 0.0004
## 20 1.3033 nan 0.0010 0.0004
## 40 1.2858 nan 0.0010 0.0004
## 60 1.2695 nan 0.0010 0.0004
## 80 1.2535 nan 0.0010 0.0003
## 100 1.2380 nan 0.0010 0.0003
## 120 1.2234 nan 0.0010 0.0003
## 140 1.2087 nan 0.0010 0.0004
## 160 1.1949 nan 0.0010 0.0003
## 180 1.1814 nan 0.0010 0.0002
## 200 1.1684 nan 0.0010 0.0003
## 220 1.1554 nan 0.0010 0.0003
## 240 1.1431 nan 0.0010 0.0002
## 260 1.1313 nan 0.0010 0.0003
## 280 1.1198 nan 0.0010 0.0002
## 300 1.1087 nan 0.0010 0.0003
## 320 1.0974 nan 0.0010 0.0003
## 340 1.0868 nan 0.0010 0.0002
## 360 1.0763 nan 0.0010 0.0002
## 380 1.0664 nan 0.0010 0.0002
## 400 1.0567 nan 0.0010 0.0002
## 420 1.0470 nan 0.0010 0.0002
## 440 1.0374 nan 0.0010 0.0002
## 460 1.0282 nan 0.0010 0.0002
## 480 1.0193 nan 0.0010 0.0002
## 500 1.0106 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3196 nan 0.0010 0.0005
## 2 1.3186 nan 0.0010 0.0004
## 3 1.3178 nan 0.0010 0.0004
## 4 1.3168 nan 0.0010 0.0004
## 5 1.3158 nan 0.0010 0.0005
## 6 1.3149 nan 0.0010 0.0004
## 7 1.3139 nan 0.0010 0.0004
## 8 1.3129 nan 0.0010 0.0005
## 9 1.3120 nan 0.0010 0.0004
## 10 1.3111 nan 0.0010 0.0004
## 20 1.3018 nan 0.0010 0.0004
## 40 1.2841 nan 0.0010 0.0004
## 60 1.2666 nan 0.0010 0.0004
## 80 1.2497 nan 0.0010 0.0003
## 100 1.2335 nan 0.0010 0.0004
## 120 1.2178 nan 0.0010 0.0003
## 140 1.2027 nan 0.0010 0.0003
## 160 1.1878 nan 0.0010 0.0003
## 180 1.1736 nan 0.0010 0.0004
## 200 1.1596 nan 0.0010 0.0003
## 220 1.1462 nan 0.0010 0.0003
## 240 1.1332 nan 0.0010 0.0003
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## 280 1.1087 nan 0.0010 0.0002
## 300 1.0967 nan 0.0010 0.0003
## 320 1.0849 nan 0.0010 0.0003
## 340 1.0735 nan 0.0010 0.0003
## 360 1.0626 nan 0.0010 0.0002
## 380 1.0520 nan 0.0010 0.0002
## 400 1.0415 nan 0.0010 0.0002
## 420 1.0314 nan 0.0010 0.0002
## 440 1.0217 nan 0.0010 0.0002
## 460 1.0120 nan 0.0010 0.0002
## 480 1.0027 nan 0.0010 0.0002
## 500 0.9936 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3178 nan 0.0010 0.0005
## 4 1.3168 nan 0.0010 0.0004
## 5 1.3160 nan 0.0010 0.0004
## 6 1.3151 nan 0.0010 0.0004
## 7 1.3141 nan 0.0010 0.0005
## 8 1.3131 nan 0.0010 0.0004
## 9 1.3122 nan 0.0010 0.0004
## 10 1.3113 nan 0.0010 0.0004
## 20 1.3019 nan 0.0010 0.0004
## 40 1.2842 nan 0.0010 0.0004
## 60 1.2671 nan 0.0010 0.0004
## 80 1.2504 nan 0.0010 0.0004
## 100 1.2342 nan 0.0010 0.0003
## 120 1.2185 nan 0.0010 0.0004
## 140 1.2034 nan 0.0010 0.0003
## 160 1.1888 nan 0.0010 0.0003
## 180 1.1747 nan 0.0010 0.0003
## 200 1.1609 nan 0.0010 0.0003
## 220 1.1474 nan 0.0010 0.0003
## 240 1.1344 nan 0.0010 0.0003
## 260 1.1219 nan 0.0010 0.0003
## 280 1.1101 nan 0.0010 0.0002
## 300 1.0985 nan 0.0010 0.0003
## 320 1.0868 nan 0.0010 0.0002
## 340 1.0755 nan 0.0010 0.0002
## 360 1.0648 nan 0.0010 0.0002
## 380 1.0542 nan 0.0010 0.0002
## 400 1.0440 nan 0.0010 0.0002
## 420 1.0341 nan 0.0010 0.0002
## 440 1.0243 nan 0.0010 0.0002
## 460 1.0146 nan 0.0010 0.0002
## 480 1.0055 nan 0.0010 0.0002
## 500 0.9964 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3187 nan 0.0010 0.0004
## 3 1.3179 nan 0.0010 0.0004
## 4 1.3170 nan 0.0010 0.0004
## 5 1.3162 nan 0.0010 0.0004
## 6 1.3153 nan 0.0010 0.0004
## 7 1.3145 nan 0.0010 0.0004
## 8 1.3135 nan 0.0010 0.0004
## 9 1.3127 nan 0.0010 0.0004
## 10 1.3118 nan 0.0010 0.0004
## 20 1.3028 nan 0.0010 0.0004
## 40 1.2850 nan 0.0010 0.0004
## 60 1.2678 nan 0.0010 0.0004
## 80 1.2512 nan 0.0010 0.0003
## 100 1.2352 nan 0.0010 0.0003
## 120 1.2195 nan 0.0010 0.0003
## 140 1.2044 nan 0.0010 0.0003
## 160 1.1901 nan 0.0010 0.0003
## 180 1.1764 nan 0.0010 0.0003
## 200 1.1627 nan 0.0010 0.0003
## 220 1.1494 nan 0.0010 0.0003
## 240 1.1367 nan 0.0010 0.0003
## 260 1.1245 nan 0.0010 0.0003
## 280 1.1121 nan 0.0010 0.0003
## 300 1.1006 nan 0.0010 0.0002
## 320 1.0895 nan 0.0010 0.0002
## 340 1.0783 nan 0.0010 0.0002
## 360 1.0674 nan 0.0010 0.0002
## 380 1.0570 nan 0.0010 0.0002
## 400 1.0469 nan 0.0010 0.0002
## 420 1.0367 nan 0.0010 0.0002
## 440 1.0270 nan 0.0010 0.0002
## 460 1.0178 nan 0.0010 0.0002
## 480 1.0086 nan 0.0010 0.0002
## 500 0.9997 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3124 nan 0.0100 0.0042
## 2 1.3038 nan 0.0100 0.0040
## 3 1.2965 nan 0.0100 0.0032
## 4 1.2887 nan 0.0100 0.0035
## 5 1.2804 nan 0.0100 0.0039
## 6 1.2725 nan 0.0100 0.0036
## 7 1.2645 nan 0.0100 0.0037
## 8 1.2567 nan 0.0100 0.0035
## 9 1.2495 nan 0.0100 0.0035
## 10 1.2420 nan 0.0100 0.0033
## 20 1.1724 nan 0.0100 0.0029
## 40 1.0642 nan 0.0100 0.0018
## 60 0.9809 nan 0.0100 0.0017
## 80 0.9198 nan 0.0100 0.0011
## 100 0.8680 nan 0.0100 0.0009
## 120 0.8262 nan 0.0100 0.0007
## 140 0.7906 nan 0.0100 0.0004
## 160 0.7623 nan 0.0100 0.0004
## 180 0.7373 nan 0.0100 0.0004
## 200 0.7152 nan 0.0100 0.0001
## 220 0.6952 nan 0.0100 0.0002
## 240 0.6768 nan 0.0100 0.0003
## 260 0.6622 nan 0.0100 -0.0001
## 280 0.6481 nan 0.0100 0.0001
## 300 0.6342 nan 0.0100 0.0001
## 320 0.6217 nan 0.0100 -0.0000
## 340 0.6095 nan 0.0100 0.0001
## 360 0.5997 nan 0.0100 0.0000
## 380 0.5898 nan 0.0100 0.0000
## 400 0.5784 nan 0.0100 0.0001
## 420 0.5686 nan 0.0100 -0.0001
## 440 0.5595 nan 0.0100 -0.0000
## 460 0.5505 nan 0.0100 -0.0000
## 480 0.5425 nan 0.0100 -0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3131 nan 0.0100 0.0031
## 2 1.3045 nan 0.0100 0.0040
## 3 1.2956 nan 0.0100 0.0039
## 4 1.2878 nan 0.0100 0.0033
## 5 1.2795 nan 0.0100 0.0039
## 6 1.2715 nan 0.0100 0.0037
## 7 1.2625 nan 0.0100 0.0038
## 8 1.2553 nan 0.0100 0.0031
## 9 1.2480 nan 0.0100 0.0031
## 10 1.2405 nan 0.0100 0.0033
## 20 1.1748 nan 0.0100 0.0032
## 40 1.0669 nan 0.0100 0.0017
## 60 0.9851 nan 0.0100 0.0015
## 80 0.9210 nan 0.0100 0.0013
## 100 0.8678 nan 0.0100 0.0007
## 120 0.8270 nan 0.0100 0.0006
## 140 0.7920 nan 0.0100 0.0005
## 160 0.7628 nan 0.0100 0.0004
## 180 0.7391 nan 0.0100 0.0003
## 200 0.7192 nan 0.0100 0.0001
## 220 0.7020 nan 0.0100 0.0002
## 240 0.6855 nan 0.0100 -0.0000
## 260 0.6701 nan 0.0100 -0.0001
## 280 0.6567 nan 0.0100 0.0001
## 300 0.6447 nan 0.0100 -0.0000
## 320 0.6318 nan 0.0100 0.0001
## 340 0.6208 nan 0.0100 0.0001
## 360 0.6102 nan 0.0100 0.0001
## 380 0.6001 nan 0.0100 -0.0003
## 400 0.5915 nan 0.0100 -0.0002
## 420 0.5816 nan 0.0100 0.0001
## 440 0.5720 nan 0.0100 -0.0001
## 460 0.5627 nan 0.0100 0.0000
## 480 0.5541 nan 0.0100 0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3118 nan 0.0100 0.0039
## 2 1.3042 nan 0.0100 0.0034
## 3 1.2958 nan 0.0100 0.0040
## 4 1.2885 nan 0.0100 0.0033
## 5 1.2807 nan 0.0100 0.0035
## 6 1.2735 nan 0.0100 0.0029
## 7 1.2657 nan 0.0100 0.0034
## 8 1.2580 nan 0.0100 0.0031
## 9 1.2503 nan 0.0100 0.0034
## 10 1.2428 nan 0.0100 0.0034
## 20 1.1773 nan 0.0100 0.0031
## 40 1.0717 nan 0.0100 0.0021
## 60 0.9886 nan 0.0100 0.0013
## 80 0.9234 nan 0.0100 0.0011
## 100 0.8695 nan 0.0100 0.0007
## 120 0.8274 nan 0.0100 0.0006
## 140 0.7929 nan 0.0100 0.0004
## 160 0.7656 nan 0.0100 0.0002
## 180 0.7412 nan 0.0100 0.0004
## 200 0.7212 nan 0.0100 0.0003
## 220 0.7028 nan 0.0100 0.0003
## 240 0.6872 nan 0.0100 -0.0000
## 260 0.6746 nan 0.0100 -0.0000
## 280 0.6611 nan 0.0100 -0.0000
## 300 0.6496 nan 0.0100 0.0000
## 320 0.6388 nan 0.0100 0.0002
## 340 0.6283 nan 0.0100 -0.0001
## 360 0.6180 nan 0.0100 -0.0000
## 380 0.6087 nan 0.0100 0.0000
## 400 0.5992 nan 0.0100 -0.0001
## 420 0.5906 nan 0.0100 -0.0000
## 440 0.5816 nan 0.0100 -0.0001
## 460 0.5734 nan 0.0100 -0.0001
## 480 0.5654 nan 0.0100 -0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3112 nan 0.0100 0.0039
## 2 1.3021 nan 0.0100 0.0041
## 3 1.2928 nan 0.0100 0.0045
## 4 1.2846 nan 0.0100 0.0036
## 5 1.2771 nan 0.0100 0.0036
## 6 1.2690 nan 0.0100 0.0031
## 7 1.2615 nan 0.0100 0.0032
## 8 1.2529 nan 0.0100 0.0037
## 9 1.2452 nan 0.0100 0.0035
## 10 1.2376 nan 0.0100 0.0031
## 20 1.1674 nan 0.0100 0.0025
## 40 1.0558 nan 0.0100 0.0020
## 60 0.9697 nan 0.0100 0.0016
## 80 0.9015 nan 0.0100 0.0014
## 100 0.8470 nan 0.0100 0.0008
## 120 0.8022 nan 0.0100 0.0006
## 140 0.7655 nan 0.0100 0.0006
## 160 0.7343 nan 0.0100 0.0004
## 180 0.7078 nan 0.0100 0.0003
## 200 0.6829 nan 0.0100 0.0001
## 220 0.6617 nan 0.0100 0.0002
## 240 0.6424 nan 0.0100 0.0002
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## 280 0.6086 nan 0.0100 0.0002
## 300 0.5938 nan 0.0100 0.0001
## 320 0.5793 nan 0.0100 0.0002
## 340 0.5647 nan 0.0100 -0.0000
## 360 0.5524 nan 0.0100 0.0000
## 380 0.5408 nan 0.0100 0.0000
## 400 0.5306 nan 0.0100 -0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3112 nan 0.0100 0.0041
## 2 1.3027 nan 0.0100 0.0034
## 3 1.2940 nan 0.0100 0.0045
## 4 1.2853 nan 0.0100 0.0040
## 5 1.2772 nan 0.0100 0.0036
## 6 1.2674 nan 0.0100 0.0044
## 7 1.2592 nan 0.0100 0.0037
## 8 1.2511 nan 0.0100 0.0034
## 9 1.2430 nan 0.0100 0.0039
## 10 1.2354 nan 0.0100 0.0031
## 20 1.1653 nan 0.0100 0.0029
## 40 1.0527 nan 0.0100 0.0022
## 60 0.9651 nan 0.0100 0.0015
## 80 0.8987 nan 0.0100 0.0014
## 100 0.8451 nan 0.0100 0.0009
## 120 0.8004 nan 0.0100 0.0007
## 140 0.7659 nan 0.0100 0.0003
## 160 0.7360 nan 0.0100 0.0004
## 180 0.7122 nan 0.0100 0.0003
## 200 0.6893 nan 0.0100 0.0003
## 220 0.6696 nan 0.0100 0.0004
## 240 0.6508 nan 0.0100 -0.0000
## 260 0.6343 nan 0.0100 0.0001
## 280 0.6192 nan 0.0100 0.0002
## 300 0.6058 nan 0.0100 0.0002
## 320 0.5921 nan 0.0100 -0.0000
## 340 0.5792 nan 0.0100 0.0000
## 360 0.5668 nan 0.0100 -0.0001
## 380 0.5548 nan 0.0100 0.0000
## 400 0.5427 nan 0.0100 -0.0001
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## 440 0.5220 nan 0.0100 -0.0001
## 460 0.5131 nan 0.0100 -0.0001
## 480 0.5033 nan 0.0100 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3114 nan 0.0100 0.0044
## 2 1.3025 nan 0.0100 0.0042
## 3 1.2939 nan 0.0100 0.0037
## 4 1.2853 nan 0.0100 0.0037
## 5 1.2766 nan 0.0100 0.0039
## 6 1.2685 nan 0.0100 0.0036
## 7 1.2605 nan 0.0100 0.0037
## 8 1.2528 nan 0.0100 0.0034
## 9 1.2461 nan 0.0100 0.0030
## 10 1.2388 nan 0.0100 0.0036
## 20 1.1659 nan 0.0100 0.0030
## 40 1.0519 nan 0.0100 0.0019
## 60 0.9672 nan 0.0100 0.0015
## 80 0.9000 nan 0.0100 0.0014
## 100 0.8488 nan 0.0100 0.0008
## 120 0.8058 nan 0.0100 0.0007
## 140 0.7708 nan 0.0100 0.0004
## 160 0.7417 nan 0.0100 0.0003
## 180 0.7161 nan 0.0100 0.0001
## 200 0.6932 nan 0.0100 0.0004
## 220 0.6740 nan 0.0100 0.0002
## 240 0.6568 nan 0.0100 0.0004
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## 280 0.6259 nan 0.0100 -0.0001
## 300 0.6121 nan 0.0100 0.0000
## 320 0.6001 nan 0.0100 -0.0000
## 340 0.5883 nan 0.0100 -0.0000
## 360 0.5770 nan 0.0100 -0.0002
## 380 0.5666 nan 0.0100 0.0001
## 400 0.5544 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3114 nan 0.0100 0.0043
## 2 1.3023 nan 0.0100 0.0045
## 3 1.2928 nan 0.0100 0.0040
## 4 1.2840 nan 0.0100 0.0038
## 5 1.2748 nan 0.0100 0.0044
## 6 1.2663 nan 0.0100 0.0039
## 7 1.2577 nan 0.0100 0.0041
## 8 1.2491 nan 0.0100 0.0037
## 9 1.2409 nan 0.0100 0.0035
## 10 1.2330 nan 0.0100 0.0036
## 20 1.1597 nan 0.0100 0.0032
## 40 1.0406 nan 0.0100 0.0019
## 60 0.9508 nan 0.0100 0.0014
## 80 0.8823 nan 0.0100 0.0014
## 100 0.8259 nan 0.0100 0.0009
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## 140 0.7417 nan 0.0100 0.0006
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## 320 0.5413 nan 0.0100 0.0000
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## 380 0.4998 nan 0.0100 -0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3114 nan 0.0100 0.0041
## 2 1.3021 nan 0.0100 0.0043
## 3 1.2927 nan 0.0100 0.0043
## 4 1.2842 nan 0.0100 0.0035
## 5 1.2748 nan 0.0100 0.0042
## 6 1.2670 nan 0.0100 0.0034
## 7 1.2581 nan 0.0100 0.0040
## 8 1.2505 nan 0.0100 0.0031
## 9 1.2429 nan 0.0100 0.0036
## 10 1.2351 nan 0.0100 0.0033
## 20 1.1603 nan 0.0100 0.0030
## 40 1.0435 nan 0.0100 0.0019
## 60 0.9539 nan 0.0100 0.0013
## 80 0.8859 nan 0.0100 0.0010
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## 160 0.7164 nan 0.0100 0.0002
## 180 0.6889 nan 0.0100 0.0005
## 200 0.6635 nan 0.0100 0.0004
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## 240 0.6227 nan 0.0100 0.0003
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## 280 0.5874 nan 0.0100 -0.0001
## 300 0.5726 nan 0.0100 0.0000
## 320 0.5587 nan 0.0100 -0.0000
## 340 0.5448 nan 0.0100 0.0001
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## 380 0.5193 nan 0.0100 -0.0000
## 400 0.5068 nan 0.0100 0.0001
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## 460 0.4740 nan 0.0100 -0.0002
## 480 0.4644 nan 0.0100 -0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3119 nan 0.0100 0.0041
## 2 1.3033 nan 0.0100 0.0033
## 3 1.2939 nan 0.0100 0.0043
## 4 1.2847 nan 0.0100 0.0042
## 5 1.2761 nan 0.0100 0.0039
## 6 1.2669 nan 0.0100 0.0042
## 7 1.2576 nan 0.0100 0.0042
## 8 1.2501 nan 0.0100 0.0032
## 9 1.2425 nan 0.0100 0.0034
## 10 1.2339 nan 0.0100 0.0041
## 20 1.1614 nan 0.0100 0.0032
## 40 1.0457 nan 0.0100 0.0023
## 60 0.9563 nan 0.0100 0.0018
## 80 0.8896 nan 0.0100 0.0013
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## 180 0.6943 nan 0.0100 0.0006
## 200 0.6708 nan 0.0100 0.0001
## 220 0.6502 nan 0.0100 -0.0000
## 240 0.6302 nan 0.0100 0.0002
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## 300 0.5823 nan 0.0100 0.0000
## 320 0.5691 nan 0.0100 -0.0000
## 340 0.5561 nan 0.0100 0.0000
## 360 0.5440 nan 0.0100 -0.0001
## 380 0.5322 nan 0.0100 -0.0001
## 400 0.5206 nan 0.0100 -0.0001
## 420 0.5083 nan 0.0100 -0.0001
## 440 0.4979 nan 0.0100 -0.0000
## 460 0.4871 nan 0.0100 -0.0001
## 480 0.4771 nan 0.0100 -0.0001
## 500 0.4677 nan 0.0100 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2501 nan 0.1000 0.0289
## 2 1.1745 nan 0.1000 0.0341
## 3 1.1301 nan 0.1000 0.0171
## 4 1.0727 nan 0.1000 0.0228
## 5 1.0225 nan 0.1000 0.0227
## 6 0.9845 nan 0.1000 0.0151
## 7 0.9489 nan 0.1000 0.0132
## 8 0.9224 nan 0.1000 0.0102
## 9 0.8961 nan 0.1000 0.0099
## 10 0.8693 nan 0.1000 0.0116
## 20 0.7129 nan 0.1000 0.0043
## 40 0.5715 nan 0.1000 -0.0003
## 60 0.4917 nan 0.1000 -0.0014
## 80 0.4298 nan 0.1000 0.0007
## 100 0.3799 nan 0.1000 -0.0016
## 120 0.3331 nan 0.1000 -0.0004
## 140 0.2960 nan 0.1000 -0.0003
## 160 0.2665 nan 0.1000 -0.0005
## 180 0.2431 nan 0.1000 0.0005
## 200 0.2176 nan 0.1000 -0.0004
## 220 0.1952 nan 0.1000 0.0000
## 240 0.1757 nan 0.1000 -0.0004
## 260 0.1599 nan 0.1000 0.0000
## 280 0.1461 nan 0.1000 -0.0003
## 300 0.1348 nan 0.1000 -0.0003
## 320 0.1230 nan 0.1000 -0.0003
## 340 0.1128 nan 0.1000 -0.0001
## 360 0.1034 nan 0.1000 -0.0001
## 380 0.0949 nan 0.1000 -0.0003
## 400 0.0872 nan 0.1000 -0.0002
## 420 0.0810 nan 0.1000 -0.0002
## 440 0.0750 nan 0.1000 -0.0001
## 460 0.0689 nan 0.1000 -0.0003
## 480 0.0633 nan 0.1000 -0.0001
## 500 0.0585 nan 0.1000 0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2389 nan 0.1000 0.0376
## 2 1.1674 nan 0.1000 0.0334
## 3 1.1051 nan 0.1000 0.0279
## 4 1.0553 nan 0.1000 0.0222
## 5 1.0120 nan 0.1000 0.0189
## 6 0.9776 nan 0.1000 0.0133
## 7 0.9485 nan 0.1000 0.0117
## 8 0.9161 nan 0.1000 0.0140
## 9 0.8881 nan 0.1000 0.0113
## 10 0.8651 nan 0.1000 0.0107
## 20 0.7188 nan 0.1000 0.0038
## 40 0.5994 nan 0.1000 -0.0009
## 60 0.5107 nan 0.1000 0.0001
## 80 0.4574 nan 0.1000 -0.0015
## 100 0.4027 nan 0.1000 -0.0016
## 120 0.3570 nan 0.1000 0.0000
## 140 0.3235 nan 0.1000 -0.0008
## 160 0.2957 nan 0.1000 -0.0006
## 180 0.2625 nan 0.1000 -0.0010
## 200 0.2363 nan 0.1000 -0.0002
## 220 0.2176 nan 0.1000 -0.0008
## 240 0.2013 nan 0.1000 0.0001
## 260 0.1834 nan 0.1000 -0.0001
## 280 0.1668 nan 0.1000 -0.0010
## 300 0.1521 nan 0.1000 -0.0005
## 320 0.1394 nan 0.1000 -0.0002
## 340 0.1283 nan 0.1000 -0.0002
## 360 0.1172 nan 0.1000 -0.0002
## 380 0.1080 nan 0.1000 -0.0001
## 400 0.0987 nan 0.1000 -0.0001
## 420 0.0907 nan 0.1000 -0.0002
## 440 0.0841 nan 0.1000 -0.0002
## 460 0.0782 nan 0.1000 -0.0001
## 480 0.0726 nan 0.1000 -0.0004
## 500 0.0672 nan 0.1000 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2340 nan 0.1000 0.0380
## 2 1.1709 nan 0.1000 0.0277
## 3 1.1142 nan 0.1000 0.0230
## 4 1.0646 nan 0.1000 0.0205
## 5 1.0194 nan 0.1000 0.0212
## 6 0.9825 nan 0.1000 0.0159
## 7 0.9459 nan 0.1000 0.0134
## 8 0.9166 nan 0.1000 0.0121
## 9 0.8876 nan 0.1000 0.0116
## 10 0.8640 nan 0.1000 0.0104
## 20 0.7158 nan 0.1000 0.0032
## 40 0.5890 nan 0.1000 -0.0014
## 60 0.5133 nan 0.1000 -0.0005
## 80 0.4494 nan 0.1000 -0.0008
## 100 0.4031 nan 0.1000 -0.0008
## 120 0.3605 nan 0.1000 0.0001
## 140 0.3306 nan 0.1000 0.0000
## 160 0.2955 nan 0.1000 -0.0002
## 180 0.2705 nan 0.1000 -0.0008
## 200 0.2442 nan 0.1000 0.0000
## 220 0.2234 nan 0.1000 -0.0001
## 240 0.2045 nan 0.1000 -0.0006
## 260 0.1877 nan 0.1000 -0.0000
## 280 0.1704 nan 0.1000 -0.0004
## 300 0.1551 nan 0.1000 -0.0003
## 320 0.1437 nan 0.1000 -0.0002
## 340 0.1304 nan 0.1000 -0.0004
## 360 0.1212 nan 0.1000 -0.0003
## 380 0.1117 nan 0.1000 0.0001
## 400 0.1043 nan 0.1000 0.0001
## 420 0.0961 nan 0.1000 -0.0002
## 440 0.0886 nan 0.1000 -0.0004
## 460 0.0817 nan 0.1000 -0.0003
## 480 0.0757 nan 0.1000 -0.0003
## 500 0.0710 nan 0.1000 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2329 nan 0.1000 0.0415
## 2 1.1613 nan 0.1000 0.0309
## 3 1.0943 nan 0.1000 0.0281
## 4 1.0443 nan 0.1000 0.0207
## 5 1.0003 nan 0.1000 0.0192
## 6 0.9699 nan 0.1000 0.0097
## 7 0.9334 nan 0.1000 0.0156
## 8 0.8994 nan 0.1000 0.0125
## 9 0.8727 nan 0.1000 0.0074
## 10 0.8466 nan 0.1000 0.0076
## 20 0.6920 nan 0.1000 0.0010
## 40 0.5355 nan 0.1000 -0.0003
## 60 0.4382 nan 0.1000 -0.0012
## 80 0.3686 nan 0.1000 -0.0000
## 100 0.3146 nan 0.1000 -0.0009
## 120 0.2742 nan 0.1000 -0.0005
## 140 0.2395 nan 0.1000 -0.0011
## 160 0.2074 nan 0.1000 -0.0005
## 180 0.1845 nan 0.1000 -0.0010
## 200 0.1623 nan 0.1000 -0.0002
## 220 0.1437 nan 0.1000 -0.0001
## 240 0.1276 nan 0.1000 -0.0001
## 260 0.1152 nan 0.1000 -0.0006
## 280 0.1036 nan 0.1000 -0.0003
## 300 0.0929 nan 0.1000 0.0000
## 320 0.0828 nan 0.1000 0.0000
## 340 0.0734 nan 0.1000 -0.0001
## 360 0.0661 nan 0.1000 -0.0001
## 380 0.0596 nan 0.1000 -0.0002
## 400 0.0532 nan 0.1000 -0.0001
## 420 0.0482 nan 0.1000 -0.0002
## 440 0.0438 nan 0.1000 -0.0001
## 460 0.0395 nan 0.1000 -0.0001
## 480 0.0357 nan 0.1000 -0.0002
## 500 0.0321 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2383 nan 0.1000 0.0367
## 2 1.1624 nan 0.1000 0.0313
## 3 1.1016 nan 0.1000 0.0281
## 4 1.0516 nan 0.1000 0.0233
## 5 1.0105 nan 0.1000 0.0179
## 6 0.9675 nan 0.1000 0.0178
## 7 0.9301 nan 0.1000 0.0157
## 8 0.8989 nan 0.1000 0.0115
## 9 0.8703 nan 0.1000 0.0116
## 10 0.8465 nan 0.1000 0.0090
## 20 0.6862 nan 0.1000 0.0035
## 40 0.5443 nan 0.1000 0.0001
## 60 0.4581 nan 0.1000 -0.0013
## 80 0.3891 nan 0.1000 -0.0003
## 100 0.3367 nan 0.1000 -0.0008
## 120 0.2916 nan 0.1000 0.0000
## 140 0.2570 nan 0.1000 -0.0006
## 160 0.2230 nan 0.1000 0.0001
## 180 0.1988 nan 0.1000 -0.0007
## 200 0.1752 nan 0.1000 -0.0003
## 220 0.1520 nan 0.1000 -0.0002
## 240 0.1340 nan 0.1000 -0.0005
## 260 0.1213 nan 0.1000 -0.0004
## 280 0.1091 nan 0.1000 -0.0002
## 300 0.0974 nan 0.1000 -0.0002
## 320 0.0874 nan 0.1000 -0.0001
## 340 0.0780 nan 0.1000 -0.0003
## 360 0.0711 nan 0.1000 -0.0002
## 380 0.0634 nan 0.1000 -0.0003
## 400 0.0567 nan 0.1000 -0.0002
## 420 0.0512 nan 0.1000 -0.0001
## 440 0.0467 nan 0.1000 -0.0002
## 460 0.0418 nan 0.1000 -0.0001
## 480 0.0376 nan 0.1000 -0.0000
## 500 0.0339 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2438 nan 0.1000 0.0347
## 2 1.1778 nan 0.1000 0.0290
## 3 1.1053 nan 0.1000 0.0303
## 4 1.0515 nan 0.1000 0.0249
## 5 0.9984 nan 0.1000 0.0225
## 6 0.9579 nan 0.1000 0.0162
## 7 0.9215 nan 0.1000 0.0152
## 8 0.8907 nan 0.1000 0.0112
## 9 0.8666 nan 0.1000 0.0098
## 10 0.8424 nan 0.1000 0.0095
## 20 0.6922 nan 0.1000 0.0016
## 40 0.5648 nan 0.1000 -0.0021
## 60 0.4738 nan 0.1000 -0.0022
## 80 0.4131 nan 0.1000 -0.0021
## 100 0.3609 nan 0.1000 0.0000
## 120 0.3197 nan 0.1000 -0.0014
## 140 0.2821 nan 0.1000 -0.0010
## 160 0.2502 nan 0.1000 -0.0005
## 180 0.2185 nan 0.1000 -0.0007
## 200 0.1952 nan 0.1000 -0.0005
## 220 0.1743 nan 0.1000 -0.0003
## 240 0.1561 nan 0.1000 -0.0005
## 260 0.1421 nan 0.1000 -0.0006
## 280 0.1280 nan 0.1000 -0.0003
## 300 0.1146 nan 0.1000 -0.0000
## 320 0.1021 nan 0.1000 -0.0001
## 340 0.0914 nan 0.1000 -0.0002
## 360 0.0829 nan 0.1000 -0.0002
## 380 0.0753 nan 0.1000 -0.0003
## 400 0.0688 nan 0.1000 -0.0001
## 420 0.0628 nan 0.1000 -0.0001
## 440 0.0564 nan 0.1000 -0.0001
## 460 0.0519 nan 0.1000 -0.0001
## 480 0.0472 nan 0.1000 -0.0002
## 500 0.0433 nan 0.1000 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2283 nan 0.1000 0.0395
## 2 1.1577 nan 0.1000 0.0303
## 3 1.0896 nan 0.1000 0.0310
## 4 1.0343 nan 0.1000 0.0242
## 5 0.9949 nan 0.1000 0.0148
## 6 0.9516 nan 0.1000 0.0172
## 7 0.9121 nan 0.1000 0.0131
## 8 0.8814 nan 0.1000 0.0116
## 9 0.8507 nan 0.1000 0.0112
## 10 0.8199 nan 0.1000 0.0122
## 20 0.6605 nan 0.1000 0.0029
## 40 0.4998 nan 0.1000 -0.0014
## 60 0.3944 nan 0.1000 -0.0010
## 80 0.3314 nan 0.1000 -0.0017
## 100 0.2782 nan 0.1000 -0.0002
## 120 0.2316 nan 0.1000 -0.0008
## 140 0.1982 nan 0.1000 -0.0009
## 160 0.1693 nan 0.1000 -0.0005
## 180 0.1457 nan 0.1000 -0.0005
## 200 0.1258 nan 0.1000 0.0002
## 220 0.1106 nan 0.1000 -0.0005
## 240 0.0967 nan 0.1000 -0.0003
## 260 0.0851 nan 0.1000 -0.0001
## 280 0.0734 nan 0.1000 -0.0001
## 300 0.0645 nan 0.1000 -0.0000
## 320 0.0557 nan 0.1000 -0.0001
## 340 0.0492 nan 0.1000 -0.0001
## 360 0.0432 nan 0.1000 -0.0000
## 380 0.0379 nan 0.1000 -0.0001
## 400 0.0337 nan 0.1000 -0.0003
## 420 0.0296 nan 0.1000 -0.0001
## 440 0.0260 nan 0.1000 -0.0000
## 460 0.0228 nan 0.1000 0.0000
## 480 0.0203 nan 0.1000 -0.0001
## 500 0.0179 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2350 nan 0.1000 0.0420
## 2 1.1697 nan 0.1000 0.0292
## 3 1.1099 nan 0.1000 0.0268
## 4 1.0557 nan 0.1000 0.0250
## 5 1.0031 nan 0.1000 0.0240
## 6 0.9662 nan 0.1000 0.0163
## 7 0.9270 nan 0.1000 0.0179
## 8 0.8885 nan 0.1000 0.0157
## 9 0.8603 nan 0.1000 0.0101
## 10 0.8333 nan 0.1000 0.0087
## 20 0.6693 nan 0.1000 0.0026
## 40 0.5194 nan 0.1000 -0.0015
## 60 0.4271 nan 0.1000 -0.0005
## 80 0.3539 nan 0.1000 -0.0009
## 100 0.2923 nan 0.1000 0.0005
## 120 0.2431 nan 0.1000 -0.0014
## 140 0.2067 nan 0.1000 -0.0004
## 160 0.1772 nan 0.1000 0.0000
## 180 0.1513 nan 0.1000 -0.0006
## 200 0.1303 nan 0.1000 -0.0002
## 220 0.1140 nan 0.1000 -0.0004
## 240 0.0996 nan 0.1000 -0.0002
## 260 0.0880 nan 0.1000 -0.0003
## 280 0.0781 nan 0.1000 -0.0002
## 300 0.0690 nan 0.1000 -0.0002
## 320 0.0609 nan 0.1000 -0.0001
## 340 0.0540 nan 0.1000 -0.0003
## 360 0.0483 nan 0.1000 -0.0001
## 380 0.0423 nan 0.1000 -0.0001
## 400 0.0368 nan 0.1000 -0.0000
## 420 0.0327 nan 0.1000 0.0000
## 440 0.0285 nan 0.1000 -0.0001
## 460 0.0253 nan 0.1000 -0.0001
## 480 0.0223 nan 0.1000 -0.0001
## 500 0.0197 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2371 nan 0.1000 0.0391
## 2 1.1684 nan 0.1000 0.0290
## 3 1.1038 nan 0.1000 0.0277
## 4 1.0436 nan 0.1000 0.0255
## 5 1.0002 nan 0.1000 0.0151
## 6 0.9588 nan 0.1000 0.0161
## 7 0.9208 nan 0.1000 0.0170
## 8 0.8884 nan 0.1000 0.0160
## 9 0.8605 nan 0.1000 0.0126
## 10 0.8355 nan 0.1000 0.0074
## 20 0.6695 nan 0.1000 0.0054
## 40 0.5187 nan 0.1000 -0.0009
## 60 0.4299 nan 0.1000 -0.0001
## 80 0.3569 nan 0.1000 -0.0002
## 100 0.2994 nan 0.1000 -0.0000
## 120 0.2529 nan 0.1000 -0.0010
## 140 0.2174 nan 0.1000 -0.0010
## 160 0.1856 nan 0.1000 -0.0005
## 180 0.1611 nan 0.1000 0.0000
## 200 0.1405 nan 0.1000 -0.0000
## 220 0.1228 nan 0.1000 -0.0006
## 240 0.1060 nan 0.1000 -0.0003
## 260 0.0929 nan 0.1000 -0.0005
## 280 0.0824 nan 0.1000 -0.0002
## 300 0.0719 nan 0.1000 -0.0002
## 320 0.0628 nan 0.1000 -0.0002
## 340 0.0554 nan 0.1000 -0.0001
## 360 0.0496 nan 0.1000 -0.0001
## 380 0.0449 nan 0.1000 -0.0003
## 400 0.0399 nan 0.1000 -0.0002
## 420 0.0353 nan 0.1000 -0.0002
## 440 0.0314 nan 0.1000 -0.0001
## 460 0.0280 nan 0.1000 -0.0002
## 480 0.0249 nan 0.1000 -0.0000
## 500 0.0218 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3199 nan 0.0010 0.0003
## 2 1.3191 nan 0.0010 0.0003
## 3 1.3182 nan 0.0010 0.0004
## 4 1.3172 nan 0.0010 0.0004
## 5 1.3164 nan 0.0010 0.0004
## 6 1.3156 nan 0.0010 0.0003
## 7 1.3147 nan 0.0010 0.0004
## 8 1.3139 nan 0.0010 0.0004
## 9 1.3131 nan 0.0010 0.0004
## 10 1.3122 nan 0.0010 0.0003
## 20 1.3039 nan 0.0010 0.0004
## 40 1.2877 nan 0.0010 0.0004
## 60 1.2719 nan 0.0010 0.0004
## 80 1.2568 nan 0.0010 0.0004
## 100 1.2420 nan 0.0010 0.0003
## 120 1.2281 nan 0.0010 0.0003
## 140 1.2145 nan 0.0010 0.0003
## 160 1.2010 nan 0.0010 0.0003
## 180 1.1882 nan 0.0010 0.0003
## 200 1.1758 nan 0.0010 0.0002
## 220 1.1636 nan 0.0010 0.0002
## 240 1.1521 nan 0.0010 0.0002
## 260 1.1405 nan 0.0010 0.0002
## 280 1.1291 nan 0.0010 0.0002
## 300 1.1178 nan 0.0010 0.0002
## 320 1.1069 nan 0.0010 0.0002
## 340 1.0964 nan 0.0010 0.0003
## 360 1.0865 nan 0.0010 0.0002
## 380 1.0768 nan 0.0010 0.0002
## 400 1.0672 nan 0.0010 0.0002
## 420 1.0578 nan 0.0010 0.0002
## 440 1.0488 nan 0.0010 0.0002
## 460 1.0403 nan 0.0010 0.0002
## 480 1.0318 nan 0.0010 0.0002
## 500 1.0236 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3199 nan 0.0010 0.0003
## 2 1.3190 nan 0.0010 0.0004
## 3 1.3182 nan 0.0010 0.0004
## 4 1.3173 nan 0.0010 0.0004
## 5 1.3164 nan 0.0010 0.0004
## 6 1.3156 nan 0.0010 0.0004
## 7 1.3147 nan 0.0010 0.0004
## 8 1.3138 nan 0.0010 0.0004
## 9 1.3129 nan 0.0010 0.0004
## 10 1.3121 nan 0.0010 0.0004
## 20 1.3038 nan 0.0010 0.0004
## 40 1.2876 nan 0.0010 0.0003
## 60 1.2719 nan 0.0010 0.0003
## 80 1.2565 nan 0.0010 0.0003
## 100 1.2419 nan 0.0010 0.0003
## 120 1.2274 nan 0.0010 0.0003
## 140 1.2140 nan 0.0010 0.0003
## 160 1.2009 nan 0.0010 0.0003
## 180 1.1880 nan 0.0010 0.0003
## 200 1.1753 nan 0.0010 0.0003
## 220 1.1633 nan 0.0010 0.0003
## 240 1.1516 nan 0.0010 0.0003
## 260 1.1400 nan 0.0010 0.0002
## 280 1.1286 nan 0.0010 0.0002
## 300 1.1179 nan 0.0010 0.0002
## 320 1.1074 nan 0.0010 0.0002
## 340 1.0972 nan 0.0010 0.0002
## 360 1.0873 nan 0.0010 0.0002
## 380 1.0775 nan 0.0010 0.0002
## 400 1.0680 nan 0.0010 0.0002
## 420 1.0589 nan 0.0010 0.0002
## 440 1.0500 nan 0.0010 0.0002
## 460 1.0412 nan 0.0010 0.0002
## 480 1.0327 nan 0.0010 0.0002
## 500 1.0244 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3180 nan 0.0010 0.0004
## 4 1.3173 nan 0.0010 0.0003
## 5 1.3163 nan 0.0010 0.0004
## 6 1.3155 nan 0.0010 0.0004
## 7 1.3147 nan 0.0010 0.0003
## 8 1.3139 nan 0.0010 0.0004
## 9 1.3131 nan 0.0010 0.0003
## 10 1.3122 nan 0.0010 0.0004
## 20 1.3041 nan 0.0010 0.0003
## 40 1.2884 nan 0.0010 0.0003
## 60 1.2732 nan 0.0010 0.0003
## 80 1.2589 nan 0.0010 0.0003
## 100 1.2445 nan 0.0010 0.0003
## 120 1.2303 nan 0.0010 0.0003
## 140 1.2168 nan 0.0010 0.0003
## 160 1.2036 nan 0.0010 0.0003
## 180 1.1909 nan 0.0010 0.0003
## 200 1.1778 nan 0.0010 0.0003
## 220 1.1655 nan 0.0010 0.0003
## 240 1.1537 nan 0.0010 0.0002
## 260 1.1425 nan 0.0010 0.0003
## 280 1.1311 nan 0.0010 0.0003
## 300 1.1202 nan 0.0010 0.0002
## 320 1.1097 nan 0.0010 0.0002
## 340 1.0996 nan 0.0010 0.0002
## 360 1.0897 nan 0.0010 0.0002
## 380 1.0801 nan 0.0010 0.0002
## 400 1.0708 nan 0.0010 0.0002
## 420 1.0618 nan 0.0010 0.0002
## 440 1.0529 nan 0.0010 0.0002
## 460 1.0442 nan 0.0010 0.0002
## 480 1.0360 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0003
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3180 nan 0.0010 0.0004
## 4 1.3170 nan 0.0010 0.0004
## 5 1.3161 nan 0.0010 0.0004
## 6 1.3151 nan 0.0010 0.0005
## 7 1.3142 nan 0.0010 0.0004
## 8 1.3134 nan 0.0010 0.0004
## 9 1.3125 nan 0.0010 0.0004
## 10 1.3116 nan 0.0010 0.0004
## 20 1.3028 nan 0.0010 0.0004
## 40 1.2854 nan 0.0010 0.0004
## 60 1.2686 nan 0.0010 0.0004
## 80 1.2524 nan 0.0010 0.0003
## 100 1.2364 nan 0.0010 0.0003
## 120 1.2214 nan 0.0010 0.0004
## 140 1.2069 nan 0.0010 0.0003
## 160 1.1928 nan 0.0010 0.0003
## 180 1.1791 nan 0.0010 0.0003
## 200 1.1656 nan 0.0010 0.0003
## 220 1.1529 nan 0.0010 0.0003
## 240 1.1404 nan 0.0010 0.0002
## 260 1.1284 nan 0.0010 0.0003
## 280 1.1166 nan 0.0010 0.0003
## 300 1.1052 nan 0.0010 0.0002
## 320 1.0939 nan 0.0010 0.0002
## 340 1.0831 nan 0.0010 0.0002
## 360 1.0723 nan 0.0010 0.0002
## 380 1.0622 nan 0.0010 0.0002
## 400 1.0524 nan 0.0010 0.0002
## 420 1.0425 nan 0.0010 0.0002
## 440 1.0331 nan 0.0010 0.0002
## 460 1.0242 nan 0.0010 0.0002
## 480 1.0155 nan 0.0010 0.0002
## 500 1.0069 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3179 nan 0.0010 0.0004
## 4 1.3171 nan 0.0010 0.0004
## 5 1.3161 nan 0.0010 0.0004
## 6 1.3153 nan 0.0010 0.0004
## 7 1.3143 nan 0.0010 0.0004
## 8 1.3135 nan 0.0010 0.0004
## 9 1.3126 nan 0.0010 0.0004
## 10 1.3118 nan 0.0010 0.0004
## 20 1.3031 nan 0.0010 0.0004
## 40 1.2858 nan 0.0010 0.0004
## 60 1.2692 nan 0.0010 0.0004
## 80 1.2533 nan 0.0010 0.0003
## 100 1.2379 nan 0.0010 0.0004
## 120 1.2230 nan 0.0010 0.0003
## 140 1.2083 nan 0.0010 0.0003
## 160 1.1943 nan 0.0010 0.0003
## 180 1.1809 nan 0.0010 0.0003
## 200 1.1678 nan 0.0010 0.0003
## 220 1.1551 nan 0.0010 0.0003
## 240 1.1427 nan 0.0010 0.0002
## 260 1.1307 nan 0.0010 0.0003
## 280 1.1189 nan 0.0010 0.0002
## 300 1.1077 nan 0.0010 0.0003
## 320 1.0970 nan 0.0010 0.0002
## 340 1.0861 nan 0.0010 0.0003
## 360 1.0756 nan 0.0010 0.0002
## 380 1.0655 nan 0.0010 0.0002
## 400 1.0557 nan 0.0010 0.0002
## 420 1.0462 nan 0.0010 0.0002
## 440 1.0369 nan 0.0010 0.0002
## 460 1.0276 nan 0.0010 0.0002
## 480 1.0188 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3180 nan 0.0010 0.0004
## 4 1.3171 nan 0.0010 0.0004
## 5 1.3162 nan 0.0010 0.0004
## 6 1.3153 nan 0.0010 0.0004
## 7 1.3144 nan 0.0010 0.0004
## 8 1.3135 nan 0.0010 0.0004
## 9 1.3126 nan 0.0010 0.0004
## 10 1.3118 nan 0.0010 0.0004
## 20 1.3031 nan 0.0010 0.0004
## 40 1.2863 nan 0.0010 0.0004
## 60 1.2702 nan 0.0010 0.0004
## 80 1.2548 nan 0.0010 0.0003
## 100 1.2393 nan 0.0010 0.0003
## 120 1.2246 nan 0.0010 0.0003
## 140 1.2106 nan 0.0010 0.0003
## 160 1.1965 nan 0.0010 0.0004
## 180 1.1829 nan 0.0010 0.0003
## 200 1.1699 nan 0.0010 0.0003
## 220 1.1573 nan 0.0010 0.0003
## 240 1.1450 nan 0.0010 0.0002
## 260 1.1333 nan 0.0010 0.0003
## 280 1.1219 nan 0.0010 0.0002
## 300 1.1106 nan 0.0010 0.0003
## 320 1.0999 nan 0.0010 0.0002
## 340 1.0893 nan 0.0010 0.0002
## 360 1.0792 nan 0.0010 0.0002
## 380 1.0690 nan 0.0010 0.0002
## 400 1.0590 nan 0.0010 0.0002
## 420 1.0496 nan 0.0010 0.0002
## 440 1.0403 nan 0.0010 0.0002
## 460 1.0312 nan 0.0010 0.0002
## 480 1.0224 nan 0.0010 0.0002
## 500 1.0141 nan 0.0010 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3187 nan 0.0010 0.0005
## 3 1.3179 nan 0.0010 0.0004
## 4 1.3169 nan 0.0010 0.0004
## 5 1.3159 nan 0.0010 0.0005
## 6 1.3150 nan 0.0010 0.0004
## 7 1.3140 nan 0.0010 0.0005
## 8 1.3130 nan 0.0010 0.0004
## 9 1.3121 nan 0.0010 0.0004
## 10 1.3112 nan 0.0010 0.0004
## 20 1.3020 nan 0.0010 0.0004
## 40 1.2840 nan 0.0010 0.0004
## 60 1.2666 nan 0.0010 0.0004
## 80 1.2498 nan 0.0010 0.0004
## 100 1.2335 nan 0.0010 0.0004
## 120 1.2176 nan 0.0010 0.0004
## 140 1.2019 nan 0.0010 0.0003
## 160 1.1872 nan 0.0010 0.0003
## 180 1.1732 nan 0.0010 0.0003
## 200 1.1595 nan 0.0010 0.0003
## 220 1.1460 nan 0.0010 0.0003
## 240 1.1329 nan 0.0010 0.0003
## 260 1.1203 nan 0.0010 0.0003
## 280 1.1081 nan 0.0010 0.0002
## 300 1.0960 nan 0.0010 0.0002
## 320 1.0844 nan 0.0010 0.0003
## 340 1.0732 nan 0.0010 0.0002
## 360 1.0625 nan 0.0010 0.0002
## 380 1.0517 nan 0.0010 0.0002
## 400 1.0414 nan 0.0010 0.0002
## 420 1.0312 nan 0.0010 0.0002
## 440 1.0215 nan 0.0010 0.0002
## 460 1.0119 nan 0.0010 0.0002
## 480 1.0024 nan 0.0010 0.0002
## 500 0.9934 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3187 nan 0.0010 0.0004
## 3 1.3178 nan 0.0010 0.0004
## 4 1.3169 nan 0.0010 0.0004
## 5 1.3160 nan 0.0010 0.0004
## 6 1.3150 nan 0.0010 0.0005
## 7 1.3141 nan 0.0010 0.0004
## 8 1.3132 nan 0.0010 0.0004
## 9 1.3122 nan 0.0010 0.0004
## 10 1.3114 nan 0.0010 0.0004
## 20 1.3019 nan 0.0010 0.0004
## 40 1.2839 nan 0.0010 0.0004
## 60 1.2668 nan 0.0010 0.0004
## 80 1.2502 nan 0.0010 0.0004
## 100 1.2339 nan 0.0010 0.0004
## 120 1.2184 nan 0.0010 0.0003
## 140 1.2034 nan 0.0010 0.0003
## 160 1.1888 nan 0.0010 0.0003
## 180 1.1748 nan 0.0010 0.0003
## 200 1.1608 nan 0.0010 0.0003
## 220 1.1476 nan 0.0010 0.0003
## 240 1.1349 nan 0.0010 0.0003
## 260 1.1224 nan 0.0010 0.0002
## 280 1.1101 nan 0.0010 0.0002
## 300 1.0982 nan 0.0010 0.0003
## 320 1.0868 nan 0.0010 0.0002
## 340 1.0756 nan 0.0010 0.0002
## 360 1.0648 nan 0.0010 0.0002
## 380 1.0543 nan 0.0010 0.0002
## 400 1.0441 nan 0.0010 0.0002
## 420 1.0343 nan 0.0010 0.0002
## 440 1.0244 nan 0.0010 0.0002
## 460 1.0152 nan 0.0010 0.0002
## 480 1.0061 nan 0.0010 0.0002
## 500 0.9973 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3178 nan 0.0010 0.0004
## 4 1.3169 nan 0.0010 0.0004
## 5 1.3160 nan 0.0010 0.0004
## 6 1.3152 nan 0.0010 0.0004
## 7 1.3142 nan 0.0010 0.0004
## 8 1.3133 nan 0.0010 0.0004
## 9 1.3125 nan 0.0010 0.0004
## 10 1.3115 nan 0.0010 0.0004
## 20 1.3023 nan 0.0010 0.0004
## 40 1.2853 nan 0.0010 0.0003
## 60 1.2687 nan 0.0010 0.0004
## 80 1.2522 nan 0.0010 0.0003
## 100 1.2365 nan 0.0010 0.0003
## 120 1.2210 nan 0.0010 0.0003
## 140 1.2060 nan 0.0010 0.0003
## 160 1.1918 nan 0.0010 0.0003
## 180 1.1780 nan 0.0010 0.0003
## 200 1.1646 nan 0.0010 0.0003
## 220 1.1514 nan 0.0010 0.0003
## 240 1.1388 nan 0.0010 0.0003
## 260 1.1264 nan 0.0010 0.0003
## 280 1.1146 nan 0.0010 0.0003
## 300 1.1029 nan 0.0010 0.0002
## 320 1.0915 nan 0.0010 0.0002
## 340 1.0806 nan 0.0010 0.0002
## 360 1.0698 nan 0.0010 0.0002
## 380 1.0594 nan 0.0010 0.0002
## 400 1.0495 nan 0.0010 0.0002
## 420 1.0397 nan 0.0010 0.0002
## 440 1.0301 nan 0.0010 0.0002
## 460 1.0206 nan 0.0010 0.0002
## 480 1.0116 nan 0.0010 0.0002
## 500 1.0027 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3122 nan 0.0100 0.0036
## 2 1.3038 nan 0.0100 0.0037
## 3 1.2960 nan 0.0100 0.0034
## 4 1.2884 nan 0.0100 0.0035
## 5 1.2803 nan 0.0100 0.0036
## 6 1.2720 nan 0.0100 0.0038
## 7 1.2643 nan 0.0100 0.0033
## 8 1.2570 nan 0.0100 0.0035
## 9 1.2498 nan 0.0100 0.0029
## 10 1.2417 nan 0.0100 0.0036
## 20 1.1747 nan 0.0100 0.0030
## 40 1.0670 nan 0.0100 0.0021
## 60 0.9837 nan 0.0100 0.0018
## 80 0.9198 nan 0.0100 0.0010
## 100 0.8685 nan 0.0100 0.0009
## 120 0.8269 nan 0.0100 0.0007
## 140 0.7915 nan 0.0100 0.0005
## 160 0.7628 nan 0.0100 0.0002
## 180 0.7358 nan 0.0100 0.0005
## 200 0.7114 nan 0.0100 0.0002
## 220 0.6916 nan 0.0100 0.0001
## 240 0.6741 nan 0.0100 0.0001
## 260 0.6581 nan 0.0100 0.0002
## 280 0.6433 nan 0.0100 -0.0000
## 300 0.6294 nan 0.0100 0.0000
## 320 0.6169 nan 0.0100 -0.0000
## 340 0.6052 nan 0.0100 -0.0001
## 360 0.5934 nan 0.0100 0.0001
## 380 0.5837 nan 0.0100 -0.0000
## 400 0.5734 nan 0.0100 -0.0000
## 420 0.5641 nan 0.0100 -0.0001
## 440 0.5547 nan 0.0100 0.0000
## 460 0.5451 nan 0.0100 -0.0000
## 480 0.5372 nan 0.0100 -0.0001
## 500 0.5292 nan 0.0100 0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3133 nan 0.0100 0.0034
## 2 1.3039 nan 0.0100 0.0041
## 3 1.2963 nan 0.0100 0.0032
## 4 1.2879 nan 0.0100 0.0037
## 5 1.2806 nan 0.0100 0.0032
## 6 1.2725 nan 0.0100 0.0038
## 7 1.2641 nan 0.0100 0.0038
## 8 1.2572 nan 0.0100 0.0031
## 9 1.2504 nan 0.0100 0.0034
## 10 1.2431 nan 0.0100 0.0035
## 20 1.1769 nan 0.0100 0.0029
## 40 1.0682 nan 0.0100 0.0022
## 60 0.9872 nan 0.0100 0.0014
## 80 0.9245 nan 0.0100 0.0013
## 100 0.8722 nan 0.0100 0.0009
## 120 0.8307 nan 0.0100 0.0006
## 140 0.7954 nan 0.0100 0.0005
## 160 0.7672 nan 0.0100 0.0005
## 180 0.7428 nan 0.0100 0.0003
## 200 0.7204 nan 0.0100 0.0004
## 220 0.7011 nan 0.0100 0.0003
## 240 0.6837 nan 0.0100 0.0002
## 260 0.6673 nan 0.0100 -0.0001
## 280 0.6529 nan 0.0100 0.0000
## 300 0.6397 nan 0.0100 -0.0000
## 320 0.6271 nan 0.0100 0.0000
## 340 0.6151 nan 0.0100 0.0001
## 360 0.6049 nan 0.0100 0.0001
## 380 0.5941 nan 0.0100 -0.0001
## 400 0.5840 nan 0.0100 -0.0000
## 420 0.5748 nan 0.0100 -0.0000
## 440 0.5652 nan 0.0100 0.0000
## 460 0.5568 nan 0.0100 -0.0000
## 480 0.5482 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3126 nan 0.0100 0.0037
## 2 1.3036 nan 0.0100 0.0042
## 3 1.2958 nan 0.0100 0.0036
## 4 1.2875 nan 0.0100 0.0037
## 5 1.2791 nan 0.0100 0.0038
## 6 1.2713 nan 0.0100 0.0034
## 7 1.2632 nan 0.0100 0.0037
## 8 1.2553 nan 0.0100 0.0036
## 9 1.2479 nan 0.0100 0.0029
## 10 1.2405 nan 0.0100 0.0033
## 20 1.1749 nan 0.0100 0.0028
## 40 1.0678 nan 0.0100 0.0020
## 60 0.9855 nan 0.0100 0.0017
## 80 0.9229 nan 0.0100 0.0011
## 100 0.8716 nan 0.0100 0.0007
## 120 0.8301 nan 0.0100 0.0008
## 140 0.7946 nan 0.0100 0.0005
## 160 0.7657 nan 0.0100 0.0002
## 180 0.7414 nan 0.0100 0.0003
## 200 0.7204 nan 0.0100 0.0002
## 220 0.7026 nan 0.0100 0.0002
## 240 0.6851 nan 0.0100 0.0002
## 260 0.6700 nan 0.0100 0.0003
## 280 0.6553 nan 0.0100 0.0001
## 300 0.6434 nan 0.0100 -0.0001
## 320 0.6308 nan 0.0100 0.0000
## 340 0.6203 nan 0.0100 0.0000
## 360 0.6105 nan 0.0100 -0.0000
## 380 0.6006 nan 0.0100 -0.0001
## 400 0.5918 nan 0.0100 0.0000
## 420 0.5830 nan 0.0100 -0.0002
## 440 0.5745 nan 0.0100 -0.0000
## 460 0.5656 nan 0.0100 -0.0001
## 480 0.5576 nan 0.0100 -0.0002
## 500 0.5493 nan 0.0100 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3110 nan 0.0100 0.0045
## 2 1.3022 nan 0.0100 0.0038
## 3 1.2931 nan 0.0100 0.0043
## 4 1.2855 nan 0.0100 0.0037
## 5 1.2771 nan 0.0100 0.0038
## 6 1.2693 nan 0.0100 0.0038
## 7 1.2609 nan 0.0100 0.0037
## 8 1.2527 nan 0.0100 0.0039
## 9 1.2455 nan 0.0100 0.0032
## 10 1.2378 nan 0.0100 0.0033
## 20 1.1681 nan 0.0100 0.0029
## 40 1.0547 nan 0.0100 0.0023
## 60 0.9663 nan 0.0100 0.0014
## 80 0.8995 nan 0.0100 0.0011
## 100 0.8454 nan 0.0100 0.0010
## 120 0.8030 nan 0.0100 0.0006
## 140 0.7654 nan 0.0100 0.0006
## 160 0.7340 nan 0.0100 0.0004
## 180 0.7076 nan 0.0100 0.0003
## 200 0.6850 nan 0.0100 0.0001
## 220 0.6634 nan 0.0100 0.0001
## 240 0.6442 nan 0.0100 0.0001
## 260 0.6258 nan 0.0100 0.0000
## 280 0.6092 nan 0.0100 0.0001
## 300 0.5944 nan 0.0100 0.0000
## 320 0.5813 nan 0.0100 0.0001
## 340 0.5680 nan 0.0100 0.0000
## 360 0.5558 nan 0.0100 -0.0001
## 380 0.5440 nan 0.0100 -0.0000
## 400 0.5321 nan 0.0100 0.0001
## 420 0.5210 nan 0.0100 -0.0000
## 440 0.5099 nan 0.0100 -0.0001
## 460 0.5001 nan 0.0100 -0.0000
## 480 0.4910 nan 0.0100 -0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3116 nan 0.0100 0.0042
## 2 1.3028 nan 0.0100 0.0040
## 3 1.2928 nan 0.0100 0.0043
## 4 1.2842 nan 0.0100 0.0043
## 5 1.2757 nan 0.0100 0.0036
## 6 1.2675 nan 0.0100 0.0037
## 7 1.2592 nan 0.0100 0.0033
## 8 1.2512 nan 0.0100 0.0037
## 9 1.2430 nan 0.0100 0.0037
## 10 1.2355 nan 0.0100 0.0039
## 20 1.1673 nan 0.0100 0.0029
## 40 1.0541 nan 0.0100 0.0022
## 60 0.9683 nan 0.0100 0.0016
## 80 0.9019 nan 0.0100 0.0009
## 100 0.8498 nan 0.0100 0.0009
## 120 0.8057 nan 0.0100 0.0006
## 140 0.7697 nan 0.0100 0.0006
## 160 0.7377 nan 0.0100 0.0004
## 180 0.7108 nan 0.0100 0.0004
## 200 0.6880 nan 0.0100 0.0002
## 220 0.6676 nan 0.0100 -0.0002
## 240 0.6500 nan 0.0100 0.0001
## 260 0.6326 nan 0.0100 0.0001
## 280 0.6173 nan 0.0100 0.0002
## 300 0.6022 nan 0.0100 0.0002
## 320 0.5882 nan 0.0100 0.0001
## 340 0.5749 nan 0.0100 0.0000
## 360 0.5627 nan 0.0100 -0.0000
## 380 0.5520 nan 0.0100 0.0001
## 400 0.5400 nan 0.0100 0.0000
## 420 0.5297 nan 0.0100 0.0001
## 440 0.5195 nan 0.0100 -0.0000
## 460 0.5103 nan 0.0100 -0.0001
## 480 0.5013 nan 0.0100 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3111 nan 0.0100 0.0046
## 2 1.3021 nan 0.0100 0.0036
## 3 1.2937 nan 0.0100 0.0037
## 4 1.2848 nan 0.0100 0.0037
## 5 1.2762 nan 0.0100 0.0037
## 6 1.2687 nan 0.0100 0.0033
## 7 1.2605 nan 0.0100 0.0037
## 8 1.2518 nan 0.0100 0.0037
## 9 1.2438 nan 0.0100 0.0034
## 10 1.2362 nan 0.0100 0.0033
## 20 1.1664 nan 0.0100 0.0032
## 40 1.0565 nan 0.0100 0.0018
## 60 0.9696 nan 0.0100 0.0014
## 80 0.9049 nan 0.0100 0.0009
## 100 0.8521 nan 0.0100 0.0009
## 120 0.8097 nan 0.0100 0.0007
## 140 0.7744 nan 0.0100 0.0005
## 160 0.7443 nan 0.0100 0.0003
## 180 0.7184 nan 0.0100 0.0002
## 200 0.6965 nan 0.0100 0.0001
## 220 0.6762 nan 0.0100 -0.0001
## 240 0.6575 nan 0.0100 -0.0000
## 260 0.6396 nan 0.0100 0.0001
## 280 0.6256 nan 0.0100 0.0002
## 300 0.6119 nan 0.0100 0.0001
## 320 0.5991 nan 0.0100 0.0001
## 340 0.5864 nan 0.0100 -0.0001
## 360 0.5746 nan 0.0100 -0.0000
## 380 0.5632 nan 0.0100 0.0001
## 400 0.5522 nan 0.0100 0.0001
## 420 0.5430 nan 0.0100 -0.0000
## 440 0.5326 nan 0.0100 0.0002
## 460 0.5232 nan 0.0100 -0.0000
## 480 0.5143 nan 0.0100 0.0001
## 500 0.5051 nan 0.0100 0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3119 nan 0.0100 0.0040
## 2 1.3027 nan 0.0100 0.0042
## 3 1.2939 nan 0.0100 0.0038
## 4 1.2847 nan 0.0100 0.0035
## 5 1.2766 nan 0.0100 0.0032
## 6 1.2675 nan 0.0100 0.0043
## 7 1.2592 nan 0.0100 0.0038
## 8 1.2508 nan 0.0100 0.0037
## 9 1.2420 nan 0.0100 0.0039
## 10 1.2335 nan 0.0100 0.0035
## 20 1.1587 nan 0.0100 0.0030
## 40 1.0391 nan 0.0100 0.0025
## 60 0.9497 nan 0.0100 0.0019
## 80 0.8793 nan 0.0100 0.0011
## 100 0.8224 nan 0.0100 0.0009
## 120 0.7742 nan 0.0100 0.0006
## 140 0.7332 nan 0.0100 0.0005
## 160 0.7006 nan 0.0100 0.0003
## 180 0.6726 nan 0.0100 0.0001
## 200 0.6479 nan 0.0100 0.0002
## 220 0.6244 nan 0.0100 0.0002
## 240 0.6051 nan 0.0100 0.0002
## 260 0.5864 nan 0.0100 0.0001
## 280 0.5685 nan 0.0100 0.0001
## 300 0.5527 nan 0.0100 0.0000
## 320 0.5371 nan 0.0100 0.0001
## 340 0.5226 nan 0.0100 0.0001
## 360 0.5091 nan 0.0100 0.0001
## 380 0.4966 nan 0.0100 0.0000
## 400 0.4843 nan 0.0100 0.0000
## 420 0.4735 nan 0.0100 -0.0000
## 440 0.4624 nan 0.0100 0.0001
## 460 0.4518 nan 0.0100 -0.0000
## 480 0.4417 nan 0.0100 -0.0002
## 500 0.4322 nan 0.0100 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3118 nan 0.0100 0.0038
## 2 1.3026 nan 0.0100 0.0042
## 3 1.2935 nan 0.0100 0.0037
## 4 1.2846 nan 0.0100 0.0040
## 5 1.2768 nan 0.0100 0.0034
## 6 1.2683 nan 0.0100 0.0033
## 7 1.2595 nan 0.0100 0.0041
## 8 1.2514 nan 0.0100 0.0036
## 9 1.2432 nan 0.0100 0.0040
## 10 1.2351 nan 0.0100 0.0038
## 20 1.1611 nan 0.0100 0.0031
## 40 1.0456 nan 0.0100 0.0022
## 60 0.9545 nan 0.0100 0.0016
## 80 0.8841 nan 0.0100 0.0014
## 100 0.8279 nan 0.0100 0.0010
## 120 0.7838 nan 0.0100 0.0006
## 140 0.7453 nan 0.0100 0.0006
## 160 0.7126 nan 0.0100 0.0004
## 180 0.6853 nan 0.0100 0.0002
## 200 0.6596 nan 0.0100 0.0003
## 220 0.6380 nan 0.0100 0.0001
## 240 0.6178 nan 0.0100 0.0001
## 260 0.5989 nan 0.0100 0.0002
## 280 0.5829 nan 0.0100 0.0000
## 300 0.5661 nan 0.0100 0.0002
## 320 0.5505 nan 0.0100 -0.0001
## 340 0.5364 nan 0.0100 -0.0001
## 360 0.5238 nan 0.0100 0.0000
## 380 0.5118 nan 0.0100 0.0000
## 400 0.4997 nan 0.0100 -0.0001
## 420 0.4889 nan 0.0100 -0.0001
## 440 0.4784 nan 0.0100 -0.0000
## 460 0.4678 nan 0.0100 -0.0001
## 480 0.4578 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3107 nan 0.0100 0.0044
## 2 1.3014 nan 0.0100 0.0044
## 3 1.2937 nan 0.0100 0.0035
## 4 1.2852 nan 0.0100 0.0039
## 5 1.2767 nan 0.0100 0.0034
## 6 1.2679 nan 0.0100 0.0039
## 7 1.2596 nan 0.0100 0.0033
## 8 1.2517 nan 0.0100 0.0034
## 9 1.2429 nan 0.0100 0.0040
## 10 1.2347 nan 0.0100 0.0036
## 20 1.1628 nan 0.0100 0.0028
## 40 1.0485 nan 0.0100 0.0021
## 60 0.9613 nan 0.0100 0.0015
## 80 0.8934 nan 0.0100 0.0013
## 100 0.8411 nan 0.0100 0.0010
## 120 0.7944 nan 0.0100 0.0007
## 140 0.7574 nan 0.0100 0.0003
## 160 0.7240 nan 0.0100 0.0007
## 180 0.6959 nan 0.0100 0.0003
## 200 0.6736 nan 0.0100 0.0002
## 220 0.6520 nan 0.0100 0.0000
## 240 0.6326 nan 0.0100 0.0000
## 260 0.6137 nan 0.0100 0.0003
## 280 0.5977 nan 0.0100 0.0001
## 300 0.5817 nan 0.0100 -0.0001
## 320 0.5662 nan 0.0100 0.0002
## 340 0.5537 nan 0.0100 -0.0000
## 360 0.5408 nan 0.0100 -0.0001
## 380 0.5270 nan 0.0100 -0.0001
## 400 0.5152 nan 0.0100 0.0001
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## 460 0.4848 nan 0.0100 -0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2442 nan 0.1000 0.0379
## 2 1.1770 nan 0.1000 0.0305
## 3 1.1243 nan 0.1000 0.0251
## 4 1.0739 nan 0.1000 0.0218
## 5 1.0295 nan 0.1000 0.0198
## 6 0.9918 nan 0.1000 0.0157
## 7 0.9542 nan 0.1000 0.0156
## 8 0.9259 nan 0.1000 0.0115
## 9 0.9003 nan 0.1000 0.0098
## 10 0.8788 nan 0.1000 0.0073
## 20 0.7228 nan 0.1000 0.0025
## 40 0.5800 nan 0.1000 -0.0001
## 60 0.5008 nan 0.1000 -0.0002
## 80 0.4411 nan 0.1000 0.0000
## 100 0.3915 nan 0.1000 -0.0010
## 120 0.3509 nan 0.1000 -0.0005
## 140 0.3083 nan 0.1000 -0.0005
## 160 0.2763 nan 0.1000 -0.0011
## 180 0.2475 nan 0.1000 -0.0008
## 200 0.2241 nan 0.1000 -0.0007
## 220 0.1994 nan 0.1000 -0.0002
## 240 0.1804 nan 0.1000 0.0001
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## 280 0.1495 nan 0.1000 -0.0001
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## 320 0.1235 nan 0.1000 -0.0003
## 340 0.1135 nan 0.1000 -0.0003
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## 380 0.0966 nan 0.1000 -0.0002
## 400 0.0892 nan 0.1000 -0.0002
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## 460 0.0708 nan 0.1000 -0.0001
## 480 0.0656 nan 0.1000 -0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2429 nan 0.1000 0.0330
## 2 1.1750 nan 0.1000 0.0307
## 3 1.1208 nan 0.1000 0.0242
## 4 1.0707 nan 0.1000 0.0239
## 5 1.0276 nan 0.1000 0.0166
## 6 0.9881 nan 0.1000 0.0183
## 7 0.9527 nan 0.1000 0.0144
## 8 0.9250 nan 0.1000 0.0113
## 9 0.8982 nan 0.1000 0.0093
## 10 0.8723 nan 0.1000 0.0105
## 20 0.7249 nan 0.1000 0.0010
## 40 0.5830 nan 0.1000 -0.0010
## 60 0.5021 nan 0.1000 -0.0024
## 80 0.4461 nan 0.1000 -0.0003
## 100 0.3945 nan 0.1000 -0.0006
## 120 0.3555 nan 0.1000 -0.0005
## 140 0.3146 nan 0.1000 -0.0013
## 160 0.2810 nan 0.1000 -0.0004
## 180 0.2562 nan 0.1000 -0.0006
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## 220 0.2104 nan 0.1000 -0.0008
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## 320 0.1334 nan 0.1000 -0.0003
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## 380 0.1048 nan 0.1000 -0.0004
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## 460 0.0765 nan 0.1000 -0.0001
## 480 0.0709 nan 0.1000 -0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2477 nan 0.1000 0.0355
## 2 1.1845 nan 0.1000 0.0327
## 3 1.1242 nan 0.1000 0.0276
## 4 1.0688 nan 0.1000 0.0214
## 5 1.0243 nan 0.1000 0.0193
## 6 0.9920 nan 0.1000 0.0126
## 7 0.9621 nan 0.1000 0.0146
## 8 0.9329 nan 0.1000 0.0119
## 9 0.9035 nan 0.1000 0.0116
## 10 0.8815 nan 0.1000 0.0067
## 20 0.7298 nan 0.1000 0.0030
## 40 0.5922 nan 0.1000 -0.0008
## 60 0.5056 nan 0.1000 -0.0000
## 80 0.4498 nan 0.1000 -0.0007
## 100 0.4063 nan 0.1000 0.0001
## 120 0.3590 nan 0.1000 0.0000
## 140 0.3287 nan 0.1000 -0.0010
## 160 0.2967 nan 0.1000 -0.0008
## 180 0.2689 nan 0.1000 -0.0011
## 200 0.2460 nan 0.1000 -0.0012
## 220 0.2259 nan 0.1000 -0.0015
## 240 0.2077 nan 0.1000 -0.0007
## 260 0.1912 nan 0.1000 -0.0006
## 280 0.1758 nan 0.1000 -0.0006
## 300 0.1629 nan 0.1000 -0.0005
## 320 0.1497 nan 0.1000 -0.0003
## 340 0.1371 nan 0.1000 -0.0008
## 360 0.1261 nan 0.1000 -0.0007
## 380 0.1173 nan 0.1000 -0.0004
## 400 0.1095 nan 0.1000 -0.0005
## 420 0.1019 nan 0.1000 -0.0006
## 440 0.0948 nan 0.1000 -0.0002
## 460 0.0881 nan 0.1000 -0.0002
## 480 0.0815 nan 0.1000 -0.0002
## 500 0.0759 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2369 nan 0.1000 0.0332
## 2 1.1576 nan 0.1000 0.0338
## 3 1.0979 nan 0.1000 0.0232
## 4 1.0453 nan 0.1000 0.0233
## 5 1.0016 nan 0.1000 0.0179
## 6 0.9618 nan 0.1000 0.0156
## 7 0.9269 nan 0.1000 0.0127
## 8 0.8961 nan 0.1000 0.0116
## 9 0.8737 nan 0.1000 0.0097
## 10 0.8492 nan 0.1000 0.0099
## 20 0.6857 nan 0.1000 0.0027
## 40 0.5318 nan 0.1000 -0.0011
## 60 0.4432 nan 0.1000 -0.0004
## 80 0.3800 nan 0.1000 -0.0002
## 100 0.3256 nan 0.1000 -0.0010
## 120 0.2817 nan 0.1000 -0.0003
## 140 0.2483 nan 0.1000 -0.0010
## 160 0.2197 nan 0.1000 -0.0008
## 180 0.1928 nan 0.1000 -0.0011
## 200 0.1686 nan 0.1000 -0.0003
## 220 0.1497 nan 0.1000 -0.0003
## 240 0.1326 nan 0.1000 -0.0004
## 260 0.1188 nan 0.1000 -0.0002
## 280 0.1077 nan 0.1000 -0.0005
## 300 0.0979 nan 0.1000 -0.0000
## 320 0.0872 nan 0.1000 -0.0004
## 340 0.0789 nan 0.1000 -0.0002
## 360 0.0712 nan 0.1000 -0.0002
## 380 0.0639 nan 0.1000 -0.0001
## 400 0.0574 nan 0.1000 -0.0000
## 420 0.0523 nan 0.1000 -0.0002
## 440 0.0472 nan 0.1000 -0.0000
## 460 0.0432 nan 0.1000 -0.0001
## 480 0.0383 nan 0.1000 -0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2300 nan 0.1000 0.0407
## 2 1.1688 nan 0.1000 0.0266
## 3 1.1066 nan 0.1000 0.0272
## 4 1.0567 nan 0.1000 0.0208
## 5 1.0101 nan 0.1000 0.0190
## 6 0.9738 nan 0.1000 0.0163
## 7 0.9391 nan 0.1000 0.0129
## 8 0.9063 nan 0.1000 0.0119
## 9 0.8750 nan 0.1000 0.0119
## 10 0.8487 nan 0.1000 0.0084
## 20 0.6857 nan 0.1000 0.0026
## 40 0.5574 nan 0.1000 -0.0001
## 60 0.4652 nan 0.1000 -0.0023
## 80 0.3967 nan 0.1000 -0.0012
## 100 0.3432 nan 0.1000 -0.0014
## 120 0.3005 nan 0.1000 -0.0007
## 140 0.2629 nan 0.1000 -0.0017
## 160 0.2294 nan 0.1000 -0.0005
## 180 0.2034 nan 0.1000 -0.0010
## 200 0.1855 nan 0.1000 -0.0010
## 220 0.1670 nan 0.1000 -0.0002
## 240 0.1464 nan 0.1000 -0.0000
## 260 0.1307 nan 0.1000 -0.0006
## 280 0.1170 nan 0.1000 -0.0002
## 300 0.1057 nan 0.1000 -0.0003
## 320 0.0946 nan 0.1000 -0.0003
## 340 0.0847 nan 0.1000 -0.0004
## 360 0.0758 nan 0.1000 -0.0003
## 380 0.0697 nan 0.1000 -0.0003
## 400 0.0623 nan 0.1000 -0.0003
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## 460 0.0456 nan 0.1000 -0.0001
## 480 0.0411 nan 0.1000 -0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2341 nan 0.1000 0.0414
## 2 1.1665 nan 0.1000 0.0306
## 3 1.1110 nan 0.1000 0.0224
## 4 1.0585 nan 0.1000 0.0170
## 5 1.0111 nan 0.1000 0.0212
## 6 0.9705 nan 0.1000 0.0180
## 7 0.9375 nan 0.1000 0.0140
## 8 0.9051 nan 0.1000 0.0123
## 9 0.8798 nan 0.1000 0.0091
## 10 0.8530 nan 0.1000 0.0108
## 20 0.6957 nan 0.1000 0.0040
## 40 0.5621 nan 0.1000 0.0015
## 60 0.4668 nan 0.1000 0.0001
## 80 0.3989 nan 0.1000 0.0003
## 100 0.3425 nan 0.1000 -0.0001
## 120 0.3014 nan 0.1000 -0.0008
## 140 0.2658 nan 0.1000 -0.0017
## 160 0.2348 nan 0.1000 -0.0011
## 180 0.2115 nan 0.1000 -0.0009
## 200 0.1875 nan 0.1000 -0.0007
## 220 0.1662 nan 0.1000 -0.0005
## 240 0.1502 nan 0.1000 -0.0006
## 260 0.1347 nan 0.1000 -0.0008
## 280 0.1219 nan 0.1000 -0.0004
## 300 0.1099 nan 0.1000 -0.0004
## 320 0.1003 nan 0.1000 -0.0005
## 340 0.0902 nan 0.1000 -0.0003
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## 380 0.0742 nan 0.1000 -0.0002
## 400 0.0669 nan 0.1000 -0.0003
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2292 nan 0.1000 0.0391
## 2 1.1599 nan 0.1000 0.0313
## 3 1.0946 nan 0.1000 0.0278
## 4 1.0397 nan 0.1000 0.0216
## 5 0.9902 nan 0.1000 0.0193
## 6 0.9422 nan 0.1000 0.0178
## 7 0.9064 nan 0.1000 0.0132
## 8 0.8781 nan 0.1000 0.0085
## 9 0.8510 nan 0.1000 0.0098
## 10 0.8261 nan 0.1000 0.0092
## 20 0.6554 nan 0.1000 0.0022
## 40 0.5017 nan 0.1000 0.0003
## 60 0.4048 nan 0.1000 -0.0002
## 80 0.3334 nan 0.1000 -0.0009
## 100 0.2762 nan 0.1000 -0.0002
## 120 0.2322 nan 0.1000 -0.0009
## 140 0.1962 nan 0.1000 -0.0008
## 160 0.1680 nan 0.1000 -0.0002
## 180 0.1464 nan 0.1000 -0.0002
## 200 0.1274 nan 0.1000 -0.0003
## 220 0.1103 nan 0.1000 -0.0003
## 240 0.0968 nan 0.1000 -0.0000
## 260 0.0847 nan 0.1000 -0.0002
## 280 0.0744 nan 0.1000 -0.0003
## 300 0.0659 nan 0.1000 -0.0001
## 320 0.0579 nan 0.1000 -0.0000
## 340 0.0513 nan 0.1000 -0.0003
## 360 0.0455 nan 0.1000 -0.0001
## 380 0.0402 nan 0.1000 -0.0001
## 400 0.0351 nan 0.1000 -0.0001
## 420 0.0309 nan 0.1000 0.0000
## 440 0.0272 nan 0.1000 0.0000
## 460 0.0240 nan 0.1000 -0.0001
## 480 0.0213 nan 0.1000 -0.0001
## 500 0.0190 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2314 nan 0.1000 0.0401
## 2 1.1590 nan 0.1000 0.0310
## 3 1.0965 nan 0.1000 0.0233
## 4 1.0397 nan 0.1000 0.0248
## 5 0.9911 nan 0.1000 0.0205
## 6 0.9499 nan 0.1000 0.0188
## 7 0.9164 nan 0.1000 0.0140
## 8 0.8847 nan 0.1000 0.0111
## 9 0.8560 nan 0.1000 0.0128
## 10 0.8278 nan 0.1000 0.0112
## 20 0.6675 nan 0.1000 0.0014
## 40 0.5026 nan 0.1000 -0.0004
## 60 0.4144 nan 0.1000 0.0001
## 80 0.3421 nan 0.1000 -0.0014
## 100 0.2897 nan 0.1000 -0.0018
## 120 0.2494 nan 0.1000 -0.0007
## 140 0.2130 nan 0.1000 -0.0004
## 160 0.1828 nan 0.1000 -0.0003
## 180 0.1554 nan 0.1000 -0.0006
## 200 0.1312 nan 0.1000 -0.0003
## 220 0.1127 nan 0.1000 -0.0005
## 240 0.0997 nan 0.1000 -0.0006
## 260 0.0868 nan 0.1000 -0.0002
## 280 0.0762 nan 0.1000 -0.0003
## 300 0.0679 nan 0.1000 -0.0001
## 320 0.0591 nan 0.1000 -0.0003
## 340 0.0526 nan 0.1000 -0.0001
## 360 0.0471 nan 0.1000 -0.0001
## 380 0.0411 nan 0.1000 -0.0001
## 400 0.0366 nan 0.1000 -0.0002
## 420 0.0320 nan 0.1000 -0.0000
## 440 0.0287 nan 0.1000 -0.0002
## 460 0.0252 nan 0.1000 -0.0001
## 480 0.0223 nan 0.1000 -0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2391 nan 0.1000 0.0368
## 2 1.1585 nan 0.1000 0.0343
## 3 1.0956 nan 0.1000 0.0289
## 4 1.0422 nan 0.1000 0.0208
## 5 0.9990 nan 0.1000 0.0172
## 6 0.9563 nan 0.1000 0.0165
## 7 0.9228 nan 0.1000 0.0135
## 8 0.8905 nan 0.1000 0.0129
## 9 0.8628 nan 0.1000 0.0121
## 10 0.8342 nan 0.1000 0.0097
## 20 0.6641 nan 0.1000 0.0013
## 40 0.5125 nan 0.1000 -0.0001
## 60 0.4160 nan 0.1000 0.0012
## 80 0.3433 nan 0.1000 -0.0007
## 100 0.2933 nan 0.1000 -0.0009
## 120 0.2498 nan 0.1000 -0.0004
## 140 0.2126 nan 0.1000 -0.0003
## 160 0.1828 nan 0.1000 -0.0002
## 180 0.1615 nan 0.1000 -0.0010
## 200 0.1411 nan 0.1000 -0.0002
## 220 0.1237 nan 0.1000 -0.0003
## 240 0.1100 nan 0.1000 -0.0003
## 260 0.0970 nan 0.1000 -0.0004
## 280 0.0858 nan 0.1000 -0.0002
## 300 0.0763 nan 0.1000 -0.0003
## 320 0.0673 nan 0.1000 -0.0002
## 340 0.0593 nan 0.1000 -0.0003
## 360 0.0523 nan 0.1000 -0.0001
## 380 0.0462 nan 0.1000 -0.0001
## 400 0.0409 nan 0.1000 -0.0002
## 420 0.0369 nan 0.1000 -0.0000
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## 460 0.0294 nan 0.1000 -0.0002
## 480 0.0262 nan 0.1000 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3204 nan 0.0010 0.0004
## 2 1.3196 nan 0.0010 0.0004
## 3 1.3188 nan 0.0010 0.0003
## 4 1.3181 nan 0.0010 0.0004
## 5 1.3172 nan 0.0010 0.0004
## 6 1.3166 nan 0.0010 0.0003
## 7 1.3158 nan 0.0010 0.0003
## 8 1.3150 nan 0.0010 0.0003
## 9 1.3143 nan 0.0010 0.0003
## 10 1.3135 nan 0.0010 0.0003
## 20 1.3060 nan 0.0010 0.0004
## 40 1.2910 nan 0.0010 0.0003
## 60 1.2765 nan 0.0010 0.0003
## 80 1.2623 nan 0.0010 0.0003
## 100 1.2486 nan 0.0010 0.0003
## 120 1.2354 nan 0.0010 0.0002
## 140 1.2223 nan 0.0010 0.0003
## 160 1.2097 nan 0.0010 0.0003
## 180 1.1977 nan 0.0010 0.0003
## 200 1.1858 nan 0.0010 0.0002
## 220 1.1744 nan 0.0010 0.0002
## 240 1.1632 nan 0.0010 0.0002
## 260 1.1525 nan 0.0010 0.0001
## 280 1.1418 nan 0.0010 0.0002
## 300 1.1315 nan 0.0010 0.0003
## 320 1.1213 nan 0.0010 0.0002
## 340 1.1111 nan 0.0010 0.0002
## 360 1.1015 nan 0.0010 0.0002
## 380 1.0922 nan 0.0010 0.0002
## 400 1.0827 nan 0.0010 0.0002
## 420 1.0738 nan 0.0010 0.0002
## 440 1.0652 nan 0.0010 0.0002
## 460 1.0567 nan 0.0010 0.0002
## 480 1.0485 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3204 nan 0.0010 0.0003
## 2 1.3196 nan 0.0010 0.0004
## 3 1.3188 nan 0.0010 0.0004
## 4 1.3179 nan 0.0010 0.0004
## 5 1.3171 nan 0.0010 0.0004
## 6 1.3163 nan 0.0010 0.0003
## 7 1.3155 nan 0.0010 0.0004
## 8 1.3147 nan 0.0010 0.0003
## 9 1.3140 nan 0.0010 0.0004
## 10 1.3132 nan 0.0010 0.0003
## 20 1.3053 nan 0.0010 0.0004
## 40 1.2903 nan 0.0010 0.0004
## 60 1.2759 nan 0.0010 0.0003
## 80 1.2617 nan 0.0010 0.0003
## 100 1.2480 nan 0.0010 0.0003
## 120 1.2345 nan 0.0010 0.0003
## 140 1.2215 nan 0.0010 0.0003
## 160 1.2092 nan 0.0010 0.0003
## 180 1.1971 nan 0.0010 0.0002
## 200 1.1852 nan 0.0010 0.0002
## 220 1.1738 nan 0.0010 0.0002
## 240 1.1625 nan 0.0010 0.0002
## 260 1.1516 nan 0.0010 0.0002
## 280 1.1412 nan 0.0010 0.0002
## 300 1.1309 nan 0.0010 0.0002
## 320 1.1210 nan 0.0010 0.0002
## 340 1.1113 nan 0.0010 0.0002
## 360 1.1018 nan 0.0010 0.0002
## 380 1.0925 nan 0.0010 0.0002
## 400 1.0832 nan 0.0010 0.0002
## 420 1.0744 nan 0.0010 0.0002
## 440 1.0659 nan 0.0010 0.0002
## 460 1.0575 nan 0.0010 0.0002
## 480 1.0491 nan 0.0010 0.0002
## 500 1.0413 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3204 nan 0.0010 0.0004
## 2 1.3197 nan 0.0010 0.0003
## 3 1.3189 nan 0.0010 0.0004
## 4 1.3181 nan 0.0010 0.0004
## 5 1.3173 nan 0.0010 0.0003
## 6 1.3165 nan 0.0010 0.0003
## 7 1.3158 nan 0.0010 0.0004
## 8 1.3149 nan 0.0010 0.0004
## 9 1.3141 nan 0.0010 0.0004
## 10 1.3133 nan 0.0010 0.0004
## 20 1.3057 nan 0.0010 0.0003
## 40 1.2909 nan 0.0010 0.0003
## 60 1.2766 nan 0.0010 0.0003
## 80 1.2624 nan 0.0010 0.0003
## 100 1.2490 nan 0.0010 0.0003
## 120 1.2356 nan 0.0010 0.0003
## 140 1.2227 nan 0.0010 0.0003
## 160 1.2103 nan 0.0010 0.0003
## 180 1.1983 nan 0.0010 0.0003
## 200 1.1865 nan 0.0010 0.0003
## 220 1.1750 nan 0.0010 0.0002
## 240 1.1639 nan 0.0010 0.0002
## 260 1.1528 nan 0.0010 0.0003
## 280 1.1422 nan 0.0010 0.0002
## 300 1.1320 nan 0.0010 0.0002
## 320 1.1221 nan 0.0010 0.0002
## 340 1.1122 nan 0.0010 0.0002
## 360 1.1027 nan 0.0010 0.0002
## 380 1.0936 nan 0.0010 0.0002
## 400 1.0847 nan 0.0010 0.0002
## 420 1.0758 nan 0.0010 0.0002
## 440 1.0671 nan 0.0010 0.0002
## 460 1.0587 nan 0.0010 0.0002
## 480 1.0505 nan 0.0010 0.0002
## 500 1.0423 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3194 nan 0.0010 0.0004
## 3 1.3186 nan 0.0010 0.0004
## 4 1.3178 nan 0.0010 0.0003
## 5 1.3169 nan 0.0010 0.0003
## 6 1.3160 nan 0.0010 0.0004
## 7 1.3152 nan 0.0010 0.0004
## 8 1.3143 nan 0.0010 0.0004
## 9 1.3135 nan 0.0010 0.0003
## 10 1.3127 nan 0.0010 0.0004
## 20 1.3042 nan 0.0010 0.0004
## 40 1.2879 nan 0.0010 0.0004
## 60 1.2719 nan 0.0010 0.0004
## 80 1.2570 nan 0.0010 0.0003
## 100 1.2424 nan 0.0010 0.0003
## 120 1.2284 nan 0.0010 0.0003
## 140 1.2142 nan 0.0010 0.0002
## 160 1.2009 nan 0.0010 0.0003
## 180 1.1879 nan 0.0010 0.0003
## 200 1.1752 nan 0.0010 0.0003
## 220 1.1626 nan 0.0010 0.0003
## 240 1.1506 nan 0.0010 0.0003
## 260 1.1388 nan 0.0010 0.0003
## 280 1.1275 nan 0.0010 0.0002
## 300 1.1164 nan 0.0010 0.0003
## 320 1.1055 nan 0.0010 0.0003
## 340 1.0950 nan 0.0010 0.0002
## 360 1.0849 nan 0.0010 0.0002
## 380 1.0746 nan 0.0010 0.0002
## 400 1.0650 nan 0.0010 0.0002
## 420 1.0558 nan 0.0010 0.0002
## 440 1.0468 nan 0.0010 0.0001
## 460 1.0378 nan 0.0010 0.0002
## 480 1.0291 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3195 nan 0.0010 0.0004
## 3 1.3186 nan 0.0010 0.0004
## 4 1.3178 nan 0.0010 0.0003
## 5 1.3169 nan 0.0010 0.0004
## 6 1.3161 nan 0.0010 0.0004
## 7 1.3152 nan 0.0010 0.0003
## 8 1.3143 nan 0.0010 0.0004
## 9 1.3135 nan 0.0010 0.0004
## 10 1.3127 nan 0.0010 0.0003
## 20 1.3045 nan 0.0010 0.0004
## 40 1.2883 nan 0.0010 0.0004
## 60 1.2727 nan 0.0010 0.0003
## 80 1.2575 nan 0.0010 0.0003
## 100 1.2429 nan 0.0010 0.0003
## 120 1.2287 nan 0.0010 0.0003
## 140 1.2150 nan 0.0010 0.0003
## 160 1.2015 nan 0.0010 0.0003
## 180 1.1885 nan 0.0010 0.0003
## 200 1.1757 nan 0.0010 0.0003
## 220 1.1636 nan 0.0010 0.0002
## 240 1.1516 nan 0.0010 0.0002
## 260 1.1399 nan 0.0010 0.0002
## 280 1.1287 nan 0.0010 0.0003
## 300 1.1176 nan 0.0010 0.0002
## 320 1.1069 nan 0.0010 0.0002
## 340 1.0964 nan 0.0010 0.0002
## 360 1.0863 nan 0.0010 0.0002
## 380 1.0762 nan 0.0010 0.0001
## 400 1.0666 nan 0.0010 0.0002
## 420 1.0570 nan 0.0010 0.0002
## 440 1.0481 nan 0.0010 0.0002
## 460 1.0394 nan 0.0010 0.0002
## 480 1.0306 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3194 nan 0.0010 0.0004
## 3 1.3185 nan 0.0010 0.0004
## 4 1.3177 nan 0.0010 0.0004
## 5 1.3168 nan 0.0010 0.0004
## 6 1.3160 nan 0.0010 0.0003
## 7 1.3153 nan 0.0010 0.0004
## 8 1.3145 nan 0.0010 0.0003
## 9 1.3137 nan 0.0010 0.0004
## 10 1.3128 nan 0.0010 0.0004
## 20 1.3045 nan 0.0010 0.0003
## 40 1.2882 nan 0.0010 0.0004
## 60 1.2726 nan 0.0010 0.0003
## 80 1.2572 nan 0.0010 0.0004
## 100 1.2427 nan 0.0010 0.0002
## 120 1.2286 nan 0.0010 0.0003
## 140 1.2150 nan 0.0010 0.0003
## 160 1.2014 nan 0.0010 0.0003
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## 400 1.0674 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3194 nan 0.0010 0.0004
## 3 1.3184 nan 0.0010 0.0004
## 4 1.3176 nan 0.0010 0.0004
## 5 1.3166 nan 0.0010 0.0004
## 6 1.3158 nan 0.0010 0.0004
## 7 1.3148 nan 0.0010 0.0004
## 8 1.3139 nan 0.0010 0.0004
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## 10 1.3121 nan 0.0010 0.0004
## 20 1.3033 nan 0.0010 0.0004
## 40 1.2860 nan 0.0010 0.0004
## 60 1.2693 nan 0.0010 0.0003
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3194 nan 0.0010 0.0004
## 3 1.3184 nan 0.0010 0.0004
## 4 1.3175 nan 0.0010 0.0004
## 5 1.3166 nan 0.0010 0.0004
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## 360 1.0746 nan 0.0010 0.0003
## 380 1.0641 nan 0.0010 0.0002
## 400 1.0543 nan 0.0010 0.0002
## 420 1.0445 nan 0.0010 0.0002
## 440 1.0351 nan 0.0010 0.0002
## 460 1.0260 nan 0.0010 0.0002
## 480 1.0170 nan 0.0010 0.0002
## 500 1.0082 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3194 nan 0.0010 0.0004
## 3 1.3184 nan 0.0010 0.0004
## 4 1.3175 nan 0.0010 0.0004
## 5 1.3166 nan 0.0010 0.0004
## 6 1.3157 nan 0.0010 0.0004
## 7 1.3149 nan 0.0010 0.0004
## 8 1.3141 nan 0.0010 0.0003
## 9 1.3132 nan 0.0010 0.0004
## 10 1.3124 nan 0.0010 0.0004
## 20 1.3037 nan 0.0010 0.0004
## 40 1.2863 nan 0.0010 0.0004
## 60 1.2697 nan 0.0010 0.0004
## 80 1.2540 nan 0.0010 0.0004
## 100 1.2387 nan 0.0010 0.0003
## 120 1.2238 nan 0.0010 0.0003
## 140 1.2094 nan 0.0010 0.0003
## 160 1.1958 nan 0.0010 0.0003
## 180 1.1822 nan 0.0010 0.0003
## 200 1.1691 nan 0.0010 0.0003
## 220 1.1564 nan 0.0010 0.0003
## 240 1.1442 nan 0.0010 0.0003
## 260 1.1320 nan 0.0010 0.0003
## 280 1.1205 nan 0.0010 0.0003
## 300 1.1091 nan 0.0010 0.0002
## 320 1.0980 nan 0.0010 0.0003
## 340 1.0873 nan 0.0010 0.0002
## 360 1.0768 nan 0.0010 0.0002
## 380 1.0665 nan 0.0010 0.0002
## 400 1.0566 nan 0.0010 0.0002
## 420 1.0468 nan 0.0010 0.0002
## 440 1.0373 nan 0.0010 0.0002
## 460 1.0282 nan 0.0010 0.0002
## 480 1.0194 nan 0.0010 0.0002
## 500 1.0107 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3135 nan 0.0100 0.0037
## 2 1.3055 nan 0.0100 0.0039
## 3 1.2977 nan 0.0100 0.0037
## 4 1.2900 nan 0.0100 0.0036
## 5 1.2822 nan 0.0100 0.0032
## 6 1.2750 nan 0.0100 0.0033
## 7 1.2680 nan 0.0100 0.0031
## 8 1.2606 nan 0.0100 0.0032
## 9 1.2535 nan 0.0100 0.0033
## 10 1.2468 nan 0.0100 0.0031
## 20 1.1838 nan 0.0100 0.0025
## 40 1.0827 nan 0.0100 0.0019
## 60 1.0027 nan 0.0100 0.0017
## 80 0.9396 nan 0.0100 0.0008
## 100 0.8883 nan 0.0100 0.0010
## 120 0.8468 nan 0.0100 0.0006
## 140 0.8127 nan 0.0100 0.0005
## 160 0.7827 nan 0.0100 0.0005
## 180 0.7576 nan 0.0100 0.0004
## 200 0.7359 nan 0.0100 0.0003
## 220 0.7154 nan 0.0100 0.0002
## 240 0.6983 nan 0.0100 0.0002
## 260 0.6822 nan 0.0100 0.0002
## 280 0.6681 nan 0.0100 0.0001
## 300 0.6546 nan 0.0100 0.0000
## 320 0.6419 nan 0.0100 -0.0000
## 340 0.6304 nan 0.0100 0.0001
## 360 0.6197 nan 0.0100 0.0001
## 380 0.6095 nan 0.0100 0.0001
## 400 0.5991 nan 0.0100 0.0000
## 420 0.5895 nan 0.0100 -0.0001
## 440 0.5800 nan 0.0100 0.0000
## 460 0.5714 nan 0.0100 -0.0001
## 480 0.5628 nan 0.0100 -0.0001
## 500 0.5537 nan 0.0100 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3135 nan 0.0100 0.0035
## 2 1.3056 nan 0.0100 0.0035
## 3 1.2981 nan 0.0100 0.0036
## 4 1.2902 nan 0.0100 0.0031
## 5 1.2824 nan 0.0100 0.0033
## 6 1.2756 nan 0.0100 0.0028
## 7 1.2688 nan 0.0100 0.0031
## 8 1.2612 nan 0.0100 0.0032
## 9 1.2544 nan 0.0100 0.0033
## 10 1.2476 nan 0.0100 0.0028
## 20 1.1852 nan 0.0100 0.0024
## 40 1.0832 nan 0.0100 0.0020
## 60 1.0038 nan 0.0100 0.0016
## 80 0.9420 nan 0.0100 0.0010
## 100 0.8911 nan 0.0100 0.0009
## 120 0.8499 nan 0.0100 0.0006
## 140 0.8160 nan 0.0100 0.0006
## 160 0.7882 nan 0.0100 0.0005
## 180 0.7632 nan 0.0100 0.0003
## 200 0.7414 nan 0.0100 0.0002
## 220 0.7223 nan 0.0100 0.0003
## 240 0.7051 nan 0.0100 0.0002
## 260 0.6903 nan 0.0100 0.0000
## 280 0.6754 nan 0.0100 -0.0000
## 300 0.6620 nan 0.0100 0.0001
## 320 0.6498 nan 0.0100 -0.0001
## 340 0.6382 nan 0.0100 -0.0001
## 360 0.6286 nan 0.0100 0.0001
## 380 0.6177 nan 0.0100 0.0001
## 400 0.6078 nan 0.0100 -0.0001
## 420 0.5987 nan 0.0100 -0.0001
## 440 0.5898 nan 0.0100 -0.0001
## 460 0.5810 nan 0.0100 -0.0001
## 480 0.5723 nan 0.0100 -0.0000
## 500 0.5636 nan 0.0100 0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3131 nan 0.0100 0.0035
## 2 1.3056 nan 0.0100 0.0032
## 3 1.2978 nan 0.0100 0.0033
## 4 1.2898 nan 0.0100 0.0035
## 5 1.2829 nan 0.0100 0.0031
## 6 1.2756 nan 0.0100 0.0034
## 7 1.2681 nan 0.0100 0.0033
## 8 1.2603 nan 0.0100 0.0033
## 9 1.2540 nan 0.0100 0.0029
## 10 1.2465 nan 0.0100 0.0033
## 20 1.1833 nan 0.0100 0.0024
## 40 1.0830 nan 0.0100 0.0018
## 60 1.0031 nan 0.0100 0.0015
## 80 0.9411 nan 0.0100 0.0011
## 100 0.8904 nan 0.0100 0.0009
## 120 0.8498 nan 0.0100 0.0005
## 140 0.8159 nan 0.0100 0.0004
## 160 0.7867 nan 0.0100 0.0003
## 180 0.7624 nan 0.0100 0.0001
## 200 0.7416 nan 0.0100 0.0003
## 220 0.7227 nan 0.0100 0.0003
## 240 0.7057 nan 0.0100 0.0001
## 260 0.6905 nan 0.0100 0.0001
## 280 0.6769 nan 0.0100 -0.0000
## 300 0.6638 nan 0.0100 0.0000
## 320 0.6519 nan 0.0100 0.0000
## 340 0.6409 nan 0.0100 -0.0002
## 360 0.6302 nan 0.0100 0.0000
## 380 0.6210 nan 0.0100 0.0001
## 400 0.6110 nan 0.0100 -0.0000
## 420 0.6026 nan 0.0100 -0.0000
## 440 0.5937 nan 0.0100 -0.0002
## 460 0.5847 nan 0.0100 -0.0000
## 480 0.5769 nan 0.0100 -0.0001
## 500 0.5678 nan 0.0100 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3132 nan 0.0100 0.0034
## 2 1.3049 nan 0.0100 0.0035
## 3 1.2968 nan 0.0100 0.0036
## 4 1.2891 nan 0.0100 0.0036
## 5 1.2803 nan 0.0100 0.0038
## 6 1.2729 nan 0.0100 0.0033
## 7 1.2647 nan 0.0100 0.0035
## 8 1.2568 nan 0.0100 0.0036
## 9 1.2492 nan 0.0100 0.0036
## 10 1.2418 nan 0.0100 0.0037
## 20 1.1762 nan 0.0100 0.0025
## 40 1.0678 nan 0.0100 0.0020
## 60 0.9818 nan 0.0100 0.0016
## 80 0.9160 nan 0.0100 0.0008
## 100 0.8624 nan 0.0100 0.0009
## 120 0.8192 nan 0.0100 0.0009
## 140 0.7836 nan 0.0100 0.0005
## 160 0.7540 nan 0.0100 0.0004
## 180 0.7264 nan 0.0100 0.0004
## 200 0.7029 nan 0.0100 0.0002
## 220 0.6826 nan 0.0100 0.0001
## 240 0.6641 nan 0.0100 0.0002
## 260 0.6459 nan 0.0100 0.0000
## 280 0.6298 nan 0.0100 0.0000
## 300 0.6153 nan 0.0100 0.0001
## 320 0.6013 nan 0.0100 0.0003
## 340 0.5887 nan 0.0100 0.0001
## 360 0.5764 nan 0.0100 0.0000
## 380 0.5642 nan 0.0100 -0.0001
## 400 0.5524 nan 0.0100 -0.0000
## 420 0.5415 nan 0.0100 -0.0001
## 440 0.5309 nan 0.0100 -0.0002
## 460 0.5211 nan 0.0100 -0.0000
## 480 0.5115 nan 0.0100 -0.0001
## 500 0.5030 nan 0.0100 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3126 nan 0.0100 0.0038
## 2 1.3036 nan 0.0100 0.0038
## 3 1.2950 nan 0.0100 0.0038
## 4 1.2880 nan 0.0100 0.0034
## 5 1.2804 nan 0.0100 0.0034
## 6 1.2721 nan 0.0100 0.0038
## 7 1.2640 nan 0.0100 0.0036
## 8 1.2565 nan 0.0100 0.0030
## 9 1.2489 nan 0.0100 0.0028
## 10 1.2414 nan 0.0100 0.0031
## 20 1.1726 nan 0.0100 0.0023
## 40 1.0647 nan 0.0100 0.0018
## 60 0.9801 nan 0.0100 0.0016
## 80 0.9127 nan 0.0100 0.0011
## 100 0.8602 nan 0.0100 0.0009
## 120 0.8187 nan 0.0100 0.0006
## 140 0.7839 nan 0.0100 0.0004
## 160 0.7528 nan 0.0100 0.0005
## 180 0.7271 nan 0.0100 0.0005
## 200 0.7042 nan 0.0100 0.0002
## 220 0.6840 nan 0.0100 0.0002
## 240 0.6663 nan 0.0100 -0.0000
## 260 0.6499 nan 0.0100 -0.0000
## 280 0.6341 nan 0.0100 0.0001
## 300 0.6196 nan 0.0100 0.0001
## 320 0.6053 nan 0.0100 0.0000
## 340 0.5934 nan 0.0100 -0.0000
## 360 0.5815 nan 0.0100 -0.0001
## 380 0.5703 nan 0.0100 -0.0001
## 400 0.5603 nan 0.0100 0.0000
## 420 0.5497 nan 0.0100 -0.0000
## 440 0.5402 nan 0.0100 -0.0001
## 460 0.5309 nan 0.0100 -0.0001
## 480 0.5218 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3123 nan 0.0100 0.0036
## 2 1.3043 nan 0.0100 0.0035
## 3 1.2963 nan 0.0100 0.0034
## 4 1.2889 nan 0.0100 0.0035
## 5 1.2809 nan 0.0100 0.0037
## 6 1.2734 nan 0.0100 0.0029
## 7 1.2651 nan 0.0100 0.0038
## 8 1.2569 nan 0.0100 0.0031
## 9 1.2497 nan 0.0100 0.0030
## 10 1.2421 nan 0.0100 0.0029
## 20 1.1741 nan 0.0100 0.0027
## 40 1.0652 nan 0.0100 0.0018
## 60 0.9831 nan 0.0100 0.0016
## 80 0.9192 nan 0.0100 0.0011
## 100 0.8675 nan 0.0100 0.0009
## 120 0.8244 nan 0.0100 0.0007
## 140 0.7901 nan 0.0100 0.0003
## 160 0.7603 nan 0.0100 0.0003
## 180 0.7354 nan 0.0100 0.0004
## 200 0.7140 nan 0.0100 0.0001
## 220 0.6933 nan 0.0100 0.0000
## 240 0.6766 nan 0.0100 0.0001
## 260 0.6607 nan 0.0100 0.0000
## 280 0.6461 nan 0.0100 -0.0001
## 300 0.6314 nan 0.0100 0.0000
## 320 0.6180 nan 0.0100 0.0000
## 340 0.6056 nan 0.0100 -0.0000
## 360 0.5938 nan 0.0100 0.0000
## 380 0.5819 nan 0.0100 -0.0000
## 400 0.5711 nan 0.0100 0.0001
## 420 0.5618 nan 0.0100 -0.0001
## 440 0.5515 nan 0.0100 0.0000
## 460 0.5412 nan 0.0100 -0.0001
## 480 0.5322 nan 0.0100 0.0000
## 500 0.5241 nan 0.0100 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3117 nan 0.0100 0.0043
## 2 1.3031 nan 0.0100 0.0039
## 3 1.2945 nan 0.0100 0.0037
## 4 1.2861 nan 0.0100 0.0033
## 5 1.2777 nan 0.0100 0.0034
## 6 1.2692 nan 0.0100 0.0036
## 7 1.2619 nan 0.0100 0.0036
## 8 1.2535 nan 0.0100 0.0037
## 9 1.2462 nan 0.0100 0.0030
## 10 1.2374 nan 0.0100 0.0036
## 20 1.1674 nan 0.0100 0.0029
## 40 1.0518 nan 0.0100 0.0022
## 60 0.9638 nan 0.0100 0.0018
## 80 0.8971 nan 0.0100 0.0008
## 100 0.8418 nan 0.0100 0.0006
## 120 0.7957 nan 0.0100 0.0004
## 140 0.7585 nan 0.0100 0.0005
## 160 0.7251 nan 0.0100 0.0006
## 180 0.6958 nan 0.0100 0.0003
## 200 0.6710 nan 0.0100 0.0001
## 220 0.6492 nan 0.0100 0.0002
## 240 0.6281 nan 0.0100 0.0002
## 260 0.6091 nan 0.0100 0.0001
## 280 0.5913 nan 0.0100 -0.0001
## 300 0.5746 nan 0.0100 0.0001
## 320 0.5598 nan 0.0100 0.0000
## 340 0.5466 nan 0.0100 0.0000
## 360 0.5331 nan 0.0100 0.0000
## 380 0.5205 nan 0.0100 0.0001
## 400 0.5086 nan 0.0100 -0.0001
## 420 0.4964 nan 0.0100 0.0000
## 440 0.4852 nan 0.0100 0.0000
## 460 0.4747 nan 0.0100 0.0001
## 480 0.4644 nan 0.0100 0.0000
## 500 0.4553 nan 0.0100 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3117 nan 0.0100 0.0045
## 2 1.3025 nan 0.0100 0.0042
## 3 1.2938 nan 0.0100 0.0042
## 4 1.2847 nan 0.0100 0.0040
## 5 1.2765 nan 0.0100 0.0037
## 6 1.2685 nan 0.0100 0.0034
## 7 1.2599 nan 0.0100 0.0037
## 8 1.2522 nan 0.0100 0.0035
## 9 1.2449 nan 0.0100 0.0030
## 10 1.2375 nan 0.0100 0.0032
## 20 1.1653 nan 0.0100 0.0029
## 40 1.0506 nan 0.0100 0.0024
## 60 0.9663 nan 0.0100 0.0010
## 80 0.8980 nan 0.0100 0.0011
## 100 0.8430 nan 0.0100 0.0009
## 120 0.7990 nan 0.0100 0.0007
## 140 0.7608 nan 0.0100 0.0005
## 160 0.7295 nan 0.0100 0.0004
## 180 0.7018 nan 0.0100 0.0004
## 200 0.6776 nan 0.0100 0.0001
## 220 0.6554 nan 0.0100 0.0002
## 240 0.6366 nan 0.0100 0.0002
## 260 0.6192 nan 0.0100 0.0000
## 280 0.6032 nan 0.0100 0.0001
## 300 0.5875 nan 0.0100 0.0002
## 320 0.5735 nan 0.0100 0.0000
## 340 0.5602 nan 0.0100 0.0001
## 360 0.5451 nan 0.0100 -0.0001
## 380 0.5322 nan 0.0100 -0.0001
## 400 0.5201 nan 0.0100 0.0000
## 420 0.5083 nan 0.0100 -0.0000
## 440 0.4969 nan 0.0100 -0.0001
## 460 0.4858 nan 0.0100 0.0002
## 480 0.4752 nan 0.0100 -0.0001
## 500 0.4658 nan 0.0100 0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3123 nan 0.0100 0.0042
## 2 1.3042 nan 0.0100 0.0035
## 3 1.2952 nan 0.0100 0.0042
## 4 1.2866 nan 0.0100 0.0039
## 5 1.2790 nan 0.0100 0.0035
## 6 1.2709 nan 0.0100 0.0034
## 7 1.2630 nan 0.0100 0.0034
## 8 1.2557 nan 0.0100 0.0033
## 9 1.2481 nan 0.0100 0.0034
## 10 1.2406 nan 0.0100 0.0031
## 20 1.1706 nan 0.0100 0.0027
## 40 1.0581 nan 0.0100 0.0024
## 60 0.9715 nan 0.0100 0.0015
## 80 0.9044 nan 0.0100 0.0011
## 100 0.8508 nan 0.0100 0.0010
## 120 0.8074 nan 0.0100 0.0004
## 140 0.7699 nan 0.0100 0.0003
## 160 0.7382 nan 0.0100 0.0004
## 180 0.7108 nan 0.0100 0.0002
## 200 0.6869 nan 0.0100 0.0002
## 220 0.6654 nan 0.0100 -0.0001
## 240 0.6466 nan 0.0100 0.0002
## 260 0.6298 nan 0.0100 -0.0001
## 280 0.6143 nan 0.0100 -0.0001
## 300 0.5997 nan 0.0100 0.0000
## 320 0.5851 nan 0.0100 0.0002
## 340 0.5705 nan 0.0100 0.0001
## 360 0.5571 nan 0.0100 -0.0000
## 380 0.5451 nan 0.0100 -0.0002
## 400 0.5329 nan 0.0100 -0.0001
## 420 0.5221 nan 0.0100 -0.0001
## 440 0.5116 nan 0.0100 -0.0000
## 460 0.5013 nan 0.0100 -0.0001
## 480 0.4914 nan 0.0100 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2455 nan 0.1000 0.0356
## 2 1.1801 nan 0.1000 0.0263
## 3 1.1242 nan 0.1000 0.0253
## 4 1.0772 nan 0.1000 0.0209
## 5 1.0382 nan 0.1000 0.0162
## 6 1.0034 nan 0.1000 0.0128
## 7 0.9698 nan 0.1000 0.0117
## 8 0.9359 nan 0.1000 0.0129
## 9 0.9077 nan 0.1000 0.0123
## 10 0.8843 nan 0.1000 0.0081
## 20 0.7317 nan 0.1000 0.0035
## 40 0.5999 nan 0.1000 0.0002
## 60 0.5085 nan 0.1000 0.0007
## 80 0.4494 nan 0.1000 0.0002
## 100 0.3962 nan 0.1000 -0.0003
## 120 0.3496 nan 0.1000 -0.0010
## 140 0.3158 nan 0.1000 -0.0001
## 160 0.2825 nan 0.1000 -0.0003
## 180 0.2582 nan 0.1000 -0.0002
## 200 0.2334 nan 0.1000 -0.0006
## 220 0.2125 nan 0.1000 -0.0007
## 240 0.1941 nan 0.1000 -0.0002
## 260 0.1784 nan 0.1000 -0.0002
## 280 0.1641 nan 0.1000 -0.0001
## 300 0.1498 nan 0.1000 -0.0001
## 320 0.1383 nan 0.1000 -0.0003
## 340 0.1280 nan 0.1000 0.0001
## 360 0.1197 nan 0.1000 -0.0005
## 380 0.1109 nan 0.1000 -0.0002
## 400 0.1028 nan 0.1000 -0.0001
## 420 0.0950 nan 0.1000 -0.0003
## 440 0.0884 nan 0.1000 -0.0003
## 460 0.0819 nan 0.1000 -0.0002
## 480 0.0758 nan 0.1000 -0.0002
## 500 0.0703 nan 0.1000 -0.0003
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2370 nan 0.1000 0.0368
## 2 1.1663 nan 0.1000 0.0293
## 3 1.1083 nan 0.1000 0.0237
## 4 1.0638 nan 0.1000 0.0183
## 5 1.0288 nan 0.1000 0.0136
## 6 0.9911 nan 0.1000 0.0144
## 7 0.9591 nan 0.1000 0.0123
## 8 0.9286 nan 0.1000 0.0132
## 9 0.9045 nan 0.1000 0.0085
## 10 0.8808 nan 0.1000 0.0059
## 20 0.7413 nan 0.1000 0.0020
## 40 0.6143 nan 0.1000 -0.0013
## 60 0.5282 nan 0.1000 -0.0001
## 80 0.4724 nan 0.1000 -0.0002
## 100 0.4165 nan 0.1000 -0.0010
## 120 0.3702 nan 0.1000 -0.0009
## 140 0.3289 nan 0.1000 -0.0000
## 160 0.2963 nan 0.1000 -0.0008
## 180 0.2704 nan 0.1000 -0.0007
## 200 0.2479 nan 0.1000 -0.0004
## 220 0.2257 nan 0.1000 -0.0011
## 240 0.2049 nan 0.1000 -0.0002
## 260 0.1877 nan 0.1000 0.0000
## 280 0.1729 nan 0.1000 -0.0005
## 300 0.1591 nan 0.1000 -0.0003
## 320 0.1472 nan 0.1000 -0.0004
## 340 0.1351 nan 0.1000 -0.0003
## 360 0.1247 nan 0.1000 -0.0003
## 380 0.1154 nan 0.1000 -0.0005
## 400 0.1068 nan 0.1000 -0.0002
## 420 0.1005 nan 0.1000 -0.0003
## 440 0.0928 nan 0.1000 -0.0001
## 460 0.0858 nan 0.1000 -0.0000
## 480 0.0805 nan 0.1000 -0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2426 nan 0.1000 0.0295
## 2 1.1792 nan 0.1000 0.0242
## 3 1.1208 nan 0.1000 0.0249
## 4 1.0708 nan 0.1000 0.0195
## 5 1.0384 nan 0.1000 0.0133
## 6 0.9981 nan 0.1000 0.0146
## 7 0.9640 nan 0.1000 0.0134
## 8 0.9349 nan 0.1000 0.0123
## 9 0.9080 nan 0.1000 0.0108
## 10 0.8859 nan 0.1000 0.0091
## 20 0.7442 nan 0.1000 0.0022
## 40 0.6243 nan 0.1000 -0.0008
## 60 0.5509 nan 0.1000 -0.0011
## 80 0.4842 nan 0.1000 -0.0008
## 100 0.4358 nan 0.1000 -0.0007
## 120 0.3898 nan 0.1000 -0.0005
## 140 0.3544 nan 0.1000 -0.0004
## 160 0.3225 nan 0.1000 -0.0004
## 180 0.2961 nan 0.1000 -0.0011
## 200 0.2712 nan 0.1000 -0.0004
## 220 0.2462 nan 0.1000 -0.0016
## 240 0.2246 nan 0.1000 -0.0005
## 260 0.2074 nan 0.1000 -0.0005
## 280 0.1913 nan 0.1000 -0.0011
## 300 0.1766 nan 0.1000 -0.0002
## 320 0.1628 nan 0.1000 -0.0002
## 340 0.1493 nan 0.1000 -0.0002
## 360 0.1358 nan 0.1000 -0.0003
## 380 0.1262 nan 0.1000 -0.0004
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## 480 0.0884 nan 0.1000 -0.0002
## 500 0.0830 nan 0.1000 -0.0003
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2426 nan 0.1000 0.0350
## 2 1.1684 nan 0.1000 0.0326
## 3 1.1100 nan 0.1000 0.0224
## 4 1.0600 nan 0.1000 0.0204
## 5 1.0126 nan 0.1000 0.0188
## 6 0.9727 nan 0.1000 0.0154
## 7 0.9395 nan 0.1000 0.0138
## 8 0.9096 nan 0.1000 0.0129
## 9 0.8808 nan 0.1000 0.0112
## 10 0.8576 nan 0.1000 0.0094
## 20 0.7002 nan 0.1000 0.0010
## 40 0.5580 nan 0.1000 -0.0006
## 60 0.4611 nan 0.1000 0.0006
## 80 0.3901 nan 0.1000 -0.0001
## 100 0.3328 nan 0.1000 0.0001
## 120 0.2872 nan 0.1000 0.0000
## 140 0.2487 nan 0.1000 -0.0000
## 160 0.2204 nan 0.1000 0.0001
## 180 0.1932 nan 0.1000 -0.0003
## 200 0.1696 nan 0.1000 -0.0002
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## 240 0.1337 nan 0.1000 0.0001
## 260 0.1189 nan 0.1000 -0.0003
## 280 0.1071 nan 0.1000 -0.0000
## 300 0.0964 nan 0.1000 -0.0001
## 320 0.0858 nan 0.1000 -0.0001
## 340 0.0778 nan 0.1000 -0.0001
## 360 0.0698 nan 0.1000 -0.0003
## 380 0.0631 nan 0.1000 -0.0000
## 400 0.0575 nan 0.1000 -0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2444 nan 0.1000 0.0326
## 2 1.1729 nan 0.1000 0.0338
## 3 1.1099 nan 0.1000 0.0232
## 4 1.0545 nan 0.1000 0.0227
## 5 1.0113 nan 0.1000 0.0188
## 6 0.9757 nan 0.1000 0.0143
## 7 0.9388 nan 0.1000 0.0148
## 8 0.9118 nan 0.1000 0.0114
## 9 0.8860 nan 0.1000 0.0070
## 10 0.8628 nan 0.1000 0.0081
## 20 0.7133 nan 0.1000 0.0011
## 40 0.5673 nan 0.1000 0.0010
## 60 0.4748 nan 0.1000 -0.0009
## 80 0.4060 nan 0.1000 -0.0003
## 100 0.3530 nan 0.1000 -0.0012
## 120 0.3069 nan 0.1000 0.0009
## 140 0.2654 nan 0.1000 -0.0006
## 160 0.2336 nan 0.1000 -0.0002
## 180 0.2064 nan 0.1000 -0.0010
## 200 0.1848 nan 0.1000 -0.0001
## 220 0.1643 nan 0.1000 -0.0006
## 240 0.1472 nan 0.1000 -0.0009
## 260 0.1332 nan 0.1000 -0.0002
## 280 0.1202 nan 0.1000 -0.0005
## 300 0.1084 nan 0.1000 -0.0003
## 320 0.0980 nan 0.1000 -0.0002
## 340 0.0878 nan 0.1000 -0.0004
## 360 0.0788 nan 0.1000 -0.0001
## 380 0.0708 nan 0.1000 -0.0003
## 400 0.0645 nan 0.1000 -0.0001
## 420 0.0582 nan 0.1000 -0.0002
## 440 0.0530 nan 0.1000 -0.0001
## 460 0.0487 nan 0.1000 -0.0002
## 480 0.0442 nan 0.1000 -0.0001
## 500 0.0399 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2418 nan 0.1000 0.0329
## 2 1.1758 nan 0.1000 0.0265
## 3 1.1156 nan 0.1000 0.0274
## 4 1.0612 nan 0.1000 0.0214
## 5 1.0198 nan 0.1000 0.0153
## 6 0.9773 nan 0.1000 0.0156
## 7 0.9396 nan 0.1000 0.0141
## 8 0.9162 nan 0.1000 0.0081
## 9 0.8891 nan 0.1000 0.0098
## 10 0.8622 nan 0.1000 0.0099
## 20 0.7152 nan 0.1000 0.0027
## 40 0.5785 nan 0.1000 0.0006
## 60 0.4802 nan 0.1000 -0.0008
## 80 0.4102 nan 0.1000 0.0002
## 100 0.3653 nan 0.1000 -0.0013
## 120 0.3241 nan 0.1000 -0.0006
## 140 0.2857 nan 0.1000 -0.0010
## 160 0.2534 nan 0.1000 -0.0010
## 180 0.2257 nan 0.1000 -0.0003
## 200 0.2037 nan 0.1000 -0.0009
## 220 0.1854 nan 0.1000 -0.0007
## 240 0.1687 nan 0.1000 -0.0001
## 260 0.1526 nan 0.1000 -0.0009
## 280 0.1391 nan 0.1000 -0.0006
## 300 0.1262 nan 0.1000 -0.0003
## 320 0.1155 nan 0.1000 -0.0006
## 340 0.1060 nan 0.1000 -0.0003
## 360 0.0959 nan 0.1000 -0.0003
## 380 0.0884 nan 0.1000 -0.0003
## 400 0.0805 nan 0.1000 -0.0001
## 420 0.0732 nan 0.1000 -0.0003
## 440 0.0670 nan 0.1000 -0.0003
## 460 0.0611 nan 0.1000 -0.0002
## 480 0.0554 nan 0.1000 -0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2315 nan 0.1000 0.0413
## 2 1.1582 nan 0.1000 0.0317
## 3 1.0991 nan 0.1000 0.0269
## 4 1.0496 nan 0.1000 0.0224
## 5 0.9992 nan 0.1000 0.0201
## 6 0.9581 nan 0.1000 0.0162
## 7 0.9226 nan 0.1000 0.0138
## 8 0.8906 nan 0.1000 0.0145
## 9 0.8584 nan 0.1000 0.0124
## 10 0.8314 nan 0.1000 0.0117
## 20 0.6734 nan 0.1000 0.0038
## 40 0.5177 nan 0.1000 0.0007
## 60 0.4140 nan 0.1000 -0.0007
## 80 0.3421 nan 0.1000 -0.0010
## 100 0.2886 nan 0.1000 -0.0004
## 120 0.2463 nan 0.1000 -0.0001
## 140 0.2108 nan 0.1000 -0.0009
## 160 0.1812 nan 0.1000 -0.0004
## 180 0.1557 nan 0.1000 -0.0008
## 200 0.1374 nan 0.1000 -0.0000
## 220 0.1213 nan 0.1000 -0.0003
## 240 0.1050 nan 0.1000 -0.0002
## 260 0.0934 nan 0.1000 -0.0004
## 280 0.0836 nan 0.1000 -0.0001
## 300 0.0738 nan 0.1000 -0.0002
## 320 0.0654 nan 0.1000 -0.0001
## 340 0.0575 nan 0.1000 -0.0002
## 360 0.0512 nan 0.1000 -0.0001
## 380 0.0453 nan 0.1000 -0.0001
## 400 0.0405 nan 0.1000 -0.0001
## 420 0.0359 nan 0.1000 -0.0001
## 440 0.0321 nan 0.1000 -0.0001
## 460 0.0286 nan 0.1000 -0.0001
## 480 0.0255 nan 0.1000 -0.0001
## 500 0.0224 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2322 nan 0.1000 0.0404
## 2 1.1642 nan 0.1000 0.0292
## 3 1.1028 nan 0.1000 0.0285
## 4 1.0488 nan 0.1000 0.0236
## 5 1.0046 nan 0.1000 0.0179
## 6 0.9639 nan 0.1000 0.0167
## 7 0.9286 nan 0.1000 0.0146
## 8 0.8978 nan 0.1000 0.0104
## 9 0.8741 nan 0.1000 0.0075
## 10 0.8455 nan 0.1000 0.0110
## 20 0.6748 nan 0.1000 0.0056
## 40 0.5235 nan 0.1000 -0.0004
## 60 0.4239 nan 0.1000 -0.0000
## 80 0.3485 nan 0.1000 -0.0009
## 100 0.2880 nan 0.1000 -0.0005
## 120 0.2442 nan 0.1000 -0.0004
## 140 0.2070 nan 0.1000 -0.0001
## 160 0.1786 nan 0.1000 -0.0001
## 180 0.1549 nan 0.1000 -0.0006
## 200 0.1348 nan 0.1000 -0.0008
## 220 0.1202 nan 0.1000 -0.0008
## 240 0.1048 nan 0.1000 -0.0002
## 260 0.0931 nan 0.1000 -0.0004
## 280 0.0828 nan 0.1000 -0.0003
## 300 0.0726 nan 0.1000 -0.0002
## 320 0.0642 nan 0.1000 -0.0002
## 340 0.0570 nan 0.1000 -0.0003
## 360 0.0499 nan 0.1000 -0.0002
## 380 0.0447 nan 0.1000 -0.0001
## 400 0.0395 nan 0.1000 -0.0000
## 420 0.0353 nan 0.1000 -0.0001
## 440 0.0316 nan 0.1000 -0.0001
## 460 0.0281 nan 0.1000 -0.0001
## 480 0.0251 nan 0.1000 -0.0001
## 500 0.0223 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2313 nan 0.1000 0.0392
## 2 1.1544 nan 0.1000 0.0350
## 3 1.0936 nan 0.1000 0.0268
## 4 1.0483 nan 0.1000 0.0147
## 5 1.0049 nan 0.1000 0.0198
## 6 0.9646 nan 0.1000 0.0160
## 7 0.9304 nan 0.1000 0.0132
## 8 0.8987 nan 0.1000 0.0105
## 9 0.8686 nan 0.1000 0.0117
## 10 0.8415 nan 0.1000 0.0093
## 20 0.6871 nan 0.1000 0.0019
## 40 0.5390 nan 0.1000 -0.0007
## 60 0.4441 nan 0.1000 -0.0012
## 80 0.3725 nan 0.1000 -0.0011
## 100 0.3192 nan 0.1000 0.0002
## 120 0.2683 nan 0.1000 -0.0011
## 140 0.2307 nan 0.1000 -0.0011
## 160 0.1985 nan 0.1000 -0.0004
## 180 0.1735 nan 0.1000 -0.0005
## 200 0.1535 nan 0.1000 -0.0007
## 220 0.1351 nan 0.1000 -0.0007
## 240 0.1190 nan 0.1000 -0.0001
## 260 0.1048 nan 0.1000 -0.0006
## 280 0.0928 nan 0.1000 -0.0001
## 300 0.0813 nan 0.1000 -0.0001
## 320 0.0722 nan 0.1000 -0.0004
## 340 0.0644 nan 0.1000 -0.0004
## 360 0.0581 nan 0.1000 -0.0001
## 380 0.0522 nan 0.1000 -0.0002
## 400 0.0472 nan 0.1000 -0.0002
## 420 0.0421 nan 0.1000 -0.0002
## 440 0.0376 nan 0.1000 -0.0002
## 460 0.0336 nan 0.1000 -0.0001
## 480 0.0303 nan 0.1000 -0.0002
## 500 0.0272 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3204 nan 0.0010 0.0004
## 2 1.3195 nan 0.0010 0.0004
## 3 1.3186 nan 0.0010 0.0004
## 4 1.3177 nan 0.0010 0.0004
## 5 1.3169 nan 0.0010 0.0004
## 6 1.3161 nan 0.0010 0.0003
## 7 1.3153 nan 0.0010 0.0004
## 8 1.3145 nan 0.0010 0.0003
## 9 1.3138 nan 0.0010 0.0003
## 10 1.3129 nan 0.0010 0.0004
## 20 1.3049 nan 0.0010 0.0003
## 40 1.2892 nan 0.0010 0.0003
## 60 1.2740 nan 0.0010 0.0004
## 80 1.2591 nan 0.0010 0.0003
## 100 1.2448 nan 0.0010 0.0003
## 120 1.2310 nan 0.0010 0.0003
## 140 1.2173 nan 0.0010 0.0003
## 160 1.2040 nan 0.0010 0.0003
## 180 1.1913 nan 0.0010 0.0003
## 200 1.1789 nan 0.0010 0.0002
## 220 1.1666 nan 0.0010 0.0002
## 240 1.1550 nan 0.0010 0.0002
## 260 1.1434 nan 0.0010 0.0003
## 280 1.1326 nan 0.0010 0.0002
## 300 1.1220 nan 0.0010 0.0002
## 320 1.1114 nan 0.0010 0.0002
## 340 1.1013 nan 0.0010 0.0002
## 360 1.0913 nan 0.0010 0.0002
## 380 1.0815 nan 0.0010 0.0002
## 400 1.0721 nan 0.0010 0.0002
## 420 1.0630 nan 0.0010 0.0001
## 440 1.0539 nan 0.0010 0.0002
## 460 1.0453 nan 0.0010 0.0002
## 480 1.0369 nan 0.0010 0.0002
## 500 1.0284 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3194 nan 0.0010 0.0004
## 3 1.3186 nan 0.0010 0.0004
## 4 1.3177 nan 0.0010 0.0004
## 5 1.3169 nan 0.0010 0.0003
## 6 1.3161 nan 0.0010 0.0003
## 7 1.3153 nan 0.0010 0.0004
## 8 1.3145 nan 0.0010 0.0004
## 9 1.3138 nan 0.0010 0.0003
## 10 1.3130 nan 0.0010 0.0003
## 20 1.3047 nan 0.0010 0.0004
## 40 1.2891 nan 0.0010 0.0003
## 60 1.2736 nan 0.0010 0.0004
## 80 1.2590 nan 0.0010 0.0003
## 100 1.2446 nan 0.0010 0.0003
## 120 1.2310 nan 0.0010 0.0003
## 140 1.2176 nan 0.0010 0.0003
## 160 1.2043 nan 0.0010 0.0003
## 180 1.1915 nan 0.0010 0.0002
## 200 1.1792 nan 0.0010 0.0003
## 220 1.1673 nan 0.0010 0.0003
## 240 1.1555 nan 0.0010 0.0002
## 260 1.1442 nan 0.0010 0.0002
## 280 1.1334 nan 0.0010 0.0002
## 300 1.1226 nan 0.0010 0.0002
## 320 1.1119 nan 0.0010 0.0002
## 340 1.1018 nan 0.0010 0.0002
## 360 1.0922 nan 0.0010 0.0002
## 380 1.0825 nan 0.0010 0.0002
## 400 1.0729 nan 0.0010 0.0002
## 420 1.0636 nan 0.0010 0.0002
## 440 1.0546 nan 0.0010 0.0002
## 460 1.0457 nan 0.0010 0.0002
## 480 1.0372 nan 0.0010 0.0002
## 500 1.0288 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3204 nan 0.0010 0.0004
## 2 1.3195 nan 0.0010 0.0004
## 3 1.3188 nan 0.0010 0.0004
## 4 1.3180 nan 0.0010 0.0003
## 5 1.3172 nan 0.0010 0.0004
## 6 1.3164 nan 0.0010 0.0004
## 7 1.3156 nan 0.0010 0.0003
## 8 1.3148 nan 0.0010 0.0004
## 9 1.3139 nan 0.0010 0.0004
## 10 1.3131 nan 0.0010 0.0004
## 20 1.3050 nan 0.0010 0.0004
## 40 1.2891 nan 0.0010 0.0004
## 60 1.2739 nan 0.0010 0.0003
## 80 1.2591 nan 0.0010 0.0003
## 100 1.2449 nan 0.0010 0.0003
## 120 1.2310 nan 0.0010 0.0003
## 140 1.2175 nan 0.0010 0.0003
## 160 1.2043 nan 0.0010 0.0003
## 180 1.1916 nan 0.0010 0.0003
## 200 1.1794 nan 0.0010 0.0003
## 220 1.1674 nan 0.0010 0.0002
## 240 1.1556 nan 0.0010 0.0002
## 260 1.1443 nan 0.0010 0.0002
## 280 1.1333 nan 0.0010 0.0002
## 300 1.1225 nan 0.0010 0.0002
## 320 1.1123 nan 0.0010 0.0002
## 340 1.1022 nan 0.0010 0.0002
## 360 1.0923 nan 0.0010 0.0002
## 380 1.0826 nan 0.0010 0.0002
## 400 1.0733 nan 0.0010 0.0002
## 420 1.0643 nan 0.0010 0.0002
## 440 1.0553 nan 0.0010 0.0002
## 460 1.0464 nan 0.0010 0.0002
## 480 1.0381 nan 0.0010 0.0002
## 500 1.0298 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3194 nan 0.0010 0.0004
## 3 1.3186 nan 0.0010 0.0004
## 4 1.3177 nan 0.0010 0.0004
## 5 1.3168 nan 0.0010 0.0004
## 6 1.3159 nan 0.0010 0.0004
## 7 1.3150 nan 0.0010 0.0004
## 8 1.3141 nan 0.0010 0.0004
## 9 1.3132 nan 0.0010 0.0004
## 10 1.3124 nan 0.0010 0.0004
## 20 1.3037 nan 0.0010 0.0004
## 40 1.2864 nan 0.0010 0.0004
## 60 1.2697 nan 0.0010 0.0004
## 80 1.2541 nan 0.0010 0.0003
## 100 1.2390 nan 0.0010 0.0004
## 120 1.2241 nan 0.0010 0.0003
## 140 1.2097 nan 0.0010 0.0003
## 160 1.1960 nan 0.0010 0.0003
## 180 1.1826 nan 0.0010 0.0003
## 200 1.1696 nan 0.0010 0.0003
## 220 1.1569 nan 0.0010 0.0003
## 240 1.1445 nan 0.0010 0.0003
## 260 1.1325 nan 0.0010 0.0002
## 280 1.1210 nan 0.0010 0.0002
## 300 1.1095 nan 0.0010 0.0003
## 320 1.0986 nan 0.0010 0.0002
## 340 1.0878 nan 0.0010 0.0002
## 360 1.0774 nan 0.0010 0.0002
## 380 1.0673 nan 0.0010 0.0002
## 400 1.0572 nan 0.0010 0.0002
## 420 1.0476 nan 0.0010 0.0002
## 440 1.0382 nan 0.0010 0.0002
## 460 1.0291 nan 0.0010 0.0002
## 480 1.0203 nan 0.0010 0.0002
## 500 1.0117 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3194 nan 0.0010 0.0004
## 3 1.3184 nan 0.0010 0.0004
## 4 1.3175 nan 0.0010 0.0004
## 5 1.3167 nan 0.0010 0.0004
## 6 1.3158 nan 0.0010 0.0004
## 7 1.3150 nan 0.0010 0.0004
## 8 1.3140 nan 0.0010 0.0004
## 9 1.3131 nan 0.0010 0.0004
## 10 1.3123 nan 0.0010 0.0003
## 20 1.3037 nan 0.0010 0.0003
## 40 1.2867 nan 0.0010 0.0004
## 60 1.2701 nan 0.0010 0.0004
## 80 1.2544 nan 0.0010 0.0003
## 100 1.2389 nan 0.0010 0.0004
## 120 1.2239 nan 0.0010 0.0003
## 140 1.2097 nan 0.0010 0.0003
## 160 1.1956 nan 0.0010 0.0003
## 180 1.1821 nan 0.0010 0.0003
## 200 1.1690 nan 0.0010 0.0003
## 220 1.1564 nan 0.0010 0.0003
## 240 1.1442 nan 0.0010 0.0002
## 260 1.1326 nan 0.0010 0.0002
## 280 1.1206 nan 0.0010 0.0002
## 300 1.1094 nan 0.0010 0.0002
## 320 1.0984 nan 0.0010 0.0003
## 340 1.0878 nan 0.0010 0.0002
## 360 1.0774 nan 0.0010 0.0002
## 380 1.0671 nan 0.0010 0.0002
## 400 1.0571 nan 0.0010 0.0002
## 420 1.0474 nan 0.0010 0.0002
## 440 1.0378 nan 0.0010 0.0002
## 460 1.0287 nan 0.0010 0.0002
## 480 1.0197 nan 0.0010 0.0002
## 500 1.0108 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3194 nan 0.0010 0.0004
## 3 1.3185 nan 0.0010 0.0004
## 4 1.3177 nan 0.0010 0.0003
## 5 1.3168 nan 0.0010 0.0004
## 6 1.3160 nan 0.0010 0.0003
## 7 1.3152 nan 0.0010 0.0004
## 8 1.3143 nan 0.0010 0.0004
## 9 1.3134 nan 0.0010 0.0004
## 10 1.3126 nan 0.0010 0.0004
## 20 1.3041 nan 0.0010 0.0004
## 40 1.2869 nan 0.0010 0.0003
## 60 1.2711 nan 0.0010 0.0004
## 80 1.2555 nan 0.0010 0.0004
## 100 1.2405 nan 0.0010 0.0004
## 120 1.2255 nan 0.0010 0.0003
## 140 1.2114 nan 0.0010 0.0003
## 160 1.1980 nan 0.0010 0.0003
## 180 1.1843 nan 0.0010 0.0003
## 200 1.1715 nan 0.0010 0.0003
## 220 1.1592 nan 0.0010 0.0003
## 240 1.1469 nan 0.0010 0.0003
## 260 1.1349 nan 0.0010 0.0002
## 280 1.1235 nan 0.0010 0.0002
## 300 1.1120 nan 0.0010 0.0003
## 320 1.1009 nan 0.0010 0.0003
## 340 1.0901 nan 0.0010 0.0003
## 360 1.0797 nan 0.0010 0.0002
## 380 1.0697 nan 0.0010 0.0002
## 400 1.0598 nan 0.0010 0.0002
## 420 1.0503 nan 0.0010 0.0002
## 440 1.0410 nan 0.0010 0.0002
## 460 1.0321 nan 0.0010 0.0002
## 480 1.0234 nan 0.0010 0.0002
## 500 1.0149 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3193 nan 0.0010 0.0004
## 3 1.3185 nan 0.0010 0.0004
## 4 1.3175 nan 0.0010 0.0005
## 5 1.3166 nan 0.0010 0.0004
## 6 1.3156 nan 0.0010 0.0004
## 7 1.3147 nan 0.0010 0.0004
## 8 1.3138 nan 0.0010 0.0004
## 9 1.3129 nan 0.0010 0.0004
## 10 1.3121 nan 0.0010 0.0004
## 20 1.3029 nan 0.0010 0.0004
## 40 1.2850 nan 0.0010 0.0004
## 60 1.2680 nan 0.0010 0.0003
## 80 1.2513 nan 0.0010 0.0003
## 100 1.2354 nan 0.0010 0.0003
## 120 1.2197 nan 0.0010 0.0004
## 140 1.2049 nan 0.0010 0.0003
## 160 1.1902 nan 0.0010 0.0003
## 180 1.1759 nan 0.0010 0.0003
## 200 1.1623 nan 0.0010 0.0003
## 220 1.1489 nan 0.0010 0.0003
## 240 1.1358 nan 0.0010 0.0003
## 260 1.1231 nan 0.0010 0.0002
## 280 1.1106 nan 0.0010 0.0002
## 300 1.0985 nan 0.0010 0.0003
## 320 1.0871 nan 0.0010 0.0002
## 340 1.0756 nan 0.0010 0.0003
## 360 1.0647 nan 0.0010 0.0002
## 380 1.0538 nan 0.0010 0.0002
## 400 1.0433 nan 0.0010 0.0002
## 420 1.0333 nan 0.0010 0.0002
## 440 1.0235 nan 0.0010 0.0002
## 460 1.0139 nan 0.0010 0.0002
## 480 1.0045 nan 0.0010 0.0002
## 500 0.9957 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3202 nan 0.0010 0.0004
## 2 1.3192 nan 0.0010 0.0004
## 3 1.3183 nan 0.0010 0.0004
## 4 1.3174 nan 0.0010 0.0004
## 5 1.3166 nan 0.0010 0.0003
## 6 1.3157 nan 0.0010 0.0004
## 7 1.3148 nan 0.0010 0.0003
## 8 1.3138 nan 0.0010 0.0005
## 9 1.3129 nan 0.0010 0.0004
## 10 1.3121 nan 0.0010 0.0004
## 20 1.3029 nan 0.0010 0.0004
## 40 1.2851 nan 0.0010 0.0004
## 60 1.2683 nan 0.0010 0.0004
## 80 1.2518 nan 0.0010 0.0003
## 100 1.2359 nan 0.0010 0.0004
## 120 1.2201 nan 0.0010 0.0004
## 140 1.2055 nan 0.0010 0.0003
## 160 1.1912 nan 0.0010 0.0003
## 180 1.1770 nan 0.0010 0.0003
## 200 1.1632 nan 0.0010 0.0003
## 220 1.1499 nan 0.0010 0.0002
## 240 1.1372 nan 0.0010 0.0003
## 260 1.1244 nan 0.0010 0.0003
## 280 1.1120 nan 0.0010 0.0002
## 300 1.1001 nan 0.0010 0.0003
## 320 1.0886 nan 0.0010 0.0002
## 340 1.0774 nan 0.0010 0.0002
## 360 1.0666 nan 0.0010 0.0002
## 380 1.0559 nan 0.0010 0.0002
## 400 1.0458 nan 0.0010 0.0002
## 420 1.0358 nan 0.0010 0.0002
## 440 1.0260 nan 0.0010 0.0002
## 460 1.0167 nan 0.0010 0.0002
## 480 1.0074 nan 0.0010 0.0002
## 500 0.9983 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3193 nan 0.0010 0.0004
## 3 1.3184 nan 0.0010 0.0005
## 4 1.3175 nan 0.0010 0.0004
## 5 1.3166 nan 0.0010 0.0004
## 6 1.3157 nan 0.0010 0.0004
## 7 1.3149 nan 0.0010 0.0004
## 8 1.3140 nan 0.0010 0.0004
## 9 1.3131 nan 0.0010 0.0004
## 10 1.3122 nan 0.0010 0.0004
## 20 1.3034 nan 0.0010 0.0003
## 40 1.2859 nan 0.0010 0.0003
## 60 1.2691 nan 0.0010 0.0004
## 80 1.2526 nan 0.0010 0.0004
## 100 1.2367 nan 0.0010 0.0003
## 120 1.2216 nan 0.0010 0.0003
## 140 1.2065 nan 0.0010 0.0003
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## 200 1.1647 nan 0.0010 0.0003
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## 240 1.1391 nan 0.0010 0.0002
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## 320 1.0915 nan 0.0010 0.0002
## 340 1.0804 nan 0.0010 0.0002
## 360 1.0697 nan 0.0010 0.0003
## 380 1.0592 nan 0.0010 0.0002
## 400 1.0486 nan 0.0010 0.0002
## 420 1.0387 nan 0.0010 0.0002
## 440 1.0292 nan 0.0010 0.0002
## 460 1.0198 nan 0.0010 0.0002
## 480 1.0108 nan 0.0010 0.0002
## 500 1.0018 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3129 nan 0.0100 0.0038
## 2 1.3039 nan 0.0100 0.0041
## 3 1.2957 nan 0.0100 0.0033
## 4 1.2876 nan 0.0100 0.0038
## 5 1.2794 nan 0.0100 0.0036
## 6 1.2721 nan 0.0100 0.0030
## 7 1.2647 nan 0.0100 0.0035
## 8 1.2572 nan 0.0100 0.0032
## 9 1.2496 nan 0.0100 0.0035
## 10 1.2423 nan 0.0100 0.0031
## 20 1.1775 nan 0.0100 0.0029
## 40 1.0695 nan 0.0100 0.0018
## 60 0.9858 nan 0.0100 0.0017
## 80 0.9211 nan 0.0100 0.0013
## 100 0.8687 nan 0.0100 0.0008
## 120 0.8256 nan 0.0100 0.0007
## 140 0.7887 nan 0.0100 0.0004
## 160 0.7598 nan 0.0100 0.0002
## 180 0.7347 nan 0.0100 0.0003
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## 220 0.6918 nan 0.0100 0.0003
## 240 0.6739 nan 0.0100 0.0003
## 260 0.6581 nan 0.0100 0.0000
## 280 0.6438 nan 0.0100 -0.0000
## 300 0.6315 nan 0.0100 0.0000
## 320 0.6193 nan 0.0100 0.0001
## 340 0.6080 nan 0.0100 0.0000
## 360 0.5974 nan 0.0100 -0.0000
## 380 0.5866 nan 0.0100 0.0001
## 400 0.5771 nan 0.0100 0.0001
## 420 0.5684 nan 0.0100 -0.0001
## 440 0.5595 nan 0.0100 -0.0001
## 460 0.5508 nan 0.0100 0.0000
## 480 0.5428 nan 0.0100 -0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3130 nan 0.0100 0.0033
## 2 1.3046 nan 0.0100 0.0038
## 3 1.2967 nan 0.0100 0.0037
## 4 1.2890 nan 0.0100 0.0033
## 5 1.2808 nan 0.0100 0.0036
## 6 1.2730 nan 0.0100 0.0037
## 7 1.2651 nan 0.0100 0.0033
## 8 1.2568 nan 0.0100 0.0035
## 9 1.2502 nan 0.0100 0.0026
## 10 1.2434 nan 0.0100 0.0034
## 20 1.1772 nan 0.0100 0.0027
## 40 1.0715 nan 0.0100 0.0021
## 60 0.9882 nan 0.0100 0.0015
## 80 0.9234 nan 0.0100 0.0007
## 100 0.8708 nan 0.0100 0.0009
## 120 0.8280 nan 0.0100 0.0008
## 140 0.7928 nan 0.0100 0.0005
## 160 0.7653 nan 0.0100 0.0002
## 180 0.7394 nan 0.0100 0.0003
## 200 0.7171 nan 0.0100 0.0003
## 220 0.6971 nan 0.0100 0.0004
## 240 0.6796 nan 0.0100 -0.0000
## 260 0.6647 nan 0.0100 0.0002
## 280 0.6499 nan 0.0100 0.0001
## 300 0.6369 nan 0.0100 0.0000
## 320 0.6245 nan 0.0100 0.0002
## 340 0.6129 nan 0.0100 -0.0001
## 360 0.6020 nan 0.0100 0.0000
## 380 0.5913 nan 0.0100 -0.0001
## 400 0.5807 nan 0.0100 -0.0000
## 420 0.5726 nan 0.0100 -0.0001
## 440 0.5647 nan 0.0100 0.0001
## 460 0.5566 nan 0.0100 0.0000
## 480 0.5477 nan 0.0100 0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3126 nan 0.0100 0.0037
## 2 1.3045 nan 0.0100 0.0037
## 3 1.2965 nan 0.0100 0.0038
## 4 1.2887 nan 0.0100 0.0038
## 5 1.2812 nan 0.0100 0.0032
## 6 1.2726 nan 0.0100 0.0038
## 7 1.2652 nan 0.0100 0.0031
## 8 1.2574 nan 0.0100 0.0037
## 9 1.2501 nan 0.0100 0.0035
## 10 1.2431 nan 0.0100 0.0034
## 20 1.1776 nan 0.0100 0.0027
## 40 1.0724 nan 0.0100 0.0019
## 60 0.9923 nan 0.0100 0.0012
## 80 0.9264 nan 0.0100 0.0014
## 100 0.8725 nan 0.0100 0.0010
## 120 0.8298 nan 0.0100 0.0008
## 140 0.7940 nan 0.0100 0.0006
## 160 0.7655 nan 0.0100 0.0004
## 180 0.7403 nan 0.0100 0.0003
## 200 0.7195 nan 0.0100 0.0002
## 220 0.7011 nan 0.0100 0.0001
## 240 0.6843 nan 0.0100 0.0002
## 260 0.6682 nan 0.0100 0.0001
## 280 0.6550 nan 0.0100 0.0001
## 300 0.6420 nan 0.0100 0.0002
## 320 0.6299 nan 0.0100 -0.0001
## 340 0.6194 nan 0.0100 0.0000
## 360 0.6087 nan 0.0100 0.0000
## 380 0.5984 nan 0.0100 -0.0001
## 400 0.5893 nan 0.0100 -0.0000
## 420 0.5810 nan 0.0100 -0.0000
## 440 0.5725 nan 0.0100 -0.0000
## 460 0.5638 nan 0.0100 -0.0000
## 480 0.5557 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3124 nan 0.0100 0.0042
## 2 1.3041 nan 0.0100 0.0036
## 3 1.2960 nan 0.0100 0.0035
## 4 1.2878 nan 0.0100 0.0037
## 5 1.2794 nan 0.0100 0.0038
## 6 1.2714 nan 0.0100 0.0036
## 7 1.2641 nan 0.0100 0.0025
## 8 1.2562 nan 0.0100 0.0032
## 9 1.2479 nan 0.0100 0.0035
## 10 1.2406 nan 0.0100 0.0028
## 20 1.1724 nan 0.0100 0.0026
## 40 1.0587 nan 0.0100 0.0020
## 60 0.9728 nan 0.0100 0.0015
## 80 0.9050 nan 0.0100 0.0010
## 100 0.8496 nan 0.0100 0.0006
## 120 0.8043 nan 0.0100 0.0008
## 140 0.7656 nan 0.0100 0.0006
## 160 0.7324 nan 0.0100 0.0004
## 180 0.7058 nan 0.0100 0.0004
## 200 0.6827 nan 0.0100 0.0000
## 220 0.6614 nan 0.0100 0.0003
## 240 0.6412 nan 0.0100 0.0002
## 260 0.6241 nan 0.0100 0.0000
## 280 0.6073 nan 0.0100 0.0001
## 300 0.5923 nan 0.0100 0.0000
## 320 0.5786 nan 0.0100 0.0000
## 340 0.5666 nan 0.0100 -0.0001
## 360 0.5539 nan 0.0100 -0.0000
## 380 0.5429 nan 0.0100 0.0000
## 400 0.5320 nan 0.0100 0.0001
## 420 0.5223 nan 0.0100 0.0002
## 440 0.5125 nan 0.0100 -0.0002
## 460 0.5028 nan 0.0100 -0.0000
## 480 0.4935 nan 0.0100 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3121 nan 0.0100 0.0041
## 2 1.3029 nan 0.0100 0.0040
## 3 1.2957 nan 0.0100 0.0036
## 4 1.2867 nan 0.0100 0.0039
## 5 1.2780 nan 0.0100 0.0039
## 6 1.2698 nan 0.0100 0.0035
## 7 1.2620 nan 0.0100 0.0034
## 8 1.2540 nan 0.0100 0.0038
## 9 1.2463 nan 0.0100 0.0031
## 10 1.2386 nan 0.0100 0.0032
## 20 1.1691 nan 0.0100 0.0029
## 40 1.0573 nan 0.0100 0.0022
## 60 0.9698 nan 0.0100 0.0018
## 80 0.9020 nan 0.0100 0.0013
## 100 0.8456 nan 0.0100 0.0007
## 120 0.8014 nan 0.0100 0.0007
## 140 0.7648 nan 0.0100 0.0004
## 160 0.7340 nan 0.0100 0.0004
## 180 0.7069 nan 0.0100 0.0004
## 200 0.6836 nan 0.0100 0.0001
## 220 0.6630 nan 0.0100 0.0000
## 240 0.6455 nan 0.0100 0.0004
## 260 0.6297 nan 0.0100 0.0001
## 280 0.6143 nan 0.0100 0.0001
## 300 0.5993 nan 0.0100 0.0001
## 320 0.5862 nan 0.0100 0.0001
## 340 0.5733 nan 0.0100 -0.0001
## 360 0.5616 nan 0.0100 0.0000
## 380 0.5498 nan 0.0100 -0.0001
## 400 0.5388 nan 0.0100 0.0001
## 420 0.5285 nan 0.0100 -0.0001
## 440 0.5188 nan 0.0100 0.0001
## 460 0.5090 nan 0.0100 -0.0002
## 480 0.4998 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3124 nan 0.0100 0.0040
## 2 1.3042 nan 0.0100 0.0037
## 3 1.2956 nan 0.0100 0.0042
## 4 1.2869 nan 0.0100 0.0040
## 5 1.2791 nan 0.0100 0.0032
## 6 1.2708 nan 0.0100 0.0036
## 7 1.2621 nan 0.0100 0.0037
## 8 1.2542 nan 0.0100 0.0032
## 9 1.2466 nan 0.0100 0.0035
## 10 1.2391 nan 0.0100 0.0034
## 20 1.1701 nan 0.0100 0.0026
## 40 1.0582 nan 0.0100 0.0023
## 60 0.9739 nan 0.0100 0.0016
## 80 0.9054 nan 0.0100 0.0014
## 100 0.8511 nan 0.0100 0.0008
## 120 0.8076 nan 0.0100 0.0008
## 140 0.7709 nan 0.0100 0.0005
## 160 0.7385 nan 0.0100 0.0005
## 180 0.7134 nan 0.0100 0.0002
## 200 0.6907 nan 0.0100 0.0002
## 220 0.6713 nan 0.0100 0.0001
## 240 0.6536 nan 0.0100 0.0002
## 260 0.6371 nan 0.0100 0.0001
## 280 0.6221 nan 0.0100 0.0002
## 300 0.6085 nan 0.0100 0.0000
## 320 0.5953 nan 0.0100 0.0001
## 340 0.5832 nan 0.0100 -0.0001
## 360 0.5715 nan 0.0100 -0.0001
## 380 0.5613 nan 0.0100 -0.0001
## 400 0.5516 nan 0.0100 0.0001
## 420 0.5415 nan 0.0100 -0.0000
## 440 0.5321 nan 0.0100 -0.0001
## 460 0.5228 nan 0.0100 -0.0000
## 480 0.5141 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3113 nan 0.0100 0.0043
## 2 1.3012 nan 0.0100 0.0042
## 3 1.2923 nan 0.0100 0.0038
## 4 1.2841 nan 0.0100 0.0035
## 5 1.2747 nan 0.0100 0.0044
## 6 1.2657 nan 0.0100 0.0036
## 7 1.2570 nan 0.0100 0.0036
## 8 1.2494 nan 0.0100 0.0034
## 9 1.2415 nan 0.0100 0.0032
## 10 1.2338 nan 0.0100 0.0033
## 20 1.1613 nan 0.0100 0.0030
## 40 1.0438 nan 0.0100 0.0024
## 60 0.9528 nan 0.0100 0.0016
## 80 0.8806 nan 0.0100 0.0012
## 100 0.8236 nan 0.0100 0.0009
## 120 0.7763 nan 0.0100 0.0008
## 140 0.7362 nan 0.0100 0.0005
## 160 0.7033 nan 0.0100 0.0004
## 180 0.6748 nan 0.0100 0.0003
## 200 0.6492 nan 0.0100 0.0001
## 220 0.6259 nan 0.0100 0.0003
## 240 0.6061 nan 0.0100 0.0002
## 260 0.5882 nan 0.0100 0.0002
## 280 0.5716 nan 0.0100 0.0003
## 300 0.5564 nan 0.0100 0.0000
## 320 0.5421 nan 0.0100 0.0001
## 340 0.5290 nan 0.0100 -0.0001
## 360 0.5165 nan 0.0100 -0.0000
## 380 0.5045 nan 0.0100 0.0001
## 400 0.4916 nan 0.0100 -0.0002
## 420 0.4799 nan 0.0100 0.0001
## 440 0.4695 nan 0.0100 -0.0000
## 460 0.4592 nan 0.0100 0.0002
## 480 0.4485 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3115 nan 0.0100 0.0046
## 2 1.3027 nan 0.0100 0.0041
## 3 1.2939 nan 0.0100 0.0040
## 4 1.2850 nan 0.0100 0.0041
## 5 1.2759 nan 0.0100 0.0041
## 6 1.2679 nan 0.0100 0.0035
## 7 1.2594 nan 0.0100 0.0036
## 8 1.2509 nan 0.0100 0.0038
## 9 1.2429 nan 0.0100 0.0036
## 10 1.2358 nan 0.0100 0.0030
## 20 1.1641 nan 0.0100 0.0025
## 40 1.0456 nan 0.0100 0.0019
## 60 0.9581 nan 0.0100 0.0016
## 80 0.8863 nan 0.0100 0.0012
## 100 0.8303 nan 0.0100 0.0010
## 120 0.7833 nan 0.0100 0.0007
## 140 0.7439 nan 0.0100 0.0003
## 160 0.7102 nan 0.0100 0.0004
## 180 0.6819 nan 0.0100 0.0001
## 200 0.6567 nan 0.0100 0.0002
## 220 0.6353 nan 0.0100 0.0002
## 240 0.6160 nan 0.0100 0.0001
## 260 0.5978 nan 0.0100 0.0003
## 280 0.5816 nan 0.0100 0.0001
## 300 0.5665 nan 0.0100 0.0001
## 320 0.5525 nan 0.0100 0.0000
## 340 0.5388 nan 0.0100 -0.0000
## 360 0.5261 nan 0.0100 0.0000
## 380 0.5140 nan 0.0100 0.0001
## 400 0.5033 nan 0.0100 -0.0001
## 420 0.4915 nan 0.0100 0.0001
## 440 0.4801 nan 0.0100 0.0000
## 460 0.4702 nan 0.0100 0.0000
## 480 0.4605 nan 0.0100 -0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3114 nan 0.0100 0.0044
## 2 1.3021 nan 0.0100 0.0039
## 3 1.2928 nan 0.0100 0.0042
## 4 1.2839 nan 0.0100 0.0037
## 5 1.2762 nan 0.0100 0.0036
## 6 1.2678 nan 0.0100 0.0037
## 7 1.2590 nan 0.0100 0.0036
## 8 1.2509 nan 0.0100 0.0034
## 9 1.2428 nan 0.0100 0.0032
## 10 1.2354 nan 0.0100 0.0031
## 20 1.1614 nan 0.0100 0.0031
## 40 1.0442 nan 0.0100 0.0022
## 60 0.9536 nan 0.0100 0.0016
## 80 0.8846 nan 0.0100 0.0013
## 100 0.8279 nan 0.0100 0.0009
## 120 0.7819 nan 0.0100 0.0007
## 140 0.7438 nan 0.0100 0.0005
## 160 0.7118 nan 0.0100 0.0005
## 180 0.6847 nan 0.0100 0.0004
## 200 0.6611 nan 0.0100 0.0002
## 220 0.6399 nan 0.0100 0.0001
## 240 0.6211 nan 0.0100 0.0001
## 260 0.6034 nan 0.0100 0.0001
## 280 0.5879 nan 0.0100 -0.0001
## 300 0.5723 nan 0.0100 0.0000
## 320 0.5579 nan 0.0100 -0.0003
## 340 0.5438 nan 0.0100 -0.0001
## 360 0.5306 nan 0.0100 -0.0001
## 380 0.5192 nan 0.0100 -0.0000
## 400 0.5084 nan 0.0100 -0.0001
## 420 0.4967 nan 0.0100 0.0000
## 440 0.4864 nan 0.0100 0.0000
## 460 0.4762 nan 0.0100 -0.0000
## 480 0.4664 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2371 nan 0.1000 0.0350
## 2 1.1688 nan 0.1000 0.0332
## 3 1.1141 nan 0.1000 0.0253
## 4 1.0668 nan 0.1000 0.0198
## 5 1.0290 nan 0.1000 0.0138
## 6 0.9913 nan 0.1000 0.0153
## 7 0.9629 nan 0.1000 0.0119
## 8 0.9345 nan 0.1000 0.0119
## 9 0.9094 nan 0.1000 0.0087
## 10 0.8836 nan 0.1000 0.0086
## 20 0.7168 nan 0.1000 0.0001
## 40 0.5774 nan 0.1000 -0.0001
## 60 0.4981 nan 0.1000 0.0000
## 80 0.4358 nan 0.1000 0.0002
## 100 0.3820 nan 0.1000 -0.0009
## 120 0.3404 nan 0.1000 -0.0007
## 140 0.3062 nan 0.1000 -0.0012
## 160 0.2757 nan 0.1000 -0.0002
## 180 0.2470 nan 0.1000 0.0000
## 200 0.2205 nan 0.1000 -0.0001
## 220 0.2013 nan 0.1000 -0.0007
## 240 0.1807 nan 0.1000 -0.0004
## 260 0.1646 nan 0.1000 -0.0003
## 280 0.1515 nan 0.1000 -0.0003
## 300 0.1379 nan 0.1000 -0.0004
## 320 0.1255 nan 0.1000 -0.0004
## 340 0.1129 nan 0.1000 -0.0002
## 360 0.1018 nan 0.1000 -0.0001
## 380 0.0933 nan 0.1000 -0.0002
## 400 0.0859 nan 0.1000 -0.0000
## 420 0.0788 nan 0.1000 -0.0003
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## 460 0.0678 nan 0.1000 -0.0002
## 480 0.0612 nan 0.1000 -0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2409 nan 0.1000 0.0362
## 2 1.1744 nan 0.1000 0.0340
## 3 1.1116 nan 0.1000 0.0251
## 4 1.0595 nan 0.1000 0.0213
## 5 1.0150 nan 0.1000 0.0202
## 6 0.9805 nan 0.1000 0.0126
## 7 0.9478 nan 0.1000 0.0136
## 8 0.9183 nan 0.1000 0.0127
## 9 0.8890 nan 0.1000 0.0106
## 10 0.8645 nan 0.1000 0.0108
## 20 0.7184 nan 0.1000 0.0028
## 40 0.5998 nan 0.1000 0.0001
## 60 0.5173 nan 0.1000 -0.0009
## 80 0.4584 nan 0.1000 -0.0010
## 100 0.4009 nan 0.1000 -0.0004
## 120 0.3563 nan 0.1000 -0.0014
## 140 0.3193 nan 0.1000 0.0000
## 160 0.2880 nan 0.1000 -0.0009
## 180 0.2582 nan 0.1000 -0.0004
## 200 0.2347 nan 0.1000 -0.0007
## 220 0.2129 nan 0.1000 -0.0005
## 240 0.1932 nan 0.1000 -0.0003
## 260 0.1771 nan 0.1000 -0.0008
## 280 0.1614 nan 0.1000 -0.0005
## 300 0.1475 nan 0.1000 -0.0002
## 320 0.1348 nan 0.1000 -0.0005
## 340 0.1235 nan 0.1000 -0.0001
## 360 0.1147 nan 0.1000 -0.0008
## 380 0.1056 nan 0.1000 -0.0002
## 400 0.0978 nan 0.1000 -0.0003
## 420 0.0905 nan 0.1000 -0.0003
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## 460 0.0771 nan 0.1000 -0.0004
## 480 0.0716 nan 0.1000 -0.0003
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2397 nan 0.1000 0.0378
## 2 1.1726 nan 0.1000 0.0310
## 3 1.1083 nan 0.1000 0.0290
## 4 1.0607 nan 0.1000 0.0212
## 5 1.0227 nan 0.1000 0.0179
## 6 0.9898 nan 0.1000 0.0134
## 7 0.9604 nan 0.1000 0.0117
## 8 0.9328 nan 0.1000 0.0101
## 9 0.9033 nan 0.1000 0.0128
## 10 0.8784 nan 0.1000 0.0105
## 20 0.7264 nan 0.1000 0.0015
## 40 0.6068 nan 0.1000 -0.0003
## 60 0.5203 nan 0.1000 -0.0013
## 80 0.4656 nan 0.1000 -0.0013
## 100 0.4127 nan 0.1000 -0.0009
## 120 0.3703 nan 0.1000 -0.0010
## 140 0.3309 nan 0.1000 -0.0006
## 160 0.2965 nan 0.1000 -0.0005
## 180 0.2645 nan 0.1000 -0.0003
## 200 0.2384 nan 0.1000 -0.0008
## 220 0.2177 nan 0.1000 -0.0011
## 240 0.1992 nan 0.1000 -0.0008
## 260 0.1804 nan 0.1000 -0.0004
## 280 0.1646 nan 0.1000 -0.0002
## 300 0.1511 nan 0.1000 -0.0005
## 320 0.1383 nan 0.1000 -0.0000
## 340 0.1274 nan 0.1000 -0.0006
## 360 0.1176 nan 0.1000 -0.0001
## 380 0.1084 nan 0.1000 -0.0002
## 400 0.1001 nan 0.1000 -0.0002
## 420 0.0927 nan 0.1000 -0.0003
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## 460 0.0797 nan 0.1000 -0.0002
## 480 0.0743 nan 0.1000 -0.0002
## 500 0.0694 nan 0.1000 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2401 nan 0.1000 0.0378
## 2 1.1714 nan 0.1000 0.0290
## 3 1.1112 nan 0.1000 0.0239
## 4 1.0593 nan 0.1000 0.0235
## 5 1.0093 nan 0.1000 0.0236
## 6 0.9703 nan 0.1000 0.0138
## 7 0.9365 nan 0.1000 0.0120
## 8 0.9054 nan 0.1000 0.0145
## 9 0.8767 nan 0.1000 0.0092
## 10 0.8507 nan 0.1000 0.0102
## 20 0.6876 nan 0.1000 0.0005
## 40 0.5419 nan 0.1000 -0.0012
## 60 0.4595 nan 0.1000 -0.0028
## 80 0.3933 nan 0.1000 -0.0011
## 100 0.3328 nan 0.1000 -0.0008
## 120 0.2879 nan 0.1000 -0.0012
## 140 0.2499 nan 0.1000 -0.0007
## 160 0.2202 nan 0.1000 -0.0001
## 180 0.1921 nan 0.1000 -0.0005
## 200 0.1693 nan 0.1000 -0.0007
## 220 0.1517 nan 0.1000 -0.0000
## 240 0.1347 nan 0.1000 -0.0004
## 260 0.1200 nan 0.1000 -0.0001
## 280 0.1075 nan 0.1000 -0.0001
## 300 0.0971 nan 0.1000 -0.0002
## 320 0.0867 nan 0.1000 -0.0004
## 340 0.0773 nan 0.1000 -0.0002
## 360 0.0700 nan 0.1000 -0.0002
## 380 0.0623 nan 0.1000 -0.0001
## 400 0.0569 nan 0.1000 -0.0001
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## 460 0.0426 nan 0.1000 -0.0001
## 480 0.0384 nan 0.1000 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2369 nan 0.1000 0.0407
## 2 1.1608 nan 0.1000 0.0340
## 3 1.1053 nan 0.1000 0.0279
## 4 1.0493 nan 0.1000 0.0243
## 5 1.0053 nan 0.1000 0.0171
## 6 0.9656 nan 0.1000 0.0180
## 7 0.9311 nan 0.1000 0.0160
## 8 0.9021 nan 0.1000 0.0109
## 9 0.8743 nan 0.1000 0.0088
## 10 0.8493 nan 0.1000 0.0094
## 20 0.6853 nan 0.1000 0.0018
## 40 0.5473 nan 0.1000 0.0001
## 60 0.4505 nan 0.1000 -0.0007
## 80 0.3848 nan 0.1000 -0.0016
## 100 0.3370 nan 0.1000 -0.0000
## 120 0.2907 nan 0.1000 -0.0008
## 140 0.2559 nan 0.1000 -0.0001
## 160 0.2248 nan 0.1000 -0.0003
## 180 0.1974 nan 0.1000 -0.0005
## 200 0.1760 nan 0.1000 -0.0009
## 220 0.1571 nan 0.1000 -0.0003
## 240 0.1405 nan 0.1000 -0.0003
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## 280 0.1121 nan 0.1000 -0.0003
## 300 0.1008 nan 0.1000 -0.0001
## 320 0.0904 nan 0.1000 -0.0000
## 340 0.0815 nan 0.1000 -0.0002
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## 380 0.0662 nan 0.1000 -0.0003
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2357 nan 0.1000 0.0363
## 2 1.1726 nan 0.1000 0.0275
## 3 1.1146 nan 0.1000 0.0274
## 4 1.0586 nan 0.1000 0.0256
## 5 1.0102 nan 0.1000 0.0196
## 6 0.9701 nan 0.1000 0.0157
## 7 0.9365 nan 0.1000 0.0141
## 8 0.9029 nan 0.1000 0.0139
## 9 0.8777 nan 0.1000 0.0093
## 10 0.8535 nan 0.1000 0.0102
## 20 0.6964 nan 0.1000 0.0016
## 40 0.5495 nan 0.1000 -0.0004
## 60 0.4639 nan 0.1000 -0.0011
## 80 0.3948 nan 0.1000 -0.0004
## 100 0.3454 nan 0.1000 -0.0016
## 120 0.3027 nan 0.1000 -0.0019
## 140 0.2667 nan 0.1000 -0.0003
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## 180 0.2106 nan 0.1000 -0.0007
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## 220 0.1680 nan 0.1000 -0.0000
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## 280 0.1214 nan 0.1000 -0.0006
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## 320 0.0997 nan 0.1000 -0.0003
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2403 nan 0.1000 0.0394
## 2 1.1616 nan 0.1000 0.0363
## 3 1.1005 nan 0.1000 0.0246
## 4 1.0468 nan 0.1000 0.0219
## 5 0.9977 nan 0.1000 0.0216
## 6 0.9526 nan 0.1000 0.0192
## 7 0.9180 nan 0.1000 0.0129
## 8 0.8855 nan 0.1000 0.0127
## 9 0.8546 nan 0.1000 0.0107
## 10 0.8276 nan 0.1000 0.0092
## 20 0.6545 nan 0.1000 0.0019
## 40 0.5027 nan 0.1000 -0.0013
## 60 0.4003 nan 0.1000 -0.0016
## 80 0.3313 nan 0.1000 -0.0002
## 100 0.2815 nan 0.1000 -0.0003
## 120 0.2454 nan 0.1000 -0.0009
## 140 0.2049 nan 0.1000 -0.0004
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## 420 0.0321 nan 0.1000 -0.0001
## 440 0.0286 nan 0.1000 -0.0001
## 460 0.0256 nan 0.1000 -0.0000
## 480 0.0229 nan 0.1000 -0.0001
## 500 0.0202 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2367 nan 0.1000 0.0389
## 2 1.1643 nan 0.1000 0.0291
## 3 1.0923 nan 0.1000 0.0311
## 4 1.0380 nan 0.1000 0.0219
## 5 0.9910 nan 0.1000 0.0197
## 6 0.9471 nan 0.1000 0.0167
## 7 0.9127 nan 0.1000 0.0150
## 8 0.8831 nan 0.1000 0.0113
## 9 0.8550 nan 0.1000 0.0114
## 10 0.8280 nan 0.1000 0.0098
## 20 0.6623 nan 0.1000 0.0030
## 40 0.5102 nan 0.1000 -0.0014
## 60 0.4213 nan 0.1000 -0.0016
## 80 0.3473 nan 0.1000 -0.0006
## 100 0.2955 nan 0.1000 -0.0000
## 120 0.2507 nan 0.1000 -0.0007
## 140 0.2121 nan 0.1000 -0.0007
## 160 0.1808 nan 0.1000 -0.0003
## 180 0.1544 nan 0.1000 -0.0005
## 200 0.1341 nan 0.1000 -0.0003
## 220 0.1168 nan 0.1000 -0.0005
## 240 0.1018 nan 0.1000 -0.0003
## 260 0.0892 nan 0.1000 -0.0002
## 280 0.0785 nan 0.1000 -0.0003
## 300 0.0687 nan 0.1000 -0.0003
## 320 0.0606 nan 0.1000 -0.0001
## 340 0.0528 nan 0.1000 -0.0001
## 360 0.0463 nan 0.1000 -0.0001
## 380 0.0406 nan 0.1000 -0.0002
## 400 0.0356 nan 0.1000 -0.0001
## 420 0.0317 nan 0.1000 -0.0000
## 440 0.0284 nan 0.1000 -0.0001
## 460 0.0248 nan 0.1000 -0.0001
## 480 0.0218 nan 0.1000 -0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2320 nan 0.1000 0.0410
## 2 1.1620 nan 0.1000 0.0305
## 3 1.1031 nan 0.1000 0.0272
## 4 1.0519 nan 0.1000 0.0200
## 5 1.0070 nan 0.1000 0.0199
## 6 0.9596 nan 0.1000 0.0204
## 7 0.9246 nan 0.1000 0.0112
## 8 0.8866 nan 0.1000 0.0144
## 9 0.8580 nan 0.1000 0.0098
## 10 0.8330 nan 0.1000 0.0053
## 20 0.6744 nan 0.1000 0.0006
## 40 0.5217 nan 0.1000 0.0016
## 60 0.4239 nan 0.1000 -0.0004
## 80 0.3529 nan 0.1000 -0.0010
## 100 0.2978 nan 0.1000 -0.0019
## 120 0.2534 nan 0.1000 -0.0011
## 140 0.2178 nan 0.1000 -0.0006
## 160 0.1886 nan 0.1000 -0.0009
## 180 0.1613 nan 0.1000 -0.0002
## 200 0.1413 nan 0.1000 -0.0002
## 220 0.1227 nan 0.1000 -0.0001
## 240 0.1083 nan 0.1000 -0.0002
## 260 0.0950 nan 0.1000 -0.0003
## 280 0.0845 nan 0.1000 -0.0002
## 300 0.0746 nan 0.1000 -0.0004
## 320 0.0650 nan 0.1000 -0.0002
## 340 0.0579 nan 0.1000 -0.0002
## 360 0.0515 nan 0.1000 -0.0002
## 380 0.0453 nan 0.1000 -0.0002
## 400 0.0401 nan 0.1000 -0.0003
## 420 0.0354 nan 0.1000 -0.0001
## 440 0.0315 nan 0.1000 -0.0001
## 460 0.0279 nan 0.1000 -0.0002
## 480 0.0247 nan 0.1000 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3199 nan 0.0010 0.0003
## 2 1.3192 nan 0.0010 0.0004
## 3 1.3184 nan 0.0010 0.0003
## 4 1.3176 nan 0.0010 0.0004
## 5 1.3168 nan 0.0010 0.0003
## 6 1.3160 nan 0.0010 0.0004
## 7 1.3152 nan 0.0010 0.0003
## 8 1.3145 nan 0.0010 0.0004
## 9 1.3137 nan 0.0010 0.0003
## 10 1.3129 nan 0.0010 0.0003
## 20 1.3048 nan 0.0010 0.0004
## 40 1.2896 nan 0.0010 0.0003
## 60 1.2745 nan 0.0010 0.0004
## 80 1.2602 nan 0.0010 0.0003
## 100 1.2461 nan 0.0010 0.0003
## 120 1.2327 nan 0.0010 0.0003
## 140 1.2200 nan 0.0010 0.0003
## 160 1.2071 nan 0.0010 0.0003
## 180 1.1946 nan 0.0010 0.0002
## 200 1.1826 nan 0.0010 0.0003
## 220 1.1711 nan 0.0010 0.0003
## 240 1.1598 nan 0.0010 0.0003
## 260 1.1486 nan 0.0010 0.0002
## 280 1.1381 nan 0.0010 0.0002
## 300 1.1274 nan 0.0010 0.0002
## 320 1.1173 nan 0.0010 0.0002
## 340 1.1074 nan 0.0010 0.0002
## 360 1.0979 nan 0.0010 0.0002
## 380 1.0886 nan 0.0010 0.0002
## 400 1.0790 nan 0.0010 0.0002
## 420 1.0701 nan 0.0010 0.0002
## 440 1.0614 nan 0.0010 0.0002
## 460 1.0530 nan 0.0010 0.0001
## 480 1.0448 nan 0.0010 0.0002
## 500 1.0369 nan 0.0010 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3191 nan 0.0010 0.0003
## 3 1.3183 nan 0.0010 0.0003
## 4 1.3175 nan 0.0010 0.0004
## 5 1.3167 nan 0.0010 0.0004
## 6 1.3159 nan 0.0010 0.0004
## 7 1.3150 nan 0.0010 0.0004
## 8 1.3142 nan 0.0010 0.0003
## 9 1.3135 nan 0.0010 0.0003
## 10 1.3126 nan 0.0010 0.0004
## 20 1.3049 nan 0.0010 0.0004
## 40 1.2895 nan 0.0010 0.0004
## 60 1.2749 nan 0.0010 0.0003
## 80 1.2603 nan 0.0010 0.0003
## 100 1.2465 nan 0.0010 0.0003
## 120 1.2328 nan 0.0010 0.0003
## 140 1.2196 nan 0.0010 0.0003
## 160 1.2067 nan 0.0010 0.0003
## 180 1.1945 nan 0.0010 0.0003
## 200 1.1823 nan 0.0010 0.0002
## 220 1.1703 nan 0.0010 0.0003
## 240 1.1589 nan 0.0010 0.0003
## 260 1.1479 nan 0.0010 0.0002
## 280 1.1372 nan 0.0010 0.0002
## 300 1.1267 nan 0.0010 0.0002
## 320 1.1164 nan 0.0010 0.0002
## 340 1.1065 nan 0.0010 0.0002
## 360 1.0969 nan 0.0010 0.0002
## 380 1.0875 nan 0.0010 0.0002
## 400 1.0787 nan 0.0010 0.0002
## 420 1.0697 nan 0.0010 0.0002
## 440 1.0610 nan 0.0010 0.0002
## 460 1.0527 nan 0.0010 0.0002
## 480 1.0443 nan 0.0010 0.0002
## 500 1.0365 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3191 nan 0.0010 0.0003
## 3 1.3183 nan 0.0010 0.0004
## 4 1.3175 nan 0.0010 0.0004
## 5 1.3167 nan 0.0010 0.0004
## 6 1.3159 nan 0.0010 0.0003
## 7 1.3151 nan 0.0010 0.0003
## 8 1.3143 nan 0.0010 0.0004
## 9 1.3136 nan 0.0010 0.0004
## 10 1.3128 nan 0.0010 0.0004
## 20 1.3050 nan 0.0010 0.0004
## 40 1.2896 nan 0.0010 0.0003
## 60 1.2747 nan 0.0010 0.0003
## 80 1.2603 nan 0.0010 0.0003
## 100 1.2462 nan 0.0010 0.0003
## 120 1.2326 nan 0.0010 0.0003
## 140 1.2197 nan 0.0010 0.0003
## 160 1.2070 nan 0.0010 0.0003
## 180 1.1943 nan 0.0010 0.0003
## 200 1.1825 nan 0.0010 0.0003
## 220 1.1707 nan 0.0010 0.0003
## 240 1.1594 nan 0.0010 0.0002
## 260 1.1486 nan 0.0010 0.0002
## 280 1.1380 nan 0.0010 0.0003
## 300 1.1279 nan 0.0010 0.0002
## 320 1.1176 nan 0.0010 0.0002
## 340 1.1078 nan 0.0010 0.0002
## 360 1.0983 nan 0.0010 0.0002
## 380 1.0891 nan 0.0010 0.0002
## 400 1.0798 nan 0.0010 0.0002
## 420 1.0711 nan 0.0010 0.0002
## 440 1.0625 nan 0.0010 0.0002
## 460 1.0541 nan 0.0010 0.0002
## 480 1.0459 nan 0.0010 0.0002
## 500 1.0377 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3180 nan 0.0010 0.0004
## 4 1.3172 nan 0.0010 0.0004
## 5 1.3163 nan 0.0010 0.0004
## 6 1.3153 nan 0.0010 0.0004
## 7 1.3145 nan 0.0010 0.0004
## 8 1.3137 nan 0.0010 0.0004
## 9 1.3127 nan 0.0010 0.0004
## 10 1.3119 nan 0.0010 0.0004
## 20 1.3033 nan 0.0010 0.0004
## 40 1.2867 nan 0.0010 0.0004
## 60 1.2709 nan 0.0010 0.0004
## 80 1.2553 nan 0.0010 0.0003
## 100 1.2406 nan 0.0010 0.0003
## 120 1.2259 nan 0.0010 0.0003
## 140 1.2120 nan 0.0010 0.0003
## 160 1.1986 nan 0.0010 0.0003
## 180 1.1853 nan 0.0010 0.0003
## 200 1.1726 nan 0.0010 0.0003
## 220 1.1601 nan 0.0010 0.0003
## 240 1.1481 nan 0.0010 0.0002
## 260 1.1364 nan 0.0010 0.0002
## 280 1.1249 nan 0.0010 0.0002
## 300 1.1140 nan 0.0010 0.0002
## 320 1.1032 nan 0.0010 0.0002
## 340 1.0926 nan 0.0010 0.0002
## 360 1.0823 nan 0.0010 0.0002
## 380 1.0724 nan 0.0010 0.0002
## 400 1.0627 nan 0.0010 0.0002
## 420 1.0535 nan 0.0010 0.0002
## 440 1.0445 nan 0.0010 0.0002
## 460 1.0354 nan 0.0010 0.0002
## 480 1.0267 nan 0.0010 0.0002
## 500 1.0182 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3180 nan 0.0010 0.0004
## 4 1.3172 nan 0.0010 0.0004
## 5 1.3163 nan 0.0010 0.0004
## 6 1.3155 nan 0.0010 0.0004
## 7 1.3145 nan 0.0010 0.0004
## 8 1.3136 nan 0.0010 0.0004
## 9 1.3128 nan 0.0010 0.0004
## 10 1.3119 nan 0.0010 0.0004
## 20 1.3032 nan 0.0010 0.0004
## 40 1.2871 nan 0.0010 0.0004
## 60 1.2711 nan 0.0010 0.0004
## 80 1.2558 nan 0.0010 0.0003
## 100 1.2410 nan 0.0010 0.0003
## 120 1.2268 nan 0.0010 0.0003
## 140 1.2128 nan 0.0010 0.0003
## 160 1.1991 nan 0.0010 0.0003
## 180 1.1856 nan 0.0010 0.0003
## 200 1.1729 nan 0.0010 0.0003
## 220 1.1605 nan 0.0010 0.0003
## 240 1.1486 nan 0.0010 0.0003
## 260 1.1369 nan 0.0010 0.0003
## 280 1.1256 nan 0.0010 0.0002
## 300 1.1148 nan 0.0010 0.0002
## 320 1.1041 nan 0.0010 0.0002
## 340 1.0935 nan 0.0010 0.0002
## 360 1.0834 nan 0.0010 0.0002
## 380 1.0737 nan 0.0010 0.0002
## 400 1.0640 nan 0.0010 0.0002
## 420 1.0544 nan 0.0010 0.0002
## 440 1.0456 nan 0.0010 0.0002
## 460 1.0369 nan 0.0010 0.0002
## 480 1.0281 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3181 nan 0.0010 0.0004
## 4 1.3172 nan 0.0010 0.0004
## 5 1.3164 nan 0.0010 0.0004
## 6 1.3155 nan 0.0010 0.0004
## 7 1.3147 nan 0.0010 0.0003
## 8 1.3138 nan 0.0010 0.0004
## 9 1.3129 nan 0.0010 0.0004
## 10 1.3121 nan 0.0010 0.0004
## 20 1.3035 nan 0.0010 0.0003
## 40 1.2872 nan 0.0010 0.0004
## 60 1.2711 nan 0.0010 0.0003
## 80 1.2560 nan 0.0010 0.0003
## 100 1.2409 nan 0.0010 0.0003
## 120 1.2266 nan 0.0010 0.0003
## 140 1.2127 nan 0.0010 0.0003
## 160 1.1993 nan 0.0010 0.0003
## 180 1.1860 nan 0.0010 0.0003
## 200 1.1732 nan 0.0010 0.0003
## 220 1.1610 nan 0.0010 0.0002
## 240 1.1492 nan 0.0010 0.0003
## 260 1.1375 nan 0.0010 0.0003
## 280 1.1261 nan 0.0010 0.0002
## 300 1.1151 nan 0.0010 0.0002
## 320 1.1042 nan 0.0010 0.0002
## 340 1.0938 nan 0.0010 0.0002
## 360 1.0835 nan 0.0010 0.0002
## 380 1.0738 nan 0.0010 0.0002
## 400 1.0643 nan 0.0010 0.0002
## 420 1.0550 nan 0.0010 0.0002
## 440 1.0459 nan 0.0010 0.0002
## 460 1.0373 nan 0.0010 0.0002
## 480 1.0289 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3179 nan 0.0010 0.0004
## 4 1.3170 nan 0.0010 0.0004
## 5 1.3160 nan 0.0010 0.0004
## 6 1.3150 nan 0.0010 0.0004
## 7 1.3141 nan 0.0010 0.0004
## 8 1.3133 nan 0.0010 0.0004
## 9 1.3124 nan 0.0010 0.0004
## 10 1.3114 nan 0.0010 0.0004
## 20 1.3024 nan 0.0010 0.0004
## 40 1.2854 nan 0.0010 0.0003
## 60 1.2686 nan 0.0010 0.0004
## 80 1.2523 nan 0.0010 0.0003
## 100 1.2368 nan 0.0010 0.0003
## 120 1.2214 nan 0.0010 0.0003
## 140 1.2068 nan 0.0010 0.0003
## 160 1.1926 nan 0.0010 0.0003
## 180 1.1787 nan 0.0010 0.0003
## 200 1.1657 nan 0.0010 0.0003
## 220 1.1528 nan 0.0010 0.0003
## 240 1.1403 nan 0.0010 0.0002
## 260 1.1281 nan 0.0010 0.0002
## 280 1.1160 nan 0.0010 0.0003
## 300 1.1044 nan 0.0010 0.0002
## 320 1.0932 nan 0.0010 0.0002
## 340 1.0821 nan 0.0010 0.0002
## 360 1.0714 nan 0.0010 0.0002
## 380 1.0612 nan 0.0010 0.0002
## 400 1.0510 nan 0.0010 0.0002
## 420 1.0413 nan 0.0010 0.0001
## 440 1.0317 nan 0.0010 0.0002
## 460 1.0225 nan 0.0010 0.0002
## 480 1.0135 nan 0.0010 0.0002
## 500 1.0047 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0003
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3179 nan 0.0010 0.0004
## 4 1.3170 nan 0.0010 0.0004
## 5 1.3161 nan 0.0010 0.0004
## 6 1.3151 nan 0.0010 0.0004
## 7 1.3142 nan 0.0010 0.0004
## 8 1.3134 nan 0.0010 0.0004
## 9 1.3125 nan 0.0010 0.0004
## 10 1.3116 nan 0.0010 0.0004
## 20 1.3027 nan 0.0010 0.0003
## 40 1.2855 nan 0.0010 0.0004
## 60 1.2688 nan 0.0010 0.0004
## 80 1.2527 nan 0.0010 0.0003
## 100 1.2373 nan 0.0010 0.0004
## 120 1.2222 nan 0.0010 0.0003
## 140 1.2077 nan 0.0010 0.0003
## 160 1.1935 nan 0.0010 0.0003
## 180 1.1799 nan 0.0010 0.0003
## 200 1.1667 nan 0.0010 0.0003
## 220 1.1536 nan 0.0010 0.0003
## 240 1.1411 nan 0.0010 0.0002
## 260 1.1293 nan 0.0010 0.0003
## 280 1.1174 nan 0.0010 0.0002
## 300 1.1059 nan 0.0010 0.0003
## 320 1.0948 nan 0.0010 0.0002
## 340 1.0839 nan 0.0010 0.0002
## 360 1.0735 nan 0.0010 0.0002
## 380 1.0634 nan 0.0010 0.0002
## 400 1.0534 nan 0.0010 0.0002
## 420 1.0438 nan 0.0010 0.0002
## 440 1.0342 nan 0.0010 0.0002
## 460 1.0249 nan 0.0010 0.0002
## 480 1.0161 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3180 nan 0.0010 0.0004
## 4 1.3172 nan 0.0010 0.0004
## 5 1.3162 nan 0.0010 0.0004
## 6 1.3154 nan 0.0010 0.0004
## 7 1.3145 nan 0.0010 0.0004
## 8 1.3136 nan 0.0010 0.0004
## 9 1.3128 nan 0.0010 0.0004
## 10 1.3119 nan 0.0010 0.0004
## 20 1.3033 nan 0.0010 0.0003
## 40 1.2862 nan 0.0010 0.0004
## 60 1.2697 nan 0.0010 0.0004
## 80 1.2535 nan 0.0010 0.0004
## 100 1.2378 nan 0.0010 0.0003
## 120 1.2226 nan 0.0010 0.0003
## 140 1.2083 nan 0.0010 0.0003
## 160 1.1943 nan 0.0010 0.0003
## 180 1.1807 nan 0.0010 0.0003
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## 220 1.1551 nan 0.0010 0.0002
## 240 1.1430 nan 0.0010 0.0003
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## 280 1.1191 nan 0.0010 0.0002
## 300 1.1073 nan 0.0010 0.0002
## 320 1.0964 nan 0.0010 0.0002
## 340 1.0857 nan 0.0010 0.0002
## 360 1.0751 nan 0.0010 0.0002
## 380 1.0650 nan 0.0010 0.0002
## 400 1.0551 nan 0.0010 0.0002
## 420 1.0453 nan 0.0010 0.0002
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## 460 1.0267 nan 0.0010 0.0002
## 480 1.0179 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3122 nan 0.0100 0.0039
## 2 1.3041 nan 0.0100 0.0037
## 3 1.2973 nan 0.0100 0.0031
## 4 1.2900 nan 0.0100 0.0031
## 5 1.2825 nan 0.0100 0.0033
## 6 1.2748 nan 0.0100 0.0035
## 7 1.2679 nan 0.0100 0.0027
## 8 1.2608 nan 0.0100 0.0032
## 9 1.2536 nan 0.0100 0.0031
## 10 1.2460 nan 0.0100 0.0031
## 20 1.1829 nan 0.0100 0.0026
## 40 1.0789 nan 0.0100 0.0019
## 60 1.0016 nan 0.0100 0.0016
## 80 0.9369 nan 0.0100 0.0009
## 100 0.8881 nan 0.0100 0.0009
## 120 0.8468 nan 0.0100 0.0008
## 140 0.8130 nan 0.0100 0.0003
## 160 0.7844 nan 0.0100 0.0004
## 180 0.7585 nan 0.0100 0.0002
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## 220 0.7174 nan 0.0100 0.0001
## 240 0.6999 nan 0.0100 0.0001
## 260 0.6843 nan 0.0100 -0.0001
## 280 0.6696 nan 0.0100 0.0000
## 300 0.6565 nan 0.0100 0.0002
## 320 0.6440 nan 0.0100 -0.0001
## 340 0.6330 nan 0.0100 -0.0001
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## 380 0.6121 nan 0.0100 -0.0000
## 400 0.6027 nan 0.0100 0.0001
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## 480 0.5662 nan 0.0100 -0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3131 nan 0.0100 0.0036
## 2 1.3047 nan 0.0100 0.0038
## 3 1.2964 nan 0.0100 0.0040
## 4 1.2900 nan 0.0100 0.0028
## 5 1.2830 nan 0.0100 0.0033
## 6 1.2759 nan 0.0100 0.0033
## 7 1.2686 nan 0.0100 0.0033
## 8 1.2621 nan 0.0100 0.0030
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## 10 1.2474 nan 0.0100 0.0031
## 20 1.1838 nan 0.0100 0.0025
## 40 1.0797 nan 0.0100 0.0020
## 60 0.9986 nan 0.0100 0.0013
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## 140 0.8116 nan 0.0100 0.0004
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## 340 0.6394 nan 0.0100 -0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3127 nan 0.0100 0.0035
## 2 1.3050 nan 0.0100 0.0032
## 3 1.2970 nan 0.0100 0.0036
## 4 1.2889 nan 0.0100 0.0036
## 5 1.2815 nan 0.0100 0.0033
## 6 1.2733 nan 0.0100 0.0035
## 7 1.2662 nan 0.0100 0.0029
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## 20 1.1820 nan 0.0100 0.0027
## 40 1.0790 nan 0.0100 0.0018
## 60 1.0013 nan 0.0100 0.0013
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## 240 0.7048 nan 0.0100 -0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3116 nan 0.0100 0.0041
## 2 1.3025 nan 0.0100 0.0042
## 3 1.2931 nan 0.0100 0.0038
## 4 1.2845 nan 0.0100 0.0034
## 5 1.2768 nan 0.0100 0.0031
## 6 1.2685 nan 0.0100 0.0035
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## 40 1.0603 nan 0.0100 0.0016
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## 360 0.5764 nan 0.0100 0.0001
## 380 0.5653 nan 0.0100 -0.0001
## 400 0.5538 nan 0.0100 -0.0000
## 420 0.5425 nan 0.0100 0.0001
## 440 0.5327 nan 0.0100 0.0000
## 460 0.5231 nan 0.0100 -0.0000
## 480 0.5141 nan 0.0100 -0.0000
## 500 0.5056 nan 0.0100 0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3122 nan 0.0100 0.0039
## 2 1.3034 nan 0.0100 0.0042
## 3 1.2946 nan 0.0100 0.0037
## 4 1.2860 nan 0.0100 0.0040
## 5 1.2780 nan 0.0100 0.0035
## 6 1.2703 nan 0.0100 0.0032
## 7 1.2623 nan 0.0100 0.0036
## 8 1.2548 nan 0.0100 0.0038
## 9 1.2468 nan 0.0100 0.0033
## 10 1.2393 nan 0.0100 0.0034
## 20 1.1718 nan 0.0100 0.0030
## 40 1.0631 nan 0.0100 0.0018
## 60 0.9811 nan 0.0100 0.0016
## 80 0.9158 nan 0.0100 0.0013
## 100 0.8616 nan 0.0100 0.0007
## 120 0.8181 nan 0.0100 0.0008
## 140 0.7836 nan 0.0100 0.0005
## 160 0.7548 nan 0.0100 0.0003
## 180 0.7285 nan 0.0100 0.0002
## 200 0.7048 nan 0.0100 -0.0000
## 220 0.6849 nan 0.0100 0.0002
## 240 0.6669 nan 0.0100 0.0004
## 260 0.6499 nan 0.0100 0.0000
## 280 0.6351 nan 0.0100 0.0001
## 300 0.6209 nan 0.0100 0.0003
## 320 0.6076 nan 0.0100 -0.0001
## 340 0.5954 nan 0.0100 -0.0001
## 360 0.5834 nan 0.0100 0.0001
## 380 0.5723 nan 0.0100 -0.0002
## 400 0.5621 nan 0.0100 -0.0002
## 420 0.5522 nan 0.0100 -0.0001
## 440 0.5425 nan 0.0100 -0.0000
## 460 0.5326 nan 0.0100 -0.0002
## 480 0.5228 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3121 nan 0.0100 0.0039
## 2 1.3036 nan 0.0100 0.0040
## 3 1.2949 nan 0.0100 0.0039
## 4 1.2866 nan 0.0100 0.0036
## 5 1.2793 nan 0.0100 0.0033
## 6 1.2711 nan 0.0100 0.0035
## 7 1.2630 nan 0.0100 0.0037
## 8 1.2557 nan 0.0100 0.0033
## 9 1.2489 nan 0.0100 0.0031
## 10 1.2412 nan 0.0100 0.0034
## 20 1.1745 nan 0.0100 0.0025
## 40 1.0647 nan 0.0100 0.0019
## 60 0.9813 nan 0.0100 0.0014
## 80 0.9169 nan 0.0100 0.0012
## 100 0.8654 nan 0.0100 0.0007
## 120 0.8232 nan 0.0100 0.0005
## 140 0.7892 nan 0.0100 0.0004
## 160 0.7596 nan 0.0100 0.0005
## 180 0.7351 nan 0.0100 0.0003
## 200 0.7133 nan 0.0100 0.0004
## 220 0.6940 nan 0.0100 0.0001
## 240 0.6772 nan 0.0100 0.0001
## 260 0.6611 nan 0.0100 0.0000
## 280 0.6461 nan 0.0100 0.0001
## 300 0.6314 nan 0.0100 0.0001
## 320 0.6191 nan 0.0100 0.0001
## 340 0.6066 nan 0.0100 -0.0001
## 360 0.5946 nan 0.0100 -0.0000
## 380 0.5846 nan 0.0100 -0.0000
## 400 0.5734 nan 0.0100 -0.0000
## 420 0.5626 nan 0.0100 -0.0001
## 440 0.5530 nan 0.0100 -0.0001
## 460 0.5429 nan 0.0100 0.0001
## 480 0.5335 nan 0.0100 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3115 nan 0.0100 0.0043
## 2 1.3027 nan 0.0100 0.0040
## 3 1.2937 nan 0.0100 0.0044
## 4 1.2856 nan 0.0100 0.0036
## 5 1.2775 nan 0.0100 0.0036
## 6 1.2688 nan 0.0100 0.0040
## 7 1.2604 nan 0.0100 0.0037
## 8 1.2515 nan 0.0100 0.0036
## 9 1.2430 nan 0.0100 0.0038
## 10 1.2348 nan 0.0100 0.0035
## 20 1.1648 nan 0.0100 0.0030
## 40 1.0496 nan 0.0100 0.0019
## 60 0.9615 nan 0.0100 0.0014
## 80 0.8931 nan 0.0100 0.0012
## 100 0.8387 nan 0.0100 0.0009
## 120 0.7926 nan 0.0100 0.0004
## 140 0.7536 nan 0.0100 0.0006
## 160 0.7215 nan 0.0100 0.0003
## 180 0.6953 nan 0.0100 0.0002
## 200 0.6712 nan 0.0100 0.0003
## 220 0.6479 nan 0.0100 0.0003
## 240 0.6290 nan 0.0100 0.0004
## 260 0.6100 nan 0.0100 0.0001
## 280 0.5923 nan 0.0100 0.0001
## 300 0.5751 nan 0.0100 -0.0000
## 320 0.5596 nan 0.0100 0.0002
## 340 0.5462 nan 0.0100 -0.0000
## 360 0.5321 nan 0.0100 -0.0001
## 380 0.5202 nan 0.0100 0.0000
## 400 0.5079 nan 0.0100 -0.0000
## 420 0.4969 nan 0.0100 0.0001
## 440 0.4855 nan 0.0100 0.0000
## 460 0.4751 nan 0.0100 -0.0001
## 480 0.4643 nan 0.0100 0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3114 nan 0.0100 0.0040
## 2 1.3026 nan 0.0100 0.0039
## 3 1.2939 nan 0.0100 0.0040
## 4 1.2851 nan 0.0100 0.0035
## 5 1.2769 nan 0.0100 0.0039
## 6 1.2690 nan 0.0100 0.0033
## 7 1.2614 nan 0.0100 0.0034
## 8 1.2537 nan 0.0100 0.0033
## 9 1.2460 nan 0.0100 0.0034
## 10 1.2380 nan 0.0100 0.0036
## 20 1.1663 nan 0.0100 0.0030
## 40 1.0530 nan 0.0100 0.0021
## 60 0.9661 nan 0.0100 0.0014
## 80 0.8985 nan 0.0100 0.0009
## 100 0.8440 nan 0.0100 0.0010
## 120 0.8002 nan 0.0100 0.0007
## 140 0.7635 nan 0.0100 0.0003
## 160 0.7314 nan 0.0100 0.0005
## 180 0.7040 nan 0.0100 0.0002
## 200 0.6803 nan 0.0100 0.0002
## 220 0.6604 nan 0.0100 0.0000
## 240 0.6406 nan 0.0100 -0.0002
## 260 0.6221 nan 0.0100 0.0001
## 280 0.6048 nan 0.0100 0.0001
## 300 0.5898 nan 0.0100 -0.0001
## 320 0.5758 nan 0.0100 -0.0000
## 340 0.5621 nan 0.0100 0.0001
## 360 0.5477 nan 0.0100 0.0000
## 380 0.5344 nan 0.0100 0.0001
## 400 0.5220 nan 0.0100 0.0002
## 420 0.5098 nan 0.0100 0.0001
## 440 0.4994 nan 0.0100 0.0000
## 460 0.4896 nan 0.0100 -0.0000
## 480 0.4797 nan 0.0100 0.0000
## 500 0.4691 nan 0.0100 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3110 nan 0.0100 0.0045
## 2 1.3017 nan 0.0100 0.0042
## 3 1.2932 nan 0.0100 0.0036
## 4 1.2844 nan 0.0100 0.0039
## 5 1.2761 nan 0.0100 0.0037
## 6 1.2681 nan 0.0100 0.0034
## 7 1.2597 nan 0.0100 0.0040
## 8 1.2520 nan 0.0100 0.0034
## 9 1.2437 nan 0.0100 0.0040
## 10 1.2363 nan 0.0100 0.0032
## 20 1.1660 nan 0.0100 0.0028
## 40 1.0544 nan 0.0100 0.0016
## 60 0.9678 nan 0.0100 0.0017
## 80 0.9021 nan 0.0100 0.0011
## 100 0.8469 nan 0.0100 0.0009
## 120 0.8047 nan 0.0100 0.0006
## 140 0.7676 nan 0.0100 0.0004
## 160 0.7369 nan 0.0100 0.0004
## 180 0.7115 nan 0.0100 0.0003
## 200 0.6884 nan 0.0100 0.0003
## 220 0.6680 nan 0.0100 -0.0000
## 240 0.6493 nan 0.0100 0.0002
## 260 0.6327 nan 0.0100 0.0000
## 280 0.6159 nan 0.0100 0.0000
## 300 0.6003 nan 0.0100 0.0001
## 320 0.5873 nan 0.0100 -0.0001
## 340 0.5746 nan 0.0100 0.0002
## 360 0.5615 nan 0.0100 -0.0001
## 380 0.5495 nan 0.0100 -0.0000
## 400 0.5384 nan 0.0100 0.0000
## 420 0.5272 nan 0.0100 -0.0001
## 440 0.5167 nan 0.0100 -0.0000
## 460 0.5059 nan 0.0100 -0.0001
## 480 0.4962 nan 0.0100 -0.0000
## 500 0.4873 nan 0.0100 0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2468 nan 0.1000 0.0341
## 2 1.1834 nan 0.1000 0.0235
## 3 1.1221 nan 0.1000 0.0258
## 4 1.0735 nan 0.1000 0.0201
## 5 1.0316 nan 0.1000 0.0182
## 6 0.9951 nan 0.1000 0.0140
## 7 0.9626 nan 0.1000 0.0130
## 8 0.9320 nan 0.1000 0.0107
## 9 0.9045 nan 0.1000 0.0100
## 10 0.8812 nan 0.1000 0.0096
## 20 0.7335 nan 0.1000 0.0020
## 40 0.6046 nan 0.1000 -0.0006
## 60 0.5233 nan 0.1000 -0.0010
## 80 0.4591 nan 0.1000 -0.0008
## 100 0.4024 nan 0.1000 0.0003
## 120 0.3538 nan 0.1000 -0.0002
## 140 0.3194 nan 0.1000 -0.0005
## 160 0.2860 nan 0.1000 -0.0001
## 180 0.2575 nan 0.1000 -0.0004
## 200 0.2326 nan 0.1000 -0.0002
## 220 0.2085 nan 0.1000 -0.0007
## 240 0.1906 nan 0.1000 -0.0006
## 260 0.1731 nan 0.1000 -0.0006
## 280 0.1591 nan 0.1000 -0.0004
## 300 0.1439 nan 0.1000 -0.0004
## 320 0.1325 nan 0.1000 0.0001
## 340 0.1205 nan 0.1000 -0.0003
## 360 0.1101 nan 0.1000 -0.0005
## 380 0.1013 nan 0.1000 -0.0003
## 400 0.0928 nan 0.1000 -0.0001
## 420 0.0851 nan 0.1000 -0.0001
## 440 0.0790 nan 0.1000 -0.0001
## 460 0.0729 nan 0.1000 0.0000
## 480 0.0671 nan 0.1000 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2478 nan 0.1000 0.0328
## 2 1.1834 nan 0.1000 0.0296
## 3 1.1244 nan 0.1000 0.0260
## 4 1.0736 nan 0.1000 0.0217
## 5 1.0293 nan 0.1000 0.0180
## 6 0.9950 nan 0.1000 0.0137
## 7 0.9640 nan 0.1000 0.0121
## 8 0.9377 nan 0.1000 0.0106
## 9 0.9100 nan 0.1000 0.0110
## 10 0.8833 nan 0.1000 0.0101
## 20 0.7386 nan 0.1000 0.0030
## 40 0.6094 nan 0.1000 -0.0001
## 60 0.5323 nan 0.1000 -0.0005
## 80 0.4671 nan 0.1000 -0.0010
## 100 0.4143 nan 0.1000 -0.0012
## 120 0.3653 nan 0.1000 -0.0006
## 140 0.3244 nan 0.1000 -0.0011
## 160 0.2955 nan 0.1000 -0.0012
## 180 0.2676 nan 0.1000 -0.0004
## 200 0.2401 nan 0.1000 -0.0002
## 220 0.2191 nan 0.1000 -0.0007
## 240 0.1991 nan 0.1000 -0.0007
## 260 0.1814 nan 0.1000 -0.0000
## 280 0.1689 nan 0.1000 -0.0004
## 300 0.1563 nan 0.1000 -0.0002
## 320 0.1424 nan 0.1000 -0.0004
## 340 0.1301 nan 0.1000 -0.0006
## 360 0.1205 nan 0.1000 -0.0005
## 380 0.1111 nan 0.1000 -0.0004
## 400 0.1014 nan 0.1000 -0.0001
## 420 0.0937 nan 0.1000 -0.0000
## 440 0.0859 nan 0.1000 -0.0002
## 460 0.0788 nan 0.1000 -0.0002
## 480 0.0730 nan 0.1000 -0.0003
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2446 nan 0.1000 0.0350
## 2 1.1811 nan 0.1000 0.0283
## 3 1.1292 nan 0.1000 0.0240
## 4 1.0800 nan 0.1000 0.0215
## 5 1.0386 nan 0.1000 0.0182
## 6 1.0017 nan 0.1000 0.0153
## 7 0.9710 nan 0.1000 0.0142
## 8 0.9389 nan 0.1000 0.0145
## 9 0.9153 nan 0.1000 0.0107
## 10 0.8911 nan 0.1000 0.0101
## 20 0.7474 nan 0.1000 0.0032
## 40 0.6205 nan 0.1000 -0.0004
## 60 0.5439 nan 0.1000 -0.0012
## 80 0.4863 nan 0.1000 -0.0010
## 100 0.4269 nan 0.1000 -0.0006
## 120 0.3867 nan 0.1000 -0.0016
## 140 0.3477 nan 0.1000 0.0001
## 160 0.3125 nan 0.1000 -0.0013
## 180 0.2802 nan 0.1000 -0.0002
## 200 0.2550 nan 0.1000 -0.0004
## 220 0.2323 nan 0.1000 -0.0009
## 240 0.2127 nan 0.1000 -0.0005
## 260 0.1960 nan 0.1000 -0.0008
## 280 0.1811 nan 0.1000 -0.0005
## 300 0.1674 nan 0.1000 -0.0006
## 320 0.1543 nan 0.1000 -0.0005
## 340 0.1430 nan 0.1000 -0.0003
## 360 0.1310 nan 0.1000 -0.0004
## 380 0.1211 nan 0.1000 -0.0002
## 400 0.1125 nan 0.1000 -0.0004
## 420 0.1037 nan 0.1000 -0.0003
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## 460 0.0887 nan 0.1000 -0.0002
## 480 0.0825 nan 0.1000 -0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2368 nan 0.1000 0.0357
## 2 1.1694 nan 0.1000 0.0287
## 3 1.1077 nan 0.1000 0.0287
## 4 1.0546 nan 0.1000 0.0229
## 5 1.0169 nan 0.1000 0.0160
## 6 0.9760 nan 0.1000 0.0156
## 7 0.9377 nan 0.1000 0.0141
## 8 0.9068 nan 0.1000 0.0076
## 9 0.8787 nan 0.1000 0.0096
## 10 0.8570 nan 0.1000 0.0079
## 20 0.6945 nan 0.1000 0.0024
## 40 0.5458 nan 0.1000 -0.0004
## 60 0.4578 nan 0.1000 0.0005
## 80 0.3871 nan 0.1000 0.0000
## 100 0.3310 nan 0.1000 0.0001
## 120 0.2872 nan 0.1000 -0.0011
## 140 0.2503 nan 0.1000 -0.0005
## 160 0.2213 nan 0.1000 -0.0007
## 180 0.1978 nan 0.1000 -0.0008
## 200 0.1754 nan 0.1000 -0.0004
## 220 0.1563 nan 0.1000 -0.0004
## 240 0.1402 nan 0.1000 -0.0004
## 260 0.1252 nan 0.1000 0.0000
## 280 0.1135 nan 0.1000 -0.0003
## 300 0.1004 nan 0.1000 -0.0001
## 320 0.0902 nan 0.1000 -0.0002
## 340 0.0817 nan 0.1000 -0.0004
## 360 0.0737 nan 0.1000 -0.0001
## 380 0.0662 nan 0.1000 -0.0003
## 400 0.0595 nan 0.1000 -0.0001
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## 460 0.0439 nan 0.1000 -0.0001
## 480 0.0396 nan 0.1000 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2405 nan 0.1000 0.0368
## 2 1.1686 nan 0.1000 0.0346
## 3 1.1090 nan 0.1000 0.0293
## 4 1.0579 nan 0.1000 0.0215
## 5 1.0141 nan 0.1000 0.0159
## 6 0.9773 nan 0.1000 0.0153
## 7 0.9434 nan 0.1000 0.0136
## 8 0.9140 nan 0.1000 0.0110
## 9 0.8802 nan 0.1000 0.0124
## 10 0.8554 nan 0.1000 0.0092
## 20 0.7003 nan 0.1000 0.0010
## 40 0.5591 nan 0.1000 -0.0008
## 60 0.4735 nan 0.1000 -0.0003
## 80 0.4054 nan 0.1000 -0.0005
## 100 0.3474 nan 0.1000 0.0001
## 120 0.3021 nan 0.1000 -0.0001
## 140 0.2605 nan 0.1000 -0.0008
## 160 0.2302 nan 0.1000 -0.0004
## 180 0.2027 nan 0.1000 -0.0008
## 200 0.1811 nan 0.1000 -0.0006
## 220 0.1598 nan 0.1000 0.0001
## 240 0.1415 nan 0.1000 -0.0010
## 260 0.1265 nan 0.1000 -0.0006
## 280 0.1140 nan 0.1000 -0.0002
## 300 0.1007 nan 0.1000 -0.0001
## 320 0.0910 nan 0.1000 -0.0004
## 340 0.0821 nan 0.1000 -0.0001
## 360 0.0745 nan 0.1000 -0.0002
## 380 0.0684 nan 0.1000 -0.0001
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## 460 0.0454 nan 0.1000 -0.0002
## 480 0.0413 nan 0.1000 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2418 nan 0.1000 0.0347
## 2 1.1762 nan 0.1000 0.0328
## 3 1.1105 nan 0.1000 0.0273
## 4 1.0616 nan 0.1000 0.0185
## 5 1.0153 nan 0.1000 0.0215
## 6 0.9722 nan 0.1000 0.0160
## 7 0.9378 nan 0.1000 0.0139
## 8 0.9142 nan 0.1000 0.0090
## 9 0.8889 nan 0.1000 0.0109
## 10 0.8607 nan 0.1000 0.0118
## 20 0.7068 nan 0.1000 0.0024
## 40 0.5751 nan 0.1000 -0.0014
## 60 0.4903 nan 0.1000 -0.0009
## 80 0.4284 nan 0.1000 -0.0004
## 100 0.3769 nan 0.1000 -0.0012
## 120 0.3304 nan 0.1000 -0.0006
## 140 0.2922 nan 0.1000 -0.0016
## 160 0.2558 nan 0.1000 -0.0007
## 180 0.2266 nan 0.1000 -0.0005
## 200 0.2031 nan 0.1000 -0.0004
## 220 0.1808 nan 0.1000 -0.0009
## 240 0.1618 nan 0.1000 -0.0006
## 260 0.1451 nan 0.1000 -0.0007
## 280 0.1310 nan 0.1000 -0.0003
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## 320 0.1069 nan 0.1000 -0.0006
## 340 0.0962 nan 0.1000 -0.0003
## 360 0.0876 nan 0.1000 -0.0003
## 380 0.0793 nan 0.1000 -0.0002
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## 460 0.0534 nan 0.1000 -0.0002
## 480 0.0487 nan 0.1000 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2376 nan 0.1000 0.0362
## 2 1.1634 nan 0.1000 0.0316
## 3 1.0971 nan 0.1000 0.0288
## 4 1.0398 nan 0.1000 0.0234
## 5 0.9940 nan 0.1000 0.0191
## 6 0.9582 nan 0.1000 0.0151
## 7 0.9272 nan 0.1000 0.0109
## 8 0.8945 nan 0.1000 0.0132
## 9 0.8684 nan 0.1000 0.0098
## 10 0.8423 nan 0.1000 0.0090
## 20 0.6711 nan 0.1000 0.0029
## 40 0.5180 nan 0.1000 -0.0003
## 60 0.4239 nan 0.1000 -0.0016
## 80 0.3567 nan 0.1000 -0.0004
## 100 0.2943 nan 0.1000 -0.0004
## 120 0.2479 nan 0.1000 -0.0003
## 140 0.2162 nan 0.1000 -0.0005
## 160 0.1842 nan 0.1000 -0.0002
## 180 0.1600 nan 0.1000 -0.0001
## 200 0.1396 nan 0.1000 -0.0000
## 220 0.1228 nan 0.1000 -0.0002
## 240 0.1077 nan 0.1000 -0.0008
## 260 0.0947 nan 0.1000 -0.0001
## 280 0.0821 nan 0.1000 -0.0001
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## 320 0.0632 nan 0.1000 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2376 nan 0.1000 0.0374
## 2 1.1699 nan 0.1000 0.0318
## 3 1.1079 nan 0.1000 0.0257
## 4 1.0550 nan 0.1000 0.0245
## 5 1.0074 nan 0.1000 0.0199
## 6 0.9664 nan 0.1000 0.0160
## 7 0.9312 nan 0.1000 0.0127
## 8 0.8967 nan 0.1000 0.0130
## 9 0.8722 nan 0.1000 0.0100
## 10 0.8477 nan 0.1000 0.0076
## 20 0.6880 nan 0.1000 0.0027
## 40 0.5311 nan 0.1000 0.0009
## 60 0.4227 nan 0.1000 -0.0002
## 80 0.3560 nan 0.1000 -0.0009
## 100 0.2962 nan 0.1000 -0.0004
## 120 0.2492 nan 0.1000 -0.0004
## 140 0.2099 nan 0.1000 -0.0007
## 160 0.1786 nan 0.1000 -0.0005
## 180 0.1545 nan 0.1000 -0.0004
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## 220 0.1143 nan 0.1000 -0.0004
## 240 0.0994 nan 0.1000 -0.0003
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## 280 0.0778 nan 0.1000 -0.0002
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## 320 0.0607 nan 0.1000 -0.0001
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## 380 0.0433 nan 0.1000 -0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2352 nan 0.1000 0.0333
## 2 1.1661 nan 0.1000 0.0334
## 3 1.1136 nan 0.1000 0.0236
## 4 1.0588 nan 0.1000 0.0245
## 5 1.0065 nan 0.1000 0.0203
## 6 0.9671 nan 0.1000 0.0162
## 7 0.9315 nan 0.1000 0.0130
## 8 0.8992 nan 0.1000 0.0125
## 9 0.8720 nan 0.1000 0.0109
## 10 0.8491 nan 0.1000 0.0077
## 20 0.6896 nan 0.1000 0.0010
## 40 0.5499 nan 0.1000 -0.0014
## 60 0.4583 nan 0.1000 -0.0016
## 80 0.3850 nan 0.1000 -0.0011
## 100 0.3288 nan 0.1000 -0.0004
## 120 0.2830 nan 0.1000 -0.0013
## 140 0.2413 nan 0.1000 -0.0011
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0005
## 2 1.3189 nan 0.0010 0.0003
## 3 1.3180 nan 0.0010 0.0004
## 4 1.3172 nan 0.0010 0.0004
## 5 1.3163 nan 0.0010 0.0004
## 6 1.3154 nan 0.0010 0.0004
## 7 1.3146 nan 0.0010 0.0004
## 8 1.3137 nan 0.0010 0.0004
## 9 1.3128 nan 0.0010 0.0004
## 10 1.3119 nan 0.0010 0.0004
## 20 1.3034 nan 0.0010 0.0004
## 40 1.2869 nan 0.0010 0.0004
## 60 1.2711 nan 0.0010 0.0004
## 80 1.2558 nan 0.0010 0.0004
## 100 1.2406 nan 0.0010 0.0003
## 120 1.2266 nan 0.0010 0.0003
## 140 1.2127 nan 0.0010 0.0003
## 160 1.1993 nan 0.0010 0.0002
## 180 1.1867 nan 0.0010 0.0002
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## 340 1.0952 nan 0.0010 0.0002
## 360 1.0855 nan 0.0010 0.0002
## 380 1.0759 nan 0.0010 0.0002
## 400 1.0666 nan 0.0010 0.0002
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## 480 1.0309 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3190 nan 0.0010 0.0004
## 3 1.3181 nan 0.0010 0.0004
## 4 1.3173 nan 0.0010 0.0004
## 5 1.3165 nan 0.0010 0.0003
## 6 1.3158 nan 0.0010 0.0003
## 7 1.3150 nan 0.0010 0.0003
## 8 1.3142 nan 0.0010 0.0004
## 9 1.3134 nan 0.0010 0.0003
## 10 1.3126 nan 0.0010 0.0004
## 20 1.3042 nan 0.0010 0.0004
## 40 1.2878 nan 0.0010 0.0003
## 60 1.2719 nan 0.0010 0.0003
## 80 1.2566 nan 0.0010 0.0003
## 100 1.2417 nan 0.0010 0.0003
## 120 1.2275 nan 0.0010 0.0003
## 140 1.2136 nan 0.0010 0.0003
## 160 1.2001 nan 0.0010 0.0003
## 180 1.1875 nan 0.0010 0.0003
## 200 1.1752 nan 0.0010 0.0002
## 220 1.1629 nan 0.0010 0.0003
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## 320 1.1071 nan 0.0010 0.0002
## 340 1.0970 nan 0.0010 0.0002
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## 380 1.0774 nan 0.0010 0.0002
## 400 1.0679 nan 0.0010 0.0002
## 420 1.0588 nan 0.0010 0.0002
## 440 1.0498 nan 0.0010 0.0002
## 460 1.0411 nan 0.0010 0.0002
## 480 1.0323 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3190 nan 0.0010 0.0004
## 3 1.3183 nan 0.0010 0.0003
## 4 1.3172 nan 0.0010 0.0004
## 5 1.3164 nan 0.0010 0.0004
## 6 1.3156 nan 0.0010 0.0004
## 7 1.3149 nan 0.0010 0.0003
## 8 1.3140 nan 0.0010 0.0004
## 9 1.3132 nan 0.0010 0.0004
## 10 1.3123 nan 0.0010 0.0004
## 20 1.3040 nan 0.0010 0.0004
## 40 1.2875 nan 0.0010 0.0003
## 60 1.2718 nan 0.0010 0.0004
## 80 1.2571 nan 0.0010 0.0003
## 100 1.2424 nan 0.0010 0.0002
## 120 1.2283 nan 0.0010 0.0003
## 140 1.2150 nan 0.0010 0.0003
## 160 1.2018 nan 0.0010 0.0002
## 180 1.1888 nan 0.0010 0.0003
## 200 1.1763 nan 0.0010 0.0003
## 220 1.1642 nan 0.0010 0.0003
## 240 1.1525 nan 0.0010 0.0002
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## 280 1.1302 nan 0.0010 0.0002
## 300 1.1195 nan 0.0010 0.0002
## 320 1.1091 nan 0.0010 0.0002
## 340 1.0993 nan 0.0010 0.0002
## 360 1.0894 nan 0.0010 0.0002
## 380 1.0794 nan 0.0010 0.0002
## 400 1.0701 nan 0.0010 0.0002
## 420 1.0610 nan 0.0010 0.0002
## 440 1.0524 nan 0.0010 0.0002
## 460 1.0435 nan 0.0010 0.0002
## 480 1.0351 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3180 nan 0.0010 0.0004
## 4 1.3171 nan 0.0010 0.0004
## 5 1.3162 nan 0.0010 0.0004
## 6 1.3152 nan 0.0010 0.0004
## 7 1.3143 nan 0.0010 0.0004
## 8 1.3134 nan 0.0010 0.0004
## 9 1.3125 nan 0.0010 0.0004
## 10 1.3115 nan 0.0010 0.0004
## 20 1.3024 nan 0.0010 0.0004
## 40 1.2849 nan 0.0010 0.0004
## 60 1.2680 nan 0.0010 0.0004
## 80 1.2520 nan 0.0010 0.0003
## 100 1.2361 nan 0.0010 0.0003
## 120 1.2211 nan 0.0010 0.0003
## 140 1.2067 nan 0.0010 0.0003
## 160 1.1927 nan 0.0010 0.0003
## 180 1.1788 nan 0.0010 0.0003
## 200 1.1657 nan 0.0010 0.0003
## 220 1.1528 nan 0.0010 0.0002
## 240 1.1403 nan 0.0010 0.0002
## 260 1.1281 nan 0.0010 0.0003
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## 300 1.1041 nan 0.0010 0.0002
## 320 1.0929 nan 0.0010 0.0002
## 340 1.0818 nan 0.0010 0.0002
## 360 1.0712 nan 0.0010 0.0002
## 380 1.0610 nan 0.0010 0.0002
## 400 1.0509 nan 0.0010 0.0002
## 420 1.0411 nan 0.0010 0.0002
## 440 1.0320 nan 0.0010 0.0002
## 460 1.0226 nan 0.0010 0.0002
## 480 1.0134 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3180 nan 0.0010 0.0003
## 4 1.3172 nan 0.0010 0.0004
## 5 1.3162 nan 0.0010 0.0004
## 6 1.3153 nan 0.0010 0.0004
## 7 1.3143 nan 0.0010 0.0004
## 8 1.3135 nan 0.0010 0.0004
## 9 1.3126 nan 0.0010 0.0004
## 10 1.3116 nan 0.0010 0.0004
## 20 1.3026 nan 0.0010 0.0003
## 40 1.2855 nan 0.0010 0.0004
## 60 1.2686 nan 0.0010 0.0004
## 80 1.2527 nan 0.0010 0.0004
## 100 1.2373 nan 0.0010 0.0003
## 120 1.2223 nan 0.0010 0.0004
## 140 1.2078 nan 0.0010 0.0003
## 160 1.1932 nan 0.0010 0.0003
## 180 1.1795 nan 0.0010 0.0003
## 200 1.1662 nan 0.0010 0.0003
## 220 1.1531 nan 0.0010 0.0003
## 240 1.1400 nan 0.0010 0.0003
## 260 1.1279 nan 0.0010 0.0002
## 280 1.1163 nan 0.0010 0.0003
## 300 1.1049 nan 0.0010 0.0002
## 320 1.0937 nan 0.0010 0.0003
## 340 1.0829 nan 0.0010 0.0002
## 360 1.0723 nan 0.0010 0.0002
## 380 1.0623 nan 0.0010 0.0002
## 400 1.0525 nan 0.0010 0.0002
## 420 1.0427 nan 0.0010 0.0002
## 440 1.0333 nan 0.0010 0.0002
## 460 1.0238 nan 0.0010 0.0002
## 480 1.0148 nan 0.0010 0.0002
## 500 1.0060 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3199 nan 0.0010 0.0004
## 2 1.3190 nan 0.0010 0.0004
## 3 1.3181 nan 0.0010 0.0004
## 4 1.3173 nan 0.0010 0.0004
## 5 1.3164 nan 0.0010 0.0004
## 6 1.3154 nan 0.0010 0.0004
## 7 1.3146 nan 0.0010 0.0004
## 8 1.3137 nan 0.0010 0.0004
## 9 1.3129 nan 0.0010 0.0004
## 10 1.3120 nan 0.0010 0.0004
## 20 1.3033 nan 0.0010 0.0004
## 40 1.2865 nan 0.0010 0.0004
## 60 1.2702 nan 0.0010 0.0003
## 80 1.2544 nan 0.0010 0.0004
## 100 1.2393 nan 0.0010 0.0003
## 120 1.2247 nan 0.0010 0.0003
## 140 1.2106 nan 0.0010 0.0003
## 160 1.1966 nan 0.0010 0.0003
## 180 1.1831 nan 0.0010 0.0003
## 200 1.1702 nan 0.0010 0.0003
## 220 1.1572 nan 0.0010 0.0003
## 240 1.1448 nan 0.0010 0.0003
## 260 1.1326 nan 0.0010 0.0003
## 280 1.1209 nan 0.0010 0.0002
## 300 1.1096 nan 0.0010 0.0003
## 320 1.0985 nan 0.0010 0.0002
## 340 1.0878 nan 0.0010 0.0002
## 360 1.0774 nan 0.0010 0.0002
## 380 1.0673 nan 0.0010 0.0002
## 400 1.0575 nan 0.0010 0.0002
## 420 1.0480 nan 0.0010 0.0002
## 440 1.0388 nan 0.0010 0.0002
## 460 1.0298 nan 0.0010 0.0002
## 480 1.0210 nan 0.0010 0.0002
## 500 1.0124 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3186 nan 0.0010 0.0005
## 3 1.3177 nan 0.0010 0.0004
## 4 1.3168 nan 0.0010 0.0004
## 5 1.3159 nan 0.0010 0.0004
## 6 1.3149 nan 0.0010 0.0005
## 7 1.3140 nan 0.0010 0.0004
## 8 1.3131 nan 0.0010 0.0004
## 9 1.3121 nan 0.0010 0.0004
## 10 1.3112 nan 0.0010 0.0004
## 20 1.3021 nan 0.0010 0.0005
## 40 1.2835 nan 0.0010 0.0003
## 60 1.2662 nan 0.0010 0.0003
## 80 1.2494 nan 0.0010 0.0004
## 100 1.2330 nan 0.0010 0.0004
## 120 1.2172 nan 0.0010 0.0003
## 140 1.2022 nan 0.0010 0.0003
## 160 1.1872 nan 0.0010 0.0003
## 180 1.1729 nan 0.0010 0.0003
## 200 1.1589 nan 0.0010 0.0003
## 220 1.1457 nan 0.0010 0.0003
## 240 1.1324 nan 0.0010 0.0003
## 260 1.1195 nan 0.0010 0.0003
## 280 1.1070 nan 0.0010 0.0003
## 300 1.0951 nan 0.0010 0.0002
## 320 1.0835 nan 0.0010 0.0003
## 340 1.0722 nan 0.0010 0.0003
## 360 1.0611 nan 0.0010 0.0002
## 380 1.0499 nan 0.0010 0.0003
## 400 1.0395 nan 0.0010 0.0002
## 420 1.0292 nan 0.0010 0.0002
## 440 1.0191 nan 0.0010 0.0003
## 460 1.0093 nan 0.0010 0.0002
## 480 1.0000 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0005
## 2 1.3186 nan 0.0010 0.0004
## 3 1.3177 nan 0.0010 0.0005
## 4 1.3168 nan 0.0010 0.0004
## 5 1.3159 nan 0.0010 0.0004
## 6 1.3149 nan 0.0010 0.0004
## 7 1.3139 nan 0.0010 0.0005
## 8 1.3130 nan 0.0010 0.0004
## 9 1.3121 nan 0.0010 0.0005
## 10 1.3111 nan 0.0010 0.0004
## 20 1.3021 nan 0.0010 0.0004
## 40 1.2841 nan 0.0010 0.0004
## 60 1.2663 nan 0.0010 0.0004
## 80 1.2495 nan 0.0010 0.0004
## 100 1.2330 nan 0.0010 0.0003
## 120 1.2176 nan 0.0010 0.0003
## 140 1.2025 nan 0.0010 0.0003
## 160 1.1878 nan 0.0010 0.0003
## 180 1.1736 nan 0.0010 0.0003
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## 220 1.1460 nan 0.0010 0.0003
## 240 1.1329 nan 0.0010 0.0003
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## 280 1.1084 nan 0.0010 0.0003
## 300 1.0965 nan 0.0010 0.0003
## 320 1.0851 nan 0.0010 0.0003
## 340 1.0738 nan 0.0010 0.0002
## 360 1.0628 nan 0.0010 0.0003
## 380 1.0520 nan 0.0010 0.0002
## 400 1.0418 nan 0.0010 0.0002
## 420 1.0314 nan 0.0010 0.0002
## 440 1.0218 nan 0.0010 0.0002
## 460 1.0122 nan 0.0010 0.0002
## 480 1.0027 nan 0.0010 0.0002
## 500 0.9935 nan 0.0010 0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3179 nan 0.0010 0.0004
## 4 1.3170 nan 0.0010 0.0004
## 5 1.3160 nan 0.0010 0.0004
## 6 1.3152 nan 0.0010 0.0004
## 7 1.3144 nan 0.0010 0.0004
## 8 1.3134 nan 0.0010 0.0005
## 9 1.3124 nan 0.0010 0.0004
## 10 1.3115 nan 0.0010 0.0005
## 20 1.3025 nan 0.0010 0.0004
## 40 1.2852 nan 0.0010 0.0004
## 60 1.2685 nan 0.0010 0.0004
## 80 1.2521 nan 0.0010 0.0003
## 100 1.2363 nan 0.0010 0.0003
## 120 1.2210 nan 0.0010 0.0004
## 140 1.2063 nan 0.0010 0.0003
## 160 1.1920 nan 0.0010 0.0003
## 180 1.1780 nan 0.0010 0.0004
## 200 1.1644 nan 0.0010 0.0002
## 220 1.1515 nan 0.0010 0.0003
## 240 1.1387 nan 0.0010 0.0003
## 260 1.1261 nan 0.0010 0.0003
## 280 1.1139 nan 0.0010 0.0003
## 300 1.1020 nan 0.0010 0.0002
## 320 1.0909 nan 0.0010 0.0002
## 340 1.0802 nan 0.0010 0.0002
## 360 1.0694 nan 0.0010 0.0002
## 380 1.0590 nan 0.0010 0.0002
## 400 1.0489 nan 0.0010 0.0002
## 420 1.0388 nan 0.0010 0.0002
## 440 1.0290 nan 0.0010 0.0002
## 460 1.0198 nan 0.0010 0.0002
## 480 1.0105 nan 0.0010 0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3120 nan 0.0100 0.0039
## 2 1.3030 nan 0.0100 0.0045
## 3 1.2944 nan 0.0100 0.0040
## 4 1.2858 nan 0.0100 0.0041
## 5 1.2784 nan 0.0100 0.0033
## 6 1.2714 nan 0.0100 0.0033
## 7 1.2640 nan 0.0100 0.0035
## 8 1.2570 nan 0.0100 0.0031
## 9 1.2494 nan 0.0100 0.0033
## 10 1.2425 nan 0.0100 0.0032
## 20 1.1734 nan 0.0100 0.0027
## 40 1.0635 nan 0.0100 0.0021
## 60 0.9836 nan 0.0100 0.0012
## 80 0.9199 nan 0.0100 0.0012
## 100 0.8683 nan 0.0100 0.0009
## 120 0.8257 nan 0.0100 0.0006
## 140 0.7911 nan 0.0100 0.0005
## 160 0.7615 nan 0.0100 0.0005
## 180 0.7354 nan 0.0100 0.0006
## 200 0.7126 nan 0.0100 0.0000
## 220 0.6926 nan 0.0100 0.0003
## 240 0.6745 nan 0.0100 -0.0001
## 260 0.6584 nan 0.0100 0.0002
## 280 0.6429 nan 0.0100 0.0002
## 300 0.6301 nan 0.0100 0.0001
## 320 0.6170 nan 0.0100 -0.0000
## 340 0.6053 nan 0.0100 0.0001
## 360 0.5939 nan 0.0100 -0.0001
## 380 0.5828 nan 0.0100 -0.0001
## 400 0.5719 nan 0.0100 0.0001
## 420 0.5614 nan 0.0100 -0.0000
## 440 0.5515 nan 0.0100 -0.0001
## 460 0.5412 nan 0.0100 0.0000
## 480 0.5328 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3127 nan 0.0100 0.0039
## 2 1.3038 nan 0.0100 0.0043
## 3 1.2962 nan 0.0100 0.0032
## 4 1.2890 nan 0.0100 0.0032
## 5 1.2803 nan 0.0100 0.0042
## 6 1.2736 nan 0.0100 0.0028
## 7 1.2661 nan 0.0100 0.0031
## 8 1.2580 nan 0.0100 0.0037
## 9 1.2500 nan 0.0100 0.0037
## 10 1.2415 nan 0.0100 0.0035
## 20 1.1760 nan 0.0100 0.0028
## 40 1.0679 nan 0.0100 0.0022
## 60 0.9845 nan 0.0100 0.0013
## 80 0.9205 nan 0.0100 0.0011
## 100 0.8690 nan 0.0100 0.0008
## 120 0.8275 nan 0.0100 0.0008
## 140 0.7927 nan 0.0100 0.0001
## 160 0.7623 nan 0.0100 0.0005
## 180 0.7376 nan 0.0100 0.0001
## 200 0.7156 nan 0.0100 0.0003
## 220 0.6973 nan 0.0100 0.0001
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## 280 0.6505 nan 0.0100 0.0001
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## 320 0.6243 nan 0.0100 0.0000
## 340 0.6126 nan 0.0100 0.0001
## 360 0.6005 nan 0.0100 0.0001
## 380 0.5895 nan 0.0100 -0.0001
## 400 0.5799 nan 0.0100 0.0000
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## 440 0.5618 nan 0.0100 -0.0001
## 460 0.5529 nan 0.0100 -0.0001
## 480 0.5445 nan 0.0100 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3134 nan 0.0100 0.0032
## 2 1.3047 nan 0.0100 0.0038
## 3 1.2957 nan 0.0100 0.0040
## 4 1.2885 nan 0.0100 0.0033
## 5 1.2806 nan 0.0100 0.0035
## 6 1.2735 nan 0.0100 0.0030
## 7 1.2656 nan 0.0100 0.0037
## 8 1.2584 nan 0.0100 0.0034
## 9 1.2513 nan 0.0100 0.0033
## 10 1.2448 nan 0.0100 0.0027
## 20 1.1773 nan 0.0100 0.0030
## 40 1.0688 nan 0.0100 0.0022
## 60 0.9880 nan 0.0100 0.0014
## 80 0.9246 nan 0.0100 0.0009
## 100 0.8732 nan 0.0100 0.0007
## 120 0.8293 nan 0.0100 0.0007
## 140 0.7938 nan 0.0100 0.0006
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## 180 0.7403 nan 0.0100 0.0002
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## 220 0.7012 nan 0.0100 0.0003
## 240 0.6848 nan 0.0100 0.0002
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## 280 0.6556 nan 0.0100 0.0001
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## 320 0.6316 nan 0.0100 0.0002
## 340 0.6197 nan 0.0100 -0.0001
## 360 0.6093 nan 0.0100 -0.0000
## 380 0.6002 nan 0.0100 -0.0001
## 400 0.5907 nan 0.0100 0.0000
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## 460 0.5641 nan 0.0100 0.0001
## 480 0.5565 nan 0.0100 -0.0003
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3117 nan 0.0100 0.0042
## 2 1.3016 nan 0.0100 0.0047
## 3 1.2922 nan 0.0100 0.0041
## 4 1.2833 nan 0.0100 0.0040
## 5 1.2750 nan 0.0100 0.0040
## 6 1.2663 nan 0.0100 0.0042
## 7 1.2584 nan 0.0100 0.0036
## 8 1.2500 nan 0.0100 0.0038
## 9 1.2417 nan 0.0100 0.0040
## 10 1.2331 nan 0.0100 0.0040
## 20 1.1612 nan 0.0100 0.0031
## 40 1.0468 nan 0.0100 0.0024
## 60 0.9597 nan 0.0100 0.0017
## 80 0.8938 nan 0.0100 0.0012
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## 120 0.7941 nan 0.0100 0.0006
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## 180 0.6990 nan 0.0100 0.0004
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## 280 0.5981 nan 0.0100 0.0001
## 300 0.5833 nan 0.0100 -0.0000
## 320 0.5685 nan 0.0100 0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3125 nan 0.0100 0.0038
## 2 1.3040 nan 0.0100 0.0037
## 3 1.2958 nan 0.0100 0.0039
## 4 1.2876 nan 0.0100 0.0036
## 5 1.2803 nan 0.0100 0.0033
## 6 1.2714 nan 0.0100 0.0041
## 7 1.2623 nan 0.0100 0.0038
## 8 1.2542 nan 0.0100 0.0036
## 9 1.2458 nan 0.0100 0.0037
## 10 1.2384 nan 0.0100 0.0034
## 20 1.1701 nan 0.0100 0.0028
## 40 1.0541 nan 0.0100 0.0024
## 60 0.9668 nan 0.0100 0.0015
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## 320 0.5819 nan 0.0100 -0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3116 nan 0.0100 0.0043
## 2 1.3024 nan 0.0100 0.0041
## 3 1.2934 nan 0.0100 0.0038
## 4 1.2847 nan 0.0100 0.0035
## 5 1.2761 nan 0.0100 0.0041
## 6 1.2678 nan 0.0100 0.0036
## 7 1.2601 nan 0.0100 0.0034
## 8 1.2517 nan 0.0100 0.0038
## 9 1.2441 nan 0.0100 0.0033
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## 20 1.1675 nan 0.0100 0.0026
## 40 1.0543 nan 0.0100 0.0021
## 60 0.9699 nan 0.0100 0.0017
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## 280 0.6206 nan 0.0100 -0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3110 nan 0.0100 0.0041
## 2 1.3007 nan 0.0100 0.0044
## 3 1.2917 nan 0.0100 0.0042
## 4 1.2819 nan 0.0100 0.0038
## 5 1.2731 nan 0.0100 0.0041
## 6 1.2645 nan 0.0100 0.0037
## 7 1.2556 nan 0.0100 0.0041
## 8 1.2471 nan 0.0100 0.0034
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## 10 1.2313 nan 0.0100 0.0032
## 20 1.1589 nan 0.0100 0.0033
## 40 1.0411 nan 0.0100 0.0019
## 60 0.9510 nan 0.0100 0.0016
## 80 0.8790 nan 0.0100 0.0014
## 100 0.8228 nan 0.0100 0.0007
## 120 0.7763 nan 0.0100 0.0009
## 140 0.7370 nan 0.0100 0.0004
## 160 0.7035 nan 0.0100 0.0005
## 180 0.6743 nan 0.0100 0.0003
## 200 0.6475 nan 0.0100 0.0004
## 220 0.6237 nan 0.0100 0.0002
## 240 0.6038 nan 0.0100 0.0001
## 260 0.5846 nan 0.0100 0.0001
## 280 0.5670 nan 0.0100 0.0003
## 300 0.5500 nan 0.0100 0.0001
## 320 0.5345 nan 0.0100 -0.0000
## 340 0.5197 nan 0.0100 -0.0000
## 360 0.5059 nan 0.0100 0.0000
## 380 0.4928 nan 0.0100 -0.0000
## 400 0.4799 nan 0.0100 -0.0000
## 420 0.4683 nan 0.0100 0.0000
## 440 0.4578 nan 0.0100 0.0000
## 460 0.4473 nan 0.0100 -0.0000
## 480 0.4381 nan 0.0100 -0.0002
## 500 0.4280 nan 0.0100 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3116 nan 0.0100 0.0040
## 2 1.3029 nan 0.0100 0.0039
## 3 1.2934 nan 0.0100 0.0044
## 4 1.2845 nan 0.0100 0.0041
## 5 1.2764 nan 0.0100 0.0035
## 6 1.2686 nan 0.0100 0.0034
## 7 1.2598 nan 0.0100 0.0036
## 8 1.2507 nan 0.0100 0.0039
## 9 1.2430 nan 0.0100 0.0037
## 10 1.2349 nan 0.0100 0.0037
## 20 1.1607 nan 0.0100 0.0027
## 40 1.0433 nan 0.0100 0.0024
## 60 0.9543 nan 0.0100 0.0016
## 80 0.8825 nan 0.0100 0.0011
## 100 0.8263 nan 0.0100 0.0007
## 120 0.7811 nan 0.0100 0.0004
## 140 0.7441 nan 0.0100 0.0006
## 160 0.7119 nan 0.0100 0.0003
## 180 0.6820 nan 0.0100 0.0003
## 200 0.6566 nan 0.0100 0.0003
## 220 0.6336 nan 0.0100 0.0004
## 240 0.6135 nan 0.0100 0.0000
## 260 0.5946 nan 0.0100 0.0003
## 280 0.5783 nan 0.0100 -0.0001
## 300 0.5631 nan 0.0100 0.0002
## 320 0.5482 nan 0.0100 0.0000
## 340 0.5338 nan 0.0100 0.0001
## 360 0.5197 nan 0.0100 0.0001
## 380 0.5073 nan 0.0100 -0.0000
## 400 0.4944 nan 0.0100 0.0001
## 420 0.4835 nan 0.0100 0.0000
## 440 0.4728 nan 0.0100 -0.0001
## 460 0.4609 nan 0.0100 -0.0001
## 480 0.4505 nan 0.0100 -0.0001
## 500 0.4405 nan 0.0100 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3112 nan 0.0100 0.0046
## 2 1.3018 nan 0.0100 0.0044
## 3 1.2936 nan 0.0100 0.0038
## 4 1.2850 nan 0.0100 0.0041
## 5 1.2757 nan 0.0100 0.0038
## 6 1.2671 nan 0.0100 0.0040
## 7 1.2588 nan 0.0100 0.0039
## 8 1.2507 nan 0.0100 0.0034
## 9 1.2426 nan 0.0100 0.0035
## 10 1.2353 nan 0.0100 0.0031
## 20 1.1636 nan 0.0100 0.0033
## 40 1.0490 nan 0.0100 0.0019
## 60 0.9603 nan 0.0100 0.0016
## 80 0.8908 nan 0.0100 0.0012
## 100 0.8381 nan 0.0100 0.0008
## 120 0.7909 nan 0.0100 0.0005
## 140 0.7527 nan 0.0100 0.0004
## 160 0.7205 nan 0.0100 0.0005
## 180 0.6913 nan 0.0100 0.0004
## 200 0.6678 nan 0.0100 0.0002
## 220 0.6460 nan 0.0100 0.0003
## 240 0.6260 nan 0.0100 0.0001
## 260 0.6082 nan 0.0100 0.0001
## 280 0.5914 nan 0.0100 0.0001
## 300 0.5763 nan 0.0100 0.0001
## 320 0.5615 nan 0.0100 0.0003
## 340 0.5478 nan 0.0100 0.0001
## 360 0.5355 nan 0.0100 0.0000
## 380 0.5236 nan 0.0100 -0.0002
## 400 0.5112 nan 0.0100 0.0000
## 420 0.4992 nan 0.0100 -0.0000
## 440 0.4892 nan 0.0100 0.0001
## 460 0.4791 nan 0.0100 -0.0002
## 480 0.4679 nan 0.0100 0.0000
## 500 0.4579 nan 0.0100 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2442 nan 0.1000 0.0350
## 2 1.1672 nan 0.1000 0.0338
## 3 1.1129 nan 0.1000 0.0205
## 4 1.0685 nan 0.1000 0.0204
## 5 1.0245 nan 0.1000 0.0168
## 6 0.9790 nan 0.1000 0.0172
## 7 0.9461 nan 0.1000 0.0153
## 8 0.9129 nan 0.1000 0.0113
## 9 0.8890 nan 0.1000 0.0102
## 10 0.8611 nan 0.1000 0.0106
## 20 0.7108 nan 0.1000 0.0017
## 40 0.5714 nan 0.1000 -0.0001
## 60 0.4883 nan 0.1000 -0.0004
## 80 0.4195 nan 0.1000 -0.0011
## 100 0.3627 nan 0.1000 -0.0005
## 120 0.3222 nan 0.1000 0.0001
## 140 0.2855 nan 0.1000 -0.0000
## 160 0.2563 nan 0.1000 -0.0004
## 180 0.2325 nan 0.1000 -0.0006
## 200 0.2111 nan 0.1000 -0.0008
## 220 0.1932 nan 0.1000 -0.0011
## 240 0.1754 nan 0.1000 -0.0001
## 260 0.1594 nan 0.1000 -0.0002
## 280 0.1459 nan 0.1000 -0.0006
## 300 0.1326 nan 0.1000 -0.0006
## 320 0.1207 nan 0.1000 -0.0001
## 340 0.1108 nan 0.1000 -0.0004
## 360 0.1023 nan 0.1000 -0.0001
## 380 0.0944 nan 0.1000 -0.0000
## 400 0.0869 nan 0.1000 -0.0001
## 420 0.0797 nan 0.1000 -0.0000
## 440 0.0731 nan 0.1000 -0.0000
## 460 0.0675 nan 0.1000 -0.0001
## 480 0.0626 nan 0.1000 -0.0001
## 500 0.0581 nan 0.1000 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2398 nan 0.1000 0.0374
## 2 1.1704 nan 0.1000 0.0286
## 3 1.1170 nan 0.1000 0.0271
## 4 1.0679 nan 0.1000 0.0211
## 5 1.0298 nan 0.1000 0.0174
## 6 0.9952 nan 0.1000 0.0137
## 7 0.9633 nan 0.1000 0.0127
## 8 0.9386 nan 0.1000 0.0106
## 9 0.9126 nan 0.1000 0.0096
## 10 0.8903 nan 0.1000 0.0086
## 20 0.7290 nan 0.1000 0.0014
## 40 0.5817 nan 0.1000 -0.0006
## 60 0.5004 nan 0.1000 0.0004
## 80 0.4446 nan 0.1000 0.0000
## 100 0.3866 nan 0.1000 -0.0014
## 120 0.3444 nan 0.1000 -0.0006
## 140 0.3085 nan 0.1000 -0.0006
## 160 0.2740 nan 0.1000 -0.0003
## 180 0.2460 nan 0.1000 0.0002
## 200 0.2222 nan 0.1000 -0.0002
## 220 0.2019 nan 0.1000 -0.0007
## 240 0.1832 nan 0.1000 -0.0005
## 260 0.1657 nan 0.1000 -0.0003
## 280 0.1506 nan 0.1000 -0.0007
## 300 0.1382 nan 0.1000 -0.0002
## 320 0.1269 nan 0.1000 -0.0006
## 340 0.1162 nan 0.1000 -0.0000
## 360 0.1057 nan 0.1000 -0.0003
## 380 0.0961 nan 0.1000 -0.0001
## 400 0.0888 nan 0.1000 -0.0001
## 420 0.0816 nan 0.1000 -0.0002
## 440 0.0750 nan 0.1000 -0.0002
## 460 0.0693 nan 0.1000 -0.0001
## 480 0.0641 nan 0.1000 -0.0005
## 500 0.0595 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2502 nan 0.1000 0.0305
## 2 1.1767 nan 0.1000 0.0361
## 3 1.1154 nan 0.1000 0.0278
## 4 1.0663 nan 0.1000 0.0206
## 5 1.0214 nan 0.1000 0.0170
## 6 0.9842 nan 0.1000 0.0178
## 7 0.9530 nan 0.1000 0.0122
## 8 0.9224 nan 0.1000 0.0107
## 9 0.8952 nan 0.1000 0.0117
## 10 0.8760 nan 0.1000 0.0076
## 20 0.7186 nan 0.1000 0.0024
## 40 0.5998 nan 0.1000 -0.0018
## 60 0.5117 nan 0.1000 0.0005
## 80 0.4578 nan 0.1000 0.0002
## 100 0.3997 nan 0.1000 -0.0014
## 120 0.3590 nan 0.1000 -0.0009
## 140 0.3242 nan 0.1000 -0.0007
## 160 0.2943 nan 0.1000 -0.0011
## 180 0.2647 nan 0.1000 -0.0005
## 200 0.2412 nan 0.1000 -0.0005
## 220 0.2182 nan 0.1000 -0.0008
## 240 0.2005 nan 0.1000 -0.0004
## 260 0.1840 nan 0.1000 -0.0003
## 280 0.1690 nan 0.1000 -0.0005
## 300 0.1553 nan 0.1000 -0.0003
## 320 0.1425 nan 0.1000 -0.0004
## 340 0.1331 nan 0.1000 -0.0004
## 360 0.1218 nan 0.1000 -0.0002
## 380 0.1130 nan 0.1000 -0.0001
## 400 0.1039 nan 0.1000 -0.0002
## 420 0.0962 nan 0.1000 -0.0007
## 440 0.0886 nan 0.1000 -0.0002
## 460 0.0819 nan 0.1000 -0.0001
## 480 0.0753 nan 0.1000 -0.0001
## 500 0.0691 nan 0.1000 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2283 nan 0.1000 0.0373
## 2 1.1616 nan 0.1000 0.0255
## 3 1.1045 nan 0.1000 0.0259
## 4 1.0567 nan 0.1000 0.0207
## 5 1.0165 nan 0.1000 0.0175
## 6 0.9714 nan 0.1000 0.0188
## 7 0.9325 nan 0.1000 0.0163
## 8 0.9044 nan 0.1000 0.0103
## 9 0.8700 nan 0.1000 0.0122
## 10 0.8442 nan 0.1000 0.0092
## 20 0.6885 nan 0.1000 0.0017
## 40 0.5407 nan 0.1000 -0.0020
## 60 0.4496 nan 0.1000 -0.0011
## 80 0.3733 nan 0.1000 -0.0006
## 100 0.3170 nan 0.1000 -0.0001
## 120 0.2772 nan 0.1000 -0.0002
## 140 0.2432 nan 0.1000 -0.0005
## 160 0.2133 nan 0.1000 -0.0003
## 180 0.1877 nan 0.1000 -0.0004
## 200 0.1641 nan 0.1000 -0.0002
## 220 0.1440 nan 0.1000 -0.0004
## 240 0.1287 nan 0.1000 -0.0002
## 260 0.1151 nan 0.1000 -0.0002
## 280 0.1022 nan 0.1000 -0.0001
## 300 0.0917 nan 0.1000 -0.0003
## 320 0.0833 nan 0.1000 -0.0001
## 340 0.0742 nan 0.1000 -0.0004
## 360 0.0660 nan 0.1000 -0.0004
## 380 0.0599 nan 0.1000 0.0001
## 400 0.0541 nan 0.1000 -0.0001
## 420 0.0491 nan 0.1000 -0.0002
## 440 0.0442 nan 0.1000 -0.0000
## 460 0.0401 nan 0.1000 -0.0002
## 480 0.0364 nan 0.1000 -0.0001
## 500 0.0330 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2299 nan 0.1000 0.0411
## 2 1.1613 nan 0.1000 0.0251
## 3 1.0973 nan 0.1000 0.0251
## 4 1.0495 nan 0.1000 0.0221
## 5 1.0059 nan 0.1000 0.0201
## 6 0.9681 nan 0.1000 0.0136
## 7 0.9314 nan 0.1000 0.0159
## 8 0.8969 nan 0.1000 0.0126
## 9 0.8674 nan 0.1000 0.0110
## 10 0.8417 nan 0.1000 0.0121
## 20 0.6834 nan 0.1000 0.0030
## 40 0.5452 nan 0.1000 -0.0005
## 60 0.4552 nan 0.1000 0.0004
## 80 0.3789 nan 0.1000 -0.0005
## 100 0.3265 nan 0.1000 -0.0003
## 120 0.2820 nan 0.1000 -0.0002
## 140 0.2438 nan 0.1000 -0.0008
## 160 0.2139 nan 0.1000 -0.0006
## 180 0.1902 nan 0.1000 -0.0003
## 200 0.1707 nan 0.1000 -0.0008
## 220 0.1532 nan 0.1000 -0.0008
## 240 0.1356 nan 0.1000 -0.0007
## 260 0.1206 nan 0.1000 -0.0002
## 280 0.1074 nan 0.1000 -0.0004
## 300 0.0959 nan 0.1000 -0.0004
## 320 0.0869 nan 0.1000 -0.0003
## 340 0.0793 nan 0.1000 -0.0003
## 360 0.0708 nan 0.1000 -0.0001
## 380 0.0641 nan 0.1000 -0.0002
## 400 0.0574 nan 0.1000 -0.0003
## 420 0.0514 nan 0.1000 -0.0001
## 440 0.0458 nan 0.1000 -0.0002
## 460 0.0413 nan 0.1000 -0.0001
## 480 0.0374 nan 0.1000 -0.0001
## 500 0.0343 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2354 nan 0.1000 0.0382
## 2 1.1588 nan 0.1000 0.0357
## 3 1.0930 nan 0.1000 0.0272
## 4 1.0394 nan 0.1000 0.0214
## 5 0.9959 nan 0.1000 0.0165
## 6 0.9526 nan 0.1000 0.0172
## 7 0.9148 nan 0.1000 0.0153
## 8 0.8851 nan 0.1000 0.0115
## 9 0.8574 nan 0.1000 0.0129
## 10 0.8322 nan 0.1000 0.0087
## 20 0.6779 nan 0.1000 0.0021
## 40 0.5410 nan 0.1000 0.0004
## 60 0.4543 nan 0.1000 -0.0032
## 80 0.3930 nan 0.1000 -0.0009
## 100 0.3434 nan 0.1000 0.0001
## 120 0.3011 nan 0.1000 -0.0007
## 140 0.2612 nan 0.1000 -0.0003
## 160 0.2283 nan 0.1000 -0.0008
## 180 0.2007 nan 0.1000 -0.0000
## 200 0.1775 nan 0.1000 0.0000
## 220 0.1594 nan 0.1000 -0.0009
## 240 0.1444 nan 0.1000 -0.0005
## 260 0.1288 nan 0.1000 -0.0003
## 280 0.1164 nan 0.1000 -0.0003
## 300 0.1051 nan 0.1000 -0.0000
## 320 0.0944 nan 0.1000 -0.0004
## 340 0.0847 nan 0.1000 -0.0001
## 360 0.0761 nan 0.1000 -0.0002
## 380 0.0687 nan 0.1000 -0.0003
## 400 0.0629 nan 0.1000 -0.0002
## 420 0.0558 nan 0.1000 -0.0001
## 440 0.0508 nan 0.1000 -0.0001
## 460 0.0455 nan 0.1000 -0.0001
## 480 0.0416 nan 0.1000 -0.0001
## 500 0.0380 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2379 nan 0.1000 0.0398
## 2 1.1642 nan 0.1000 0.0283
## 3 1.0981 nan 0.1000 0.0325
## 4 1.0438 nan 0.1000 0.0238
## 5 0.9983 nan 0.1000 0.0178
## 6 0.9545 nan 0.1000 0.0139
## 7 0.9171 nan 0.1000 0.0132
## 8 0.8861 nan 0.1000 0.0096
## 9 0.8525 nan 0.1000 0.0125
## 10 0.8252 nan 0.1000 0.0080
## 20 0.6495 nan 0.1000 0.0046
## 40 0.4886 nan 0.1000 -0.0004
## 60 0.3988 nan 0.1000 -0.0011
## 80 0.3304 nan 0.1000 -0.0014
## 100 0.2740 nan 0.1000 -0.0001
## 120 0.2298 nan 0.1000 -0.0007
## 140 0.1954 nan 0.1000 -0.0003
## 160 0.1701 nan 0.1000 -0.0011
## 180 0.1457 nan 0.1000 -0.0005
## 200 0.1260 nan 0.1000 -0.0003
## 220 0.1099 nan 0.1000 -0.0001
## 240 0.0954 nan 0.1000 -0.0002
## 260 0.0835 nan 0.1000 0.0000
## 280 0.0726 nan 0.1000 -0.0001
## 300 0.0647 nan 0.1000 0.0002
## 320 0.0568 nan 0.1000 -0.0000
## 340 0.0495 nan 0.1000 -0.0000
## 360 0.0442 nan 0.1000 -0.0001
## 380 0.0386 nan 0.1000 -0.0001
## 400 0.0344 nan 0.1000 -0.0001
## 420 0.0306 nan 0.1000 0.0000
## 440 0.0270 nan 0.1000 -0.0001
## 460 0.0237 nan 0.1000 -0.0000
## 480 0.0211 nan 0.1000 0.0000
## 500 0.0186 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2288 nan 0.1000 0.0421
## 2 1.1489 nan 0.1000 0.0369
## 3 1.0851 nan 0.1000 0.0245
## 4 1.0306 nan 0.1000 0.0224
## 5 0.9900 nan 0.1000 0.0171
## 6 0.9497 nan 0.1000 0.0173
## 7 0.9080 nan 0.1000 0.0158
## 8 0.8758 nan 0.1000 0.0115
## 9 0.8437 nan 0.1000 0.0116
## 10 0.8139 nan 0.1000 0.0118
## 20 0.6454 nan 0.1000 0.0038
## 40 0.4964 nan 0.1000 0.0002
## 60 0.4013 nan 0.1000 0.0009
## 80 0.3267 nan 0.1000 -0.0001
## 100 0.2770 nan 0.1000 -0.0010
## 120 0.2318 nan 0.1000 -0.0003
## 140 0.1973 nan 0.1000 -0.0004
## 160 0.1694 nan 0.1000 -0.0008
## 180 0.1441 nan 0.1000 -0.0001
## 200 0.1238 nan 0.1000 -0.0006
## 220 0.1066 nan 0.1000 -0.0004
## 240 0.0922 nan 0.1000 -0.0002
## 260 0.0808 nan 0.1000 -0.0003
## 280 0.0708 nan 0.1000 0.0000
## 300 0.0630 nan 0.1000 -0.0002
## 320 0.0554 nan 0.1000 0.0001
## 340 0.0489 nan 0.1000 -0.0002
## 360 0.0434 nan 0.1000 -0.0002
## 380 0.0384 nan 0.1000 -0.0001
## 400 0.0338 nan 0.1000 -0.0001
## 420 0.0297 nan 0.1000 -0.0002
## 440 0.0263 nan 0.1000 -0.0001
## 460 0.0232 nan 0.1000 -0.0002
## 480 0.0207 nan 0.1000 -0.0001
## 500 0.0184 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2273 nan 0.1000 0.0428
## 2 1.1555 nan 0.1000 0.0329
## 3 1.0949 nan 0.1000 0.0236
## 4 1.0385 nan 0.1000 0.0248
## 5 0.9936 nan 0.1000 0.0166
## 6 0.9543 nan 0.1000 0.0146
## 7 0.9211 nan 0.1000 0.0119
## 8 0.8906 nan 0.1000 0.0123
## 9 0.8556 nan 0.1000 0.0121
## 10 0.8282 nan 0.1000 0.0086
## 20 0.6629 nan 0.1000 0.0024
## 40 0.5129 nan 0.1000 -0.0026
## 60 0.4177 nan 0.1000 -0.0004
## 80 0.3508 nan 0.1000 -0.0002
## 100 0.2918 nan 0.1000 -0.0008
## 120 0.2493 nan 0.1000 -0.0005
## 140 0.2121 nan 0.1000 -0.0010
## 160 0.1817 nan 0.1000 -0.0004
## 180 0.1585 nan 0.1000 -0.0001
## 200 0.1396 nan 0.1000 -0.0006
## 220 0.1209 nan 0.1000 -0.0002
## 240 0.1056 nan 0.1000 -0.0003
## 260 0.0920 nan 0.1000 -0.0004
## 280 0.0817 nan 0.1000 -0.0002
## 300 0.0719 nan 0.1000 -0.0002
## 320 0.0637 nan 0.1000 -0.0003
## 340 0.0557 nan 0.1000 -0.0001
## 360 0.0493 nan 0.1000 -0.0002
## 380 0.0439 nan 0.1000 -0.0002
## 400 0.0394 nan 0.1000 -0.0001
## 420 0.0349 nan 0.1000 -0.0001
## 440 0.0309 nan 0.1000 -0.0001
## 460 0.0274 nan 0.1000 -0.0001
## 480 0.0241 nan 0.1000 -0.0000
## 500 0.0215 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3205 nan 0.0010 0.0003
## 2 1.3196 nan 0.0010 0.0004
## 3 1.3187 nan 0.0010 0.0004
## 4 1.3178 nan 0.0010 0.0004
## 5 1.3170 nan 0.0010 0.0003
## 6 1.3162 nan 0.0010 0.0004
## 7 1.3155 nan 0.0010 0.0004
## 8 1.3146 nan 0.0010 0.0004
## 9 1.3138 nan 0.0010 0.0004
## 10 1.3130 nan 0.0010 0.0004
## 20 1.3054 nan 0.0010 0.0003
## 40 1.2900 nan 0.0010 0.0003
## 60 1.2755 nan 0.0010 0.0003
## 80 1.2611 nan 0.0010 0.0003
## 100 1.2469 nan 0.0010 0.0004
## 120 1.2337 nan 0.0010 0.0003
## 140 1.2211 nan 0.0010 0.0002
## 160 1.2084 nan 0.0010 0.0002
## 180 1.1962 nan 0.0010 0.0002
## 200 1.1846 nan 0.0010 0.0002
## 220 1.1730 nan 0.0010 0.0002
## 240 1.1621 nan 0.0010 0.0003
## 260 1.1512 nan 0.0010 0.0002
## 280 1.1406 nan 0.0010 0.0003
## 300 1.1302 nan 0.0010 0.0002
## 320 1.1205 nan 0.0010 0.0002
## 340 1.1110 nan 0.0010 0.0002
## 360 1.1012 nan 0.0010 0.0002
## 380 1.0921 nan 0.0010 0.0002
## 400 1.0833 nan 0.0010 0.0002
## 420 1.0746 nan 0.0010 0.0002
## 440 1.0660 nan 0.0010 0.0002
## 460 1.0575 nan 0.0010 0.0002
## 480 1.0493 nan 0.0010 0.0002
## 500 1.0413 nan 0.0010 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3204 nan 0.0010 0.0003
## 2 1.3196 nan 0.0010 0.0004
## 3 1.3189 nan 0.0010 0.0003
## 4 1.3181 nan 0.0010 0.0003
## 5 1.3174 nan 0.0010 0.0003
## 6 1.3166 nan 0.0010 0.0003
## 7 1.3158 nan 0.0010 0.0004
## 8 1.3151 nan 0.0010 0.0003
## 9 1.3143 nan 0.0010 0.0004
## 10 1.3135 nan 0.0010 0.0003
## 20 1.3059 nan 0.0010 0.0003
## 40 1.2910 nan 0.0010 0.0004
## 60 1.2758 nan 0.0010 0.0004
## 80 1.2619 nan 0.0010 0.0003
## 100 1.2480 nan 0.0010 0.0003
## 120 1.2353 nan 0.0010 0.0003
## 140 1.2223 nan 0.0010 0.0002
## 160 1.2097 nan 0.0010 0.0003
## 180 1.1975 nan 0.0010 0.0002
## 200 1.1856 nan 0.0010 0.0003
## 220 1.1743 nan 0.0010 0.0002
## 240 1.1634 nan 0.0010 0.0002
## 260 1.1528 nan 0.0010 0.0002
## 280 1.1425 nan 0.0010 0.0002
## 300 1.1322 nan 0.0010 0.0002
## 320 1.1225 nan 0.0010 0.0002
## 340 1.1128 nan 0.0010 0.0002
## 360 1.1036 nan 0.0010 0.0002
## 380 1.0945 nan 0.0010 0.0002
## 400 1.0857 nan 0.0010 0.0002
## 420 1.0769 nan 0.0010 0.0002
## 440 1.0685 nan 0.0010 0.0002
## 460 1.0602 nan 0.0010 0.0002
## 480 1.0521 nan 0.0010 0.0002
## 500 1.0441 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3204 nan 0.0010 0.0004
## 2 1.3197 nan 0.0010 0.0003
## 3 1.3189 nan 0.0010 0.0003
## 4 1.3181 nan 0.0010 0.0003
## 5 1.3174 nan 0.0010 0.0003
## 6 1.3166 nan 0.0010 0.0003
## 7 1.3159 nan 0.0010 0.0004
## 8 1.3152 nan 0.0010 0.0003
## 9 1.3144 nan 0.0010 0.0003
## 10 1.3137 nan 0.0010 0.0003
## 20 1.3058 nan 0.0010 0.0003
## 40 1.2906 nan 0.0010 0.0003
## 60 1.2761 nan 0.0010 0.0004
## 80 1.2617 nan 0.0010 0.0003
## 100 1.2481 nan 0.0010 0.0003
## 120 1.2344 nan 0.0010 0.0003
## 140 1.2218 nan 0.0010 0.0003
## 160 1.2094 nan 0.0010 0.0003
## 180 1.1971 nan 0.0010 0.0002
## 200 1.1855 nan 0.0010 0.0003
## 220 1.1741 nan 0.0010 0.0003
## 240 1.1629 nan 0.0010 0.0003
## 260 1.1523 nan 0.0010 0.0002
## 280 1.1421 nan 0.0010 0.0003
## 300 1.1319 nan 0.0010 0.0002
## 320 1.1221 nan 0.0010 0.0002
## 340 1.1125 nan 0.0010 0.0002
## 360 1.1028 nan 0.0010 0.0002
## 380 1.0935 nan 0.0010 0.0002
## 400 1.0845 nan 0.0010 0.0002
## 420 1.0759 nan 0.0010 0.0002
## 440 1.0677 nan 0.0010 0.0002
## 460 1.0595 nan 0.0010 0.0002
## 480 1.0515 nan 0.0010 0.0002
## 500 1.0436 nan 0.0010 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3195 nan 0.0010 0.0004
## 3 1.3187 nan 0.0010 0.0004
## 4 1.3178 nan 0.0010 0.0004
## 5 1.3170 nan 0.0010 0.0004
## 6 1.3162 nan 0.0010 0.0004
## 7 1.3154 nan 0.0010 0.0004
## 8 1.3146 nan 0.0010 0.0003
## 9 1.3138 nan 0.0010 0.0004
## 10 1.3129 nan 0.0010 0.0004
## 20 1.3046 nan 0.0010 0.0004
## 40 1.2885 nan 0.0010 0.0004
## 60 1.2727 nan 0.0010 0.0004
## 80 1.2578 nan 0.0010 0.0003
## 100 1.2431 nan 0.0010 0.0003
## 120 1.2291 nan 0.0010 0.0003
## 140 1.2155 nan 0.0010 0.0003
## 160 1.2022 nan 0.0010 0.0003
## 180 1.1894 nan 0.0010 0.0003
## 200 1.1768 nan 0.0010 0.0003
## 220 1.1644 nan 0.0010 0.0002
## 240 1.1526 nan 0.0010 0.0003
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## 280 1.1296 nan 0.0010 0.0003
## 300 1.1186 nan 0.0010 0.0002
## 320 1.1081 nan 0.0010 0.0002
## 340 1.0977 nan 0.0010 0.0002
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## 380 1.0781 nan 0.0010 0.0002
## 400 1.0686 nan 0.0010 0.0002
## 420 1.0593 nan 0.0010 0.0002
## 440 1.0501 nan 0.0010 0.0002
## 460 1.0412 nan 0.0010 0.0002
## 480 1.0327 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3204 nan 0.0010 0.0003
## 2 1.3195 nan 0.0010 0.0003
## 3 1.3187 nan 0.0010 0.0004
## 4 1.3178 nan 0.0010 0.0004
## 5 1.3170 nan 0.0010 0.0004
## 6 1.3161 nan 0.0010 0.0004
## 7 1.3153 nan 0.0010 0.0004
## 8 1.3145 nan 0.0010 0.0004
## 9 1.3137 nan 0.0010 0.0004
## 10 1.3128 nan 0.0010 0.0004
## 20 1.3046 nan 0.0010 0.0003
## 40 1.2882 nan 0.0010 0.0003
## 60 1.2725 nan 0.0010 0.0004
## 80 1.2575 nan 0.0010 0.0003
## 100 1.2429 nan 0.0010 0.0003
## 120 1.2286 nan 0.0010 0.0003
## 140 1.2151 nan 0.0010 0.0003
## 160 1.2017 nan 0.0010 0.0003
## 180 1.1889 nan 0.0010 0.0003
## 200 1.1764 nan 0.0010 0.0002
## 220 1.1647 nan 0.0010 0.0003
## 240 1.1526 nan 0.0010 0.0003
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## 280 1.1299 nan 0.0010 0.0002
## 300 1.1190 nan 0.0010 0.0002
## 320 1.1085 nan 0.0010 0.0002
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## 360 1.0886 nan 0.0010 0.0002
## 380 1.0791 nan 0.0010 0.0002
## 400 1.0698 nan 0.0010 0.0002
## 420 1.0606 nan 0.0010 0.0002
## 440 1.0517 nan 0.0010 0.0002
## 460 1.0430 nan 0.0010 0.0002
## 480 1.0346 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0003
## 2 1.3195 nan 0.0010 0.0004
## 3 1.3186 nan 0.0010 0.0004
## 4 1.3177 nan 0.0010 0.0004
## 5 1.3169 nan 0.0010 0.0004
## 6 1.3160 nan 0.0010 0.0004
## 7 1.3151 nan 0.0010 0.0004
## 8 1.3143 nan 0.0010 0.0004
## 9 1.3136 nan 0.0010 0.0004
## 10 1.3128 nan 0.0010 0.0004
## 20 1.3046 nan 0.0010 0.0004
## 40 1.2887 nan 0.0010 0.0003
## 60 1.2732 nan 0.0010 0.0004
## 80 1.2580 nan 0.0010 0.0004
## 100 1.2435 nan 0.0010 0.0003
## 120 1.2293 nan 0.0010 0.0003
## 140 1.2157 nan 0.0010 0.0003
## 160 1.2029 nan 0.0010 0.0003
## 180 1.1903 nan 0.0010 0.0003
## 200 1.1777 nan 0.0010 0.0003
## 220 1.1654 nan 0.0010 0.0002
## 240 1.1536 nan 0.0010 0.0003
## 260 1.1422 nan 0.0010 0.0002
## 280 1.1313 nan 0.0010 0.0003
## 300 1.1205 nan 0.0010 0.0002
## 320 1.1102 nan 0.0010 0.0002
## 340 1.0999 nan 0.0010 0.0002
## 360 1.0899 nan 0.0010 0.0002
## 380 1.0801 nan 0.0010 0.0002
## 400 1.0708 nan 0.0010 0.0002
## 420 1.0616 nan 0.0010 0.0002
## 440 1.0529 nan 0.0010 0.0002
## 460 1.0444 nan 0.0010 0.0002
## 480 1.0360 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3194 nan 0.0010 0.0004
## 3 1.3184 nan 0.0010 0.0004
## 4 1.3176 nan 0.0010 0.0004
## 5 1.3166 nan 0.0010 0.0005
## 6 1.3158 nan 0.0010 0.0004
## 7 1.3148 nan 0.0010 0.0004
## 8 1.3140 nan 0.0010 0.0004
## 9 1.3130 nan 0.0010 0.0004
## 10 1.3122 nan 0.0010 0.0004
## 20 1.3036 nan 0.0010 0.0004
## 40 1.2869 nan 0.0010 0.0004
## 60 1.2706 nan 0.0010 0.0003
## 80 1.2545 nan 0.0010 0.0003
## 100 1.2389 nan 0.0010 0.0003
## 120 1.2238 nan 0.0010 0.0004
## 140 1.2089 nan 0.0010 0.0003
## 160 1.1952 nan 0.0010 0.0003
## 180 1.1816 nan 0.0010 0.0003
## 200 1.1685 nan 0.0010 0.0003
## 220 1.1558 nan 0.0010 0.0003
## 240 1.1434 nan 0.0010 0.0003
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## 280 1.1198 nan 0.0010 0.0003
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## 320 1.0976 nan 0.0010 0.0002
## 340 1.0867 nan 0.0010 0.0002
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## 380 1.0659 nan 0.0010 0.0002
## 400 1.0561 nan 0.0010 0.0002
## 420 1.0468 nan 0.0010 0.0002
## 440 1.0373 nan 0.0010 0.0002
## 460 1.0282 nan 0.0010 0.0002
## 480 1.0192 nan 0.0010 0.0002
## 500 1.0105 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3195 nan 0.0010 0.0004
## 3 1.3186 nan 0.0010 0.0004
## 4 1.3178 nan 0.0010 0.0003
## 5 1.3169 nan 0.0010 0.0004
## 6 1.3161 nan 0.0010 0.0004
## 7 1.3152 nan 0.0010 0.0004
## 8 1.3143 nan 0.0010 0.0004
## 9 1.3134 nan 0.0010 0.0004
## 10 1.3126 nan 0.0010 0.0004
## 20 1.3037 nan 0.0010 0.0004
## 40 1.2863 nan 0.0010 0.0003
## 60 1.2702 nan 0.0010 0.0003
## 80 1.2543 nan 0.0010 0.0004
## 100 1.2393 nan 0.0010 0.0004
## 120 1.2247 nan 0.0010 0.0003
## 140 1.2104 nan 0.0010 0.0003
## 160 1.1966 nan 0.0010 0.0003
## 180 1.1830 nan 0.0010 0.0003
## 200 1.1703 nan 0.0010 0.0003
## 220 1.1575 nan 0.0010 0.0002
## 240 1.1454 nan 0.0010 0.0002
## 260 1.1334 nan 0.0010 0.0002
## 280 1.1220 nan 0.0010 0.0003
## 300 1.1107 nan 0.0010 0.0003
## 320 1.0998 nan 0.0010 0.0002
## 340 1.0890 nan 0.0010 0.0002
## 360 1.0785 nan 0.0010 0.0002
## 380 1.0683 nan 0.0010 0.0002
## 400 1.0586 nan 0.0010 0.0002
## 420 1.0492 nan 0.0010 0.0002
## 440 1.0397 nan 0.0010 0.0002
## 460 1.0305 nan 0.0010 0.0002
## 480 1.0216 nan 0.0010 0.0002
## 500 1.0128 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3202 nan 0.0010 0.0004
## 2 1.3194 nan 0.0010 0.0004
## 3 1.3185 nan 0.0010 0.0004
## 4 1.3176 nan 0.0010 0.0004
## 5 1.3168 nan 0.0010 0.0004
## 6 1.3159 nan 0.0010 0.0004
## 7 1.3150 nan 0.0010 0.0004
## 8 1.3141 nan 0.0010 0.0004
## 9 1.3133 nan 0.0010 0.0004
## 10 1.3125 nan 0.0010 0.0004
## 20 1.3038 nan 0.0010 0.0004
## 40 1.2872 nan 0.0010 0.0004
## 60 1.2712 nan 0.0010 0.0004
## 80 1.2555 nan 0.0010 0.0003
## 100 1.2407 nan 0.0010 0.0003
## 120 1.2259 nan 0.0010 0.0003
## 140 1.2119 nan 0.0010 0.0003
## 160 1.1977 nan 0.0010 0.0003
## 180 1.1847 nan 0.0010 0.0003
## 200 1.1715 nan 0.0010 0.0003
## 220 1.1592 nan 0.0010 0.0003
## 240 1.1470 nan 0.0010 0.0002
## 260 1.1352 nan 0.0010 0.0003
## 280 1.1236 nan 0.0010 0.0002
## 300 1.1124 nan 0.0010 0.0003
## 320 1.1015 nan 0.0010 0.0002
## 340 1.0910 nan 0.0010 0.0002
## 360 1.0805 nan 0.0010 0.0002
## 380 1.0705 nan 0.0010 0.0002
## 400 1.0608 nan 0.0010 0.0002
## 420 1.0512 nan 0.0010 0.0002
## 440 1.0421 nan 0.0010 0.0002
## 460 1.0332 nan 0.0010 0.0002
## 480 1.0245 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3128 nan 0.0100 0.0038
## 2 1.3059 nan 0.0100 0.0032
## 3 1.2981 nan 0.0100 0.0034
## 4 1.2908 nan 0.0100 0.0033
## 5 1.2839 nan 0.0100 0.0028
## 6 1.2776 nan 0.0100 0.0028
## 7 1.2700 nan 0.0100 0.0034
## 8 1.2632 nan 0.0100 0.0029
## 9 1.2557 nan 0.0100 0.0034
## 10 1.2492 nan 0.0100 0.0029
## 20 1.1862 nan 0.0100 0.0023
## 40 1.0848 nan 0.0100 0.0017
## 60 1.0071 nan 0.0100 0.0014
## 80 0.9464 nan 0.0100 0.0013
## 100 0.8968 nan 0.0100 0.0008
## 120 0.8567 nan 0.0100 0.0007
## 140 0.8218 nan 0.0100 0.0005
## 160 0.7931 nan 0.0100 0.0001
## 180 0.7674 nan 0.0100 0.0002
## 200 0.7446 nan 0.0100 0.0004
## 220 0.7246 nan 0.0100 0.0002
## 240 0.7085 nan 0.0100 0.0002
## 260 0.6920 nan 0.0100 0.0002
## 280 0.6777 nan 0.0100 -0.0000
## 300 0.6643 nan 0.0100 -0.0000
## 320 0.6515 nan 0.0100 0.0000
## 340 0.6403 nan 0.0100 0.0000
## 360 0.6281 nan 0.0100 0.0000
## 380 0.6168 nan 0.0100 0.0001
## 400 0.6059 nan 0.0100 -0.0001
## 420 0.5968 nan 0.0100 0.0000
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## 460 0.5785 nan 0.0100 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3133 nan 0.0100 0.0038
## 2 1.3045 nan 0.0100 0.0036
## 3 1.2971 nan 0.0100 0.0031
## 4 1.2903 nan 0.0100 0.0030
## 5 1.2839 nan 0.0100 0.0024
## 6 1.2766 nan 0.0100 0.0034
## 7 1.2695 nan 0.0100 0.0034
## 8 1.2625 nan 0.0100 0.0033
## 9 1.2548 nan 0.0100 0.0034
## 10 1.2484 nan 0.0100 0.0030
## 20 1.1850 nan 0.0100 0.0024
## 40 1.0839 nan 0.0100 0.0019
## 60 1.0052 nan 0.0100 0.0017
## 80 0.9431 nan 0.0100 0.0010
## 100 0.8931 nan 0.0100 0.0011
## 120 0.8510 nan 0.0100 0.0007
## 140 0.8172 nan 0.0100 0.0004
## 160 0.7894 nan 0.0100 0.0002
## 180 0.7651 nan 0.0100 0.0005
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## 220 0.7253 nan 0.0100 0.0003
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## 280 0.6770 nan 0.0100 0.0001
## 300 0.6634 nan 0.0100 0.0000
## 320 0.6516 nan 0.0100 -0.0001
## 340 0.6397 nan 0.0100 -0.0000
## 360 0.6292 nan 0.0100 -0.0000
## 380 0.6181 nan 0.0100 0.0001
## 400 0.6079 nan 0.0100 -0.0003
## 420 0.5974 nan 0.0100 -0.0001
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## 480 0.5713 nan 0.0100 0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3134 nan 0.0100 0.0038
## 2 1.3050 nan 0.0100 0.0037
## 3 1.2972 nan 0.0100 0.0035
## 4 1.2903 nan 0.0100 0.0031
## 5 1.2829 nan 0.0100 0.0035
## 6 1.2757 nan 0.0100 0.0035
## 7 1.2682 nan 0.0100 0.0034
## 8 1.2609 nan 0.0100 0.0031
## 9 1.2540 nan 0.0100 0.0030
## 10 1.2469 nan 0.0100 0.0032
## 20 1.1855 nan 0.0100 0.0025
## 40 1.0839 nan 0.0100 0.0019
## 60 1.0086 nan 0.0100 0.0015
## 80 0.9482 nan 0.0100 0.0012
## 100 0.9001 nan 0.0100 0.0008
## 120 0.8588 nan 0.0100 0.0008
## 140 0.8246 nan 0.0100 0.0005
## 160 0.7976 nan 0.0100 0.0003
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## 220 0.7317 nan 0.0100 0.0002
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## 260 0.6993 nan 0.0100 0.0000
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## 380 0.6292 nan 0.0100 0.0001
## 400 0.6188 nan 0.0100 0.0001
## 420 0.6094 nan 0.0100 -0.0002
## 440 0.6006 nan 0.0100 -0.0001
## 460 0.5920 nan 0.0100 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3124 nan 0.0100 0.0038
## 2 1.3037 nan 0.0100 0.0039
## 3 1.2955 nan 0.0100 0.0040
## 4 1.2875 nan 0.0100 0.0037
## 5 1.2793 nan 0.0100 0.0038
## 6 1.2712 nan 0.0100 0.0034
## 7 1.2636 nan 0.0100 0.0032
## 8 1.2557 nan 0.0100 0.0036
## 9 1.2484 nan 0.0100 0.0034
## 10 1.2409 nan 0.0100 0.0033
## 20 1.1745 nan 0.0100 0.0028
## 40 1.0678 nan 0.0100 0.0020
## 60 0.9852 nan 0.0100 0.0013
## 80 0.9193 nan 0.0100 0.0012
## 100 0.8652 nan 0.0100 0.0009
## 120 0.8243 nan 0.0100 0.0006
## 140 0.7875 nan 0.0100 0.0006
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## 180 0.7311 nan 0.0100 0.0003
## 200 0.7057 nan 0.0100 0.0002
## 220 0.6846 nan 0.0100 0.0000
## 240 0.6667 nan 0.0100 0.0002
## 260 0.6490 nan 0.0100 -0.0001
## 280 0.6348 nan 0.0100 0.0001
## 300 0.6196 nan 0.0100 0.0001
## 320 0.6046 nan 0.0100 -0.0002
## 340 0.5912 nan 0.0100 0.0000
## 360 0.5782 nan 0.0100 -0.0001
## 380 0.5663 nan 0.0100 -0.0001
## 400 0.5540 nan 0.0100 -0.0001
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## 460 0.5202 nan 0.0100 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3122 nan 0.0100 0.0040
## 2 1.3034 nan 0.0100 0.0039
## 3 1.2955 nan 0.0100 0.0033
## 4 1.2881 nan 0.0100 0.0034
## 5 1.2806 nan 0.0100 0.0035
## 6 1.2728 nan 0.0100 0.0032
## 7 1.2655 nan 0.0100 0.0032
## 8 1.2582 nan 0.0100 0.0031
## 9 1.2504 nan 0.0100 0.0032
## 10 1.2430 nan 0.0100 0.0034
## 20 1.1770 nan 0.0100 0.0025
## 40 1.0716 nan 0.0100 0.0018
## 60 0.9892 nan 0.0100 0.0016
## 80 0.9247 nan 0.0100 0.0013
## 100 0.8732 nan 0.0100 0.0006
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## 140 0.7950 nan 0.0100 0.0006
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## 320 0.6109 nan 0.0100 -0.0001
## 340 0.5981 nan 0.0100 0.0002
## 360 0.5851 nan 0.0100 -0.0001
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## 460 0.5314 nan 0.0100 -0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3128 nan 0.0100 0.0041
## 2 1.3052 nan 0.0100 0.0038
## 3 1.2974 nan 0.0100 0.0035
## 4 1.2895 nan 0.0100 0.0036
## 5 1.2815 nan 0.0100 0.0036
## 6 1.2736 nan 0.0100 0.0038
## 7 1.2657 nan 0.0100 0.0037
## 8 1.2589 nan 0.0100 0.0032
## 9 1.2510 nan 0.0100 0.0035
## 10 1.2442 nan 0.0100 0.0028
## 20 1.1779 nan 0.0100 0.0029
## 40 1.0712 nan 0.0100 0.0022
## 60 0.9899 nan 0.0100 0.0012
## 80 0.9283 nan 0.0100 0.0012
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## 160 0.7711 nan 0.0100 0.0002
## 180 0.7451 nan 0.0100 0.0002
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## 220 0.6999 nan 0.0100 0.0002
## 240 0.6811 nan 0.0100 0.0001
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## 320 0.6199 nan 0.0100 0.0000
## 340 0.6070 nan 0.0100 -0.0001
## 360 0.5946 nan 0.0100 0.0000
## 380 0.5827 nan 0.0100 -0.0001
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## 460 0.5413 nan 0.0100 -0.0002
## 480 0.5318 nan 0.0100 -0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3124 nan 0.0100 0.0038
## 2 1.3035 nan 0.0100 0.0041
## 3 1.2945 nan 0.0100 0.0043
## 4 1.2857 nan 0.0100 0.0038
## 5 1.2776 nan 0.0100 0.0037
## 6 1.2696 nan 0.0100 0.0037
## 7 1.2618 nan 0.0100 0.0032
## 8 1.2545 nan 0.0100 0.0034
## 9 1.2466 nan 0.0100 0.0038
## 10 1.2393 nan 0.0100 0.0031
## 20 1.1703 nan 0.0100 0.0029
## 40 1.0568 nan 0.0100 0.0025
## 60 0.9682 nan 0.0100 0.0017
## 80 0.9012 nan 0.0100 0.0012
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## 140 0.7602 nan 0.0100 0.0004
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## 180 0.7005 nan 0.0100 0.0003
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## 320 0.5615 nan 0.0100 0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3123 nan 0.0100 0.0037
## 2 1.3039 nan 0.0100 0.0034
## 3 1.2956 nan 0.0100 0.0034
## 4 1.2873 nan 0.0100 0.0038
## 5 1.2791 nan 0.0100 0.0036
## 6 1.2705 nan 0.0100 0.0038
## 7 1.2624 nan 0.0100 0.0035
## 8 1.2542 nan 0.0100 0.0034
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## 20 1.1665 nan 0.0100 0.0030
## 40 1.0568 nan 0.0100 0.0022
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3122 nan 0.0100 0.0040
## 2 1.3037 nan 0.0100 0.0036
## 3 1.2951 nan 0.0100 0.0038
## 4 1.2873 nan 0.0100 0.0031
## 5 1.2791 nan 0.0100 0.0035
## 6 1.2716 nan 0.0100 0.0035
## 7 1.2639 nan 0.0100 0.0037
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## 10 1.2416 nan 0.0100 0.0027
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## 40 1.0639 nan 0.0100 0.0022
## 60 0.9786 nan 0.0100 0.0013
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## 360 0.5544 nan 0.0100 -0.0001
## 380 0.5412 nan 0.0100 -0.0001
## 400 0.5304 nan 0.0100 -0.0001
## 420 0.5192 nan 0.0100 0.0001
## 440 0.5083 nan 0.0100 0.0001
## 460 0.4986 nan 0.0100 -0.0002
## 480 0.4886 nan 0.0100 -0.0000
## 500 0.4794 nan 0.0100 0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2466 nan 0.1000 0.0341
## 2 1.1851 nan 0.1000 0.0261
## 3 1.1371 nan 0.1000 0.0178
## 4 1.0881 nan 0.1000 0.0224
## 5 1.0454 nan 0.1000 0.0202
## 6 1.0081 nan 0.1000 0.0161
## 7 0.9755 nan 0.1000 0.0140
## 8 0.9444 nan 0.1000 0.0119
## 9 0.9206 nan 0.1000 0.0103
## 10 0.8935 nan 0.1000 0.0095
## 20 0.7499 nan 0.1000 0.0007
## 40 0.6096 nan 0.1000 0.0015
## 60 0.5238 nan 0.1000 0.0001
## 80 0.4565 nan 0.1000 -0.0006
## 100 0.4006 nan 0.1000 -0.0015
## 120 0.3560 nan 0.1000 -0.0008
## 140 0.3207 nan 0.1000 -0.0011
## 160 0.2900 nan 0.1000 -0.0002
## 180 0.2647 nan 0.1000 -0.0007
## 200 0.2404 nan 0.1000 0.0000
## 220 0.2179 nan 0.1000 0.0004
## 240 0.1978 nan 0.1000 -0.0004
## 260 0.1794 nan 0.1000 -0.0002
## 280 0.1641 nan 0.1000 -0.0006
## 300 0.1487 nan 0.1000 -0.0003
## 320 0.1338 nan 0.1000 -0.0001
## 340 0.1224 nan 0.1000 -0.0003
## 360 0.1124 nan 0.1000 -0.0003
## 380 0.1032 nan 0.1000 -0.0002
## 400 0.0948 nan 0.1000 -0.0000
## 420 0.0876 nan 0.1000 -0.0001
## 440 0.0810 nan 0.1000 -0.0002
## 460 0.0750 nan 0.1000 -0.0001
## 480 0.0697 nan 0.1000 -0.0002
## 500 0.0646 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2457 nan 0.1000 0.0316
## 2 1.1830 nan 0.1000 0.0266
## 3 1.1296 nan 0.1000 0.0240
## 4 1.0807 nan 0.1000 0.0174
## 5 1.0444 nan 0.1000 0.0180
## 6 1.0097 nan 0.1000 0.0120
## 7 0.9782 nan 0.1000 0.0127
## 8 0.9470 nan 0.1000 0.0109
## 9 0.9241 nan 0.1000 0.0099
## 10 0.8993 nan 0.1000 0.0084
## 20 0.7491 nan 0.1000 0.0003
## 40 0.6209 nan 0.1000 -0.0009
## 60 0.5344 nan 0.1000 -0.0001
## 80 0.4732 nan 0.1000 -0.0002
## 100 0.4241 nan 0.1000 -0.0019
## 120 0.3775 nan 0.1000 -0.0005
## 140 0.3362 nan 0.1000 -0.0004
## 160 0.2996 nan 0.1000 -0.0004
## 180 0.2694 nan 0.1000 -0.0002
## 200 0.2450 nan 0.1000 -0.0009
## 220 0.2198 nan 0.1000 0.0002
## 240 0.1993 nan 0.1000 -0.0008
## 260 0.1823 nan 0.1000 -0.0007
## 280 0.1681 nan 0.1000 -0.0006
## 300 0.1545 nan 0.1000 -0.0005
## 320 0.1432 nan 0.1000 -0.0005
## 340 0.1315 nan 0.1000 -0.0003
## 360 0.1211 nan 0.1000 -0.0004
## 380 0.1116 nan 0.1000 -0.0005
## 400 0.1029 nan 0.1000 -0.0003
## 420 0.0955 nan 0.1000 -0.0003
## 440 0.0886 nan 0.1000 -0.0002
## 460 0.0827 nan 0.1000 -0.0002
## 480 0.0761 nan 0.1000 -0.0001
## 500 0.0703 nan 0.1000 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2498 nan 0.1000 0.0301
## 2 1.1844 nan 0.1000 0.0287
## 3 1.1393 nan 0.1000 0.0164
## 4 1.0858 nan 0.1000 0.0251
## 5 1.0491 nan 0.1000 0.0147
## 6 1.0090 nan 0.1000 0.0157
## 7 0.9732 nan 0.1000 0.0146
## 8 0.9430 nan 0.1000 0.0125
## 9 0.9198 nan 0.1000 0.0078
## 10 0.8973 nan 0.1000 0.0091
## 20 0.7612 nan 0.1000 -0.0007
## 40 0.6302 nan 0.1000 -0.0000
## 60 0.5407 nan 0.1000 -0.0002
## 80 0.4783 nan 0.1000 0.0000
## 100 0.4218 nan 0.1000 -0.0010
## 120 0.3789 nan 0.1000 -0.0011
## 140 0.3393 nan 0.1000 -0.0007
## 160 0.3048 nan 0.1000 -0.0010
## 180 0.2789 nan 0.1000 -0.0014
## 200 0.2567 nan 0.1000 -0.0006
## 220 0.2373 nan 0.1000 -0.0010
## 240 0.2165 nan 0.1000 -0.0006
## 260 0.1981 nan 0.1000 -0.0009
## 280 0.1805 nan 0.1000 -0.0003
## 300 0.1658 nan 0.1000 -0.0005
## 320 0.1527 nan 0.1000 0.0000
## 340 0.1407 nan 0.1000 -0.0004
## 360 0.1301 nan 0.1000 -0.0003
## 380 0.1196 nan 0.1000 -0.0004
## 400 0.1100 nan 0.1000 -0.0002
## 420 0.1015 nan 0.1000 -0.0002
## 440 0.0933 nan 0.1000 -0.0002
## 460 0.0864 nan 0.1000 -0.0003
## 480 0.0799 nan 0.1000 -0.0002
## 500 0.0740 nan 0.1000 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2460 nan 0.1000 0.0314
## 2 1.1746 nan 0.1000 0.0307
## 3 1.1172 nan 0.1000 0.0226
## 4 1.0678 nan 0.1000 0.0215
## 5 1.0243 nan 0.1000 0.0196
## 6 0.9826 nan 0.1000 0.0169
## 7 0.9441 nan 0.1000 0.0144
## 8 0.9181 nan 0.1000 0.0092
## 9 0.8907 nan 0.1000 0.0096
## 10 0.8694 nan 0.1000 0.0071
## 20 0.7170 nan 0.1000 0.0027
## 40 0.5588 nan 0.1000 -0.0020
## 60 0.4697 nan 0.1000 -0.0002
## 80 0.3926 nan 0.1000 0.0002
## 100 0.3378 nan 0.1000 -0.0012
## 120 0.2923 nan 0.1000 -0.0004
## 140 0.2523 nan 0.1000 -0.0001
## 160 0.2209 nan 0.1000 0.0002
## 180 0.1964 nan 0.1000 -0.0004
## 200 0.1738 nan 0.1000 -0.0004
## 220 0.1548 nan 0.1000 -0.0005
## 240 0.1370 nan 0.1000 -0.0003
## 260 0.1208 nan 0.1000 -0.0001
## 280 0.1083 nan 0.1000 -0.0001
## 300 0.0981 nan 0.1000 -0.0004
## 320 0.0883 nan 0.1000 -0.0004
## 340 0.0794 nan 0.1000 -0.0003
## 360 0.0713 nan 0.1000 -0.0001
## 380 0.0649 nan 0.1000 -0.0003
## 400 0.0597 nan 0.1000 -0.0000
## 420 0.0546 nan 0.1000 -0.0002
## 440 0.0497 nan 0.1000 -0.0002
## 460 0.0452 nan 0.1000 -0.0001
## 480 0.0408 nan 0.1000 -0.0001
## 500 0.0369 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2452 nan 0.1000 0.0345
## 2 1.1755 nan 0.1000 0.0311
## 3 1.1188 nan 0.1000 0.0223
## 4 1.0779 nan 0.1000 0.0189
## 5 1.0334 nan 0.1000 0.0207
## 6 0.9928 nan 0.1000 0.0171
## 7 0.9565 nan 0.1000 0.0156
## 8 0.9266 nan 0.1000 0.0111
## 9 0.8946 nan 0.1000 0.0110
## 10 0.8696 nan 0.1000 0.0079
## 20 0.7127 nan 0.1000 0.0009
## 40 0.5664 nan 0.1000 0.0002
## 60 0.4744 nan 0.1000 -0.0013
## 80 0.4055 nan 0.1000 -0.0001
## 100 0.3510 nan 0.1000 -0.0000
## 120 0.3077 nan 0.1000 -0.0007
## 140 0.2696 nan 0.1000 -0.0003
## 160 0.2345 nan 0.1000 -0.0010
## 180 0.2083 nan 0.1000 -0.0006
## 200 0.1852 nan 0.1000 -0.0007
## 220 0.1661 nan 0.1000 -0.0012
## 240 0.1484 nan 0.1000 -0.0005
## 260 0.1336 nan 0.1000 -0.0004
## 280 0.1188 nan 0.1000 -0.0002
## 300 0.1080 nan 0.1000 -0.0003
## 320 0.0970 nan 0.1000 -0.0001
## 340 0.0881 nan 0.1000 -0.0004
## 360 0.0787 nan 0.1000 0.0000
## 380 0.0709 nan 0.1000 -0.0003
## 400 0.0642 nan 0.1000 -0.0000
## 420 0.0582 nan 0.1000 -0.0001
## 440 0.0526 nan 0.1000 -0.0000
## 460 0.0475 nan 0.1000 -0.0001
## 480 0.0433 nan 0.1000 -0.0001
## 500 0.0390 nan 0.1000 0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2384 nan 0.1000 0.0374
## 2 1.1670 nan 0.1000 0.0269
## 3 1.1085 nan 0.1000 0.0227
## 4 1.0553 nan 0.1000 0.0222
## 5 1.0126 nan 0.1000 0.0172
## 6 0.9778 nan 0.1000 0.0154
## 7 0.9428 nan 0.1000 0.0126
## 8 0.9125 nan 0.1000 0.0111
## 9 0.8877 nan 0.1000 0.0097
## 10 0.8667 nan 0.1000 0.0061
## 20 0.7228 nan 0.1000 0.0027
## 40 0.5806 nan 0.1000 -0.0006
## 60 0.4966 nan 0.1000 -0.0021
## 80 0.4272 nan 0.1000 -0.0008
## 100 0.3699 nan 0.1000 -0.0017
## 120 0.3246 nan 0.1000 -0.0004
## 140 0.2824 nan 0.1000 -0.0006
## 160 0.2525 nan 0.1000 -0.0008
## 180 0.2239 nan 0.1000 -0.0007
## 200 0.2012 nan 0.1000 -0.0009
## 220 0.1792 nan 0.1000 -0.0005
## 240 0.1591 nan 0.1000 -0.0001
## 260 0.1421 nan 0.1000 -0.0007
## 280 0.1279 nan 0.1000 -0.0004
## 300 0.1145 nan 0.1000 -0.0007
## 320 0.1028 nan 0.1000 -0.0004
## 340 0.0920 nan 0.1000 -0.0003
## 360 0.0839 nan 0.1000 -0.0004
## 380 0.0769 nan 0.1000 -0.0003
## 400 0.0699 nan 0.1000 -0.0003
## 420 0.0637 nan 0.1000 -0.0004
## 440 0.0576 nan 0.1000 -0.0003
## 460 0.0527 nan 0.1000 -0.0000
## 480 0.0479 nan 0.1000 -0.0003
## 500 0.0438 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2361 nan 0.1000 0.0401
## 2 1.1665 nan 0.1000 0.0298
## 3 1.1087 nan 0.1000 0.0203
## 4 1.0538 nan 0.1000 0.0207
## 5 1.0068 nan 0.1000 0.0184
## 6 0.9611 nan 0.1000 0.0165
## 7 0.9291 nan 0.1000 0.0108
## 8 0.8982 nan 0.1000 0.0118
## 9 0.8722 nan 0.1000 0.0076
## 10 0.8465 nan 0.1000 0.0088
## 20 0.6692 nan 0.1000 0.0036
## 40 0.5076 nan 0.1000 0.0012
## 60 0.4065 nan 0.1000 0.0001
## 80 0.3323 nan 0.1000 -0.0002
## 100 0.2780 nan 0.1000 -0.0005
## 120 0.2296 nan 0.1000 -0.0002
## 140 0.1962 nan 0.1000 -0.0005
## 160 0.1701 nan 0.1000 -0.0001
## 180 0.1474 nan 0.1000 -0.0005
## 200 0.1276 nan 0.1000 -0.0003
## 220 0.1111 nan 0.1000 0.0002
## 240 0.0975 nan 0.1000 -0.0002
## 260 0.0852 nan 0.1000 -0.0002
## 280 0.0754 nan 0.1000 -0.0001
## 300 0.0661 nan 0.1000 -0.0002
## 320 0.0586 nan 0.1000 -0.0003
## 340 0.0520 nan 0.1000 -0.0002
## 360 0.0461 nan 0.1000 -0.0002
## 380 0.0408 nan 0.1000 -0.0002
## 400 0.0367 nan 0.1000 -0.0001
## 420 0.0324 nan 0.1000 -0.0001
## 440 0.0286 nan 0.1000 -0.0000
## 460 0.0255 nan 0.1000 -0.0001
## 480 0.0227 nan 0.1000 -0.0000
## 500 0.0202 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2361 nan 0.1000 0.0369
## 2 1.1675 nan 0.1000 0.0286
## 3 1.1065 nan 0.1000 0.0263
## 4 1.0605 nan 0.1000 0.0192
## 5 1.0169 nan 0.1000 0.0199
## 6 0.9747 nan 0.1000 0.0176
## 7 0.9404 nan 0.1000 0.0130
## 8 0.9103 nan 0.1000 0.0105
## 9 0.8796 nan 0.1000 0.0120
## 10 0.8524 nan 0.1000 0.0091
## 20 0.6867 nan 0.1000 0.0006
## 40 0.5318 nan 0.1000 -0.0024
## 60 0.4401 nan 0.1000 -0.0014
## 80 0.3595 nan 0.1000 -0.0005
## 100 0.3075 nan 0.1000 -0.0005
## 120 0.2630 nan 0.1000 -0.0008
## 140 0.2268 nan 0.1000 -0.0017
## 160 0.1960 nan 0.1000 -0.0004
## 180 0.1690 nan 0.1000 -0.0005
## 200 0.1475 nan 0.1000 -0.0007
## 220 0.1267 nan 0.1000 -0.0006
## 240 0.1100 nan 0.1000 -0.0003
## 260 0.0964 nan 0.1000 -0.0004
## 280 0.0844 nan 0.1000 -0.0006
## 300 0.0737 nan 0.1000 -0.0002
## 320 0.0651 nan 0.1000 -0.0002
## 340 0.0571 nan 0.1000 -0.0001
## 360 0.0504 nan 0.1000 -0.0002
## 380 0.0443 nan 0.1000 -0.0000
## 400 0.0393 nan 0.1000 -0.0001
## 420 0.0354 nan 0.1000 -0.0001
## 440 0.0315 nan 0.1000 -0.0001
## 460 0.0279 nan 0.1000 -0.0000
## 480 0.0251 nan 0.1000 -0.0001
## 500 0.0224 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2285 nan 0.1000 0.0381
## 2 1.1627 nan 0.1000 0.0329
## 3 1.1049 nan 0.1000 0.0245
## 4 1.0575 nan 0.1000 0.0187
## 5 1.0143 nan 0.1000 0.0190
## 6 0.9759 nan 0.1000 0.0167
## 7 0.9405 nan 0.1000 0.0135
## 8 0.9080 nan 0.1000 0.0151
## 9 0.8802 nan 0.1000 0.0088
## 10 0.8533 nan 0.1000 0.0118
## 20 0.6891 nan 0.1000 0.0027
## 40 0.5265 nan 0.1000 -0.0007
## 60 0.4257 nan 0.1000 -0.0006
## 80 0.3565 nan 0.1000 -0.0001
## 100 0.2999 nan 0.1000 -0.0014
## 120 0.2534 nan 0.1000 -0.0005
## 140 0.2190 nan 0.1000 -0.0002
## 160 0.1902 nan 0.1000 -0.0009
## 180 0.1665 nan 0.1000 -0.0006
## 200 0.1451 nan 0.1000 -0.0004
## 220 0.1278 nan 0.1000 -0.0002
## 240 0.1124 nan 0.1000 -0.0002
## 260 0.1000 nan 0.1000 -0.0006
## 280 0.0870 nan 0.1000 -0.0005
## 300 0.0782 nan 0.1000 -0.0003
## 320 0.0698 nan 0.1000 -0.0004
## 340 0.0623 nan 0.1000 -0.0001
## 360 0.0554 nan 0.1000 -0.0003
## 380 0.0495 nan 0.1000 -0.0003
## 400 0.0437 nan 0.1000 -0.0002
## 420 0.0390 nan 0.1000 -0.0001
## 440 0.0345 nan 0.1000 -0.0001
## 460 0.0304 nan 0.1000 -0.0001
## 480 0.0269 nan 0.1000 -0.0001
## 500 0.0242 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3204 nan 0.0010 0.0004
## 2 1.3196 nan 0.0010 0.0004
## 3 1.3187 nan 0.0010 0.0005
## 4 1.3179 nan 0.0010 0.0003
## 5 1.3171 nan 0.0010 0.0004
## 6 1.3163 nan 0.0010 0.0003
## 7 1.3155 nan 0.0010 0.0004
## 8 1.3147 nan 0.0010 0.0004
## 9 1.3138 nan 0.0010 0.0004
## 10 1.3130 nan 0.0010 0.0004
## 20 1.3050 nan 0.0010 0.0003
## 40 1.2891 nan 0.0010 0.0004
## 60 1.2738 nan 0.0010 0.0003
## 80 1.2587 nan 0.0010 0.0003
## 100 1.2442 nan 0.0010 0.0004
## 120 1.2304 nan 0.0010 0.0003
## 140 1.2170 nan 0.0010 0.0003
## 160 1.2037 nan 0.0010 0.0003
## 180 1.1911 nan 0.0010 0.0003
## 200 1.1787 nan 0.0010 0.0003
## 220 1.1667 nan 0.0010 0.0003
## 240 1.1552 nan 0.0010 0.0002
## 260 1.1441 nan 0.0010 0.0002
## 280 1.1331 nan 0.0010 0.0003
## 300 1.1224 nan 0.0010 0.0002
## 320 1.1119 nan 0.0010 0.0002
## 340 1.1017 nan 0.0010 0.0002
## 360 1.0917 nan 0.0010 0.0002
## 380 1.0824 nan 0.0010 0.0002
## 400 1.0729 nan 0.0010 0.0002
## 420 1.0638 nan 0.0010 0.0002
## 440 1.0551 nan 0.0010 0.0001
## 460 1.0464 nan 0.0010 0.0002
## 480 1.0379 nan 0.0010 0.0002
## 500 1.0296 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3204 nan 0.0010 0.0004
## 2 1.3196 nan 0.0010 0.0004
## 3 1.3189 nan 0.0010 0.0004
## 4 1.3181 nan 0.0010 0.0004
## 5 1.3173 nan 0.0010 0.0004
## 6 1.3166 nan 0.0010 0.0003
## 7 1.3157 nan 0.0010 0.0004
## 8 1.3150 nan 0.0010 0.0003
## 9 1.3141 nan 0.0010 0.0004
## 10 1.3134 nan 0.0010 0.0003
## 20 1.3051 nan 0.0010 0.0004
## 40 1.2894 nan 0.0010 0.0003
## 60 1.2741 nan 0.0010 0.0004
## 80 1.2591 nan 0.0010 0.0003
## 100 1.2447 nan 0.0010 0.0003
## 120 1.2308 nan 0.0010 0.0003
## 140 1.2177 nan 0.0010 0.0003
## 160 1.2042 nan 0.0010 0.0003
## 180 1.1913 nan 0.0010 0.0002
## 200 1.1791 nan 0.0010 0.0003
## 220 1.1673 nan 0.0010 0.0002
## 240 1.1553 nan 0.0010 0.0003
## 260 1.1440 nan 0.0010 0.0003
## 280 1.1331 nan 0.0010 0.0002
## 300 1.1224 nan 0.0010 0.0003
## 320 1.1123 nan 0.0010 0.0002
## 340 1.1023 nan 0.0010 0.0002
## 360 1.0924 nan 0.0010 0.0002
## 380 1.0829 nan 0.0010 0.0002
## 400 1.0734 nan 0.0010 0.0002
## 420 1.0639 nan 0.0010 0.0002
## 440 1.0548 nan 0.0010 0.0002
## 460 1.0464 nan 0.0010 0.0002
## 480 1.0383 nan 0.0010 0.0002
## 500 1.0303 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3204 nan 0.0010 0.0003
## 2 1.3196 nan 0.0010 0.0004
## 3 1.3188 nan 0.0010 0.0004
## 4 1.3180 nan 0.0010 0.0003
## 5 1.3173 nan 0.0010 0.0003
## 6 1.3165 nan 0.0010 0.0004
## 7 1.3157 nan 0.0010 0.0003
## 8 1.3149 nan 0.0010 0.0003
## 9 1.3141 nan 0.0010 0.0004
## 10 1.3132 nan 0.0010 0.0004
## 20 1.3050 nan 0.0010 0.0004
## 40 1.2894 nan 0.0010 0.0004
## 60 1.2740 nan 0.0010 0.0003
## 80 1.2590 nan 0.0010 0.0003
## 100 1.2452 nan 0.0010 0.0003
## 120 1.2314 nan 0.0010 0.0003
## 140 1.2180 nan 0.0010 0.0003
## 160 1.2050 nan 0.0010 0.0003
## 180 1.1922 nan 0.0010 0.0003
## 200 1.1801 nan 0.0010 0.0002
## 220 1.1679 nan 0.0010 0.0002
## 240 1.1564 nan 0.0010 0.0003
## 260 1.1451 nan 0.0010 0.0003
## 280 1.1345 nan 0.0010 0.0002
## 300 1.1240 nan 0.0010 0.0002
## 320 1.1137 nan 0.0010 0.0002
## 340 1.1037 nan 0.0010 0.0002
## 360 1.0939 nan 0.0010 0.0002
## 380 1.0845 nan 0.0010 0.0002
## 400 1.0749 nan 0.0010 0.0002
## 420 1.0655 nan 0.0010 0.0002
## 440 1.0567 nan 0.0010 0.0002
## 460 1.0480 nan 0.0010 0.0002
## 480 1.0397 nan 0.0010 0.0002
## 500 1.0313 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3194 nan 0.0010 0.0004
## 3 1.3186 nan 0.0010 0.0003
## 4 1.3178 nan 0.0010 0.0004
## 5 1.3169 nan 0.0010 0.0004
## 6 1.3160 nan 0.0010 0.0004
## 7 1.3151 nan 0.0010 0.0004
## 8 1.3142 nan 0.0010 0.0004
## 9 1.3133 nan 0.0010 0.0004
## 10 1.3125 nan 0.0010 0.0004
## 20 1.3039 nan 0.0010 0.0004
## 40 1.2873 nan 0.0010 0.0004
## 60 1.2711 nan 0.0010 0.0003
## 80 1.2554 nan 0.0010 0.0004
## 100 1.2398 nan 0.0010 0.0004
## 120 1.2251 nan 0.0010 0.0003
## 140 1.2108 nan 0.0010 0.0003
## 160 1.1971 nan 0.0010 0.0003
## 180 1.1834 nan 0.0010 0.0003
## 200 1.1702 nan 0.0010 0.0002
## 220 1.1577 nan 0.0010 0.0003
## 240 1.1456 nan 0.0010 0.0003
## 260 1.1334 nan 0.0010 0.0003
## 280 1.1218 nan 0.0010 0.0002
## 300 1.1105 nan 0.0010 0.0003
## 320 1.0998 nan 0.0010 0.0002
## 340 1.0894 nan 0.0010 0.0002
## 360 1.0791 nan 0.0010 0.0002
## 380 1.0690 nan 0.0010 0.0002
## 400 1.0591 nan 0.0010 0.0002
## 420 1.0495 nan 0.0010 0.0002
## 440 1.0399 nan 0.0010 0.0002
## 460 1.0307 nan 0.0010 0.0002
## 480 1.0217 nan 0.0010 0.0002
## 500 1.0132 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3196 nan 0.0010 0.0003
## 3 1.3187 nan 0.0010 0.0004
## 4 1.3178 nan 0.0010 0.0005
## 5 1.3169 nan 0.0010 0.0005
## 6 1.3159 nan 0.0010 0.0004
## 7 1.3151 nan 0.0010 0.0004
## 8 1.3141 nan 0.0010 0.0004
## 9 1.3132 nan 0.0010 0.0004
## 10 1.3123 nan 0.0010 0.0004
## 20 1.3035 nan 0.0010 0.0004
## 40 1.2868 nan 0.0010 0.0004
## 60 1.2703 nan 0.0010 0.0004
## 80 1.2546 nan 0.0010 0.0003
## 100 1.2395 nan 0.0010 0.0003
## 120 1.2247 nan 0.0010 0.0003
## 140 1.2106 nan 0.0010 0.0003
## 160 1.1970 nan 0.0010 0.0003
## 180 1.1836 nan 0.0010 0.0003
## 200 1.1705 nan 0.0010 0.0003
## 220 1.1579 nan 0.0010 0.0003
## 240 1.1457 nan 0.0010 0.0003
## 260 1.1337 nan 0.0010 0.0003
## 280 1.1218 nan 0.0010 0.0003
## 300 1.1103 nan 0.0010 0.0002
## 320 1.0993 nan 0.0010 0.0002
## 340 1.0885 nan 0.0010 0.0002
## 360 1.0783 nan 0.0010 0.0002
## 380 1.0683 nan 0.0010 0.0002
## 400 1.0586 nan 0.0010 0.0002
## 420 1.0491 nan 0.0010 0.0001
## 440 1.0400 nan 0.0010 0.0002
## 460 1.0309 nan 0.0010 0.0002
## 480 1.0219 nan 0.0010 0.0002
## 500 1.0134 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3195 nan 0.0010 0.0004
## 3 1.3185 nan 0.0010 0.0004
## 4 1.3176 nan 0.0010 0.0004
## 5 1.3167 nan 0.0010 0.0004
## 6 1.3158 nan 0.0010 0.0004
## 7 1.3150 nan 0.0010 0.0003
## 8 1.3141 nan 0.0010 0.0004
## 9 1.3133 nan 0.0010 0.0004
## 10 1.3125 nan 0.0010 0.0003
## 20 1.3040 nan 0.0010 0.0004
## 40 1.2872 nan 0.0010 0.0004
## 60 1.2709 nan 0.0010 0.0003
## 80 1.2551 nan 0.0010 0.0004
## 100 1.2399 nan 0.0010 0.0003
## 120 1.2256 nan 0.0010 0.0004
## 140 1.2117 nan 0.0010 0.0003
## 160 1.1979 nan 0.0010 0.0003
## 180 1.1844 nan 0.0010 0.0002
## 200 1.1718 nan 0.0010 0.0003
## 220 1.1591 nan 0.0010 0.0003
## 240 1.1470 nan 0.0010 0.0003
## 260 1.1350 nan 0.0010 0.0003
## 280 1.1233 nan 0.0010 0.0003
## 300 1.1121 nan 0.0010 0.0003
## 320 1.1013 nan 0.0010 0.0002
## 340 1.0908 nan 0.0010 0.0002
## 360 1.0806 nan 0.0010 0.0002
## 380 1.0705 nan 0.0010 0.0002
## 400 1.0609 nan 0.0010 0.0002
## 420 1.0514 nan 0.0010 0.0002
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## 460 1.0332 nan 0.0010 0.0002
## 480 1.0244 nan 0.0010 0.0002
## 500 1.0159 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3202 nan 0.0010 0.0005
## 2 1.3192 nan 0.0010 0.0005
## 3 1.3183 nan 0.0010 0.0004
## 4 1.3174 nan 0.0010 0.0004
## 5 1.3165 nan 0.0010 0.0004
## 6 1.3155 nan 0.0010 0.0004
## 7 1.3146 nan 0.0010 0.0004
## 8 1.3137 nan 0.0010 0.0004
## 9 1.3127 nan 0.0010 0.0005
## 10 1.3118 nan 0.0010 0.0004
## 20 1.3025 nan 0.0010 0.0004
## 40 1.2846 nan 0.0010 0.0004
## 60 1.2673 nan 0.0010 0.0004
## 80 1.2505 nan 0.0010 0.0004
## 100 1.2345 nan 0.0010 0.0003
## 120 1.2189 nan 0.0010 0.0003
## 140 1.2044 nan 0.0010 0.0003
## 160 1.1896 nan 0.0010 0.0003
## 180 1.1756 nan 0.0010 0.0003
## 200 1.1624 nan 0.0010 0.0002
## 220 1.1491 nan 0.0010 0.0003
## 240 1.1362 nan 0.0010 0.0003
## 260 1.1236 nan 0.0010 0.0002
## 280 1.1116 nan 0.0010 0.0002
## 300 1.1000 nan 0.0010 0.0003
## 320 1.0885 nan 0.0010 0.0002
## 340 1.0774 nan 0.0010 0.0002
## 360 1.0665 nan 0.0010 0.0002
## 380 1.0559 nan 0.0010 0.0002
## 400 1.0457 nan 0.0010 0.0002
## 420 1.0357 nan 0.0010 0.0002
## 440 1.0261 nan 0.0010 0.0002
## 460 1.0168 nan 0.0010 0.0002
## 480 1.0075 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3194 nan 0.0010 0.0004
## 3 1.3185 nan 0.0010 0.0004
## 4 1.3175 nan 0.0010 0.0005
## 5 1.3166 nan 0.0010 0.0004
## 6 1.3157 nan 0.0010 0.0004
## 7 1.3148 nan 0.0010 0.0004
## 8 1.3139 nan 0.0010 0.0004
## 9 1.3130 nan 0.0010 0.0004
## 10 1.3120 nan 0.0010 0.0004
## 20 1.3027 nan 0.0010 0.0004
## 40 1.2850 nan 0.0010 0.0004
## 60 1.2680 nan 0.0010 0.0004
## 80 1.2514 nan 0.0010 0.0004
## 100 1.2358 nan 0.0010 0.0003
## 120 1.2201 nan 0.0010 0.0003
## 140 1.2049 nan 0.0010 0.0004
## 160 1.1906 nan 0.0010 0.0003
## 180 1.1767 nan 0.0010 0.0003
## 200 1.1633 nan 0.0010 0.0003
## 220 1.1502 nan 0.0010 0.0003
## 240 1.1376 nan 0.0010 0.0002
## 260 1.1254 nan 0.0010 0.0003
## 280 1.1132 nan 0.0010 0.0003
## 300 1.1014 nan 0.0010 0.0002
## 320 1.0901 nan 0.0010 0.0002
## 340 1.0790 nan 0.0010 0.0002
## 360 1.0682 nan 0.0010 0.0002
## 380 1.0575 nan 0.0010 0.0002
## 400 1.0473 nan 0.0010 0.0002
## 420 1.0373 nan 0.0010 0.0002
## 440 1.0278 nan 0.0010 0.0002
## 460 1.0183 nan 0.0010 0.0002
## 480 1.0092 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3194 nan 0.0010 0.0004
## 3 1.3185 nan 0.0010 0.0004
## 4 1.3176 nan 0.0010 0.0004
## 5 1.3167 nan 0.0010 0.0004
## 6 1.3158 nan 0.0010 0.0005
## 7 1.3149 nan 0.0010 0.0004
## 8 1.3140 nan 0.0010 0.0004
## 9 1.3131 nan 0.0010 0.0004
## 10 1.3121 nan 0.0010 0.0004
## 20 1.3032 nan 0.0010 0.0004
## 40 1.2855 nan 0.0010 0.0003
## 60 1.2688 nan 0.0010 0.0004
## 80 1.2525 nan 0.0010 0.0003
## 100 1.2368 nan 0.0010 0.0003
## 120 1.2217 nan 0.0010 0.0003
## 140 1.2071 nan 0.0010 0.0003
## 160 1.1930 nan 0.0010 0.0003
## 180 1.1792 nan 0.0010 0.0003
## 200 1.1657 nan 0.0010 0.0003
## 220 1.1526 nan 0.0010 0.0003
## 240 1.1400 nan 0.0010 0.0002
## 260 1.1276 nan 0.0010 0.0003
## 280 1.1158 nan 0.0010 0.0003
## 300 1.1043 nan 0.0010 0.0003
## 320 1.0928 nan 0.0010 0.0002
## 340 1.0818 nan 0.0010 0.0003
## 360 1.0714 nan 0.0010 0.0002
## 380 1.0608 nan 0.0010 0.0002
## 400 1.0504 nan 0.0010 0.0002
## 420 1.0405 nan 0.0010 0.0002
## 440 1.0310 nan 0.0010 0.0002
## 460 1.0219 nan 0.0010 0.0002
## 480 1.0127 nan 0.0010 0.0002
## 500 1.0039 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3131 nan 0.0100 0.0036
## 2 1.3056 nan 0.0100 0.0031
## 3 1.2989 nan 0.0100 0.0031
## 4 1.2915 nan 0.0100 0.0033
## 5 1.2844 nan 0.0100 0.0034
## 6 1.2760 nan 0.0100 0.0039
## 7 1.2689 nan 0.0100 0.0030
## 8 1.2617 nan 0.0100 0.0030
## 9 1.2550 nan 0.0100 0.0028
## 10 1.2476 nan 0.0100 0.0033
## 20 1.1810 nan 0.0100 0.0026
## 40 1.0770 nan 0.0100 0.0019
## 60 0.9941 nan 0.0100 0.0015
## 80 0.9291 nan 0.0100 0.0012
## 100 0.8778 nan 0.0100 0.0008
## 120 0.8359 nan 0.0100 0.0005
## 140 0.8006 nan 0.0100 0.0001
## 160 0.7702 nan 0.0100 0.0003
## 180 0.7437 nan 0.0100 0.0003
## 200 0.7208 nan 0.0100 0.0004
## 220 0.7012 nan 0.0100 0.0002
## 240 0.6837 nan 0.0100 0.0001
## 260 0.6676 nan 0.0100 0.0000
## 280 0.6541 nan 0.0100 0.0001
## 300 0.6411 nan 0.0100 0.0001
## 320 0.6286 nan 0.0100 0.0001
## 340 0.6162 nan 0.0100 -0.0001
## 360 0.6051 nan 0.0100 0.0002
## 380 0.5945 nan 0.0100 -0.0001
## 400 0.5843 nan 0.0100 0.0000
## 420 0.5745 nan 0.0100 -0.0002
## 440 0.5655 nan 0.0100 -0.0000
## 460 0.5561 nan 0.0100 -0.0001
## 480 0.5472 nan 0.0100 -0.0000
## 500 0.5394 nan 0.0100 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3135 nan 0.0100 0.0035
## 2 1.3046 nan 0.0100 0.0040
## 3 1.2961 nan 0.0100 0.0037
## 4 1.2893 nan 0.0100 0.0031
## 5 1.2815 nan 0.0100 0.0038
## 6 1.2734 nan 0.0100 0.0038
## 7 1.2659 nan 0.0100 0.0035
## 8 1.2591 nan 0.0100 0.0031
## 9 1.2524 nan 0.0100 0.0028
## 10 1.2448 nan 0.0100 0.0035
## 20 1.1812 nan 0.0100 0.0022
## 40 1.0738 nan 0.0100 0.0018
## 60 0.9950 nan 0.0100 0.0015
## 80 0.9284 nan 0.0100 0.0010
## 100 0.8757 nan 0.0100 0.0009
## 120 0.8329 nan 0.0100 0.0005
## 140 0.7977 nan 0.0100 0.0006
## 160 0.7680 nan 0.0100 0.0004
## 180 0.7428 nan 0.0100 0.0002
## 200 0.7210 nan 0.0100 0.0003
## 220 0.7031 nan 0.0100 -0.0000
## 240 0.6867 nan 0.0100 -0.0000
## 260 0.6708 nan 0.0100 0.0001
## 280 0.6578 nan 0.0100 -0.0000
## 300 0.6446 nan 0.0100 0.0001
## 320 0.6326 nan 0.0100 0.0000
## 340 0.6218 nan 0.0100 -0.0000
## 360 0.6117 nan 0.0100 0.0000
## 380 0.6011 nan 0.0100 0.0001
## 400 0.5916 nan 0.0100 -0.0001
## 420 0.5826 nan 0.0100 0.0000
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## 460 0.5654 nan 0.0100 -0.0000
## 480 0.5569 nan 0.0100 -0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3123 nan 0.0100 0.0040
## 2 1.3049 nan 0.0100 0.0029
## 3 1.2962 nan 0.0100 0.0038
## 4 1.2886 nan 0.0100 0.0038
## 5 1.2812 nan 0.0100 0.0032
## 6 1.2732 nan 0.0100 0.0034
## 7 1.2653 nan 0.0100 0.0035
## 8 1.2582 nan 0.0100 0.0030
## 9 1.2510 nan 0.0100 0.0034
## 10 1.2441 nan 0.0100 0.0035
## 20 1.1805 nan 0.0100 0.0026
## 40 1.0739 nan 0.0100 0.0023
## 60 0.9941 nan 0.0100 0.0013
## 80 0.9314 nan 0.0100 0.0011
## 100 0.8796 nan 0.0100 0.0007
## 120 0.8379 nan 0.0100 0.0008
## 140 0.8038 nan 0.0100 0.0005
## 160 0.7737 nan 0.0100 0.0004
## 180 0.7497 nan 0.0100 0.0003
## 200 0.7289 nan 0.0100 0.0002
## 220 0.7098 nan 0.0100 0.0004
## 240 0.6931 nan 0.0100 0.0001
## 260 0.6774 nan 0.0100 -0.0000
## 280 0.6634 nan 0.0100 -0.0001
## 300 0.6515 nan 0.0100 0.0000
## 320 0.6408 nan 0.0100 -0.0001
## 340 0.6308 nan 0.0100 0.0001
## 360 0.6205 nan 0.0100 0.0001
## 380 0.6108 nan 0.0100 -0.0001
## 400 0.6019 nan 0.0100 0.0000
## 420 0.5924 nan 0.0100 0.0000
## 440 0.5833 nan 0.0100 -0.0000
## 460 0.5731 nan 0.0100 -0.0001
## 480 0.5653 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3124 nan 0.0100 0.0043
## 2 1.3038 nan 0.0100 0.0041
## 3 1.2951 nan 0.0100 0.0039
## 4 1.2865 nan 0.0100 0.0041
## 5 1.2787 nan 0.0100 0.0037
## 6 1.2698 nan 0.0100 0.0038
## 7 1.2616 nan 0.0100 0.0037
## 8 1.2542 nan 0.0100 0.0034
## 9 1.2465 nan 0.0100 0.0037
## 10 1.2394 nan 0.0100 0.0031
## 20 1.1698 nan 0.0100 0.0030
## 40 1.0587 nan 0.0100 0.0021
## 60 0.9710 nan 0.0100 0.0017
## 80 0.9029 nan 0.0100 0.0009
## 100 0.8483 nan 0.0100 0.0009
## 120 0.8030 nan 0.0100 0.0006
## 140 0.7663 nan 0.0100 0.0004
## 160 0.7354 nan 0.0100 0.0002
## 180 0.7083 nan 0.0100 0.0002
## 200 0.6859 nan 0.0100 0.0002
## 220 0.6637 nan 0.0100 0.0002
## 240 0.6442 nan 0.0100 0.0001
## 260 0.6264 nan 0.0100 0.0002
## 280 0.6114 nan 0.0100 0.0001
## 300 0.5976 nan 0.0100 -0.0001
## 320 0.5828 nan 0.0100 0.0000
## 340 0.5696 nan 0.0100 0.0001
## 360 0.5566 nan 0.0100 0.0000
## 380 0.5453 nan 0.0100 -0.0001
## 400 0.5354 nan 0.0100 -0.0001
## 420 0.5235 nan 0.0100 -0.0001
## 440 0.5137 nan 0.0100 -0.0000
## 460 0.5040 nan 0.0100 -0.0002
## 480 0.4943 nan 0.0100 -0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3124 nan 0.0100 0.0042
## 2 1.3042 nan 0.0100 0.0041
## 3 1.2958 nan 0.0100 0.0037
## 4 1.2875 nan 0.0100 0.0039
## 5 1.2789 nan 0.0100 0.0040
## 6 1.2705 nan 0.0100 0.0038
## 7 1.2628 nan 0.0100 0.0032
## 8 1.2549 nan 0.0100 0.0034
## 9 1.2478 nan 0.0100 0.0035
## 10 1.2396 nan 0.0100 0.0036
## 20 1.1691 nan 0.0100 0.0027
## 40 1.0552 nan 0.0100 0.0022
## 60 0.9716 nan 0.0100 0.0014
## 80 0.9060 nan 0.0100 0.0012
## 100 0.8526 nan 0.0100 0.0007
## 120 0.8094 nan 0.0100 0.0008
## 140 0.7729 nan 0.0100 0.0007
## 160 0.7428 nan 0.0100 0.0003
## 180 0.7174 nan 0.0100 0.0001
## 200 0.6948 nan 0.0100 0.0002
## 220 0.6748 nan 0.0100 0.0001
## 240 0.6551 nan 0.0100 0.0001
## 260 0.6387 nan 0.0100 0.0003
## 280 0.6239 nan 0.0100 0.0000
## 300 0.6099 nan 0.0100 0.0000
## 320 0.5974 nan 0.0100 -0.0001
## 340 0.5846 nan 0.0100 0.0001
## 360 0.5733 nan 0.0100 0.0000
## 380 0.5626 nan 0.0100 -0.0001
## 400 0.5509 nan 0.0100 -0.0001
## 420 0.5407 nan 0.0100 0.0000
## 440 0.5303 nan 0.0100 -0.0002
## 460 0.5197 nan 0.0100 -0.0001
## 480 0.5102 nan 0.0100 -0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3119 nan 0.0100 0.0042
## 2 1.3030 nan 0.0100 0.0036
## 3 1.2947 nan 0.0100 0.0035
## 4 1.2864 nan 0.0100 0.0036
## 5 1.2790 nan 0.0100 0.0035
## 6 1.2706 nan 0.0100 0.0037
## 7 1.2632 nan 0.0100 0.0035
## 8 1.2545 nan 0.0100 0.0036
## 9 1.2467 nan 0.0100 0.0035
## 10 1.2399 nan 0.0100 0.0032
## 20 1.1702 nan 0.0100 0.0025
## 40 1.0588 nan 0.0100 0.0022
## 60 0.9763 nan 0.0100 0.0014
## 80 0.9095 nan 0.0100 0.0014
## 100 0.8547 nan 0.0100 0.0009
## 120 0.8119 nan 0.0100 0.0007
## 140 0.7765 nan 0.0100 0.0007
## 160 0.7465 nan 0.0100 0.0003
## 180 0.7196 nan 0.0100 0.0003
## 200 0.6972 nan 0.0100 0.0003
## 220 0.6778 nan 0.0100 0.0001
## 240 0.6605 nan 0.0100 0.0003
## 260 0.6453 nan 0.0100 0.0001
## 280 0.6300 nan 0.0100 0.0001
## 300 0.6160 nan 0.0100 0.0001
## 320 0.6029 nan 0.0100 -0.0002
## 340 0.5910 nan 0.0100 -0.0001
## 360 0.5787 nan 0.0100 0.0001
## 380 0.5681 nan 0.0100 -0.0001
## 400 0.5577 nan 0.0100 -0.0000
## 420 0.5475 nan 0.0100 0.0001
## 440 0.5381 nan 0.0100 -0.0001
## 460 0.5278 nan 0.0100 -0.0001
## 480 0.5184 nan 0.0100 -0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3118 nan 0.0100 0.0039
## 2 1.3032 nan 0.0100 0.0039
## 3 1.2941 nan 0.0100 0.0041
## 4 1.2853 nan 0.0100 0.0042
## 5 1.2760 nan 0.0100 0.0037
## 6 1.2677 nan 0.0100 0.0039
## 7 1.2588 nan 0.0100 0.0039
## 8 1.2507 nan 0.0100 0.0038
## 9 1.2432 nan 0.0100 0.0036
## 10 1.2357 nan 0.0100 0.0032
## 20 1.1618 nan 0.0100 0.0028
## 40 1.0451 nan 0.0100 0.0020
## 60 0.9574 nan 0.0100 0.0015
## 80 0.8876 nan 0.0100 0.0012
## 100 0.8308 nan 0.0100 0.0009
## 120 0.7837 nan 0.0100 0.0009
## 140 0.7445 nan 0.0100 0.0005
## 160 0.7127 nan 0.0100 0.0001
## 180 0.6835 nan 0.0100 0.0003
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## 240 0.6147 nan 0.0100 0.0002
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## 280 0.5773 nan 0.0100 0.0001
## 300 0.5605 nan 0.0100 0.0000
## 320 0.5457 nan 0.0100 -0.0000
## 340 0.5309 nan 0.0100 0.0001
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## 380 0.5066 nan 0.0100 0.0001
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## 460 0.4603 nan 0.0100 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3119 nan 0.0100 0.0039
## 2 1.3032 nan 0.0100 0.0036
## 3 1.2942 nan 0.0100 0.0042
## 4 1.2854 nan 0.0100 0.0043
## 5 1.2760 nan 0.0100 0.0040
## 6 1.2669 nan 0.0100 0.0041
## 7 1.2582 nan 0.0100 0.0036
## 8 1.2502 nan 0.0100 0.0036
## 9 1.2429 nan 0.0100 0.0032
## 10 1.2346 nan 0.0100 0.0037
## 20 1.1635 nan 0.0100 0.0030
## 40 1.0476 nan 0.0100 0.0022
## 60 0.9599 nan 0.0100 0.0015
## 80 0.8907 nan 0.0100 0.0013
## 100 0.8334 nan 0.0100 0.0007
## 120 0.7874 nan 0.0100 0.0005
## 140 0.7486 nan 0.0100 0.0003
## 160 0.7170 nan 0.0100 0.0003
## 180 0.6893 nan 0.0100 0.0003
## 200 0.6647 nan 0.0100 0.0002
## 220 0.6437 nan 0.0100 -0.0000
## 240 0.6235 nan 0.0100 0.0001
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## 280 0.5885 nan 0.0100 0.0001
## 300 0.5728 nan 0.0100 -0.0001
## 320 0.5596 nan 0.0100 0.0001
## 340 0.5467 nan 0.0100 -0.0000
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## 380 0.5210 nan 0.0100 -0.0002
## 400 0.5085 nan 0.0100 0.0000
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## 460 0.4744 nan 0.0100 0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3117 nan 0.0100 0.0042
## 2 1.3032 nan 0.0100 0.0038
## 3 1.2946 nan 0.0100 0.0039
## 4 1.2859 nan 0.0100 0.0037
## 5 1.2777 nan 0.0100 0.0033
## 6 1.2686 nan 0.0100 0.0037
## 7 1.2608 nan 0.0100 0.0035
## 8 1.2526 nan 0.0100 0.0038
## 9 1.2445 nan 0.0100 0.0035
## 10 1.2367 nan 0.0100 0.0034
## 20 1.1643 nan 0.0100 0.0031
## 40 1.0478 nan 0.0100 0.0022
## 60 0.9598 nan 0.0100 0.0017
## 80 0.8910 nan 0.0100 0.0012
## 100 0.8363 nan 0.0100 0.0006
## 120 0.7915 nan 0.0100 0.0005
## 140 0.7534 nan 0.0100 0.0005
## 160 0.7220 nan 0.0100 0.0006
## 180 0.6960 nan 0.0100 0.0002
## 200 0.6714 nan 0.0100 0.0001
## 220 0.6508 nan 0.0100 0.0001
## 240 0.6315 nan 0.0100 -0.0001
## 260 0.6138 nan 0.0100 0.0002
## 280 0.5967 nan 0.0100 0.0001
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## 320 0.5672 nan 0.0100 -0.0001
## 340 0.5548 nan 0.0100 -0.0001
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## 400 0.5199 nan 0.0100 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2436 nan 0.1000 0.0331
## 2 1.1719 nan 0.1000 0.0327
## 3 1.1169 nan 0.1000 0.0219
## 4 1.0648 nan 0.1000 0.0199
## 5 1.0278 nan 0.1000 0.0159
## 6 0.9878 nan 0.1000 0.0180
## 7 0.9507 nan 0.1000 0.0138
## 8 0.9179 nan 0.1000 0.0117
## 9 0.8921 nan 0.1000 0.0094
## 10 0.8644 nan 0.1000 0.0113
## 20 0.7172 nan 0.1000 0.0037
## 40 0.5838 nan 0.1000 -0.0001
## 60 0.5030 nan 0.1000 -0.0006
## 80 0.4408 nan 0.1000 0.0008
## 100 0.3873 nan 0.1000 -0.0003
## 120 0.3478 nan 0.1000 -0.0019
## 140 0.3101 nan 0.1000 0.0002
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## 180 0.2507 nan 0.1000 -0.0009
## 200 0.2245 nan 0.1000 -0.0003
## 220 0.2047 nan 0.1000 -0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2400 nan 0.1000 0.0331
## 2 1.1701 nan 0.1000 0.0312
## 3 1.1141 nan 0.1000 0.0224
## 4 1.0693 nan 0.1000 0.0215
## 5 1.0268 nan 0.1000 0.0191
## 6 0.9905 nan 0.1000 0.0123
## 7 0.9549 nan 0.1000 0.0143
## 8 0.9303 nan 0.1000 0.0060
## 9 0.9049 nan 0.1000 0.0107
## 10 0.8813 nan 0.1000 0.0080
## 20 0.7258 nan 0.1000 0.0036
## 40 0.6031 nan 0.1000 -0.0012
## 60 0.5170 nan 0.1000 -0.0001
## 80 0.4544 nan 0.1000 -0.0010
## 100 0.4002 nan 0.1000 -0.0003
## 120 0.3515 nan 0.1000 0.0000
## 140 0.3152 nan 0.1000 -0.0005
## 160 0.2830 nan 0.1000 -0.0003
## 180 0.2551 nan 0.1000 -0.0003
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2423 nan 0.1000 0.0317
## 2 1.1759 nan 0.1000 0.0272
## 3 1.1235 nan 0.1000 0.0233
## 4 1.0776 nan 0.1000 0.0190
## 5 1.0380 nan 0.1000 0.0173
## 6 0.9965 nan 0.1000 0.0162
## 7 0.9659 nan 0.1000 0.0141
## 8 0.9307 nan 0.1000 0.0136
## 9 0.9049 nan 0.1000 0.0098
## 10 0.8817 nan 0.1000 0.0103
## 20 0.7282 nan 0.1000 0.0036
## 40 0.5988 nan 0.1000 -0.0012
## 60 0.5198 nan 0.1000 -0.0009
## 80 0.4583 nan 0.1000 -0.0017
## 100 0.4085 nan 0.1000 -0.0007
## 120 0.3698 nan 0.1000 0.0001
## 140 0.3329 nan 0.1000 -0.0010
## 160 0.3020 nan 0.1000 -0.0005
## 180 0.2755 nan 0.1000 -0.0003
## 200 0.2487 nan 0.1000 -0.0012
## 220 0.2263 nan 0.1000 -0.0005
## 240 0.2048 nan 0.1000 -0.0005
## 260 0.1860 nan 0.1000 -0.0003
## 280 0.1703 nan 0.1000 -0.0000
## 300 0.1567 nan 0.1000 -0.0005
## 320 0.1444 nan 0.1000 -0.0004
## 340 0.1325 nan 0.1000 -0.0003
## 360 0.1220 nan 0.1000 -0.0005
## 380 0.1112 nan 0.1000 -0.0002
## 400 0.1027 nan 0.1000 -0.0001
## 420 0.0945 nan 0.1000 -0.0000
## 440 0.0878 nan 0.1000 -0.0003
## 460 0.0803 nan 0.1000 -0.0003
## 480 0.0748 nan 0.1000 -0.0003
## 500 0.0698 nan 0.1000 -0.0003
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2412 nan 0.1000 0.0374
## 2 1.1728 nan 0.1000 0.0306
## 3 1.1056 nan 0.1000 0.0285
## 4 1.0541 nan 0.1000 0.0207
## 5 1.0081 nan 0.1000 0.0212
## 6 0.9691 nan 0.1000 0.0161
## 7 0.9348 nan 0.1000 0.0141
## 8 0.9001 nan 0.1000 0.0132
## 9 0.8728 nan 0.1000 0.0106
## 10 0.8433 nan 0.1000 0.0123
## 20 0.6791 nan 0.1000 0.0036
## 40 0.5331 nan 0.1000 0.0013
## 60 0.4468 nan 0.1000 -0.0013
## 80 0.3822 nan 0.1000 -0.0006
## 100 0.3189 nan 0.1000 -0.0003
## 120 0.2758 nan 0.1000 -0.0009
## 140 0.2392 nan 0.1000 -0.0003
## 160 0.2095 nan 0.1000 -0.0004
## 180 0.1843 nan 0.1000 -0.0007
## 200 0.1630 nan 0.1000 -0.0004
## 220 0.1451 nan 0.1000 -0.0000
## 240 0.1300 nan 0.1000 -0.0003
## 260 0.1153 nan 0.1000 -0.0005
## 280 0.1010 nan 0.1000 -0.0001
## 300 0.0893 nan 0.1000 0.0001
## 320 0.0800 nan 0.1000 -0.0001
## 340 0.0721 nan 0.1000 -0.0003
## 360 0.0651 nan 0.1000 -0.0001
## 380 0.0589 nan 0.1000 -0.0001
## 400 0.0531 nan 0.1000 -0.0001
## 420 0.0481 nan 0.1000 -0.0001
## 440 0.0438 nan 0.1000 -0.0001
## 460 0.0393 nan 0.1000 -0.0001
## 480 0.0352 nan 0.1000 -0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2403 nan 0.1000 0.0369
## 2 1.1657 nan 0.1000 0.0336
## 3 1.1088 nan 0.1000 0.0239
## 4 1.0575 nan 0.1000 0.0199
## 5 1.0073 nan 0.1000 0.0189
## 6 0.9711 nan 0.1000 0.0143
## 7 0.9346 nan 0.1000 0.0143
## 8 0.9008 nan 0.1000 0.0131
## 9 0.8750 nan 0.1000 0.0096
## 10 0.8458 nan 0.1000 0.0114
## 20 0.6868 nan 0.1000 0.0038
## 40 0.5439 nan 0.1000 -0.0001
## 60 0.4617 nan 0.1000 -0.0004
## 80 0.3869 nan 0.1000 -0.0008
## 100 0.3362 nan 0.1000 0.0002
## 120 0.2920 nan 0.1000 -0.0024
## 140 0.2517 nan 0.1000 -0.0009
## 160 0.2223 nan 0.1000 -0.0007
## 180 0.1955 nan 0.1000 -0.0004
## 200 0.1717 nan 0.1000 -0.0007
## 220 0.1521 nan 0.1000 0.0000
## 240 0.1340 nan 0.1000 -0.0002
## 260 0.1206 nan 0.1000 -0.0002
## 280 0.1088 nan 0.1000 -0.0001
## 300 0.0968 nan 0.1000 -0.0003
## 320 0.0864 nan 0.1000 -0.0002
## 340 0.0769 nan 0.1000 -0.0000
## 360 0.0690 nan 0.1000 -0.0002
## 380 0.0633 nan 0.1000 -0.0002
## 400 0.0570 nan 0.1000 -0.0002
## 420 0.0522 nan 0.1000 -0.0003
## 440 0.0470 nan 0.1000 -0.0001
## 460 0.0421 nan 0.1000 -0.0002
## 480 0.0378 nan 0.1000 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2396 nan 0.1000 0.0396
## 2 1.1711 nan 0.1000 0.0288
## 3 1.1114 nan 0.1000 0.0274
## 4 1.0533 nan 0.1000 0.0265
## 5 1.0075 nan 0.1000 0.0216
## 6 0.9690 nan 0.1000 0.0170
## 7 0.9355 nan 0.1000 0.0131
## 8 0.9022 nan 0.1000 0.0121
## 9 0.8735 nan 0.1000 0.0117
## 10 0.8495 nan 0.1000 0.0109
## 20 0.7045 nan 0.1000 0.0019
## 40 0.5598 nan 0.1000 -0.0003
## 60 0.4729 nan 0.1000 -0.0021
## 80 0.4054 nan 0.1000 -0.0011
## 100 0.3555 nan 0.1000 -0.0005
## 120 0.3114 nan 0.1000 -0.0012
## 140 0.2722 nan 0.1000 -0.0008
## 160 0.2413 nan 0.1000 -0.0012
## 180 0.2133 nan 0.1000 -0.0003
## 200 0.1898 nan 0.1000 -0.0008
## 220 0.1686 nan 0.1000 -0.0006
## 240 0.1499 nan 0.1000 -0.0004
## 260 0.1341 nan 0.1000 -0.0003
## 280 0.1196 nan 0.1000 -0.0002
## 300 0.1076 nan 0.1000 -0.0002
## 320 0.0978 nan 0.1000 -0.0003
## 340 0.0871 nan 0.1000 -0.0003
## 360 0.0780 nan 0.1000 -0.0001
## 380 0.0705 nan 0.1000 -0.0002
## 400 0.0626 nan 0.1000 -0.0003
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## 440 0.0516 nan 0.1000 -0.0001
## 460 0.0465 nan 0.1000 -0.0001
## 480 0.0429 nan 0.1000 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2250 nan 0.1000 0.0400
## 2 1.1572 nan 0.1000 0.0306
## 3 1.0904 nan 0.1000 0.0275
## 4 1.0394 nan 0.1000 0.0202
## 5 0.9936 nan 0.1000 0.0192
## 6 0.9503 nan 0.1000 0.0179
## 7 0.9198 nan 0.1000 0.0117
## 8 0.8849 nan 0.1000 0.0126
## 9 0.8546 nan 0.1000 0.0119
## 10 0.8336 nan 0.1000 0.0076
## 20 0.6669 nan 0.1000 0.0013
## 40 0.5028 nan 0.1000 -0.0004
## 60 0.4057 nan 0.1000 0.0008
## 80 0.3339 nan 0.1000 -0.0004
## 100 0.2793 nan 0.1000 -0.0008
## 120 0.2359 nan 0.1000 -0.0003
## 140 0.1971 nan 0.1000 -0.0003
## 160 0.1673 nan 0.1000 -0.0003
## 180 0.1440 nan 0.1000 -0.0001
## 200 0.1229 nan 0.1000 -0.0003
## 220 0.1066 nan 0.1000 -0.0001
## 240 0.0922 nan 0.1000 -0.0001
## 260 0.0803 nan 0.1000 -0.0001
## 280 0.0693 nan 0.1000 -0.0002
## 300 0.0607 nan 0.1000 -0.0001
## 320 0.0536 nan 0.1000 -0.0002
## 340 0.0469 nan 0.1000 0.0000
## 360 0.0407 nan 0.1000 -0.0001
## 380 0.0362 nan 0.1000 -0.0001
## 400 0.0322 nan 0.1000 -0.0000
## 420 0.0284 nan 0.1000 -0.0000
## 440 0.0250 nan 0.1000 -0.0001
## 460 0.0221 nan 0.1000 -0.0000
## 480 0.0191 nan 0.1000 -0.0000
## 500 0.0166 nan 0.1000 0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2260 nan 0.1000 0.0424
## 2 1.1547 nan 0.1000 0.0328
## 3 1.0917 nan 0.1000 0.0276
## 4 1.0383 nan 0.1000 0.0232
## 5 0.9924 nan 0.1000 0.0199
## 6 0.9546 nan 0.1000 0.0165
## 7 0.9212 nan 0.1000 0.0088
## 8 0.8891 nan 0.1000 0.0127
## 9 0.8605 nan 0.1000 0.0099
## 10 0.8311 nan 0.1000 0.0102
## 20 0.6672 nan 0.1000 0.0010
## 40 0.5128 nan 0.1000 0.0010
## 60 0.4136 nan 0.1000 -0.0005
## 80 0.3448 nan 0.1000 0.0008
## 100 0.2927 nan 0.1000 -0.0013
## 120 0.2409 nan 0.1000 -0.0002
## 140 0.2067 nan 0.1000 -0.0004
## 160 0.1750 nan 0.1000 -0.0008
## 180 0.1503 nan 0.1000 -0.0005
## 200 0.1301 nan 0.1000 -0.0005
## 220 0.1137 nan 0.1000 -0.0005
## 240 0.0973 nan 0.1000 -0.0004
## 260 0.0839 nan 0.1000 -0.0003
## 280 0.0725 nan 0.1000 0.0002
## 300 0.0629 nan 0.1000 -0.0001
## 320 0.0558 nan 0.1000 -0.0000
## 340 0.0492 nan 0.1000 -0.0001
## 360 0.0437 nan 0.1000 -0.0002
## 380 0.0385 nan 0.1000 -0.0001
## 400 0.0343 nan 0.1000 -0.0002
## 420 0.0302 nan 0.1000 -0.0000
## 440 0.0266 nan 0.1000 -0.0000
## 460 0.0233 nan 0.1000 -0.0000
## 480 0.0205 nan 0.1000 -0.0000
## 500 0.0181 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2337 nan 0.1000 0.0382
## 2 1.1610 nan 0.1000 0.0312
## 3 1.0937 nan 0.1000 0.0269
## 4 1.0439 nan 0.1000 0.0224
## 5 0.9954 nan 0.1000 0.0192
## 6 0.9569 nan 0.1000 0.0150
## 7 0.9237 nan 0.1000 0.0131
## 8 0.8890 nan 0.1000 0.0119
## 9 0.8635 nan 0.1000 0.0097
## 10 0.8387 nan 0.1000 0.0087
## 20 0.6812 nan 0.1000 0.0029
## 40 0.5281 nan 0.1000 -0.0007
## 60 0.4291 nan 0.1000 -0.0010
## 80 0.3561 nan 0.1000 0.0002
## 100 0.3033 nan 0.1000 -0.0001
## 120 0.2546 nan 0.1000 -0.0004
## 140 0.2191 nan 0.1000 -0.0005
## 160 0.1897 nan 0.1000 -0.0013
## 180 0.1620 nan 0.1000 -0.0000
## 200 0.1396 nan 0.1000 0.0000
## 220 0.1208 nan 0.1000 -0.0001
## 240 0.1044 nan 0.1000 -0.0001
## 260 0.0920 nan 0.1000 -0.0004
## 280 0.0803 nan 0.1000 -0.0006
## 300 0.0710 nan 0.1000 -0.0001
## 320 0.0627 nan 0.1000 -0.0002
## 340 0.0558 nan 0.1000 -0.0001
## 360 0.0493 nan 0.1000 -0.0002
## 380 0.0434 nan 0.1000 -0.0001
## 400 0.0385 nan 0.1000 -0.0001
## 420 0.0341 nan 0.1000 -0.0000
## 440 0.0303 nan 0.1000 -0.0001
## 460 0.0267 nan 0.1000 -0.0001
## 480 0.0239 nan 0.1000 -0.0001
## 500 0.0213 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3180 nan 0.0010 0.0004
## 4 1.3171 nan 0.0010 0.0004
## 5 1.3163 nan 0.0010 0.0004
## 6 1.3154 nan 0.0010 0.0004
## 7 1.3145 nan 0.0010 0.0004
## 8 1.3137 nan 0.0010 0.0004
## 9 1.3129 nan 0.0010 0.0003
## 10 1.3119 nan 0.0010 0.0004
## 20 1.3033 nan 0.0010 0.0003
## 40 1.2868 nan 0.0010 0.0004
## 60 1.2710 nan 0.0010 0.0004
## 80 1.2558 nan 0.0010 0.0003
## 100 1.2412 nan 0.0010 0.0003
## 120 1.2271 nan 0.0010 0.0003
## 140 1.2133 nan 0.0010 0.0003
## 160 1.1999 nan 0.0010 0.0003
## 180 1.1873 nan 0.0010 0.0003
## 200 1.1747 nan 0.0010 0.0002
## 220 1.1626 nan 0.0010 0.0002
## 240 1.1507 nan 0.0010 0.0002
## 260 1.1392 nan 0.0010 0.0002
## 280 1.1281 nan 0.0010 0.0003
## 300 1.1172 nan 0.0010 0.0003
## 320 1.1064 nan 0.0010 0.0002
## 340 1.0960 nan 0.0010 0.0002
## 360 1.0858 nan 0.0010 0.0002
## 380 1.0761 nan 0.0010 0.0002
## 400 1.0664 nan 0.0010 0.0002
## 420 1.0571 nan 0.0010 0.0002
## 440 1.0481 nan 0.0010 0.0002
## 460 1.0394 nan 0.0010 0.0002
## 480 1.0308 nan 0.0010 0.0002
## 500 1.0224 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3199 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3181 nan 0.0010 0.0004
## 4 1.3172 nan 0.0010 0.0004
## 5 1.3164 nan 0.0010 0.0004
## 6 1.3155 nan 0.0010 0.0004
## 7 1.3146 nan 0.0010 0.0004
## 8 1.3138 nan 0.0010 0.0003
## 9 1.3129 nan 0.0010 0.0004
## 10 1.3121 nan 0.0010 0.0003
## 20 1.3039 nan 0.0010 0.0004
## 40 1.2877 nan 0.0010 0.0003
## 60 1.2720 nan 0.0010 0.0003
## 80 1.2568 nan 0.0010 0.0004
## 100 1.2420 nan 0.0010 0.0003
## 120 1.2275 nan 0.0010 0.0003
## 140 1.2135 nan 0.0010 0.0003
## 160 1.2000 nan 0.0010 0.0003
## 180 1.1870 nan 0.0010 0.0003
## 200 1.1744 nan 0.0010 0.0002
## 220 1.1620 nan 0.0010 0.0003
## 240 1.1499 nan 0.0010 0.0003
## 260 1.1383 nan 0.0010 0.0002
## 280 1.1269 nan 0.0010 0.0002
## 300 1.1162 nan 0.0010 0.0002
## 320 1.1057 nan 0.0010 0.0002
## 340 1.0956 nan 0.0010 0.0002
## 360 1.0855 nan 0.0010 0.0002
## 380 1.0757 nan 0.0010 0.0002
## 400 1.0661 nan 0.0010 0.0002
## 420 1.0567 nan 0.0010 0.0002
## 440 1.0476 nan 0.0010 0.0002
## 460 1.0389 nan 0.0010 0.0002
## 480 1.0302 nan 0.0010 0.0002
## 500 1.0218 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3181 nan 0.0010 0.0004
## 4 1.3172 nan 0.0010 0.0003
## 5 1.3164 nan 0.0010 0.0004
## 6 1.3156 nan 0.0010 0.0003
## 7 1.3146 nan 0.0010 0.0004
## 8 1.3138 nan 0.0010 0.0004
## 9 1.3129 nan 0.0010 0.0004
## 10 1.3121 nan 0.0010 0.0004
## 20 1.3039 nan 0.0010 0.0003
## 40 1.2876 nan 0.0010 0.0004
## 60 1.2716 nan 0.0010 0.0003
## 80 1.2564 nan 0.0010 0.0003
## 100 1.2417 nan 0.0010 0.0003
## 120 1.2274 nan 0.0010 0.0003
## 140 1.2139 nan 0.0010 0.0003
## 160 1.2007 nan 0.0010 0.0003
## 180 1.1877 nan 0.0010 0.0003
## 200 1.1752 nan 0.0010 0.0003
## 220 1.1629 nan 0.0010 0.0003
## 240 1.1512 nan 0.0010 0.0003
## 260 1.1397 nan 0.0010 0.0003
## 280 1.1286 nan 0.0010 0.0003
## 300 1.1178 nan 0.0010 0.0002
## 320 1.1073 nan 0.0010 0.0002
## 340 1.0971 nan 0.0010 0.0002
## 360 1.0870 nan 0.0010 0.0002
## 380 1.0774 nan 0.0010 0.0002
## 400 1.0681 nan 0.0010 0.0002
## 420 1.0587 nan 0.0010 0.0002
## 440 1.0496 nan 0.0010 0.0002
## 460 1.0408 nan 0.0010 0.0002
## 480 1.0324 nan 0.0010 0.0002
## 500 1.0241 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3179 nan 0.0010 0.0004
## 4 1.3170 nan 0.0010 0.0004
## 5 1.3162 nan 0.0010 0.0004
## 6 1.3153 nan 0.0010 0.0004
## 7 1.3144 nan 0.0010 0.0004
## 8 1.3135 nan 0.0010 0.0004
## 9 1.3126 nan 0.0010 0.0004
## 10 1.3116 nan 0.0010 0.0004
## 20 1.3026 nan 0.0010 0.0004
## 40 1.2850 nan 0.0010 0.0004
## 60 1.2685 nan 0.0010 0.0003
## 80 1.2525 nan 0.0010 0.0003
## 100 1.2367 nan 0.0010 0.0003
## 120 1.2215 nan 0.0010 0.0003
## 140 1.2070 nan 0.0010 0.0003
## 160 1.1928 nan 0.0010 0.0003
## 180 1.1793 nan 0.0010 0.0003
## 200 1.1656 nan 0.0010 0.0003
## 220 1.1528 nan 0.0010 0.0002
## 240 1.1403 nan 0.0010 0.0002
## 260 1.1280 nan 0.0010 0.0003
## 280 1.1162 nan 0.0010 0.0002
## 300 1.1048 nan 0.0010 0.0002
## 320 1.0933 nan 0.0010 0.0002
## 340 1.0823 nan 0.0010 0.0002
## 360 1.0717 nan 0.0010 0.0002
## 380 1.0611 nan 0.0010 0.0002
## 400 1.0514 nan 0.0010 0.0002
## 420 1.0416 nan 0.0010 0.0002
## 440 1.0319 nan 0.0010 0.0002
## 460 1.0225 nan 0.0010 0.0002
## 480 1.0133 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3180 nan 0.0010 0.0004
## 4 1.3170 nan 0.0010 0.0004
## 5 1.3161 nan 0.0010 0.0004
## 6 1.3153 nan 0.0010 0.0003
## 7 1.3144 nan 0.0010 0.0004
## 8 1.3134 nan 0.0010 0.0004
## 9 1.3125 nan 0.0010 0.0004
## 10 1.3117 nan 0.0010 0.0004
## 20 1.3028 nan 0.0010 0.0004
## 40 1.2858 nan 0.0010 0.0004
## 60 1.2694 nan 0.0010 0.0004
## 80 1.2532 nan 0.0010 0.0004
## 100 1.2372 nan 0.0010 0.0004
## 120 1.2222 nan 0.0010 0.0003
## 140 1.2076 nan 0.0010 0.0003
## 160 1.1936 nan 0.0010 0.0003
## 180 1.1798 nan 0.0010 0.0003
## 200 1.1666 nan 0.0010 0.0003
## 220 1.1536 nan 0.0010 0.0003
## 240 1.1409 nan 0.0010 0.0003
## 260 1.1285 nan 0.0010 0.0003
## 280 1.1169 nan 0.0010 0.0003
## 300 1.1053 nan 0.0010 0.0003
## 320 1.0941 nan 0.0010 0.0002
## 340 1.0831 nan 0.0010 0.0002
## 360 1.0724 nan 0.0010 0.0002
## 380 1.0623 nan 0.0010 0.0002
## 400 1.0524 nan 0.0010 0.0002
## 420 1.0427 nan 0.0010 0.0002
## 440 1.0334 nan 0.0010 0.0002
## 460 1.0242 nan 0.0010 0.0002
## 480 1.0153 nan 0.0010 0.0002
## 500 1.0066 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0005
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3179 nan 0.0010 0.0004
## 4 1.3170 nan 0.0010 0.0004
## 5 1.3160 nan 0.0010 0.0004
## 6 1.3151 nan 0.0010 0.0004
## 7 1.3143 nan 0.0010 0.0004
## 8 1.3134 nan 0.0010 0.0004
## 9 1.3125 nan 0.0010 0.0005
## 10 1.3116 nan 0.0010 0.0004
## 20 1.3029 nan 0.0010 0.0004
## 40 1.2858 nan 0.0010 0.0004
## 60 1.2690 nan 0.0010 0.0004
## 80 1.2531 nan 0.0010 0.0003
## 100 1.2375 nan 0.0010 0.0003
## 120 1.2225 nan 0.0010 0.0004
## 140 1.2080 nan 0.0010 0.0003
## 160 1.1939 nan 0.0010 0.0003
## 180 1.1802 nan 0.0010 0.0003
## 200 1.1671 nan 0.0010 0.0003
## 220 1.1544 nan 0.0010 0.0002
## 240 1.1420 nan 0.0010 0.0003
## 260 1.1301 nan 0.0010 0.0003
## 280 1.1181 nan 0.0010 0.0003
## 300 1.1065 nan 0.0010 0.0002
## 320 1.0954 nan 0.0010 0.0002
## 340 1.0846 nan 0.0010 0.0003
## 360 1.0741 nan 0.0010 0.0002
## 380 1.0639 nan 0.0010 0.0002
## 400 1.0540 nan 0.0010 0.0002
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## 460 1.0259 nan 0.0010 0.0002
## 480 1.0170 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3196 nan 0.0010 0.0004
## 2 1.3187 nan 0.0010 0.0004
## 3 1.3177 nan 0.0010 0.0004
## 4 1.3168 nan 0.0010 0.0004
## 5 1.3158 nan 0.0010 0.0004
## 6 1.3148 nan 0.0010 0.0004
## 7 1.3139 nan 0.0010 0.0005
## 8 1.3130 nan 0.0010 0.0004
## 9 1.3120 nan 0.0010 0.0004
## 10 1.3111 nan 0.0010 0.0004
## 20 1.3018 nan 0.0010 0.0003
## 40 1.2835 nan 0.0010 0.0004
## 60 1.2659 nan 0.0010 0.0004
## 80 1.2492 nan 0.0010 0.0003
## 100 1.2326 nan 0.0010 0.0004
## 120 1.2166 nan 0.0010 0.0003
## 140 1.2012 nan 0.0010 0.0004
## 160 1.1864 nan 0.0010 0.0003
## 180 1.1717 nan 0.0010 0.0003
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## 280 1.1062 nan 0.0010 0.0002
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## 320 1.0824 nan 0.0010 0.0003
## 340 1.0710 nan 0.0010 0.0002
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## 380 1.0491 nan 0.0010 0.0002
## 400 1.0387 nan 0.0010 0.0002
## 420 1.0284 nan 0.0010 0.0002
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## 460 1.0088 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3178 nan 0.0010 0.0004
## 4 1.3169 nan 0.0010 0.0004
## 5 1.3159 nan 0.0010 0.0004
## 6 1.3150 nan 0.0010 0.0004
## 7 1.3141 nan 0.0010 0.0004
## 8 1.3132 nan 0.0010 0.0004
## 9 1.3123 nan 0.0010 0.0004
## 10 1.3114 nan 0.0010 0.0004
## 20 1.3020 nan 0.0010 0.0004
## 40 1.2840 nan 0.0010 0.0004
## 60 1.2667 nan 0.0010 0.0004
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## 100 1.2341 nan 0.0010 0.0004
## 120 1.2186 nan 0.0010 0.0003
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## 340 1.0752 nan 0.0010 0.0002
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## 380 1.0538 nan 0.0010 0.0002
## 400 1.0434 nan 0.0010 0.0002
## 420 1.0333 nan 0.0010 0.0002
## 440 1.0234 nan 0.0010 0.0002
## 460 1.0135 nan 0.0010 0.0002
## 480 1.0042 nan 0.0010 0.0002
## 500 0.9952 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3178 nan 0.0010 0.0005
## 4 1.3169 nan 0.0010 0.0004
## 5 1.3160 nan 0.0010 0.0004
## 6 1.3150 nan 0.0010 0.0004
## 7 1.3141 nan 0.0010 0.0004
## 8 1.3131 nan 0.0010 0.0004
## 9 1.3122 nan 0.0010 0.0004
## 10 1.3113 nan 0.0010 0.0004
## 20 1.3022 nan 0.0010 0.0004
## 40 1.2842 nan 0.0010 0.0004
## 60 1.2672 nan 0.0010 0.0004
## 80 1.2506 nan 0.0010 0.0004
## 100 1.2345 nan 0.0010 0.0004
## 120 1.2189 nan 0.0010 0.0003
## 140 1.2038 nan 0.0010 0.0003
## 160 1.1891 nan 0.0010 0.0003
## 180 1.1749 nan 0.0010 0.0003
## 200 1.1612 nan 0.0010 0.0003
## 220 1.1481 nan 0.0010 0.0003
## 240 1.1354 nan 0.0010 0.0003
## 260 1.1229 nan 0.0010 0.0003
## 280 1.1108 nan 0.0010 0.0003
## 300 1.0989 nan 0.0010 0.0002
## 320 1.0874 nan 0.0010 0.0002
## 340 1.0762 nan 0.0010 0.0002
## 360 1.0654 nan 0.0010 0.0003
## 380 1.0547 nan 0.0010 0.0002
## 400 1.0444 nan 0.0010 0.0002
## 420 1.0344 nan 0.0010 0.0002
## 440 1.0245 nan 0.0010 0.0002
## 460 1.0150 nan 0.0010 0.0002
## 480 1.0057 nan 0.0010 0.0002
## 500 0.9967 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3117 nan 0.0100 0.0040
## 2 1.3035 nan 0.0100 0.0039
## 3 1.2956 nan 0.0100 0.0038
## 4 1.2875 nan 0.0100 0.0033
## 5 1.2794 nan 0.0100 0.0035
## 6 1.2715 nan 0.0100 0.0037
## 7 1.2634 nan 0.0100 0.0033
## 8 1.2567 nan 0.0100 0.0030
## 9 1.2492 nan 0.0100 0.0035
## 10 1.2422 nan 0.0100 0.0031
## 20 1.1765 nan 0.0100 0.0030
## 40 1.0656 nan 0.0100 0.0022
## 60 0.9816 nan 0.0100 0.0013
## 80 0.9162 nan 0.0100 0.0010
## 100 0.8624 nan 0.0100 0.0010
## 120 0.8194 nan 0.0100 0.0006
## 140 0.7824 nan 0.0100 0.0006
## 160 0.7522 nan 0.0100 0.0002
## 180 0.7261 nan 0.0100 0.0003
## 200 0.7033 nan 0.0100 0.0002
## 220 0.6842 nan 0.0100 0.0002
## 240 0.6664 nan 0.0100 0.0000
## 260 0.6507 nan 0.0100 0.0001
## 280 0.6369 nan 0.0100 0.0001
## 300 0.6232 nan 0.0100 0.0000
## 320 0.6113 nan 0.0100 -0.0001
## 340 0.5994 nan 0.0100 0.0001
## 360 0.5888 nan 0.0100 -0.0000
## 380 0.5790 nan 0.0100 -0.0001
## 400 0.5696 nan 0.0100 0.0000
## 420 0.5605 nan 0.0100 -0.0000
## 440 0.5518 nan 0.0100 0.0000
## 460 0.5426 nan 0.0100 -0.0001
## 480 0.5336 nan 0.0100 -0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3126 nan 0.0100 0.0037
## 2 1.3044 nan 0.0100 0.0039
## 3 1.2961 nan 0.0100 0.0037
## 4 1.2885 nan 0.0100 0.0033
## 5 1.2804 nan 0.0100 0.0038
## 6 1.2727 nan 0.0100 0.0037
## 7 1.2648 nan 0.0100 0.0040
## 8 1.2580 nan 0.0100 0.0034
## 9 1.2504 nan 0.0100 0.0034
## 10 1.2431 nan 0.0100 0.0032
## 20 1.1762 nan 0.0100 0.0030
## 40 1.0685 nan 0.0100 0.0020
## 60 0.9842 nan 0.0100 0.0017
## 80 0.9164 nan 0.0100 0.0014
## 100 0.8655 nan 0.0100 0.0011
## 120 0.8213 nan 0.0100 0.0007
## 140 0.7872 nan 0.0100 0.0004
## 160 0.7553 nan 0.0100 0.0006
## 180 0.7306 nan 0.0100 0.0005
## 200 0.7083 nan 0.0100 0.0003
## 220 0.6891 nan 0.0100 0.0002
## 240 0.6717 nan 0.0100 0.0002
## 260 0.6561 nan 0.0100 0.0001
## 280 0.6423 nan 0.0100 0.0001
## 300 0.6290 nan 0.0100 0.0000
## 320 0.6169 nan 0.0100 0.0001
## 340 0.6054 nan 0.0100 0.0002
## 360 0.5942 nan 0.0100 0.0001
## 380 0.5841 nan 0.0100 0.0000
## 400 0.5752 nan 0.0100 -0.0000
## 420 0.5660 nan 0.0100 -0.0000
## 440 0.5575 nan 0.0100 0.0001
## 460 0.5483 nan 0.0100 -0.0001
## 480 0.5404 nan 0.0100 0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3114 nan 0.0100 0.0039
## 2 1.3036 nan 0.0100 0.0037
## 3 1.2950 nan 0.0100 0.0040
## 4 1.2872 nan 0.0100 0.0038
## 5 1.2795 nan 0.0100 0.0035
## 6 1.2717 nan 0.0100 0.0035
## 7 1.2640 nan 0.0100 0.0038
## 8 1.2567 nan 0.0100 0.0034
## 9 1.2489 nan 0.0100 0.0034
## 10 1.2415 nan 0.0100 0.0032
## 20 1.1739 nan 0.0100 0.0022
## 40 1.0672 nan 0.0100 0.0020
## 60 0.9831 nan 0.0100 0.0016
## 80 0.9191 nan 0.0100 0.0011
## 100 0.8649 nan 0.0100 0.0009
## 120 0.8228 nan 0.0100 0.0007
## 140 0.7885 nan 0.0100 0.0006
## 160 0.7607 nan 0.0100 0.0003
## 180 0.7354 nan 0.0100 0.0002
## 200 0.7132 nan 0.0100 0.0002
## 220 0.6937 nan 0.0100 0.0001
## 240 0.6769 nan 0.0100 0.0001
## 260 0.6617 nan 0.0100 0.0000
## 280 0.6475 nan 0.0100 0.0001
## 300 0.6347 nan 0.0100 0.0000
## 320 0.6225 nan 0.0100 -0.0001
## 340 0.6121 nan 0.0100 -0.0001
## 360 0.6026 nan 0.0100 0.0001
## 380 0.5925 nan 0.0100 0.0002
## 400 0.5831 nan 0.0100 -0.0000
## 420 0.5736 nan 0.0100 0.0002
## 440 0.5654 nan 0.0100 -0.0002
## 460 0.5575 nan 0.0100 -0.0002
## 480 0.5494 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3119 nan 0.0100 0.0038
## 2 1.3025 nan 0.0100 0.0042
## 3 1.2931 nan 0.0100 0.0041
## 4 1.2851 nan 0.0100 0.0036
## 5 1.2764 nan 0.0100 0.0040
## 6 1.2678 nan 0.0100 0.0038
## 7 1.2594 nan 0.0100 0.0037
## 8 1.2508 nan 0.0100 0.0037
## 9 1.2427 nan 0.0100 0.0037
## 10 1.2347 nan 0.0100 0.0034
## 20 1.1647 nan 0.0100 0.0029
## 40 1.0524 nan 0.0100 0.0020
## 60 0.9646 nan 0.0100 0.0014
## 80 0.8959 nan 0.0100 0.0010
## 100 0.8411 nan 0.0100 0.0008
## 120 0.7965 nan 0.0100 0.0006
## 140 0.7581 nan 0.0100 0.0004
## 160 0.7251 nan 0.0100 0.0006
## 180 0.6979 nan 0.0100 0.0004
## 200 0.6740 nan 0.0100 0.0005
## 220 0.6519 nan 0.0100 0.0002
## 240 0.6323 nan 0.0100 0.0001
## 260 0.6153 nan 0.0100 0.0003
## 280 0.6001 nan 0.0100 0.0001
## 300 0.5855 nan 0.0100 0.0002
## 320 0.5724 nan 0.0100 0.0001
## 340 0.5594 nan 0.0100 0.0001
## 360 0.5480 nan 0.0100 -0.0002
## 380 0.5369 nan 0.0100 -0.0000
## 400 0.5266 nan 0.0100 0.0001
## 420 0.5157 nan 0.0100 0.0001
## 440 0.5057 nan 0.0100 -0.0001
## 460 0.4958 nan 0.0100 -0.0001
## 480 0.4863 nan 0.0100 0.0000
## 500 0.4772 nan 0.0100 0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3119 nan 0.0100 0.0040
## 2 1.3035 nan 0.0100 0.0037
## 3 1.2944 nan 0.0100 0.0038
## 4 1.2860 nan 0.0100 0.0040
## 5 1.2773 nan 0.0100 0.0038
## 6 1.2692 nan 0.0100 0.0037
## 7 1.2613 nan 0.0100 0.0034
## 8 1.2535 nan 0.0100 0.0035
## 9 1.2456 nan 0.0100 0.0033
## 10 1.2377 nan 0.0100 0.0036
## 20 1.1664 nan 0.0100 0.0029
## 40 1.0519 nan 0.0100 0.0022
## 60 0.9647 nan 0.0100 0.0017
## 80 0.8951 nan 0.0100 0.0012
## 100 0.8421 nan 0.0100 0.0008
## 120 0.7991 nan 0.0100 0.0006
## 140 0.7600 nan 0.0100 0.0005
## 160 0.7280 nan 0.0100 0.0002
## 180 0.7007 nan 0.0100 0.0004
## 200 0.6771 nan 0.0100 0.0002
## 220 0.6566 nan 0.0100 0.0003
## 240 0.6381 nan 0.0100 0.0001
## 260 0.6211 nan 0.0100 0.0001
## 280 0.6062 nan 0.0100 0.0001
## 300 0.5919 nan 0.0100 -0.0001
## 320 0.5793 nan 0.0100 0.0000
## 340 0.5671 nan 0.0100 -0.0000
## 360 0.5552 nan 0.0100 -0.0000
## 380 0.5431 nan 0.0100 -0.0001
## 400 0.5318 nan 0.0100 0.0001
## 420 0.5211 nan 0.0100 0.0000
## 440 0.5117 nan 0.0100 0.0000
## 460 0.5014 nan 0.0100 0.0000
## 480 0.4926 nan 0.0100 -0.0001
## 500 0.4835 nan 0.0100 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3122 nan 0.0100 0.0036
## 2 1.3031 nan 0.0100 0.0040
## 3 1.2941 nan 0.0100 0.0041
## 4 1.2857 nan 0.0100 0.0036
## 5 1.2769 nan 0.0100 0.0039
## 6 1.2687 nan 0.0100 0.0039
## 7 1.2598 nan 0.0100 0.0038
## 8 1.2520 nan 0.0100 0.0034
## 9 1.2439 nan 0.0100 0.0038
## 10 1.2357 nan 0.0100 0.0035
## 20 1.1656 nan 0.0100 0.0030
## 40 1.0531 nan 0.0100 0.0021
## 60 0.9670 nan 0.0100 0.0015
## 80 0.8999 nan 0.0100 0.0013
## 100 0.8451 nan 0.0100 0.0010
## 120 0.8003 nan 0.0100 0.0005
## 140 0.7638 nan 0.0100 0.0003
## 160 0.7325 nan 0.0100 0.0003
## 180 0.7054 nan 0.0100 0.0002
## 200 0.6831 nan 0.0100 0.0003
## 220 0.6637 nan 0.0100 0.0001
## 240 0.6457 nan 0.0100 0.0001
## 260 0.6284 nan 0.0100 0.0001
## 280 0.6136 nan 0.0100 0.0000
## 300 0.6007 nan 0.0100 0.0000
## 320 0.5875 nan 0.0100 -0.0000
## 340 0.5760 nan 0.0100 -0.0001
## 360 0.5653 nan 0.0100 0.0001
## 380 0.5542 nan 0.0100 -0.0001
## 400 0.5434 nan 0.0100 -0.0000
## 420 0.5342 nan 0.0100 -0.0000
## 440 0.5252 nan 0.0100 -0.0002
## 460 0.5167 nan 0.0100 -0.0001
## 480 0.5080 nan 0.0100 -0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3108 nan 0.0100 0.0048
## 2 1.3014 nan 0.0100 0.0042
## 3 1.2927 nan 0.0100 0.0040
## 4 1.2835 nan 0.0100 0.0044
## 5 1.2746 nan 0.0100 0.0041
## 6 1.2660 nan 0.0100 0.0037
## 7 1.2567 nan 0.0100 0.0039
## 8 1.2481 nan 0.0100 0.0039
## 9 1.2398 nan 0.0100 0.0041
## 10 1.2316 nan 0.0100 0.0035
## 20 1.1558 nan 0.0100 0.0028
## 40 1.0371 nan 0.0100 0.0018
## 60 0.9458 nan 0.0100 0.0015
## 80 0.8743 nan 0.0100 0.0012
## 100 0.8173 nan 0.0100 0.0009
## 120 0.7714 nan 0.0100 0.0006
## 140 0.7307 nan 0.0100 0.0005
## 160 0.6976 nan 0.0100 0.0003
## 180 0.6688 nan 0.0100 0.0005
## 200 0.6428 nan 0.0100 0.0002
## 220 0.6200 nan 0.0100 0.0003
## 240 0.5990 nan 0.0100 0.0001
## 260 0.5806 nan 0.0100 0.0001
## 280 0.5635 nan 0.0100 0.0001
## 300 0.5476 nan 0.0100 0.0001
## 320 0.5320 nan 0.0100 -0.0000
## 340 0.5171 nan 0.0100 0.0000
## 360 0.5033 nan 0.0100 0.0001
## 380 0.4902 nan 0.0100 -0.0000
## 400 0.4791 nan 0.0100 -0.0001
## 420 0.4689 nan 0.0100 -0.0001
## 440 0.4590 nan 0.0100 0.0001
## 460 0.4489 nan 0.0100 -0.0001
## 480 0.4390 nan 0.0100 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3112 nan 0.0100 0.0042
## 2 1.3019 nan 0.0100 0.0044
## 3 1.2929 nan 0.0100 0.0038
## 4 1.2841 nan 0.0100 0.0040
## 5 1.2751 nan 0.0100 0.0037
## 6 1.2664 nan 0.0100 0.0040
## 7 1.2580 nan 0.0100 0.0036
## 8 1.2499 nan 0.0100 0.0035
## 9 1.2414 nan 0.0100 0.0037
## 10 1.2331 nan 0.0100 0.0036
## 20 1.1581 nan 0.0100 0.0032
## 40 1.0423 nan 0.0100 0.0022
## 60 0.9514 nan 0.0100 0.0016
## 80 0.8823 nan 0.0100 0.0014
## 100 0.8229 nan 0.0100 0.0009
## 120 0.7755 nan 0.0100 0.0006
## 140 0.7382 nan 0.0100 0.0004
## 160 0.7049 nan 0.0100 0.0004
## 180 0.6768 nan 0.0100 0.0003
## 200 0.6514 nan 0.0100 0.0002
## 220 0.6289 nan 0.0100 0.0002
## 240 0.6098 nan 0.0100 0.0001
## 260 0.5919 nan 0.0100 0.0002
## 280 0.5756 nan 0.0100 0.0000
## 300 0.5596 nan 0.0100 0.0001
## 320 0.5454 nan 0.0100 0.0000
## 340 0.5312 nan 0.0100 -0.0000
## 360 0.5183 nan 0.0100 -0.0000
## 380 0.5065 nan 0.0100 0.0001
## 400 0.4949 nan 0.0100 -0.0000
## 420 0.4839 nan 0.0100 -0.0001
## 440 0.4731 nan 0.0100 -0.0001
## 460 0.4636 nan 0.0100 -0.0001
## 480 0.4540 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3109 nan 0.0100 0.0043
## 2 1.3020 nan 0.0100 0.0040
## 3 1.2929 nan 0.0100 0.0040
## 4 1.2845 nan 0.0100 0.0035
## 5 1.2750 nan 0.0100 0.0043
## 6 1.2665 nan 0.0100 0.0040
## 7 1.2577 nan 0.0100 0.0040
## 8 1.2496 nan 0.0100 0.0038
## 9 1.2411 nan 0.0100 0.0035
## 10 1.2325 nan 0.0100 0.0034
## 20 1.1614 nan 0.0100 0.0031
## 40 1.0446 nan 0.0100 0.0021
## 60 0.9539 nan 0.0100 0.0017
## 80 0.8837 nan 0.0100 0.0010
## 100 0.8266 nan 0.0100 0.0011
## 120 0.7802 nan 0.0100 0.0007
## 140 0.7426 nan 0.0100 0.0003
## 160 0.7102 nan 0.0100 0.0003
## 180 0.6833 nan 0.0100 0.0002
## 200 0.6600 nan 0.0100 0.0003
## 220 0.6386 nan 0.0100 0.0002
## 240 0.6185 nan 0.0100 0.0002
## 260 0.6005 nan 0.0100 0.0001
## 280 0.5840 nan 0.0100 0.0000
## 300 0.5680 nan 0.0100 0.0001
## 320 0.5540 nan 0.0100 0.0000
## 340 0.5414 nan 0.0100 0.0000
## 360 0.5294 nan 0.0100 -0.0000
## 380 0.5177 nan 0.0100 -0.0000
## 400 0.5059 nan 0.0100 -0.0001
## 420 0.4954 nan 0.0100 -0.0002
## 440 0.4849 nan 0.0100 0.0002
## 460 0.4753 nan 0.0100 -0.0001
## 480 0.4665 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2377 nan 0.1000 0.0329
## 2 1.1714 nan 0.1000 0.0296
## 3 1.1159 nan 0.1000 0.0245
## 4 1.0606 nan 0.1000 0.0222
## 5 1.0134 nan 0.1000 0.0209
## 6 0.9734 nan 0.1000 0.0154
## 7 0.9407 nan 0.1000 0.0129
## 8 0.9124 nan 0.1000 0.0115
## 9 0.8865 nan 0.1000 0.0079
## 10 0.8650 nan 0.1000 0.0094
## 20 0.7155 nan 0.1000 0.0009
## 40 0.5787 nan 0.1000 -0.0012
## 60 0.4938 nan 0.1000 -0.0003
## 80 0.4313 nan 0.1000 -0.0006
## 100 0.3868 nan 0.1000 -0.0010
## 120 0.3428 nan 0.1000 -0.0001
## 140 0.3062 nan 0.1000 0.0002
## 160 0.2771 nan 0.1000 -0.0005
## 180 0.2489 nan 0.1000 -0.0001
## 200 0.2235 nan 0.1000 -0.0002
## 220 0.2005 nan 0.1000 -0.0001
## 240 0.1821 nan 0.1000 -0.0006
## 260 0.1664 nan 0.1000 -0.0007
## 280 0.1514 nan 0.1000 -0.0002
## 300 0.1385 nan 0.1000 -0.0007
## 320 0.1265 nan 0.1000 -0.0001
## 340 0.1160 nan 0.1000 -0.0006
## 360 0.1062 nan 0.1000 -0.0001
## 380 0.0975 nan 0.1000 -0.0003
## 400 0.0898 nan 0.1000 -0.0003
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## 440 0.0768 nan 0.1000 -0.0004
## 460 0.0706 nan 0.1000 -0.0004
## 480 0.0650 nan 0.1000 -0.0003
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2333 nan 0.1000 0.0347
## 2 1.1655 nan 0.1000 0.0310
## 3 1.1108 nan 0.1000 0.0218
## 4 1.0556 nan 0.1000 0.0226
## 5 1.0129 nan 0.1000 0.0165
## 6 0.9762 nan 0.1000 0.0158
## 7 0.9421 nan 0.1000 0.0133
## 8 0.9158 nan 0.1000 0.0114
## 9 0.8858 nan 0.1000 0.0109
## 10 0.8624 nan 0.1000 0.0068
## 20 0.7191 nan 0.1000 0.0025
## 40 0.5778 nan 0.1000 0.0001
## 60 0.4976 nan 0.1000 -0.0018
## 80 0.4386 nan 0.1000 0.0007
## 100 0.3899 nan 0.1000 -0.0003
## 120 0.3442 nan 0.1000 -0.0008
## 140 0.3062 nan 0.1000 -0.0007
## 160 0.2768 nan 0.1000 -0.0016
## 180 0.2490 nan 0.1000 -0.0006
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## 220 0.2046 nan 0.1000 -0.0003
## 240 0.1867 nan 0.1000 -0.0001
## 260 0.1695 nan 0.1000 -0.0003
## 280 0.1547 nan 0.1000 -0.0000
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## 320 0.1294 nan 0.1000 -0.0006
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## 380 0.1004 nan 0.1000 -0.0001
## 400 0.0928 nan 0.1000 -0.0003
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## 460 0.0723 nan 0.1000 -0.0002
## 480 0.0671 nan 0.1000 -0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2412 nan 0.1000 0.0365
## 2 1.1751 nan 0.1000 0.0274
## 3 1.1175 nan 0.1000 0.0233
## 4 1.0627 nan 0.1000 0.0249
## 5 1.0152 nan 0.1000 0.0206
## 6 0.9796 nan 0.1000 0.0125
## 7 0.9441 nan 0.1000 0.0156
## 8 0.9135 nan 0.1000 0.0114
## 9 0.8863 nan 0.1000 0.0108
## 10 0.8602 nan 0.1000 0.0099
## 20 0.7106 nan 0.1000 0.0013
## 40 0.5746 nan 0.1000 -0.0004
## 60 0.5084 nan 0.1000 -0.0017
## 80 0.4550 nan 0.1000 -0.0010
## 100 0.4051 nan 0.1000 -0.0013
## 120 0.3624 nan 0.1000 -0.0010
## 140 0.3214 nan 0.1000 -0.0005
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## 180 0.2676 nan 0.1000 -0.0013
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## 240 0.2061 nan 0.1000 -0.0006
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## 320 0.1484 nan 0.1000 -0.0004
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2291 nan 0.1000 0.0394
## 2 1.1562 nan 0.1000 0.0360
## 3 1.0966 nan 0.1000 0.0242
## 4 1.0431 nan 0.1000 0.0241
## 5 1.0002 nan 0.1000 0.0164
## 6 0.9651 nan 0.1000 0.0137
## 7 0.9258 nan 0.1000 0.0170
## 8 0.8933 nan 0.1000 0.0115
## 9 0.8643 nan 0.1000 0.0101
## 10 0.8358 nan 0.1000 0.0090
## 20 0.6692 nan 0.1000 0.0053
## 40 0.5183 nan 0.1000 0.0013
## 60 0.4231 nan 0.1000 0.0001
## 80 0.3563 nan 0.1000 -0.0005
## 100 0.3125 nan 0.1000 -0.0008
## 120 0.2695 nan 0.1000 0.0003
## 140 0.2364 nan 0.1000 -0.0007
## 160 0.2052 nan 0.1000 0.0002
## 180 0.1806 nan 0.1000 -0.0004
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## 220 0.1415 nan 0.1000 -0.0001
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## 280 0.1015 nan 0.1000 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2375 nan 0.1000 0.0394
## 2 1.1607 nan 0.1000 0.0333
## 3 1.1010 nan 0.1000 0.0256
## 4 1.0490 nan 0.1000 0.0233
## 5 1.0087 nan 0.1000 0.0193
## 6 0.9665 nan 0.1000 0.0163
## 7 0.9349 nan 0.1000 0.0115
## 8 0.9056 nan 0.1000 0.0106
## 9 0.8796 nan 0.1000 0.0081
## 10 0.8581 nan 0.1000 0.0072
## 20 0.6824 nan 0.1000 -0.0005
## 40 0.5409 nan 0.1000 -0.0003
## 60 0.4578 nan 0.1000 -0.0009
## 80 0.3836 nan 0.1000 -0.0009
## 100 0.3261 nan 0.1000 -0.0006
## 120 0.2803 nan 0.1000 -0.0010
## 140 0.2475 nan 0.1000 -0.0005
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## 180 0.1911 nan 0.1000 -0.0005
## 200 0.1668 nan 0.1000 -0.0004
## 220 0.1497 nan 0.1000 -0.0008
## 240 0.1335 nan 0.1000 -0.0002
## 260 0.1191 nan 0.1000 -0.0004
## 280 0.1067 nan 0.1000 -0.0002
## 300 0.0951 nan 0.1000 -0.0000
## 320 0.0841 nan 0.1000 -0.0001
## 340 0.0759 nan 0.1000 -0.0000
## 360 0.0677 nan 0.1000 -0.0003
## 380 0.0612 nan 0.1000 -0.0003
## 400 0.0548 nan 0.1000 -0.0000
## 420 0.0497 nan 0.1000 -0.0001
## 440 0.0453 nan 0.1000 -0.0002
## 460 0.0410 nan 0.1000 -0.0000
## 480 0.0375 nan 0.1000 -0.0002
## 500 0.0340 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2323 nan 0.1000 0.0426
## 2 1.1593 nan 0.1000 0.0311
## 3 1.0959 nan 0.1000 0.0260
## 4 1.0466 nan 0.1000 0.0237
## 5 1.0039 nan 0.1000 0.0190
## 6 0.9678 nan 0.1000 0.0154
## 7 0.9352 nan 0.1000 0.0125
## 8 0.9030 nan 0.1000 0.0127
## 9 0.8756 nan 0.1000 0.0104
## 10 0.8536 nan 0.1000 0.0078
## 20 0.6988 nan 0.1000 -0.0001
## 40 0.5580 nan 0.1000 -0.0001
## 60 0.4674 nan 0.1000 -0.0011
## 80 0.4048 nan 0.1000 -0.0016
## 100 0.3544 nan 0.1000 -0.0011
## 120 0.3129 nan 0.1000 -0.0004
## 140 0.2743 nan 0.1000 -0.0002
## 160 0.2419 nan 0.1000 -0.0014
## 180 0.2175 nan 0.1000 -0.0014
## 200 0.1918 nan 0.1000 -0.0009
## 220 0.1705 nan 0.1000 -0.0007
## 240 0.1541 nan 0.1000 -0.0006
## 260 0.1391 nan 0.1000 -0.0006
## 280 0.1257 nan 0.1000 -0.0008
## 300 0.1126 nan 0.1000 -0.0001
## 320 0.1026 nan 0.1000 -0.0007
## 340 0.0927 nan 0.1000 -0.0005
## 360 0.0832 nan 0.1000 -0.0003
## 380 0.0760 nan 0.1000 -0.0003
## 400 0.0692 nan 0.1000 -0.0004
## 420 0.0627 nan 0.1000 -0.0000
## 440 0.0565 nan 0.1000 -0.0001
## 460 0.0509 nan 0.1000 -0.0001
## 480 0.0462 nan 0.1000 -0.0001
## 500 0.0421 nan 0.1000 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2248 nan 0.1000 0.0403
## 2 1.1497 nan 0.1000 0.0344
## 3 1.0846 nan 0.1000 0.0313
## 4 1.0259 nan 0.1000 0.0236
## 5 0.9801 nan 0.1000 0.0214
## 6 0.9396 nan 0.1000 0.0152
## 7 0.9047 nan 0.1000 0.0148
## 8 0.8753 nan 0.1000 0.0119
## 9 0.8443 nan 0.1000 0.0108
## 10 0.8201 nan 0.1000 0.0092
## 20 0.6546 nan 0.1000 0.0014
## 40 0.4964 nan 0.1000 0.0002
## 60 0.4024 nan 0.1000 -0.0012
## 80 0.3299 nan 0.1000 -0.0003
## 100 0.2741 nan 0.1000 -0.0003
## 120 0.2324 nan 0.1000 -0.0002
## 140 0.2001 nan 0.1000 -0.0005
## 160 0.1711 nan 0.1000 -0.0003
## 180 0.1460 nan 0.1000 -0.0004
## 200 0.1267 nan 0.1000 -0.0006
## 220 0.1083 nan 0.1000 -0.0001
## 240 0.0945 nan 0.1000 -0.0002
## 260 0.0824 nan 0.1000 -0.0001
## 280 0.0719 nan 0.1000 -0.0002
## 300 0.0634 nan 0.1000 -0.0003
## 320 0.0561 nan 0.1000 -0.0001
## 340 0.0494 nan 0.1000 -0.0000
## 360 0.0430 nan 0.1000 -0.0001
## 380 0.0381 nan 0.1000 -0.0000
## 400 0.0335 nan 0.1000 -0.0001
## 420 0.0297 nan 0.1000 -0.0001
## 440 0.0260 nan 0.1000 -0.0001
## 460 0.0231 nan 0.1000 0.0000
## 480 0.0204 nan 0.1000 -0.0000
## 500 0.0180 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2255 nan 0.1000 0.0443
## 2 1.1543 nan 0.1000 0.0319
## 3 1.0906 nan 0.1000 0.0276
## 4 1.0399 nan 0.1000 0.0207
## 5 0.9941 nan 0.1000 0.0200
## 6 0.9510 nan 0.1000 0.0162
## 7 0.9140 nan 0.1000 0.0155
## 8 0.8790 nan 0.1000 0.0127
## 9 0.8482 nan 0.1000 0.0111
## 10 0.8225 nan 0.1000 0.0102
## 20 0.6573 nan 0.1000 0.0012
## 40 0.5057 nan 0.1000 -0.0024
## 60 0.4092 nan 0.1000 -0.0001
## 80 0.3386 nan 0.1000 -0.0004
## 100 0.2903 nan 0.1000 -0.0009
## 120 0.2455 nan 0.1000 0.0005
## 140 0.2094 nan 0.1000 -0.0000
## 160 0.1835 nan 0.1000 -0.0010
## 180 0.1525 nan 0.1000 -0.0004
## 200 0.1326 nan 0.1000 -0.0009
## 220 0.1151 nan 0.1000 -0.0003
## 240 0.0988 nan 0.1000 -0.0004
## 260 0.0857 nan 0.1000 -0.0001
## 280 0.0765 nan 0.1000 -0.0006
## 300 0.0673 nan 0.1000 -0.0002
## 320 0.0591 nan 0.1000 0.0000
## 340 0.0523 nan 0.1000 -0.0002
## 360 0.0458 nan 0.1000 -0.0001
## 380 0.0400 nan 0.1000 -0.0002
## 400 0.0351 nan 0.1000 -0.0001
## 420 0.0311 nan 0.1000 -0.0001
## 440 0.0276 nan 0.1000 -0.0001
## 460 0.0246 nan 0.1000 -0.0000
## 480 0.0218 nan 0.1000 -0.0000
## 500 0.0193 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2282 nan 0.1000 0.0434
## 2 1.1482 nan 0.1000 0.0367
## 3 1.0902 nan 0.1000 0.0237
## 4 1.0405 nan 0.1000 0.0174
## 5 0.9875 nan 0.1000 0.0217
## 6 0.9459 nan 0.1000 0.0184
## 7 0.9103 nan 0.1000 0.0133
## 8 0.8746 nan 0.1000 0.0151
## 9 0.8450 nan 0.1000 0.0120
## 10 0.8189 nan 0.1000 0.0100
## 20 0.6598 nan 0.1000 0.0009
## 40 0.5050 nan 0.1000 -0.0001
## 60 0.4150 nan 0.1000 -0.0001
## 80 0.3510 nan 0.1000 -0.0016
## 100 0.2980 nan 0.1000 -0.0007
## 120 0.2560 nan 0.1000 -0.0002
## 140 0.2217 nan 0.1000 -0.0010
## 160 0.1919 nan 0.1000 -0.0006
## 180 0.1683 nan 0.1000 -0.0005
## 200 0.1439 nan 0.1000 -0.0004
## 220 0.1260 nan 0.1000 -0.0006
## 240 0.1105 nan 0.1000 -0.0001
## 260 0.0963 nan 0.1000 -0.0005
## 280 0.0855 nan 0.1000 -0.0003
## 300 0.0759 nan 0.1000 -0.0002
## 320 0.0676 nan 0.1000 -0.0001
## 340 0.0608 nan 0.1000 -0.0003
## 360 0.0537 nan 0.1000 -0.0001
## 380 0.0474 nan 0.1000 -0.0001
## 400 0.0420 nan 0.1000 -0.0002
## 420 0.0374 nan 0.1000 -0.0001
## 440 0.0331 nan 0.1000 -0.0001
## 460 0.0291 nan 0.1000 -0.0000
## 480 0.0260 nan 0.1000 -0.0001
## 500 0.0235 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3199 nan 0.0010 0.0003
## 2 1.3191 nan 0.0010 0.0004
## 3 1.3183 nan 0.0010 0.0004
## 4 1.3175 nan 0.0010 0.0004
## 5 1.3168 nan 0.0010 0.0003
## 6 1.3160 nan 0.0010 0.0003
## 7 1.3152 nan 0.0010 0.0003
## 8 1.3145 nan 0.0010 0.0004
## 9 1.3136 nan 0.0010 0.0003
## 10 1.3128 nan 0.0010 0.0004
## 20 1.3047 nan 0.0010 0.0004
## 40 1.2893 nan 0.0010 0.0003
## 60 1.2744 nan 0.0010 0.0003
## 80 1.2603 nan 0.0010 0.0003
## 100 1.2460 nan 0.0010 0.0003
## 120 1.2323 nan 0.0010 0.0003
## 140 1.2191 nan 0.0010 0.0003
## 160 1.2064 nan 0.0010 0.0003
## 180 1.1936 nan 0.0010 0.0002
## 200 1.1815 nan 0.0010 0.0002
## 220 1.1696 nan 0.0010 0.0003
## 240 1.1581 nan 0.0010 0.0003
## 260 1.1467 nan 0.0010 0.0002
## 280 1.1359 nan 0.0010 0.0003
## 300 1.1254 nan 0.0010 0.0002
## 320 1.1152 nan 0.0010 0.0002
## 340 1.1053 nan 0.0010 0.0002
## 360 1.0956 nan 0.0010 0.0002
## 380 1.0864 nan 0.0010 0.0002
## 400 1.0771 nan 0.0010 0.0002
## 420 1.0680 nan 0.0010 0.0002
## 440 1.0595 nan 0.0010 0.0002
## 460 1.0512 nan 0.0010 0.0001
## 480 1.0428 nan 0.0010 0.0002
## 500 1.0345 nan 0.0010 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3199 nan 0.0010 0.0004
## 2 1.3191 nan 0.0010 0.0004
## 3 1.3183 nan 0.0010 0.0004
## 4 1.3174 nan 0.0010 0.0004
## 5 1.3167 nan 0.0010 0.0003
## 6 1.3159 nan 0.0010 0.0003
## 7 1.3151 nan 0.0010 0.0004
## 8 1.3142 nan 0.0010 0.0004
## 9 1.3134 nan 0.0010 0.0004
## 10 1.3126 nan 0.0010 0.0004
## 20 1.3048 nan 0.0010 0.0004
## 40 1.2892 nan 0.0010 0.0004
## 60 1.2744 nan 0.0010 0.0003
## 80 1.2596 nan 0.0010 0.0003
## 100 1.2455 nan 0.0010 0.0003
## 120 1.2316 nan 0.0010 0.0003
## 140 1.2186 nan 0.0010 0.0003
## 160 1.2057 nan 0.0010 0.0003
## 180 1.1931 nan 0.0010 0.0003
## 200 1.1812 nan 0.0010 0.0003
## 220 1.1696 nan 0.0010 0.0003
## 240 1.1583 nan 0.0010 0.0002
## 260 1.1473 nan 0.0010 0.0003
## 280 1.1360 nan 0.0010 0.0003
## 300 1.1256 nan 0.0010 0.0002
## 320 1.1153 nan 0.0010 0.0002
## 340 1.1055 nan 0.0010 0.0002
## 360 1.0957 nan 0.0010 0.0002
## 380 1.0863 nan 0.0010 0.0002
## 400 1.0771 nan 0.0010 0.0002
## 420 1.0683 nan 0.0010 0.0002
## 440 1.0594 nan 0.0010 0.0002
## 460 1.0506 nan 0.0010 0.0002
## 480 1.0423 nan 0.0010 0.0001
## 500 1.0343 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3190 nan 0.0010 0.0004
## 3 1.3182 nan 0.0010 0.0004
## 4 1.3174 nan 0.0010 0.0004
## 5 1.3166 nan 0.0010 0.0004
## 6 1.3158 nan 0.0010 0.0003
## 7 1.3150 nan 0.0010 0.0004
## 8 1.3141 nan 0.0010 0.0003
## 9 1.3133 nan 0.0010 0.0003
## 10 1.3126 nan 0.0010 0.0003
## 20 1.3045 nan 0.0010 0.0004
## 40 1.2889 nan 0.0010 0.0003
## 60 1.2738 nan 0.0010 0.0003
## 80 1.2591 nan 0.0010 0.0003
## 100 1.2451 nan 0.0010 0.0003
## 120 1.2316 nan 0.0010 0.0003
## 140 1.2182 nan 0.0010 0.0003
## 160 1.2051 nan 0.0010 0.0003
## 180 1.1929 nan 0.0010 0.0003
## 200 1.1810 nan 0.0010 0.0002
## 220 1.1691 nan 0.0010 0.0003
## 240 1.1579 nan 0.0010 0.0003
## 260 1.1468 nan 0.0010 0.0002
## 280 1.1359 nan 0.0010 0.0002
## 300 1.1251 nan 0.0010 0.0002
## 320 1.1150 nan 0.0010 0.0002
## 340 1.1050 nan 0.0010 0.0002
## 360 1.0957 nan 0.0010 0.0002
## 380 1.0865 nan 0.0010 0.0002
## 400 1.0775 nan 0.0010 0.0002
## 420 1.0684 nan 0.0010 0.0002
## 440 1.0599 nan 0.0010 0.0002
## 460 1.0515 nan 0.0010 0.0002
## 480 1.0434 nan 0.0010 0.0002
## 500 1.0354 nan 0.0010 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3180 nan 0.0010 0.0004
## 4 1.3172 nan 0.0010 0.0004
## 5 1.3163 nan 0.0010 0.0004
## 6 1.3154 nan 0.0010 0.0004
## 7 1.3145 nan 0.0010 0.0004
## 8 1.3137 nan 0.0010 0.0003
## 9 1.3128 nan 0.0010 0.0004
## 10 1.3120 nan 0.0010 0.0004
## 20 1.3035 nan 0.0010 0.0004
## 40 1.2867 nan 0.0010 0.0004
## 60 1.2707 nan 0.0010 0.0004
## 80 1.2553 nan 0.0010 0.0003
## 100 1.2404 nan 0.0010 0.0003
## 120 1.2258 nan 0.0010 0.0003
## 140 1.2115 nan 0.0010 0.0003
## 160 1.1978 nan 0.0010 0.0003
## 180 1.1844 nan 0.0010 0.0003
## 200 1.1715 nan 0.0010 0.0003
## 220 1.1592 nan 0.0010 0.0003
## 240 1.1472 nan 0.0010 0.0003
## 260 1.1352 nan 0.0010 0.0003
## 280 1.1239 nan 0.0010 0.0003
## 300 1.1128 nan 0.0010 0.0002
## 320 1.1023 nan 0.0010 0.0002
## 340 1.0917 nan 0.0010 0.0002
## 360 1.0814 nan 0.0010 0.0002
## 380 1.0718 nan 0.0010 0.0002
## 400 1.0622 nan 0.0010 0.0002
## 420 1.0525 nan 0.0010 0.0002
## 440 1.0433 nan 0.0010 0.0002
## 460 1.0345 nan 0.0010 0.0002
## 480 1.0257 nan 0.0010 0.0002
## 500 1.0169 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0003
## 3 1.3181 nan 0.0010 0.0003
## 4 1.3172 nan 0.0010 0.0004
## 5 1.3163 nan 0.0010 0.0004
## 6 1.3154 nan 0.0010 0.0004
## 7 1.3146 nan 0.0010 0.0004
## 8 1.3136 nan 0.0010 0.0004
## 9 1.3127 nan 0.0010 0.0004
## 10 1.3119 nan 0.0010 0.0004
## 20 1.3036 nan 0.0010 0.0004
## 40 1.2870 nan 0.0010 0.0004
## 60 1.2710 nan 0.0010 0.0004
## 80 1.2555 nan 0.0010 0.0004
## 100 1.2405 nan 0.0010 0.0003
## 120 1.2261 nan 0.0010 0.0004
## 140 1.2120 nan 0.0010 0.0003
## 160 1.1984 nan 0.0010 0.0003
## 180 1.1852 nan 0.0010 0.0003
## 200 1.1725 nan 0.0010 0.0002
## 220 1.1598 nan 0.0010 0.0003
## 240 1.1479 nan 0.0010 0.0002
## 260 1.1363 nan 0.0010 0.0003
## 280 1.1249 nan 0.0010 0.0003
## 300 1.1138 nan 0.0010 0.0002
## 320 1.1032 nan 0.0010 0.0003
## 340 1.0927 nan 0.0010 0.0002
## 360 1.0825 nan 0.0010 0.0002
## 380 1.0724 nan 0.0010 0.0002
## 400 1.0626 nan 0.0010 0.0002
## 420 1.0531 nan 0.0010 0.0002
## 440 1.0439 nan 0.0010 0.0002
## 460 1.0352 nan 0.0010 0.0002
## 480 1.0264 nan 0.0010 0.0002
## 500 1.0178 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3190 nan 0.0010 0.0004
## 3 1.3181 nan 0.0010 0.0004
## 4 1.3173 nan 0.0010 0.0004
## 5 1.3165 nan 0.0010 0.0004
## 6 1.3157 nan 0.0010 0.0004
## 7 1.3149 nan 0.0010 0.0004
## 8 1.3141 nan 0.0010 0.0004
## 9 1.3133 nan 0.0010 0.0004
## 10 1.3124 nan 0.0010 0.0004
## 20 1.3041 nan 0.0010 0.0004
## 40 1.2880 nan 0.0010 0.0003
## 60 1.2721 nan 0.0010 0.0004
## 80 1.2570 nan 0.0010 0.0003
## 100 1.2419 nan 0.0010 0.0004
## 120 1.2278 nan 0.0010 0.0003
## 140 1.2141 nan 0.0010 0.0003
## 160 1.2007 nan 0.0010 0.0003
## 180 1.1874 nan 0.0010 0.0003
## 200 1.1746 nan 0.0010 0.0003
## 220 1.1620 nan 0.0010 0.0003
## 240 1.1502 nan 0.0010 0.0003
## 260 1.1383 nan 0.0010 0.0003
## 280 1.1272 nan 0.0010 0.0002
## 300 1.1163 nan 0.0010 0.0002
## 320 1.1055 nan 0.0010 0.0003
## 340 1.0951 nan 0.0010 0.0002
## 360 1.0849 nan 0.0010 0.0002
## 380 1.0753 nan 0.0010 0.0002
## 400 1.0657 nan 0.0010 0.0002
## 420 1.0563 nan 0.0010 0.0002
## 440 1.0471 nan 0.0010 0.0002
## 460 1.0383 nan 0.0010 0.0002
## 480 1.0294 nan 0.0010 0.0002
## 500 1.0210 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3190 nan 0.0010 0.0004
## 3 1.3180 nan 0.0010 0.0004
## 4 1.3171 nan 0.0010 0.0004
## 5 1.3163 nan 0.0010 0.0004
## 6 1.3154 nan 0.0010 0.0004
## 7 1.3145 nan 0.0010 0.0004
## 8 1.3136 nan 0.0010 0.0003
## 9 1.3127 nan 0.0010 0.0004
## 10 1.3117 nan 0.0010 0.0004
## 20 1.3031 nan 0.0010 0.0004
## 40 1.2855 nan 0.0010 0.0004
## 60 1.2684 nan 0.0010 0.0003
## 80 1.2521 nan 0.0010 0.0004
## 100 1.2363 nan 0.0010 0.0003
## 120 1.2210 nan 0.0010 0.0003
## 140 1.2062 nan 0.0010 0.0003
## 160 1.1920 nan 0.0010 0.0003
## 180 1.1784 nan 0.0010 0.0003
## 200 1.1650 nan 0.0010 0.0003
## 220 1.1523 nan 0.0010 0.0003
## 240 1.1396 nan 0.0010 0.0003
## 260 1.1270 nan 0.0010 0.0002
## 280 1.1151 nan 0.0010 0.0003
## 300 1.1036 nan 0.0010 0.0002
## 320 1.0923 nan 0.0010 0.0002
## 340 1.0810 nan 0.0010 0.0002
## 360 1.0705 nan 0.0010 0.0002
## 380 1.0601 nan 0.0010 0.0002
## 400 1.0501 nan 0.0010 0.0002
## 420 1.0402 nan 0.0010 0.0002
## 440 1.0305 nan 0.0010 0.0002
## 460 1.0210 nan 0.0010 0.0002
## 480 1.0120 nan 0.0010 0.0002
## 500 1.0032 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3179 nan 0.0010 0.0004
## 4 1.3170 nan 0.0010 0.0004
## 5 1.3161 nan 0.0010 0.0004
## 6 1.3153 nan 0.0010 0.0004
## 7 1.3143 nan 0.0010 0.0004
## 8 1.3134 nan 0.0010 0.0004
## 9 1.3125 nan 0.0010 0.0004
## 10 1.3117 nan 0.0010 0.0004
## 20 1.3028 nan 0.0010 0.0003
## 40 1.2855 nan 0.0010 0.0003
## 60 1.2689 nan 0.0010 0.0004
## 80 1.2530 nan 0.0010 0.0004
## 100 1.2373 nan 0.0010 0.0003
## 120 1.2222 nan 0.0010 0.0004
## 140 1.2075 nan 0.0010 0.0003
## 160 1.1931 nan 0.0010 0.0003
## 180 1.1795 nan 0.0010 0.0003
## 200 1.1662 nan 0.0010 0.0002
## 220 1.1532 nan 0.0010 0.0003
## 240 1.1406 nan 0.0010 0.0003
## 260 1.1288 nan 0.0010 0.0003
## 280 1.1169 nan 0.0010 0.0003
## 300 1.1055 nan 0.0010 0.0002
## 320 1.0942 nan 0.0010 0.0002
## 340 1.0835 nan 0.0010 0.0002
## 360 1.0729 nan 0.0010 0.0002
## 380 1.0624 nan 0.0010 0.0002
## 400 1.0523 nan 0.0010 0.0002
## 420 1.0425 nan 0.0010 0.0002
## 440 1.0328 nan 0.0010 0.0002
## 460 1.0236 nan 0.0010 0.0002
## 480 1.0150 nan 0.0010 0.0002
## 500 1.0060 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3180 nan 0.0010 0.0004
## 4 1.3171 nan 0.0010 0.0004
## 5 1.3163 nan 0.0010 0.0004
## 6 1.3154 nan 0.0010 0.0004
## 7 1.3145 nan 0.0010 0.0004
## 8 1.3136 nan 0.0010 0.0004
## 9 1.3127 nan 0.0010 0.0004
## 10 1.3118 nan 0.0010 0.0004
## 20 1.3031 nan 0.0010 0.0004
## 40 1.2863 nan 0.0010 0.0003
## 60 1.2699 nan 0.0010 0.0003
## 80 1.2537 nan 0.0010 0.0004
## 100 1.2381 nan 0.0010 0.0003
## 120 1.2230 nan 0.0010 0.0003
## 140 1.2086 nan 0.0010 0.0003
## 160 1.1948 nan 0.0010 0.0003
## 180 1.1812 nan 0.0010 0.0003
## 200 1.1681 nan 0.0010 0.0003
## 220 1.1554 nan 0.0010 0.0003
## 240 1.1430 nan 0.0010 0.0003
## 260 1.1308 nan 0.0010 0.0002
## 280 1.1193 nan 0.0010 0.0002
## 300 1.1080 nan 0.0010 0.0002
## 320 1.0970 nan 0.0010 0.0003
## 340 1.0864 nan 0.0010 0.0002
## 360 1.0760 nan 0.0010 0.0002
## 380 1.0657 nan 0.0010 0.0002
## 400 1.0558 nan 0.0010 0.0002
## 420 1.0463 nan 0.0010 0.0002
## 440 1.0371 nan 0.0010 0.0002
## 460 1.0279 nan 0.0010 0.0002
## 480 1.0191 nan 0.0010 0.0002
## 500 1.0104 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3119 nan 0.0100 0.0038
## 2 1.3037 nan 0.0100 0.0033
## 3 1.2955 nan 0.0100 0.0037
## 4 1.2878 nan 0.0100 0.0034
## 5 1.2792 nan 0.0100 0.0037
## 6 1.2717 nan 0.0100 0.0033
## 7 1.2644 nan 0.0100 0.0033
## 8 1.2568 nan 0.0100 0.0030
## 9 1.2498 nan 0.0100 0.0031
## 10 1.2435 nan 0.0100 0.0028
## 20 1.1798 nan 0.0100 0.0028
## 40 1.0760 nan 0.0100 0.0016
## 60 0.9961 nan 0.0100 0.0016
## 80 0.9339 nan 0.0100 0.0011
## 100 0.8844 nan 0.0100 0.0007
## 120 0.8438 nan 0.0100 0.0006
## 140 0.8088 nan 0.0100 0.0005
## 160 0.7789 nan 0.0100 0.0003
## 180 0.7526 nan 0.0100 0.0002
## 200 0.7313 nan 0.0100 0.0001
## 220 0.7124 nan 0.0100 0.0002
## 240 0.6943 nan 0.0100 0.0002
## 260 0.6789 nan 0.0100 0.0000
## 280 0.6636 nan 0.0100 -0.0002
## 300 0.6492 nan 0.0100 0.0001
## 320 0.6371 nan 0.0100 0.0000
## 340 0.6249 nan 0.0100 0.0001
## 360 0.6138 nan 0.0100 0.0000
## 380 0.6051 nan 0.0100 -0.0001
## 400 0.5950 nan 0.0100 -0.0001
## 420 0.5842 nan 0.0100 0.0000
## 440 0.5744 nan 0.0100 -0.0000
## 460 0.5661 nan 0.0100 -0.0001
## 480 0.5568 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3121 nan 0.0100 0.0039
## 2 1.3038 nan 0.0100 0.0039
## 3 1.2957 nan 0.0100 0.0030
## 4 1.2886 nan 0.0100 0.0030
## 5 1.2805 nan 0.0100 0.0036
## 6 1.2733 nan 0.0100 0.0030
## 7 1.2656 nan 0.0100 0.0034
## 8 1.2581 nan 0.0100 0.0036
## 9 1.2509 nan 0.0100 0.0032
## 10 1.2439 nan 0.0100 0.0032
## 20 1.1807 nan 0.0100 0.0024
## 40 1.0763 nan 0.0100 0.0017
## 60 0.9954 nan 0.0100 0.0015
## 80 0.9334 nan 0.0100 0.0011
## 100 0.8846 nan 0.0100 0.0009
## 120 0.8429 nan 0.0100 0.0007
## 140 0.8085 nan 0.0100 0.0004
## 160 0.7797 nan 0.0100 0.0005
## 180 0.7549 nan 0.0100 0.0003
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## 240 0.6992 nan 0.0100 0.0000
## 260 0.6839 nan 0.0100 0.0003
## 280 0.6704 nan 0.0100 0.0000
## 300 0.6580 nan 0.0100 -0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3121 nan 0.0100 0.0037
## 2 1.3045 nan 0.0100 0.0035
## 3 1.2965 nan 0.0100 0.0037
## 4 1.2895 nan 0.0100 0.0025
## 5 1.2816 nan 0.0100 0.0034
## 6 1.2743 nan 0.0100 0.0029
## 7 1.2673 nan 0.0100 0.0029
## 8 1.2602 nan 0.0100 0.0030
## 9 1.2527 nan 0.0100 0.0032
## 10 1.2451 nan 0.0100 0.0031
## 20 1.1789 nan 0.0100 0.0029
## 40 1.0746 nan 0.0100 0.0020
## 60 0.9955 nan 0.0100 0.0014
## 80 0.9346 nan 0.0100 0.0009
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## 120 0.8432 nan 0.0100 0.0008
## 140 0.8104 nan 0.0100 0.0004
## 160 0.7818 nan 0.0100 0.0005
## 180 0.7579 nan 0.0100 0.0003
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## 220 0.7186 nan 0.0100 0.0001
## 240 0.7026 nan 0.0100 0.0001
## 260 0.6871 nan 0.0100 -0.0002
## 280 0.6740 nan 0.0100 -0.0000
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## 320 0.6506 nan 0.0100 -0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3125 nan 0.0100 0.0032
## 2 1.3046 nan 0.0100 0.0036
## 3 1.2965 nan 0.0100 0.0038
## 4 1.2878 nan 0.0100 0.0041
## 5 1.2795 nan 0.0100 0.0035
## 6 1.2720 nan 0.0100 0.0032
## 7 1.2642 nan 0.0100 0.0036
## 8 1.2565 nan 0.0100 0.0034
## 9 1.2486 nan 0.0100 0.0039
## 10 1.2404 nan 0.0100 0.0035
## 20 1.1695 nan 0.0100 0.0028
## 40 1.0617 nan 0.0100 0.0022
## 60 0.9772 nan 0.0100 0.0015
## 80 0.9108 nan 0.0100 0.0013
## 100 0.8576 nan 0.0100 0.0009
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## 140 0.7768 nan 0.0100 0.0006
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## 320 0.5967 nan 0.0100 0.0001
## 340 0.5840 nan 0.0100 -0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3111 nan 0.0100 0.0041
## 2 1.3023 nan 0.0100 0.0039
## 3 1.2942 nan 0.0100 0.0036
## 4 1.2856 nan 0.0100 0.0036
## 5 1.2780 nan 0.0100 0.0033
## 6 1.2701 nan 0.0100 0.0037
## 7 1.2620 nan 0.0100 0.0036
## 8 1.2547 nan 0.0100 0.0033
## 9 1.2472 nan 0.0100 0.0029
## 10 1.2394 nan 0.0100 0.0036
## 20 1.1716 nan 0.0100 0.0025
## 40 1.0618 nan 0.0100 0.0020
## 60 0.9782 nan 0.0100 0.0016
## 80 0.9135 nan 0.0100 0.0011
## 100 0.8621 nan 0.0100 0.0008
## 120 0.8185 nan 0.0100 0.0008
## 140 0.7830 nan 0.0100 0.0003
## 160 0.7519 nan 0.0100 0.0003
## 180 0.7262 nan 0.0100 0.0004
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## 220 0.6816 nan 0.0100 0.0003
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## 320 0.6030 nan 0.0100 0.0001
## 340 0.5911 nan 0.0100 -0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3121 nan 0.0100 0.0038
## 2 1.3034 nan 0.0100 0.0041
## 3 1.2954 nan 0.0100 0.0032
## 4 1.2877 nan 0.0100 0.0035
## 5 1.2797 nan 0.0100 0.0034
## 6 1.2713 nan 0.0100 0.0035
## 7 1.2635 nan 0.0100 0.0037
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## 9 1.2485 nan 0.0100 0.0031
## 10 1.2410 nan 0.0100 0.0029
## 20 1.1732 nan 0.0100 0.0026
## 40 1.0638 nan 0.0100 0.0021
## 60 0.9798 nan 0.0100 0.0014
## 80 0.9134 nan 0.0100 0.0011
## 100 0.8619 nan 0.0100 0.0007
## 120 0.8197 nan 0.0100 0.0006
## 140 0.7847 nan 0.0100 0.0004
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## 180 0.7313 nan 0.0100 0.0004
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## 220 0.6895 nan 0.0100 0.0003
## 240 0.6719 nan 0.0100 -0.0002
## 260 0.6568 nan 0.0100 0.0002
## 280 0.6424 nan 0.0100 0.0000
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## 320 0.6148 nan 0.0100 0.0001
## 340 0.6030 nan 0.0100 0.0001
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## 380 0.5807 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3117 nan 0.0100 0.0039
## 2 1.3031 nan 0.0100 0.0040
## 3 1.2942 nan 0.0100 0.0042
## 4 1.2858 nan 0.0100 0.0039
## 5 1.2770 nan 0.0100 0.0037
## 6 1.2683 nan 0.0100 0.0037
## 7 1.2601 nan 0.0100 0.0037
## 8 1.2520 nan 0.0100 0.0035
## 9 1.2432 nan 0.0100 0.0034
## 10 1.2352 nan 0.0100 0.0033
## 20 1.1631 nan 0.0100 0.0027
## 40 1.0471 nan 0.0100 0.0021
## 60 0.9591 nan 0.0100 0.0017
## 80 0.8903 nan 0.0100 0.0015
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## 180 0.6902 nan 0.0100 0.0001
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## 220 0.6453 nan 0.0100 0.0002
## 240 0.6249 nan 0.0100 0.0003
## 260 0.6048 nan 0.0100 0.0000
## 280 0.5875 nan 0.0100 0.0000
## 300 0.5702 nan 0.0100 -0.0001
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## 380 0.5168 nan 0.0100 0.0001
## 400 0.5056 nan 0.0100 0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3118 nan 0.0100 0.0040
## 2 1.3027 nan 0.0100 0.0041
## 3 1.2944 nan 0.0100 0.0034
## 4 1.2856 nan 0.0100 0.0038
## 5 1.2774 nan 0.0100 0.0038
## 6 1.2688 nan 0.0100 0.0036
## 7 1.2602 nan 0.0100 0.0038
## 8 1.2522 nan 0.0100 0.0039
## 9 1.2446 nan 0.0100 0.0033
## 10 1.2370 nan 0.0100 0.0034
## 20 1.1670 nan 0.0100 0.0031
## 40 1.0506 nan 0.0100 0.0021
## 60 0.9655 nan 0.0100 0.0017
## 80 0.8963 nan 0.0100 0.0009
## 100 0.8422 nan 0.0100 0.0009
## 120 0.7967 nan 0.0100 0.0004
## 140 0.7594 nan 0.0100 0.0004
## 160 0.7273 nan 0.0100 0.0004
## 180 0.7002 nan 0.0100 0.0003
## 200 0.6755 nan 0.0100 0.0003
## 220 0.6550 nan 0.0100 -0.0001
## 240 0.6351 nan 0.0100 0.0002
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## 280 0.6009 nan 0.0100 -0.0002
## 300 0.5860 nan 0.0100 -0.0001
## 320 0.5713 nan 0.0100 0.0000
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## 360 0.5446 nan 0.0100 -0.0000
## 380 0.5314 nan 0.0100 0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3119 nan 0.0100 0.0039
## 2 1.3033 nan 0.0100 0.0037
## 3 1.2952 nan 0.0100 0.0035
## 4 1.2870 nan 0.0100 0.0037
## 5 1.2786 nan 0.0100 0.0037
## 6 1.2701 nan 0.0100 0.0038
## 7 1.2618 nan 0.0100 0.0033
## 8 1.2534 nan 0.0100 0.0034
## 9 1.2453 nan 0.0100 0.0035
## 10 1.2376 nan 0.0100 0.0033
## 20 1.1687 nan 0.0100 0.0033
## 40 1.0562 nan 0.0100 0.0020
## 60 0.9708 nan 0.0100 0.0014
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## 180 0.7108 nan 0.0100 0.0004
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## 220 0.6645 nan 0.0100 0.0001
## 240 0.6462 nan 0.0100 0.0002
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## 280 0.6123 nan 0.0100 -0.0000
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## 380 0.5439 nan 0.0100 -0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2502 nan 0.1000 0.0310
## 2 1.1941 nan 0.1000 0.0222
## 3 1.1409 nan 0.1000 0.0222
## 4 1.0922 nan 0.1000 0.0219
## 5 1.0514 nan 0.1000 0.0165
## 6 1.0108 nan 0.1000 0.0163
## 7 0.9734 nan 0.1000 0.0130
## 8 0.9370 nan 0.1000 0.0137
## 9 0.9158 nan 0.1000 0.0071
## 10 0.8921 nan 0.1000 0.0095
## 20 0.7340 nan 0.1000 0.0037
## 40 0.6019 nan 0.1000 -0.0008
## 60 0.5167 nan 0.1000 0.0003
## 80 0.4604 nan 0.1000 -0.0008
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## 140 0.3205 nan 0.1000 -0.0006
## 160 0.2892 nan 0.1000 0.0000
## 180 0.2606 nan 0.1000 -0.0010
## 200 0.2373 nan 0.1000 -0.0004
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## 240 0.1981 nan 0.1000 -0.0003
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## 280 0.1665 nan 0.1000 -0.0004
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2359 nan 0.1000 0.0365
## 2 1.1720 nan 0.1000 0.0259
## 3 1.1154 nan 0.1000 0.0232
## 4 1.0671 nan 0.1000 0.0201
## 5 1.0294 nan 0.1000 0.0165
## 6 0.9922 nan 0.1000 0.0157
## 7 0.9595 nan 0.1000 0.0156
## 8 0.9266 nan 0.1000 0.0118
## 9 0.9019 nan 0.1000 0.0098
## 10 0.8812 nan 0.1000 0.0067
## 20 0.7315 nan 0.1000 0.0032
## 40 0.6053 nan 0.1000 -0.0018
## 60 0.5237 nan 0.1000 -0.0013
## 80 0.4635 nan 0.1000 -0.0009
## 100 0.4139 nan 0.1000 0.0006
## 120 0.3709 nan 0.1000 0.0004
## 140 0.3322 nan 0.1000 -0.0008
## 160 0.2983 nan 0.1000 -0.0005
## 180 0.2710 nan 0.1000 -0.0001
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## 320 0.1447 nan 0.1000 -0.0003
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2404 nan 0.1000 0.0370
## 2 1.1727 nan 0.1000 0.0342
## 3 1.1160 nan 0.1000 0.0276
## 4 1.0706 nan 0.1000 0.0190
## 5 1.0299 nan 0.1000 0.0162
## 6 0.9940 nan 0.1000 0.0157
## 7 0.9625 nan 0.1000 0.0115
## 8 0.9338 nan 0.1000 0.0101
## 9 0.9036 nan 0.1000 0.0126
## 10 0.8813 nan 0.1000 0.0074
## 20 0.7389 nan 0.1000 0.0046
## 40 0.6106 nan 0.1000 -0.0000
## 60 0.5407 nan 0.1000 -0.0002
## 80 0.4790 nan 0.1000 -0.0016
## 100 0.4314 nan 0.1000 -0.0013
## 120 0.3893 nan 0.1000 -0.0014
## 140 0.3459 nan 0.1000 -0.0003
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## 180 0.2882 nan 0.1000 -0.0005
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2371 nan 0.1000 0.0378
## 2 1.1645 nan 0.1000 0.0292
## 3 1.0998 nan 0.1000 0.0284
## 4 1.0539 nan 0.1000 0.0199
## 5 1.0144 nan 0.1000 0.0153
## 6 0.9810 nan 0.1000 0.0160
## 7 0.9457 nan 0.1000 0.0143
## 8 0.9146 nan 0.1000 0.0125
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## 10 0.8645 nan 0.1000 0.0099
## 20 0.7009 nan 0.1000 0.0041
## 40 0.5558 nan 0.1000 -0.0005
## 60 0.4610 nan 0.1000 -0.0010
## 80 0.3883 nan 0.1000 0.0003
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## 140 0.2567 nan 0.1000 -0.0007
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## 180 0.2023 nan 0.1000 -0.0004
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2422 nan 0.1000 0.0362
## 2 1.1708 nan 0.1000 0.0272
## 3 1.1153 nan 0.1000 0.0231
## 4 1.0612 nan 0.1000 0.0217
## 5 1.0188 nan 0.1000 0.0169
## 6 0.9747 nan 0.1000 0.0172
## 7 0.9379 nan 0.1000 0.0149
## 8 0.9057 nan 0.1000 0.0138
## 9 0.8797 nan 0.1000 0.0101
## 10 0.8546 nan 0.1000 0.0075
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## 40 0.5569 nan 0.1000 0.0001
## 60 0.4713 nan 0.1000 -0.0013
## 80 0.4063 nan 0.1000 -0.0017
## 100 0.3488 nan 0.1000 -0.0004
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2399 nan 0.1000 0.0379
## 2 1.1668 nan 0.1000 0.0329
## 3 1.1041 nan 0.1000 0.0294
## 4 1.0533 nan 0.1000 0.0197
## 5 1.0094 nan 0.1000 0.0175
## 6 0.9747 nan 0.1000 0.0151
## 7 0.9389 nan 0.1000 0.0139
## 8 0.9067 nan 0.1000 0.0147
## 9 0.8793 nan 0.1000 0.0096
## 10 0.8552 nan 0.1000 0.0082
## 20 0.7078 nan 0.1000 0.0035
## 40 0.5789 nan 0.1000 -0.0006
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2401 nan 0.1000 0.0366
## 2 1.1668 nan 0.1000 0.0334
## 3 1.1072 nan 0.1000 0.0234
## 4 1.0437 nan 0.1000 0.0276
## 5 0.9959 nan 0.1000 0.0201
## 6 0.9506 nan 0.1000 0.0165
## 7 0.9180 nan 0.1000 0.0115
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## 460 0.0297 nan 0.1000 -0.0001
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## 500 0.0236 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2310 nan 0.1000 0.0418
## 2 1.1637 nan 0.1000 0.0273
## 3 1.0971 nan 0.1000 0.0283
## 4 1.0471 nan 0.1000 0.0224
## 5 0.9999 nan 0.1000 0.0193
## 6 0.9579 nan 0.1000 0.0168
## 7 0.9190 nan 0.1000 0.0172
## 8 0.8923 nan 0.1000 0.0094
## 9 0.8653 nan 0.1000 0.0098
## 10 0.8384 nan 0.1000 0.0105
## 20 0.6777 nan 0.1000 0.0015
## 40 0.5352 nan 0.1000 -0.0016
## 60 0.4294 nan 0.1000 -0.0012
## 80 0.3525 nan 0.1000 -0.0010
## 100 0.2941 nan 0.1000 0.0004
## 120 0.2472 nan 0.1000 0.0002
## 140 0.2127 nan 0.1000 -0.0004
## 160 0.1822 nan 0.1000 -0.0005
## 180 0.1591 nan 0.1000 -0.0003
## 200 0.1377 nan 0.1000 -0.0004
## 220 0.1190 nan 0.1000 -0.0004
## 240 0.1047 nan 0.1000 -0.0005
## 260 0.0933 nan 0.1000 -0.0001
## 280 0.0830 nan 0.1000 -0.0002
## 300 0.0735 nan 0.1000 -0.0001
## 320 0.0654 nan 0.1000 -0.0002
## 340 0.0575 nan 0.1000 -0.0002
## 360 0.0511 nan 0.1000 -0.0000
## 380 0.0457 nan 0.1000 -0.0001
## 400 0.0409 nan 0.1000 0.0000
## 420 0.0365 nan 0.1000 -0.0001
## 440 0.0332 nan 0.1000 -0.0001
## 460 0.0300 nan 0.1000 -0.0001
## 480 0.0268 nan 0.1000 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2344 nan 0.1000 0.0383
## 2 1.1662 nan 0.1000 0.0292
## 3 1.1067 nan 0.1000 0.0252
## 4 1.0516 nan 0.1000 0.0204
## 5 1.0096 nan 0.1000 0.0212
## 6 0.9658 nan 0.1000 0.0149
## 7 0.9301 nan 0.1000 0.0125
## 8 0.8990 nan 0.1000 0.0132
## 9 0.8714 nan 0.1000 0.0100
## 10 0.8471 nan 0.1000 0.0080
## 20 0.6950 nan 0.1000 -0.0013
## 40 0.5500 nan 0.1000 -0.0000
## 60 0.4511 nan 0.1000 0.0005
## 80 0.3817 nan 0.1000 -0.0023
## 100 0.3240 nan 0.1000 -0.0008
## 120 0.2821 nan 0.1000 0.0002
## 140 0.2429 nan 0.1000 -0.0005
## 160 0.2107 nan 0.1000 -0.0013
## 180 0.1811 nan 0.1000 -0.0009
## 200 0.1578 nan 0.1000 0.0001
## 220 0.1388 nan 0.1000 -0.0005
## 240 0.1231 nan 0.1000 -0.0004
## 260 0.1074 nan 0.1000 -0.0004
## 280 0.0945 nan 0.1000 -0.0003
## 300 0.0839 nan 0.1000 -0.0003
## 320 0.0743 nan 0.1000 -0.0004
## 340 0.0660 nan 0.1000 -0.0003
## 360 0.0585 nan 0.1000 -0.0003
## 380 0.0523 nan 0.1000 -0.0003
## 400 0.0470 nan 0.1000 -0.0002
## 420 0.0416 nan 0.1000 -0.0002
## 440 0.0370 nan 0.1000 -0.0002
## 460 0.0332 nan 0.1000 -0.0001
## 480 0.0293 nan 0.1000 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3204 nan 0.0010 0.0004
## 2 1.3195 nan 0.0010 0.0004
## 3 1.3186 nan 0.0010 0.0004
## 4 1.3179 nan 0.0010 0.0003
## 5 1.3170 nan 0.0010 0.0004
## 6 1.3162 nan 0.0010 0.0004
## 7 1.3153 nan 0.0010 0.0004
## 8 1.3144 nan 0.0010 0.0004
## 9 1.3135 nan 0.0010 0.0004
## 10 1.3127 nan 0.0010 0.0004
## 20 1.3042 nan 0.0010 0.0004
## 40 1.2875 nan 0.0010 0.0004
## 60 1.2714 nan 0.0010 0.0003
## 80 1.2561 nan 0.0010 0.0003
## 100 1.2412 nan 0.0010 0.0003
## 120 1.2269 nan 0.0010 0.0003
## 140 1.2132 nan 0.0010 0.0003
## 160 1.1995 nan 0.0010 0.0003
## 180 1.1862 nan 0.0010 0.0003
## 200 1.1735 nan 0.0010 0.0003
## 220 1.1608 nan 0.0010 0.0002
## 240 1.1487 nan 0.0010 0.0003
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## 280 1.1258 nan 0.0010 0.0002
## 300 1.1147 nan 0.0010 0.0002
## 320 1.1039 nan 0.0010 0.0002
## 340 1.0934 nan 0.0010 0.0002
## 360 1.0832 nan 0.0010 0.0002
## 380 1.0731 nan 0.0010 0.0002
## 400 1.0636 nan 0.0010 0.0002
## 420 1.0542 nan 0.0010 0.0002
## 440 1.0450 nan 0.0010 0.0002
## 460 1.0361 nan 0.0010 0.0002
## 480 1.0272 nan 0.0010 0.0002
## 500 1.0187 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3195 nan 0.0010 0.0004
## 3 1.3187 nan 0.0010 0.0004
## 4 1.3178 nan 0.0010 0.0004
## 5 1.3169 nan 0.0010 0.0004
## 6 1.3161 nan 0.0010 0.0004
## 7 1.3152 nan 0.0010 0.0004
## 8 1.3144 nan 0.0010 0.0004
## 9 1.3136 nan 0.0010 0.0004
## 10 1.3127 nan 0.0010 0.0004
## 20 1.3041 nan 0.0010 0.0004
## 40 1.2877 nan 0.0010 0.0004
## 60 1.2719 nan 0.0010 0.0003
## 80 1.2566 nan 0.0010 0.0003
## 100 1.2418 nan 0.0010 0.0003
## 120 1.2273 nan 0.0010 0.0003
## 140 1.2135 nan 0.0010 0.0003
## 160 1.1999 nan 0.0010 0.0003
## 180 1.1869 nan 0.0010 0.0003
## 200 1.1744 nan 0.0010 0.0003
## 220 1.1621 nan 0.0010 0.0003
## 240 1.1499 nan 0.0010 0.0003
## 260 1.1383 nan 0.0010 0.0002
## 280 1.1271 nan 0.0010 0.0002
## 300 1.1157 nan 0.0010 0.0003
## 320 1.1049 nan 0.0010 0.0002
## 340 1.0942 nan 0.0010 0.0002
## 360 1.0841 nan 0.0010 0.0002
## 380 1.0742 nan 0.0010 0.0002
## 400 1.0647 nan 0.0010 0.0002
## 420 1.0551 nan 0.0010 0.0002
## 440 1.0457 nan 0.0010 0.0002
## 460 1.0367 nan 0.0010 0.0002
## 480 1.0278 nan 0.0010 0.0001
## 500 1.0193 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3195 nan 0.0010 0.0003
## 3 1.3187 nan 0.0010 0.0004
## 4 1.3178 nan 0.0010 0.0004
## 5 1.3170 nan 0.0010 0.0004
## 6 1.3161 nan 0.0010 0.0004
## 7 1.3153 nan 0.0010 0.0004
## 8 1.3145 nan 0.0010 0.0004
## 9 1.3136 nan 0.0010 0.0004
## 10 1.3127 nan 0.0010 0.0004
## 20 1.3042 nan 0.0010 0.0004
## 40 1.2880 nan 0.0010 0.0003
## 60 1.2720 nan 0.0010 0.0003
## 80 1.2568 nan 0.0010 0.0003
## 100 1.2420 nan 0.0010 0.0004
## 120 1.2274 nan 0.0010 0.0003
## 140 1.2134 nan 0.0010 0.0003
## 160 1.2001 nan 0.0010 0.0003
## 180 1.1872 nan 0.0010 0.0003
## 200 1.1741 nan 0.0010 0.0003
## 220 1.1617 nan 0.0010 0.0002
## 240 1.1497 nan 0.0010 0.0003
## 260 1.1381 nan 0.0010 0.0002
## 280 1.1266 nan 0.0010 0.0003
## 300 1.1157 nan 0.0010 0.0003
## 320 1.1049 nan 0.0010 0.0002
## 340 1.0946 nan 0.0010 0.0002
## 360 1.0843 nan 0.0010 0.0002
## 380 1.0743 nan 0.0010 0.0002
## 400 1.0647 nan 0.0010 0.0002
## 420 1.0555 nan 0.0010 0.0002
## 440 1.0463 nan 0.0010 0.0002
## 460 1.0372 nan 0.0010 0.0002
## 480 1.0285 nan 0.0010 0.0002
## 500 1.0199 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3193 nan 0.0010 0.0004
## 3 1.3184 nan 0.0010 0.0004
## 4 1.3175 nan 0.0010 0.0004
## 5 1.3167 nan 0.0010 0.0004
## 6 1.3157 nan 0.0010 0.0005
## 7 1.3149 nan 0.0010 0.0004
## 8 1.3140 nan 0.0010 0.0004
## 9 1.3131 nan 0.0010 0.0004
## 10 1.3122 nan 0.0010 0.0004
## 20 1.3033 nan 0.0010 0.0004
## 40 1.2858 nan 0.0010 0.0004
## 60 1.2685 nan 0.0010 0.0004
## 80 1.2522 nan 0.0010 0.0003
## 100 1.2364 nan 0.0010 0.0004
## 120 1.2210 nan 0.0010 0.0004
## 140 1.2064 nan 0.0010 0.0003
## 160 1.1920 nan 0.0010 0.0003
## 180 1.1781 nan 0.0010 0.0003
## 200 1.1644 nan 0.0010 0.0003
## 220 1.1514 nan 0.0010 0.0003
## 240 1.1386 nan 0.0010 0.0003
## 260 1.1262 nan 0.0010 0.0003
## 280 1.1140 nan 0.0010 0.0003
## 300 1.1023 nan 0.0010 0.0003
## 320 1.0906 nan 0.0010 0.0002
## 340 1.0794 nan 0.0010 0.0002
## 360 1.0686 nan 0.0010 0.0002
## 380 1.0582 nan 0.0010 0.0002
## 400 1.0479 nan 0.0010 0.0002
## 420 1.0381 nan 0.0010 0.0002
## 440 1.0285 nan 0.0010 0.0002
## 460 1.0190 nan 0.0010 0.0002
## 480 1.0097 nan 0.0010 0.0002
## 500 1.0006 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3202 nan 0.0010 0.0005
## 2 1.3194 nan 0.0010 0.0004
## 3 1.3185 nan 0.0010 0.0004
## 4 1.3177 nan 0.0010 0.0004
## 5 1.3168 nan 0.0010 0.0004
## 6 1.3158 nan 0.0010 0.0004
## 7 1.3149 nan 0.0010 0.0004
## 8 1.3140 nan 0.0010 0.0004
## 9 1.3131 nan 0.0010 0.0004
## 10 1.3122 nan 0.0010 0.0004
## 20 1.3034 nan 0.0010 0.0004
## 40 1.2861 nan 0.0010 0.0004
## 60 1.2694 nan 0.0010 0.0004
## 80 1.2528 nan 0.0010 0.0003
## 100 1.2374 nan 0.0010 0.0004
## 120 1.2221 nan 0.0010 0.0004
## 140 1.2072 nan 0.0010 0.0003
## 160 1.1929 nan 0.0010 0.0003
## 180 1.1789 nan 0.0010 0.0003
## 200 1.1653 nan 0.0010 0.0003
## 220 1.1523 nan 0.0010 0.0003
## 240 1.1396 nan 0.0010 0.0003
## 260 1.1272 nan 0.0010 0.0002
## 280 1.1151 nan 0.0010 0.0002
## 300 1.1034 nan 0.0010 0.0002
## 320 1.0921 nan 0.0010 0.0003
## 340 1.0810 nan 0.0010 0.0002
## 360 1.0702 nan 0.0010 0.0002
## 380 1.0597 nan 0.0010 0.0003
## 400 1.0493 nan 0.0010 0.0002
## 420 1.0394 nan 0.0010 0.0002
## 440 1.0297 nan 0.0010 0.0002
## 460 1.0203 nan 0.0010 0.0002
## 480 1.0110 nan 0.0010 0.0002
## 500 1.0021 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3193 nan 0.0010 0.0004
## 3 1.3184 nan 0.0010 0.0004
## 4 1.3174 nan 0.0010 0.0004
## 5 1.3166 nan 0.0010 0.0004
## 6 1.3157 nan 0.0010 0.0004
## 7 1.3148 nan 0.0010 0.0004
## 8 1.3138 nan 0.0010 0.0004
## 9 1.3129 nan 0.0010 0.0004
## 10 1.3120 nan 0.0010 0.0004
## 20 1.3034 nan 0.0010 0.0004
## 40 1.2858 nan 0.0010 0.0004
## 60 1.2693 nan 0.0010 0.0004
## 80 1.2530 nan 0.0010 0.0004
## 100 1.2373 nan 0.0010 0.0004
## 120 1.2219 nan 0.0010 0.0003
## 140 1.2075 nan 0.0010 0.0003
## 160 1.1932 nan 0.0010 0.0003
## 180 1.1793 nan 0.0010 0.0003
## 200 1.1658 nan 0.0010 0.0003
## 220 1.1528 nan 0.0010 0.0003
## 240 1.1404 nan 0.0010 0.0003
## 260 1.1280 nan 0.0010 0.0003
## 280 1.1162 nan 0.0010 0.0002
## 300 1.1046 nan 0.0010 0.0003
## 320 1.0934 nan 0.0010 0.0002
## 340 1.0822 nan 0.0010 0.0002
## 360 1.0716 nan 0.0010 0.0002
## 380 1.0612 nan 0.0010 0.0002
## 400 1.0510 nan 0.0010 0.0002
## 420 1.0410 nan 0.0010 0.0002
## 440 1.0313 nan 0.0010 0.0002
## 460 1.0220 nan 0.0010 0.0002
## 480 1.0129 nan 0.0010 0.0002
## 500 1.0039 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3202 nan 0.0010 0.0005
## 2 1.3192 nan 0.0010 0.0004
## 3 1.3183 nan 0.0010 0.0004
## 4 1.3173 nan 0.0010 0.0004
## 5 1.3164 nan 0.0010 0.0004
## 6 1.3155 nan 0.0010 0.0004
## 7 1.3146 nan 0.0010 0.0004
## 8 1.3136 nan 0.0010 0.0004
## 9 1.3127 nan 0.0010 0.0004
## 10 1.3118 nan 0.0010 0.0004
## 20 1.3026 nan 0.0010 0.0004
## 40 1.2843 nan 0.0010 0.0004
## 60 1.2666 nan 0.0010 0.0004
## 80 1.2497 nan 0.0010 0.0003
## 100 1.2328 nan 0.0010 0.0004
## 120 1.2166 nan 0.0010 0.0003
## 140 1.2012 nan 0.0010 0.0003
## 160 1.1862 nan 0.0010 0.0004
## 180 1.1716 nan 0.0010 0.0003
## 200 1.1575 nan 0.0010 0.0003
## 220 1.1436 nan 0.0010 0.0003
## 240 1.1304 nan 0.0010 0.0002
## 260 1.1172 nan 0.0010 0.0003
## 280 1.1046 nan 0.0010 0.0003
## 300 1.0924 nan 0.0010 0.0003
## 320 1.0805 nan 0.0010 0.0003
## 340 1.0690 nan 0.0010 0.0002
## 360 1.0576 nan 0.0010 0.0003
## 380 1.0468 nan 0.0010 0.0003
## 400 1.0361 nan 0.0010 0.0002
## 420 1.0255 nan 0.0010 0.0002
## 440 1.0154 nan 0.0010 0.0002
## 460 1.0055 nan 0.0010 0.0002
## 480 0.9959 nan 0.0010 0.0002
## 500 0.9864 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3193 nan 0.0010 0.0005
## 3 1.3183 nan 0.0010 0.0004
## 4 1.3173 nan 0.0010 0.0004
## 5 1.3164 nan 0.0010 0.0004
## 6 1.3155 nan 0.0010 0.0004
## 7 1.3146 nan 0.0010 0.0004
## 8 1.3136 nan 0.0010 0.0004
## 9 1.3126 nan 0.0010 0.0004
## 10 1.3116 nan 0.0010 0.0004
## 20 1.3024 nan 0.0010 0.0004
## 40 1.2843 nan 0.0010 0.0004
## 60 1.2666 nan 0.0010 0.0004
## 80 1.2496 nan 0.0010 0.0003
## 100 1.2332 nan 0.0010 0.0003
## 120 1.2174 nan 0.0010 0.0004
## 140 1.2019 nan 0.0010 0.0003
## 160 1.1868 nan 0.0010 0.0003
## 180 1.1720 nan 0.0010 0.0003
## 200 1.1583 nan 0.0010 0.0003
## 220 1.1446 nan 0.0010 0.0003
## 240 1.1314 nan 0.0010 0.0003
## 260 1.1183 nan 0.0010 0.0003
## 280 1.1058 nan 0.0010 0.0003
## 300 1.0936 nan 0.0010 0.0003
## 320 1.0816 nan 0.0010 0.0002
## 340 1.0703 nan 0.0010 0.0003
## 360 1.0590 nan 0.0010 0.0002
## 380 1.0480 nan 0.0010 0.0002
## 400 1.0373 nan 0.0010 0.0002
## 420 1.0269 nan 0.0010 0.0002
## 440 1.0167 nan 0.0010 0.0002
## 460 1.0071 nan 0.0010 0.0002
## 480 0.9977 nan 0.0010 0.0002
## 500 0.9885 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3202 nan 0.0010 0.0004
## 2 1.3192 nan 0.0010 0.0005
## 3 1.3183 nan 0.0010 0.0004
## 4 1.3174 nan 0.0010 0.0004
## 5 1.3164 nan 0.0010 0.0004
## 6 1.3155 nan 0.0010 0.0004
## 7 1.3145 nan 0.0010 0.0004
## 8 1.3136 nan 0.0010 0.0004
## 9 1.3126 nan 0.0010 0.0004
## 10 1.3117 nan 0.0010 0.0005
## 20 1.3023 nan 0.0010 0.0004
## 40 1.2841 nan 0.0010 0.0004
## 60 1.2667 nan 0.0010 0.0004
## 80 1.2497 nan 0.0010 0.0003
## 100 1.2334 nan 0.0010 0.0004
## 120 1.2178 nan 0.0010 0.0003
## 140 1.2024 nan 0.0010 0.0004
## 160 1.1878 nan 0.0010 0.0003
## 180 1.1734 nan 0.0010 0.0003
## 200 1.1596 nan 0.0010 0.0003
## 220 1.1459 nan 0.0010 0.0003
## 240 1.1326 nan 0.0010 0.0003
## 260 1.1198 nan 0.0010 0.0003
## 280 1.1075 nan 0.0010 0.0002
## 300 1.0955 nan 0.0010 0.0002
## 320 1.0837 nan 0.0010 0.0002
## 340 1.0722 nan 0.0010 0.0003
## 360 1.0612 nan 0.0010 0.0002
## 380 1.0503 nan 0.0010 0.0002
## 400 1.0400 nan 0.0010 0.0002
## 420 1.0298 nan 0.0010 0.0002
## 440 1.0199 nan 0.0010 0.0002
## 460 1.0102 nan 0.0010 0.0002
## 480 1.0008 nan 0.0010 0.0001
## 500 0.9917 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3119 nan 0.0100 0.0044
## 2 1.3030 nan 0.0100 0.0041
## 3 1.2956 nan 0.0100 0.0029
## 4 1.2874 nan 0.0100 0.0035
## 5 1.2789 nan 0.0100 0.0039
## 6 1.2705 nan 0.0100 0.0035
## 7 1.2629 nan 0.0100 0.0031
## 8 1.2558 nan 0.0100 0.0034
## 9 1.2474 nan 0.0100 0.0035
## 10 1.2396 nan 0.0100 0.0032
## 20 1.1712 nan 0.0100 0.0031
## 40 1.0599 nan 0.0100 0.0020
## 60 0.9776 nan 0.0100 0.0015
## 80 0.9109 nan 0.0100 0.0010
## 100 0.8571 nan 0.0100 0.0006
## 120 0.8127 nan 0.0100 0.0005
## 140 0.7744 nan 0.0100 0.0007
## 160 0.7445 nan 0.0100 0.0004
## 180 0.7185 nan 0.0100 0.0005
## 200 0.6959 nan 0.0100 0.0002
## 220 0.6767 nan 0.0100 0.0003
## 240 0.6585 nan 0.0100 0.0001
## 260 0.6422 nan 0.0100 0.0001
## 280 0.6284 nan 0.0100 -0.0000
## 300 0.6147 nan 0.0100 0.0001
## 320 0.6012 nan 0.0100 0.0001
## 340 0.5891 nan 0.0100 -0.0000
## 360 0.5773 nan 0.0100 0.0001
## 380 0.5680 nan 0.0100 0.0001
## 400 0.5583 nan 0.0100 0.0000
## 420 0.5488 nan 0.0100 -0.0000
## 440 0.5399 nan 0.0100 0.0001
## 460 0.5309 nan 0.0100 -0.0000
## 480 0.5227 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3139 nan 0.0100 0.0033
## 2 1.3058 nan 0.0100 0.0035
## 3 1.2976 nan 0.0100 0.0038
## 4 1.2895 nan 0.0100 0.0037
## 5 1.2813 nan 0.0100 0.0038
## 6 1.2731 nan 0.0100 0.0039
## 7 1.2645 nan 0.0100 0.0036
## 8 1.2576 nan 0.0100 0.0031
## 9 1.2499 nan 0.0100 0.0035
## 10 1.2422 nan 0.0100 0.0035
## 20 1.1750 nan 0.0100 0.0028
## 40 1.0641 nan 0.0100 0.0017
## 60 0.9788 nan 0.0100 0.0012
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## 100 0.8588 nan 0.0100 0.0011
## 120 0.8144 nan 0.0100 0.0007
## 140 0.7780 nan 0.0100 0.0006
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## 180 0.7214 nan 0.0100 0.0001
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## 300 0.6202 nan 0.0100 -0.0000
## 320 0.6086 nan 0.0100 0.0002
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## 380 0.5765 nan 0.0100 -0.0000
## 400 0.5672 nan 0.0100 0.0000
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## 460 0.5413 nan 0.0100 0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3126 nan 0.0100 0.0041
## 2 1.3040 nan 0.0100 0.0039
## 3 1.2967 nan 0.0100 0.0033
## 4 1.2887 nan 0.0100 0.0034
## 5 1.2813 nan 0.0100 0.0036
## 6 1.2730 nan 0.0100 0.0040
## 7 1.2645 nan 0.0100 0.0039
## 8 1.2574 nan 0.0100 0.0034
## 9 1.2495 nan 0.0100 0.0036
## 10 1.2418 nan 0.0100 0.0033
## 20 1.1744 nan 0.0100 0.0027
## 40 1.0655 nan 0.0100 0.0020
## 60 0.9792 nan 0.0100 0.0014
## 80 0.9120 nan 0.0100 0.0009
## 100 0.8572 nan 0.0100 0.0010
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## 280 0.6369 nan 0.0100 -0.0001
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## 380 0.5820 nan 0.0100 -0.0002
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## 460 0.5475 nan 0.0100 -0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3111 nan 0.0100 0.0042
## 2 1.3021 nan 0.0100 0.0037
## 3 1.2929 nan 0.0100 0.0042
## 4 1.2840 nan 0.0100 0.0039
## 5 1.2753 nan 0.0100 0.0040
## 6 1.2666 nan 0.0100 0.0033
## 7 1.2586 nan 0.0100 0.0038
## 8 1.2502 nan 0.0100 0.0036
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## 10 1.2342 nan 0.0100 0.0032
## 20 1.1605 nan 0.0100 0.0031
## 40 1.0454 nan 0.0100 0.0023
## 60 0.9568 nan 0.0100 0.0017
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## 300 0.5759 nan 0.0100 -0.0000
## 320 0.5631 nan 0.0100 0.0000
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## 400 0.5159 nan 0.0100 -0.0002
## 420 0.5053 nan 0.0100 0.0001
## 440 0.4950 nan 0.0100 -0.0000
## 460 0.4858 nan 0.0100 -0.0001
## 480 0.4769 nan 0.0100 -0.0000
## 500 0.4676 nan 0.0100 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3118 nan 0.0100 0.0043
## 2 1.3025 nan 0.0100 0.0039
## 3 1.2939 nan 0.0100 0.0039
## 4 1.2852 nan 0.0100 0.0038
## 5 1.2767 nan 0.0100 0.0038
## 6 1.2691 nan 0.0100 0.0032
## 7 1.2607 nan 0.0100 0.0038
## 8 1.2522 nan 0.0100 0.0037
## 9 1.2438 nan 0.0100 0.0037
## 10 1.2360 nan 0.0100 0.0033
## 20 1.1618 nan 0.0100 0.0029
## 40 1.0479 nan 0.0100 0.0020
## 60 0.9604 nan 0.0100 0.0018
## 80 0.8927 nan 0.0100 0.0011
## 100 0.8366 nan 0.0100 0.0010
## 120 0.7916 nan 0.0100 0.0006
## 140 0.7526 nan 0.0100 0.0006
## 160 0.7208 nan 0.0100 0.0002
## 180 0.6927 nan 0.0100 0.0002
## 200 0.6674 nan 0.0100 0.0001
## 220 0.6472 nan 0.0100 0.0001
## 240 0.6277 nan 0.0100 0.0001
## 260 0.6111 nan 0.0100 -0.0001
## 280 0.5954 nan 0.0100 0.0002
## 300 0.5811 nan 0.0100 -0.0000
## 320 0.5692 nan 0.0100 -0.0001
## 340 0.5559 nan 0.0100 -0.0000
## 360 0.5438 nan 0.0100 -0.0001
## 380 0.5330 nan 0.0100 -0.0000
## 400 0.5228 nan 0.0100 0.0000
## 420 0.5123 nan 0.0100 -0.0000
## 440 0.5030 nan 0.0100 -0.0002
## 460 0.4938 nan 0.0100 0.0001
## 480 0.4842 nan 0.0100 -0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3123 nan 0.0100 0.0041
## 2 1.3031 nan 0.0100 0.0040
## 3 1.2944 nan 0.0100 0.0040
## 4 1.2862 nan 0.0100 0.0034
## 5 1.2775 nan 0.0100 0.0038
## 6 1.2697 nan 0.0100 0.0036
## 7 1.2618 nan 0.0100 0.0038
## 8 1.2538 nan 0.0100 0.0038
## 9 1.2456 nan 0.0100 0.0039
## 10 1.2378 nan 0.0100 0.0036
## 20 1.1665 nan 0.0100 0.0028
## 40 1.0516 nan 0.0100 0.0022
## 60 0.9637 nan 0.0100 0.0017
## 80 0.8946 nan 0.0100 0.0012
## 100 0.8396 nan 0.0100 0.0010
## 120 0.7942 nan 0.0100 0.0009
## 140 0.7574 nan 0.0100 0.0007
## 160 0.7251 nan 0.0100 0.0005
## 180 0.6976 nan 0.0100 0.0003
## 200 0.6744 nan 0.0100 0.0002
## 220 0.6538 nan 0.0100 0.0003
## 240 0.6349 nan 0.0100 -0.0000
## 260 0.6191 nan 0.0100 0.0002
## 280 0.6042 nan 0.0100 0.0001
## 300 0.5906 nan 0.0100 0.0000
## 320 0.5789 nan 0.0100 0.0001
## 340 0.5674 nan 0.0100 -0.0001
## 360 0.5554 nan 0.0100 0.0000
## 380 0.5440 nan 0.0100 -0.0001
## 400 0.5338 nan 0.0100 -0.0000
## 420 0.5246 nan 0.0100 -0.0002
## 440 0.5153 nan 0.0100 0.0001
## 460 0.5063 nan 0.0100 -0.0000
## 480 0.4970 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3124 nan 0.0100 0.0040
## 2 1.3032 nan 0.0100 0.0040
## 3 1.2942 nan 0.0100 0.0043
## 4 1.2845 nan 0.0100 0.0044
## 5 1.2759 nan 0.0100 0.0035
## 6 1.2667 nan 0.0100 0.0041
## 7 1.2578 nan 0.0100 0.0039
## 8 1.2494 nan 0.0100 0.0039
## 9 1.2403 nan 0.0100 0.0037
## 10 1.2335 nan 0.0100 0.0026
## 20 1.1586 nan 0.0100 0.0030
## 40 1.0376 nan 0.0100 0.0023
## 60 0.9473 nan 0.0100 0.0017
## 80 0.8736 nan 0.0100 0.0013
## 100 0.8149 nan 0.0100 0.0009
## 120 0.7687 nan 0.0100 0.0007
## 140 0.7279 nan 0.0100 0.0005
## 160 0.6945 nan 0.0100 0.0004
## 180 0.6654 nan 0.0100 0.0000
## 200 0.6401 nan 0.0100 0.0003
## 220 0.6169 nan 0.0100 0.0003
## 240 0.5972 nan 0.0100 0.0001
## 260 0.5777 nan 0.0100 0.0001
## 280 0.5610 nan 0.0100 0.0000
## 300 0.5451 nan 0.0100 0.0001
## 320 0.5306 nan 0.0100 0.0001
## 340 0.5176 nan 0.0100 0.0000
## 360 0.5045 nan 0.0100 -0.0000
## 380 0.4914 nan 0.0100 -0.0001
## 400 0.4798 nan 0.0100 -0.0002
## 420 0.4683 nan 0.0100 0.0001
## 440 0.4574 nan 0.0100 0.0000
## 460 0.4469 nan 0.0100 0.0000
## 480 0.4372 nan 0.0100 0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3116 nan 0.0100 0.0042
## 2 1.3020 nan 0.0100 0.0044
## 3 1.2931 nan 0.0100 0.0041
## 4 1.2849 nan 0.0100 0.0039
## 5 1.2758 nan 0.0100 0.0042
## 6 1.2671 nan 0.0100 0.0036
## 7 1.2588 nan 0.0100 0.0038
## 8 1.2498 nan 0.0100 0.0040
## 9 1.2414 nan 0.0100 0.0036
## 10 1.2331 nan 0.0100 0.0035
## 20 1.1606 nan 0.0100 0.0027
## 40 1.0404 nan 0.0100 0.0022
## 60 0.9457 nan 0.0100 0.0019
## 80 0.8722 nan 0.0100 0.0012
## 100 0.8138 nan 0.0100 0.0008
## 120 0.7662 nan 0.0100 0.0009
## 140 0.7269 nan 0.0100 0.0007
## 160 0.6947 nan 0.0100 0.0002
## 180 0.6665 nan 0.0100 0.0005
## 200 0.6424 nan 0.0100 0.0003
## 220 0.6205 nan 0.0100 0.0001
## 240 0.6007 nan 0.0100 0.0001
## 260 0.5812 nan 0.0100 0.0002
## 280 0.5650 nan 0.0100 0.0000
## 300 0.5497 nan 0.0100 0.0001
## 320 0.5353 nan 0.0100 -0.0000
## 340 0.5227 nan 0.0100 -0.0000
## 360 0.5105 nan 0.0100 0.0000
## 380 0.4990 nan 0.0100 0.0001
## 400 0.4881 nan 0.0100 0.0000
## 420 0.4765 nan 0.0100 0.0001
## 440 0.4659 nan 0.0100 -0.0000
## 460 0.4565 nan 0.0100 -0.0001
## 480 0.4471 nan 0.0100 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3115 nan 0.0100 0.0044
## 2 1.3023 nan 0.0100 0.0042
## 3 1.2928 nan 0.0100 0.0040
## 4 1.2848 nan 0.0100 0.0034
## 5 1.2759 nan 0.0100 0.0040
## 6 1.2675 nan 0.0100 0.0040
## 7 1.2590 nan 0.0100 0.0037
## 8 1.2508 nan 0.0100 0.0038
## 9 1.2424 nan 0.0100 0.0038
## 10 1.2353 nan 0.0100 0.0031
## 20 1.1625 nan 0.0100 0.0030
## 40 1.0406 nan 0.0100 0.0021
## 60 0.9489 nan 0.0100 0.0015
## 80 0.8773 nan 0.0100 0.0013
## 100 0.8196 nan 0.0100 0.0009
## 120 0.7728 nan 0.0100 0.0007
## 140 0.7353 nan 0.0100 0.0004
## 160 0.7025 nan 0.0100 0.0004
## 180 0.6747 nan 0.0100 0.0003
## 200 0.6501 nan 0.0100 0.0004
## 220 0.6299 nan 0.0100 0.0003
## 240 0.6114 nan 0.0100 0.0001
## 260 0.5942 nan 0.0100 0.0001
## 280 0.5771 nan 0.0100 0.0000
## 300 0.5618 nan 0.0100 0.0001
## 320 0.5475 nan 0.0100 -0.0000
## 340 0.5356 nan 0.0100 -0.0000
## 360 0.5231 nan 0.0100 -0.0001
## 380 0.5117 nan 0.0100 -0.0001
## 400 0.5004 nan 0.0100 -0.0002
## 420 0.4901 nan 0.0100 0.0001
## 440 0.4801 nan 0.0100 -0.0001
## 460 0.4697 nan 0.0100 -0.0001
## 480 0.4609 nan 0.0100 -0.0001
## 500 0.4522 nan 0.0100 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2405 nan 0.1000 0.0351
## 2 1.1734 nan 0.1000 0.0283
## 3 1.1110 nan 0.1000 0.0237
## 4 1.0690 nan 0.1000 0.0162
## 5 1.0245 nan 0.1000 0.0202
## 6 0.9844 nan 0.1000 0.0174
## 7 0.9471 nan 0.1000 0.0172
## 8 0.9116 nan 0.1000 0.0155
## 9 0.8835 nan 0.1000 0.0108
## 10 0.8646 nan 0.1000 0.0047
## 20 0.7063 nan 0.1000 0.0012
## 40 0.5690 nan 0.1000 0.0015
## 60 0.4826 nan 0.1000 -0.0019
## 80 0.4257 nan 0.1000 -0.0012
## 100 0.3697 nan 0.1000 -0.0001
## 120 0.3281 nan 0.1000 -0.0009
## 140 0.2925 nan 0.1000 -0.0001
## 160 0.2595 nan 0.1000 -0.0000
## 180 0.2343 nan 0.1000 -0.0004
## 200 0.2098 nan 0.1000 -0.0007
## 220 0.1909 nan 0.1000 0.0001
## 240 0.1743 nan 0.1000 -0.0002
## 260 0.1584 nan 0.1000 -0.0003
## 280 0.1459 nan 0.1000 -0.0001
## 300 0.1346 nan 0.1000 -0.0006
## 320 0.1236 nan 0.1000 -0.0002
## 340 0.1136 nan 0.1000 -0.0003
## 360 0.1041 nan 0.1000 -0.0002
## 380 0.0960 nan 0.1000 -0.0004
## 400 0.0886 nan 0.1000 -0.0000
## 420 0.0823 nan 0.1000 -0.0003
## 440 0.0763 nan 0.1000 -0.0002
## 460 0.0709 nan 0.1000 -0.0000
## 480 0.0655 nan 0.1000 -0.0001
## 500 0.0615 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2422 nan 0.1000 0.0347
## 2 1.1821 nan 0.1000 0.0275
## 3 1.1195 nan 0.1000 0.0299
## 4 1.0665 nan 0.1000 0.0206
## 5 1.0272 nan 0.1000 0.0186
## 6 0.9885 nan 0.1000 0.0165
## 7 0.9525 nan 0.1000 0.0151
## 8 0.9203 nan 0.1000 0.0143
## 9 0.8926 nan 0.1000 0.0100
## 10 0.8689 nan 0.1000 0.0082
## 20 0.7078 nan 0.1000 0.0012
## 40 0.5779 nan 0.1000 0.0004
## 60 0.4994 nan 0.1000 0.0003
## 80 0.4415 nan 0.1000 0.0002
## 100 0.3910 nan 0.1000 -0.0016
## 120 0.3499 nan 0.1000 0.0002
## 140 0.3146 nan 0.1000 -0.0006
## 160 0.2796 nan 0.1000 -0.0005
## 180 0.2527 nan 0.1000 -0.0008
## 200 0.2307 nan 0.1000 -0.0004
## 220 0.2071 nan 0.1000 -0.0000
## 240 0.1900 nan 0.1000 -0.0004
## 260 0.1737 nan 0.1000 -0.0003
## 280 0.1573 nan 0.1000 -0.0004
## 300 0.1414 nan 0.1000 -0.0004
## 320 0.1298 nan 0.1000 -0.0003
## 340 0.1197 nan 0.1000 -0.0003
## 360 0.1109 nan 0.1000 -0.0003
## 380 0.1028 nan 0.1000 -0.0004
## 400 0.0939 nan 0.1000 -0.0002
## 420 0.0867 nan 0.1000 -0.0002
## 440 0.0805 nan 0.1000 -0.0001
## 460 0.0747 nan 0.1000 -0.0003
## 480 0.0698 nan 0.1000 -0.0002
## 500 0.0650 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2388 nan 0.1000 0.0379
## 2 1.1705 nan 0.1000 0.0309
## 3 1.1107 nan 0.1000 0.0244
## 4 1.0597 nan 0.1000 0.0217
## 5 1.0178 nan 0.1000 0.0150
## 6 0.9779 nan 0.1000 0.0154
## 7 0.9437 nan 0.1000 0.0136
## 8 0.9165 nan 0.1000 0.0083
## 9 0.8901 nan 0.1000 0.0117
## 10 0.8615 nan 0.1000 0.0100
## 20 0.7104 nan 0.1000 0.0024
## 40 0.5821 nan 0.1000 -0.0007
## 60 0.5065 nan 0.1000 -0.0016
## 80 0.4468 nan 0.1000 -0.0020
## 100 0.3957 nan 0.1000 -0.0004
## 120 0.3600 nan 0.1000 -0.0002
## 140 0.3237 nan 0.1000 -0.0003
## 160 0.2895 nan 0.1000 0.0004
## 180 0.2616 nan 0.1000 -0.0012
## 200 0.2377 nan 0.1000 -0.0008
## 220 0.2179 nan 0.1000 -0.0015
## 240 0.1981 nan 0.1000 -0.0007
## 260 0.1829 nan 0.1000 -0.0008
## 280 0.1698 nan 0.1000 -0.0005
## 300 0.1558 nan 0.1000 -0.0009
## 320 0.1424 nan 0.1000 -0.0002
## 340 0.1307 nan 0.1000 -0.0004
## 360 0.1207 nan 0.1000 -0.0006
## 380 0.1120 nan 0.1000 -0.0003
## 400 0.1049 nan 0.1000 -0.0005
## 420 0.0981 nan 0.1000 -0.0004
## 440 0.0914 nan 0.1000 -0.0005
## 460 0.0840 nan 0.1000 -0.0003
## 480 0.0773 nan 0.1000 -0.0004
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2286 nan 0.1000 0.0435
## 2 1.1564 nan 0.1000 0.0330
## 3 1.0911 nan 0.1000 0.0264
## 4 1.0417 nan 0.1000 0.0201
## 5 0.9884 nan 0.1000 0.0204
## 6 0.9513 nan 0.1000 0.0168
## 7 0.9103 nan 0.1000 0.0146
## 8 0.8796 nan 0.1000 0.0118
## 9 0.8496 nan 0.1000 0.0120
## 10 0.8251 nan 0.1000 0.0104
## 20 0.6624 nan 0.1000 0.0032
## 40 0.5250 nan 0.1000 -0.0001
## 60 0.4379 nan 0.1000 0.0005
## 80 0.3727 nan 0.1000 0.0006
## 100 0.3197 nan 0.1000 -0.0010
## 120 0.2714 nan 0.1000 -0.0008
## 140 0.2351 nan 0.1000 -0.0008
## 160 0.2048 nan 0.1000 -0.0004
## 180 0.1806 nan 0.1000 -0.0003
## 200 0.1613 nan 0.1000 -0.0003
## 220 0.1419 nan 0.1000 -0.0002
## 240 0.1268 nan 0.1000 -0.0002
## 260 0.1139 nan 0.1000 -0.0004
## 280 0.1021 nan 0.1000 -0.0004
## 300 0.0908 nan 0.1000 -0.0002
## 320 0.0814 nan 0.1000 -0.0003
## 340 0.0723 nan 0.1000 -0.0002
## 360 0.0658 nan 0.1000 -0.0002
## 380 0.0601 nan 0.1000 -0.0002
## 400 0.0548 nan 0.1000 -0.0002
## 420 0.0496 nan 0.1000 -0.0000
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## 460 0.0408 nan 0.1000 -0.0001
## 480 0.0370 nan 0.1000 -0.0000
## 500 0.0335 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2394 nan 0.1000 0.0370
## 2 1.1659 nan 0.1000 0.0330
## 3 1.1021 nan 0.1000 0.0308
## 4 1.0523 nan 0.1000 0.0207
## 5 1.0028 nan 0.1000 0.0233
## 6 0.9615 nan 0.1000 0.0190
## 7 0.9270 nan 0.1000 0.0137
## 8 0.8977 nan 0.1000 0.0139
## 9 0.8678 nan 0.1000 0.0136
## 10 0.8399 nan 0.1000 0.0120
## 20 0.6668 nan 0.1000 0.0030
## 40 0.5338 nan 0.1000 -0.0007
## 60 0.4448 nan 0.1000 0.0002
## 80 0.3828 nan 0.1000 -0.0008
## 100 0.3321 nan 0.1000 -0.0016
## 120 0.2859 nan 0.1000 -0.0016
## 140 0.2501 nan 0.1000 -0.0006
## 160 0.2191 nan 0.1000 -0.0007
## 180 0.1945 nan 0.1000 -0.0005
## 200 0.1710 nan 0.1000 -0.0001
## 220 0.1513 nan 0.1000 -0.0006
## 240 0.1342 nan 0.1000 -0.0003
## 260 0.1201 nan 0.1000 -0.0003
## 280 0.1081 nan 0.1000 -0.0004
## 300 0.0970 nan 0.1000 -0.0003
## 320 0.0873 nan 0.1000 -0.0003
## 340 0.0794 nan 0.1000 -0.0000
## 360 0.0716 nan 0.1000 -0.0002
## 380 0.0641 nan 0.1000 -0.0001
## 400 0.0583 nan 0.1000 -0.0001
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## 460 0.0435 nan 0.1000 -0.0000
## 480 0.0395 nan 0.1000 -0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2358 nan 0.1000 0.0402
## 2 1.1620 nan 0.1000 0.0279
## 3 1.1022 nan 0.1000 0.0288
## 4 1.0537 nan 0.1000 0.0203
## 5 1.0004 nan 0.1000 0.0212
## 6 0.9554 nan 0.1000 0.0170
## 7 0.9207 nan 0.1000 0.0130
## 8 0.8864 nan 0.1000 0.0135
## 9 0.8573 nan 0.1000 0.0100
## 10 0.8266 nan 0.1000 0.0095
## 20 0.6691 nan 0.1000 0.0009
## 40 0.5300 nan 0.1000 -0.0012
## 60 0.4469 nan 0.1000 -0.0006
## 80 0.3738 nan 0.1000 -0.0008
## 100 0.3249 nan 0.1000 -0.0012
## 120 0.2856 nan 0.1000 -0.0011
## 140 0.2545 nan 0.1000 -0.0006
## 160 0.2278 nan 0.1000 -0.0007
## 180 0.2015 nan 0.1000 -0.0002
## 200 0.1782 nan 0.1000 -0.0006
## 220 0.1585 nan 0.1000 -0.0006
## 240 0.1431 nan 0.1000 -0.0005
## 260 0.1263 nan 0.1000 -0.0003
## 280 0.1146 nan 0.1000 -0.0004
## 300 0.1026 nan 0.1000 -0.0004
## 320 0.0937 nan 0.1000 -0.0003
## 340 0.0846 nan 0.1000 -0.0002
## 360 0.0763 nan 0.1000 -0.0002
## 380 0.0690 nan 0.1000 -0.0003
## 400 0.0625 nan 0.1000 -0.0002
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## 460 0.0477 nan 0.1000 -0.0001
## 480 0.0431 nan 0.1000 -0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2268 nan 0.1000 0.0420
## 2 1.1527 nan 0.1000 0.0333
## 3 1.0922 nan 0.1000 0.0268
## 4 1.0354 nan 0.1000 0.0242
## 5 0.9861 nan 0.1000 0.0209
## 6 0.9434 nan 0.1000 0.0176
## 7 0.9070 nan 0.1000 0.0147
## 8 0.8712 nan 0.1000 0.0139
## 9 0.8436 nan 0.1000 0.0104
## 10 0.8167 nan 0.1000 0.0095
## 20 0.6464 nan 0.1000 0.0008
## 40 0.4898 nan 0.1000 -0.0003
## 60 0.3964 nan 0.1000 -0.0006
## 80 0.3201 nan 0.1000 -0.0016
## 100 0.2658 nan 0.1000 -0.0009
## 120 0.2207 nan 0.1000 -0.0003
## 140 0.1852 nan 0.1000 -0.0002
## 160 0.1622 nan 0.1000 -0.0002
## 180 0.1426 nan 0.1000 -0.0005
## 200 0.1238 nan 0.1000 -0.0000
## 220 0.1092 nan 0.1000 -0.0002
## 240 0.0952 nan 0.1000 -0.0005
## 260 0.0829 nan 0.1000 -0.0002
## 280 0.0749 nan 0.1000 -0.0003
## 300 0.0649 nan 0.1000 -0.0000
## 320 0.0578 nan 0.1000 -0.0002
## 340 0.0514 nan 0.1000 -0.0000
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## 400 0.0352 nan 0.1000 -0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2354 nan 0.1000 0.0356
## 2 1.1627 nan 0.1000 0.0282
## 3 1.1019 nan 0.1000 0.0269
## 4 1.0429 nan 0.1000 0.0265
## 5 0.9926 nan 0.1000 0.0191
## 6 0.9510 nan 0.1000 0.0163
## 7 0.9086 nan 0.1000 0.0181
## 8 0.8713 nan 0.1000 0.0137
## 9 0.8451 nan 0.1000 0.0090
## 10 0.8190 nan 0.1000 0.0095
## 20 0.6438 nan 0.1000 0.0034
## 40 0.4907 nan 0.1000 -0.0014
## 60 0.3997 nan 0.1000 0.0002
## 80 0.3386 nan 0.1000 -0.0028
## 100 0.2837 nan 0.1000 -0.0004
## 120 0.2437 nan 0.1000 -0.0013
## 140 0.2105 nan 0.1000 -0.0006
## 160 0.1805 nan 0.1000 -0.0008
## 180 0.1528 nan 0.1000 -0.0005
## 200 0.1329 nan 0.1000 -0.0002
## 220 0.1147 nan 0.1000 -0.0002
## 240 0.0997 nan 0.1000 -0.0002
## 260 0.0882 nan 0.1000 -0.0004
## 280 0.0781 nan 0.1000 -0.0003
## 300 0.0698 nan 0.1000 -0.0002
## 320 0.0620 nan 0.1000 -0.0003
## 340 0.0547 nan 0.1000 -0.0002
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## 380 0.0423 nan 0.1000 -0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2252 nan 0.1000 0.0465
## 2 1.1522 nan 0.1000 0.0328
## 3 1.0983 nan 0.1000 0.0223
## 4 1.0410 nan 0.1000 0.0257
## 5 0.9909 nan 0.1000 0.0198
## 6 0.9490 nan 0.1000 0.0186
## 7 0.9084 nan 0.1000 0.0168
## 8 0.8750 nan 0.1000 0.0134
## 9 0.8453 nan 0.1000 0.0108
## 10 0.8186 nan 0.1000 0.0113
## 20 0.6555 nan 0.1000 0.0002
## 40 0.5053 nan 0.1000 -0.0002
## 60 0.4196 nan 0.1000 -0.0013
## 80 0.3509 nan 0.1000 -0.0010
## 100 0.2953 nan 0.1000 -0.0009
## 120 0.2539 nan 0.1000 -0.0002
## 140 0.2168 nan 0.1000 -0.0009
## 160 0.1850 nan 0.1000 -0.0002
## 180 0.1625 nan 0.1000 -0.0007
## 200 0.1429 nan 0.1000 -0.0005
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## 240 0.1092 nan 0.1000 -0.0004
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## 320 0.0659 nan 0.1000 -0.0003
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3190 nan 0.0010 0.0003
## 3 1.3182 nan 0.0010 0.0003
## 4 1.3174 nan 0.0010 0.0004
## 5 1.3165 nan 0.0010 0.0004
## 6 1.3157 nan 0.0010 0.0004
## 7 1.3149 nan 0.0010 0.0004
## 8 1.3141 nan 0.0010 0.0004
## 9 1.3132 nan 0.0010 0.0004
## 10 1.3124 nan 0.0010 0.0004
## 20 1.3045 nan 0.0010 0.0003
## 40 1.2894 nan 0.0010 0.0003
## 60 1.2746 nan 0.0010 0.0003
## 80 1.2604 nan 0.0010 0.0003
## 100 1.2461 nan 0.0010 0.0003
## 120 1.2331 nan 0.0010 0.0003
## 140 1.2201 nan 0.0010 0.0003
## 160 1.2074 nan 0.0010 0.0003
## 180 1.1952 nan 0.0010 0.0003
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## 280 1.1383 nan 0.0010 0.0002
## 300 1.1278 nan 0.0010 0.0003
## 320 1.1177 nan 0.0010 0.0002
## 340 1.1078 nan 0.0010 0.0002
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## 380 1.0891 nan 0.0010 0.0002
## 400 1.0801 nan 0.0010 0.0001
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## 480 1.0453 nan 0.0010 0.0002
## 500 1.0374 nan 0.0010 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3190 nan 0.0010 0.0003
## 3 1.3182 nan 0.0010 0.0004
## 4 1.3173 nan 0.0010 0.0004
## 5 1.3165 nan 0.0010 0.0004
## 6 1.3158 nan 0.0010 0.0003
## 7 1.3151 nan 0.0010 0.0004
## 8 1.3143 nan 0.0010 0.0004
## 9 1.3136 nan 0.0010 0.0003
## 10 1.3127 nan 0.0010 0.0004
## 20 1.3049 nan 0.0010 0.0003
## 40 1.2898 nan 0.0010 0.0004
## 60 1.2750 nan 0.0010 0.0003
## 80 1.2608 nan 0.0010 0.0004
## 100 1.2469 nan 0.0010 0.0003
## 120 1.2336 nan 0.0010 0.0003
## 140 1.2204 nan 0.0010 0.0003
## 160 1.2075 nan 0.0010 0.0003
## 180 1.1948 nan 0.0010 0.0003
## 200 1.1828 nan 0.0010 0.0003
## 220 1.1708 nan 0.0010 0.0003
## 240 1.1596 nan 0.0010 0.0002
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## 280 1.1380 nan 0.0010 0.0003
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## 320 1.1180 nan 0.0010 0.0002
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## 360 1.0983 nan 0.0010 0.0002
## 380 1.0890 nan 0.0010 0.0002
## 400 1.0799 nan 0.0010 0.0002
## 420 1.0711 nan 0.0010 0.0002
## 440 1.0623 nan 0.0010 0.0002
## 460 1.0538 nan 0.0010 0.0002
## 480 1.0454 nan 0.0010 0.0002
## 500 1.0375 nan 0.0010 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3190 nan 0.0010 0.0004
## 3 1.3183 nan 0.0010 0.0003
## 4 1.3175 nan 0.0010 0.0004
## 5 1.3166 nan 0.0010 0.0004
## 6 1.3159 nan 0.0010 0.0003
## 7 1.3151 nan 0.0010 0.0003
## 8 1.3142 nan 0.0010 0.0004
## 9 1.3134 nan 0.0010 0.0004
## 10 1.3126 nan 0.0010 0.0004
## 20 1.3049 nan 0.0010 0.0003
## 40 1.2898 nan 0.0010 0.0004
## 60 1.2750 nan 0.0010 0.0003
## 80 1.2604 nan 0.0010 0.0003
## 100 1.2464 nan 0.0010 0.0003
## 120 1.2332 nan 0.0010 0.0003
## 140 1.2203 nan 0.0010 0.0003
## 160 1.2077 nan 0.0010 0.0003
## 180 1.1955 nan 0.0010 0.0003
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## 220 1.1718 nan 0.0010 0.0003
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## 260 1.1499 nan 0.0010 0.0002
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## 320 1.1187 nan 0.0010 0.0003
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## 360 1.0993 nan 0.0010 0.0002
## 380 1.0901 nan 0.0010 0.0002
## 400 1.0812 nan 0.0010 0.0002
## 420 1.0724 nan 0.0010 0.0001
## 440 1.0640 nan 0.0010 0.0002
## 460 1.0557 nan 0.0010 0.0002
## 480 1.0477 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3181 nan 0.0010 0.0004
## 4 1.3172 nan 0.0010 0.0004
## 5 1.3164 nan 0.0010 0.0004
## 6 1.3155 nan 0.0010 0.0004
## 7 1.3147 nan 0.0010 0.0004
## 8 1.3138 nan 0.0010 0.0004
## 9 1.3130 nan 0.0010 0.0003
## 10 1.3121 nan 0.0010 0.0004
## 20 1.3034 nan 0.0010 0.0004
## 40 1.2868 nan 0.0010 0.0004
## 60 1.2709 nan 0.0010 0.0003
## 80 1.2554 nan 0.0010 0.0003
## 100 1.2407 nan 0.0010 0.0003
## 120 1.2263 nan 0.0010 0.0003
## 140 1.2124 nan 0.0010 0.0003
## 160 1.1988 nan 0.0010 0.0003
## 180 1.1857 nan 0.0010 0.0002
## 200 1.1731 nan 0.0010 0.0003
## 220 1.1609 nan 0.0010 0.0002
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## 320 1.1043 nan 0.0010 0.0002
## 340 1.0938 nan 0.0010 0.0002
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## 380 1.0737 nan 0.0010 0.0002
## 400 1.0641 nan 0.0010 0.0002
## 420 1.0546 nan 0.0010 0.0002
## 440 1.0454 nan 0.0010 0.0002
## 460 1.0366 nan 0.0010 0.0001
## 480 1.0278 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3190 nan 0.0010 0.0004
## 3 1.3181 nan 0.0010 0.0004
## 4 1.3173 nan 0.0010 0.0004
## 5 1.3165 nan 0.0010 0.0004
## 6 1.3156 nan 0.0010 0.0004
## 7 1.3148 nan 0.0010 0.0004
## 8 1.3140 nan 0.0010 0.0004
## 9 1.3131 nan 0.0010 0.0004
## 10 1.3123 nan 0.0010 0.0004
## 20 1.3040 nan 0.0010 0.0004
## 40 1.2876 nan 0.0010 0.0004
## 60 1.2717 nan 0.0010 0.0004
## 80 1.2565 nan 0.0010 0.0003
## 100 1.2417 nan 0.0010 0.0003
## 120 1.2272 nan 0.0010 0.0003
## 140 1.2130 nan 0.0010 0.0002
## 160 1.1997 nan 0.0010 0.0003
## 180 1.1863 nan 0.0010 0.0003
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## 220 1.1614 nan 0.0010 0.0003
## 240 1.1492 nan 0.0010 0.0002
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## 280 1.1262 nan 0.0010 0.0003
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## 320 1.1045 nan 0.0010 0.0002
## 340 1.0944 nan 0.0010 0.0003
## 360 1.0841 nan 0.0010 0.0002
## 380 1.0744 nan 0.0010 0.0002
## 400 1.0648 nan 0.0010 0.0002
## 420 1.0555 nan 0.0010 0.0002
## 440 1.0464 nan 0.0010 0.0002
## 460 1.0374 nan 0.0010 0.0002
## 480 1.0286 nan 0.0010 0.0002
## 500 1.0204 nan 0.0010 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3181 nan 0.0010 0.0004
## 4 1.3172 nan 0.0010 0.0004
## 5 1.3164 nan 0.0010 0.0004
## 6 1.3155 nan 0.0010 0.0004
## 7 1.3146 nan 0.0010 0.0004
## 8 1.3137 nan 0.0010 0.0004
## 9 1.3129 nan 0.0010 0.0004
## 10 1.3121 nan 0.0010 0.0004
## 20 1.3039 nan 0.0010 0.0003
## 40 1.2881 nan 0.0010 0.0004
## 60 1.2724 nan 0.0010 0.0003
## 80 1.2574 nan 0.0010 0.0003
## 100 1.2428 nan 0.0010 0.0004
## 120 1.2283 nan 0.0010 0.0003
## 140 1.2145 nan 0.0010 0.0003
## 160 1.2010 nan 0.0010 0.0003
## 180 1.1883 nan 0.0010 0.0003
## 200 1.1757 nan 0.0010 0.0003
## 220 1.1636 nan 0.0010 0.0003
## 240 1.1515 nan 0.0010 0.0002
## 260 1.1399 nan 0.0010 0.0002
## 280 1.1287 nan 0.0010 0.0002
## 300 1.1179 nan 0.0010 0.0002
## 320 1.1075 nan 0.0010 0.0002
## 340 1.0972 nan 0.0010 0.0002
## 360 1.0870 nan 0.0010 0.0002
## 380 1.0772 nan 0.0010 0.0002
## 400 1.0674 nan 0.0010 0.0002
## 420 1.0582 nan 0.0010 0.0002
## 440 1.0494 nan 0.0010 0.0002
## 460 1.0406 nan 0.0010 0.0002
## 480 1.0320 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3181 nan 0.0010 0.0004
## 4 1.3172 nan 0.0010 0.0004
## 5 1.3162 nan 0.0010 0.0004
## 6 1.3153 nan 0.0010 0.0004
## 7 1.3143 nan 0.0010 0.0004
## 8 1.3134 nan 0.0010 0.0004
## 9 1.3125 nan 0.0010 0.0004
## 10 1.3117 nan 0.0010 0.0004
## 20 1.3030 nan 0.0010 0.0004
## 40 1.2856 nan 0.0010 0.0004
## 60 1.2687 nan 0.0010 0.0004
## 80 1.2525 nan 0.0010 0.0004
## 100 1.2371 nan 0.0010 0.0003
## 120 1.2222 nan 0.0010 0.0003
## 140 1.2076 nan 0.0010 0.0003
## 160 1.1935 nan 0.0010 0.0003
## 180 1.1800 nan 0.0010 0.0003
## 200 1.1664 nan 0.0010 0.0003
## 220 1.1533 nan 0.0010 0.0003
## 240 1.1407 nan 0.0010 0.0002
## 260 1.1286 nan 0.0010 0.0003
## 280 1.1167 nan 0.0010 0.0002
## 300 1.1056 nan 0.0010 0.0002
## 320 1.0945 nan 0.0010 0.0002
## 340 1.0836 nan 0.0010 0.0002
## 360 1.0730 nan 0.0010 0.0002
## 380 1.0628 nan 0.0010 0.0002
## 400 1.0528 nan 0.0010 0.0002
## 420 1.0430 nan 0.0010 0.0002
## 440 1.0335 nan 0.0010 0.0002
## 460 1.0243 nan 0.0010 0.0002
## 480 1.0154 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0005
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3179 nan 0.0010 0.0004
## 4 1.3170 nan 0.0010 0.0004
## 5 1.3161 nan 0.0010 0.0005
## 6 1.3152 nan 0.0010 0.0004
## 7 1.3144 nan 0.0010 0.0004
## 8 1.3134 nan 0.0010 0.0004
## 9 1.3124 nan 0.0010 0.0005
## 10 1.3116 nan 0.0010 0.0004
## 20 1.3027 nan 0.0010 0.0004
## 40 1.2858 nan 0.0010 0.0004
## 60 1.2692 nan 0.0010 0.0003
## 80 1.2535 nan 0.0010 0.0003
## 100 1.2382 nan 0.0010 0.0003
## 120 1.2234 nan 0.0010 0.0003
## 140 1.2085 nan 0.0010 0.0003
## 160 1.1946 nan 0.0010 0.0003
## 180 1.1810 nan 0.0010 0.0003
## 200 1.1677 nan 0.0010 0.0002
## 220 1.1549 nan 0.0010 0.0003
## 240 1.1424 nan 0.0010 0.0003
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## 280 1.1187 nan 0.0010 0.0002
## 300 1.1073 nan 0.0010 0.0002
## 320 1.0960 nan 0.0010 0.0003
## 340 1.0850 nan 0.0010 0.0002
## 360 1.0747 nan 0.0010 0.0002
## 380 1.0646 nan 0.0010 0.0002
## 400 1.0546 nan 0.0010 0.0002
## 420 1.0449 nan 0.0010 0.0002
## 440 1.0355 nan 0.0010 0.0002
## 460 1.0263 nan 0.0010 0.0002
## 480 1.0175 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3180 nan 0.0010 0.0003
## 4 1.3171 nan 0.0010 0.0003
## 5 1.3163 nan 0.0010 0.0004
## 6 1.3155 nan 0.0010 0.0004
## 7 1.3147 nan 0.0010 0.0004
## 8 1.3138 nan 0.0010 0.0004
## 9 1.3128 nan 0.0010 0.0004
## 10 1.3120 nan 0.0010 0.0004
## 20 1.3034 nan 0.0010 0.0003
## 40 1.2867 nan 0.0010 0.0003
## 60 1.2703 nan 0.0010 0.0004
## 80 1.2545 nan 0.0010 0.0003
## 100 1.2394 nan 0.0010 0.0004
## 120 1.2243 nan 0.0010 0.0003
## 140 1.2099 nan 0.0010 0.0003
## 160 1.1960 nan 0.0010 0.0003
## 180 1.1828 nan 0.0010 0.0003
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## 220 1.1571 nan 0.0010 0.0002
## 240 1.1444 nan 0.0010 0.0003
## 260 1.1324 nan 0.0010 0.0003
## 280 1.1207 nan 0.0010 0.0002
## 300 1.1093 nan 0.0010 0.0002
## 320 1.0980 nan 0.0010 0.0002
## 340 1.0874 nan 0.0010 0.0002
## 360 1.0771 nan 0.0010 0.0002
## 380 1.0668 nan 0.0010 0.0002
## 400 1.0572 nan 0.0010 0.0002
## 420 1.0475 nan 0.0010 0.0002
## 440 1.0383 nan 0.0010 0.0002
## 460 1.0292 nan 0.0010 0.0002
## 480 1.0204 nan 0.0010 0.0002
## 500 1.0118 nan 0.0010 0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3138 nan 0.0100 0.0030
## 2 1.3058 nan 0.0100 0.0032
## 3 1.2970 nan 0.0100 0.0038
## 4 1.2891 nan 0.0100 0.0033
## 5 1.2813 nan 0.0100 0.0037
## 6 1.2742 nan 0.0100 0.0037
## 7 1.2672 nan 0.0100 0.0030
## 8 1.2600 nan 0.0100 0.0030
## 9 1.2523 nan 0.0100 0.0032
## 10 1.2454 nan 0.0100 0.0034
## 20 1.1813 nan 0.0100 0.0026
## 40 1.0780 nan 0.0100 0.0018
## 60 1.0008 nan 0.0100 0.0013
## 80 0.9375 nan 0.0100 0.0013
## 100 0.8866 nan 0.0100 0.0010
## 120 0.8459 nan 0.0100 0.0007
## 140 0.8108 nan 0.0100 0.0005
## 160 0.7813 nan 0.0100 0.0004
## 180 0.7562 nan 0.0100 0.0004
## 200 0.7358 nan 0.0100 0.0003
## 220 0.7167 nan 0.0100 0.0002
## 240 0.7000 nan 0.0100 0.0001
## 260 0.6848 nan 0.0100 0.0000
## 280 0.6689 nan 0.0100 0.0000
## 300 0.6552 nan 0.0100 0.0000
## 320 0.6421 nan 0.0100 0.0001
## 340 0.6303 nan 0.0100 0.0002
## 360 0.6192 nan 0.0100 -0.0001
## 380 0.6078 nan 0.0100 -0.0001
## 400 0.5981 nan 0.0100 -0.0000
## 420 0.5881 nan 0.0100 0.0000
## 440 0.5792 nan 0.0100 -0.0002
## 460 0.5700 nan 0.0100 -0.0001
## 480 0.5614 nan 0.0100 0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3125 nan 0.0100 0.0034
## 2 1.3057 nan 0.0100 0.0030
## 3 1.2975 nan 0.0100 0.0040
## 4 1.2896 nan 0.0100 0.0038
## 5 1.2816 nan 0.0100 0.0039
## 6 1.2740 nan 0.0100 0.0031
## 7 1.2669 nan 0.0100 0.0032
## 8 1.2603 nan 0.0100 0.0031
## 9 1.2524 nan 0.0100 0.0035
## 10 1.2452 nan 0.0100 0.0030
## 20 1.1808 nan 0.0100 0.0026
## 40 1.0794 nan 0.0100 0.0019
## 60 0.9999 nan 0.0100 0.0015
## 80 0.9377 nan 0.0100 0.0013
## 100 0.8880 nan 0.0100 0.0007
## 120 0.8471 nan 0.0100 0.0007
## 140 0.8127 nan 0.0100 0.0005
## 160 0.7839 nan 0.0100 0.0004
## 180 0.7604 nan 0.0100 0.0000
## 200 0.7394 nan 0.0100 -0.0001
## 220 0.7203 nan 0.0100 0.0001
## 240 0.7033 nan 0.0100 0.0003
## 260 0.6887 nan 0.0100 -0.0000
## 280 0.6743 nan 0.0100 0.0001
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## 320 0.6506 nan 0.0100 0.0000
## 340 0.6395 nan 0.0100 0.0000
## 360 0.6285 nan 0.0100 -0.0001
## 380 0.6181 nan 0.0100 -0.0000
## 400 0.6090 nan 0.0100 -0.0001
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## 460 0.5812 nan 0.0100 0.0001
## 480 0.5727 nan 0.0100 -0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3127 nan 0.0100 0.0037
## 2 1.3049 nan 0.0100 0.0035
## 3 1.2979 nan 0.0100 0.0033
## 4 1.2908 nan 0.0100 0.0034
## 5 1.2834 nan 0.0100 0.0036
## 6 1.2766 nan 0.0100 0.0032
## 7 1.2702 nan 0.0100 0.0028
## 8 1.2632 nan 0.0100 0.0027
## 9 1.2565 nan 0.0100 0.0030
## 10 1.2492 nan 0.0100 0.0031
## 20 1.1849 nan 0.0100 0.0024
## 40 1.0808 nan 0.0100 0.0021
## 60 1.0015 nan 0.0100 0.0015
## 80 0.9386 nan 0.0100 0.0013
## 100 0.8884 nan 0.0100 0.0009
## 120 0.8479 nan 0.0100 0.0007
## 140 0.8146 nan 0.0100 0.0007
## 160 0.7860 nan 0.0100 0.0004
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## 220 0.7211 nan 0.0100 0.0001
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## 260 0.6897 nan 0.0100 0.0002
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## 320 0.6507 nan 0.0100 0.0000
## 340 0.6399 nan 0.0100 -0.0000
## 360 0.6294 nan 0.0100 0.0001
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## 460 0.5856 nan 0.0100 -0.0002
## 480 0.5766 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3129 nan 0.0100 0.0037
## 2 1.3038 nan 0.0100 0.0039
## 3 1.2954 nan 0.0100 0.0037
## 4 1.2877 nan 0.0100 0.0033
## 5 1.2793 nan 0.0100 0.0039
## 6 1.2718 nan 0.0100 0.0032
## 7 1.2639 nan 0.0100 0.0034
## 8 1.2562 nan 0.0100 0.0035
## 9 1.2486 nan 0.0100 0.0032
## 10 1.2416 nan 0.0100 0.0030
## 20 1.1742 nan 0.0100 0.0025
## 40 1.0658 nan 0.0100 0.0018
## 60 0.9801 nan 0.0100 0.0017
## 80 0.9141 nan 0.0100 0.0011
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## 160 0.7509 nan 0.0100 0.0003
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## 220 0.6815 nan 0.0100 0.0002
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## 300 0.6130 nan 0.0100 -0.0000
## 320 0.5987 nan 0.0100 -0.0001
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## 380 0.5646 nan 0.0100 0.0001
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## 480 0.5116 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3123 nan 0.0100 0.0036
## 2 1.3045 nan 0.0100 0.0033
## 3 1.2965 nan 0.0100 0.0037
## 4 1.2885 nan 0.0100 0.0033
## 5 1.2807 nan 0.0100 0.0038
## 6 1.2733 nan 0.0100 0.0033
## 7 1.2649 nan 0.0100 0.0040
## 8 1.2571 nan 0.0100 0.0036
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## 10 1.2415 nan 0.0100 0.0033
## 20 1.1739 nan 0.0100 0.0029
## 40 1.0638 nan 0.0100 0.0018
## 60 0.9822 nan 0.0100 0.0016
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## 360 0.5805 nan 0.0100 -0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3121 nan 0.0100 0.0038
## 2 1.3032 nan 0.0100 0.0041
## 3 1.2952 nan 0.0100 0.0035
## 4 1.2873 nan 0.0100 0.0036
## 5 1.2788 nan 0.0100 0.0039
## 6 1.2707 nan 0.0100 0.0040
## 7 1.2632 nan 0.0100 0.0034
## 8 1.2556 nan 0.0100 0.0036
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## 10 1.2399 nan 0.0100 0.0033
## 20 1.1724 nan 0.0100 0.0029
## 40 1.0665 nan 0.0100 0.0021
## 60 0.9839 nan 0.0100 0.0015
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## 300 0.6297 nan 0.0100 -0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3117 nan 0.0100 0.0042
## 2 1.3029 nan 0.0100 0.0039
## 3 1.2949 nan 0.0100 0.0037
## 4 1.2859 nan 0.0100 0.0036
## 5 1.2778 nan 0.0100 0.0038
## 6 1.2696 nan 0.0100 0.0037
## 7 1.2627 nan 0.0100 0.0033
## 8 1.2550 nan 0.0100 0.0036
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## 20 1.1684 nan 0.0100 0.0029
## 40 1.0545 nan 0.0100 0.0019
## 60 0.9684 nan 0.0100 0.0015
## 80 0.9001 nan 0.0100 0.0010
## 100 0.8440 nan 0.0100 0.0008
## 120 0.7976 nan 0.0100 0.0007
## 140 0.7607 nan 0.0100 0.0005
## 160 0.7273 nan 0.0100 0.0005
## 180 0.6983 nan 0.0100 0.0003
## 200 0.6733 nan 0.0100 0.0001
## 220 0.6503 nan 0.0100 0.0003
## 240 0.6307 nan 0.0100 0.0001
## 260 0.6122 nan 0.0100 -0.0001
## 280 0.5953 nan 0.0100 0.0001
## 300 0.5799 nan 0.0100 -0.0000
## 320 0.5649 nan 0.0100 0.0001
## 340 0.5507 nan 0.0100 -0.0000
## 360 0.5379 nan 0.0100 -0.0002
## 380 0.5257 nan 0.0100 -0.0000
## 400 0.5139 nan 0.0100 0.0000
## 420 0.5022 nan 0.0100 -0.0000
## 440 0.4904 nan 0.0100 0.0001
## 460 0.4807 nan 0.0100 -0.0000
## 480 0.4701 nan 0.0100 -0.0002
## 500 0.4611 nan 0.0100 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3124 nan 0.0100 0.0037
## 2 1.3036 nan 0.0100 0.0042
## 3 1.2948 nan 0.0100 0.0040
## 4 1.2869 nan 0.0100 0.0036
## 5 1.2788 nan 0.0100 0.0037
## 6 1.2708 nan 0.0100 0.0039
## 7 1.2634 nan 0.0100 0.0034
## 8 1.2554 nan 0.0100 0.0035
## 9 1.2473 nan 0.0100 0.0036
## 10 1.2388 nan 0.0100 0.0037
## 20 1.1679 nan 0.0100 0.0031
## 40 1.0550 nan 0.0100 0.0016
## 60 0.9661 nan 0.0100 0.0017
## 80 0.8958 nan 0.0100 0.0009
## 100 0.8410 nan 0.0100 0.0009
## 120 0.7978 nan 0.0100 0.0006
## 140 0.7602 nan 0.0100 0.0007
## 160 0.7270 nan 0.0100 0.0004
## 180 0.6994 nan 0.0100 0.0001
## 200 0.6752 nan 0.0100 0.0003
## 220 0.6542 nan 0.0100 0.0001
## 240 0.6345 nan 0.0100 -0.0001
## 260 0.6175 nan 0.0100 0.0001
## 280 0.5993 nan 0.0100 0.0001
## 300 0.5841 nan 0.0100 -0.0000
## 320 0.5690 nan 0.0100 0.0003
## 340 0.5553 nan 0.0100 -0.0000
## 360 0.5424 nan 0.0100 -0.0000
## 380 0.5293 nan 0.0100 -0.0002
## 400 0.5179 nan 0.0100 -0.0000
## 420 0.5067 nan 0.0100 0.0000
## 440 0.4963 nan 0.0100 -0.0000
## 460 0.4849 nan 0.0100 0.0000
## 480 0.4751 nan 0.0100 -0.0000
## 500 0.4658 nan 0.0100 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3113 nan 0.0100 0.0045
## 2 1.3028 nan 0.0100 0.0040
## 3 1.2941 nan 0.0100 0.0040
## 4 1.2855 nan 0.0100 0.0034
## 5 1.2774 nan 0.0100 0.0032
## 6 1.2692 nan 0.0100 0.0037
## 7 1.2622 nan 0.0100 0.0032
## 8 1.2543 nan 0.0100 0.0034
## 9 1.2468 nan 0.0100 0.0033
## 10 1.2387 nan 0.0100 0.0038
## 20 1.1695 nan 0.0100 0.0029
## 40 1.0574 nan 0.0100 0.0019
## 60 0.9736 nan 0.0100 0.0015
## 80 0.9050 nan 0.0100 0.0014
## 100 0.8513 nan 0.0100 0.0008
## 120 0.8074 nan 0.0100 0.0003
## 140 0.7704 nan 0.0100 0.0005
## 160 0.7397 nan 0.0100 0.0003
## 180 0.7116 nan 0.0100 0.0002
## 200 0.6886 nan 0.0100 0.0004
## 220 0.6659 nan 0.0100 -0.0001
## 240 0.6460 nan 0.0100 0.0001
## 260 0.6283 nan 0.0100 -0.0000
## 280 0.6123 nan 0.0100 0.0003
## 300 0.5967 nan 0.0100 0.0001
## 320 0.5822 nan 0.0100 0.0001
## 340 0.5683 nan 0.0100 -0.0000
## 360 0.5549 nan 0.0100 0.0000
## 380 0.5422 nan 0.0100 0.0001
## 400 0.5310 nan 0.0100 0.0002
## 420 0.5188 nan 0.0100 -0.0001
## 440 0.5080 nan 0.0100 0.0000
## 460 0.4980 nan 0.0100 -0.0001
## 480 0.4878 nan 0.0100 -0.0001
## 500 0.4776 nan 0.0100 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2361 nan 0.1000 0.0350
## 2 1.1733 nan 0.1000 0.0280
## 3 1.1280 nan 0.1000 0.0191
## 4 1.0820 nan 0.1000 0.0184
## 5 1.0378 nan 0.1000 0.0182
## 6 1.0019 nan 0.1000 0.0148
## 7 0.9726 nan 0.1000 0.0119
## 8 0.9393 nan 0.1000 0.0129
## 9 0.9099 nan 0.1000 0.0109
## 10 0.8858 nan 0.1000 0.0105
## 20 0.7379 nan 0.1000 0.0024
## 40 0.6069 nan 0.1000 0.0001
## 60 0.5181 nan 0.1000 -0.0016
## 80 0.4547 nan 0.1000 -0.0010
## 100 0.4008 nan 0.1000 -0.0012
## 120 0.3568 nan 0.1000 -0.0003
## 140 0.3215 nan 0.1000 -0.0008
## 160 0.2887 nan 0.1000 -0.0002
## 180 0.2617 nan 0.1000 -0.0004
## 200 0.2410 nan 0.1000 -0.0005
## 220 0.2152 nan 0.1000 -0.0000
## 240 0.1949 nan 0.1000 -0.0009
## 260 0.1787 nan 0.1000 -0.0005
## 280 0.1637 nan 0.1000 -0.0002
## 300 0.1497 nan 0.1000 -0.0004
## 320 0.1382 nan 0.1000 -0.0005
## 340 0.1260 nan 0.1000 -0.0003
## 360 0.1180 nan 0.1000 -0.0000
## 380 0.1097 nan 0.1000 -0.0002
## 400 0.1026 nan 0.1000 -0.0003
## 420 0.0949 nan 0.1000 -0.0004
## 440 0.0885 nan 0.1000 -0.0001
## 460 0.0819 nan 0.1000 -0.0001
## 480 0.0756 nan 0.1000 -0.0001
## 500 0.0695 nan 0.1000 -0.0003
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2417 nan 0.1000 0.0394
## 2 1.1764 nan 0.1000 0.0282
## 3 1.1244 nan 0.1000 0.0217
## 4 1.0789 nan 0.1000 0.0211
## 5 1.0379 nan 0.1000 0.0162
## 6 1.0056 nan 0.1000 0.0129
## 7 0.9750 nan 0.1000 0.0116
## 8 0.9404 nan 0.1000 0.0139
## 9 0.9137 nan 0.1000 0.0109
## 10 0.8926 nan 0.1000 0.0062
## 20 0.7431 nan 0.1000 0.0011
## 40 0.6024 nan 0.1000 0.0011
## 60 0.5230 nan 0.1000 -0.0006
## 80 0.4642 nan 0.1000 -0.0006
## 100 0.4084 nan 0.1000 -0.0021
## 120 0.3643 nan 0.1000 0.0002
## 140 0.3284 nan 0.1000 -0.0007
## 160 0.2961 nan 0.1000 -0.0008
## 180 0.2695 nan 0.1000 -0.0010
## 200 0.2417 nan 0.1000 -0.0017
## 220 0.2231 nan 0.1000 -0.0011
## 240 0.2047 nan 0.1000 -0.0010
## 260 0.1894 nan 0.1000 -0.0005
## 280 0.1734 nan 0.1000 -0.0005
## 300 0.1592 nan 0.1000 -0.0004
## 320 0.1476 nan 0.1000 -0.0000
## 340 0.1368 nan 0.1000 -0.0002
## 360 0.1249 nan 0.1000 -0.0003
## 380 0.1170 nan 0.1000 -0.0003
## 400 0.1084 nan 0.1000 -0.0005
## 420 0.1010 nan 0.1000 -0.0004
## 440 0.0941 nan 0.1000 -0.0003
## 460 0.0885 nan 0.1000 -0.0002
## 480 0.0823 nan 0.1000 -0.0003
## 500 0.0766 nan 0.1000 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2456 nan 0.1000 0.0333
## 2 1.1762 nan 0.1000 0.0320
## 3 1.1269 nan 0.1000 0.0225
## 4 1.0732 nan 0.1000 0.0246
## 5 1.0299 nan 0.1000 0.0165
## 6 0.9871 nan 0.1000 0.0165
## 7 0.9544 nan 0.1000 0.0107
## 8 0.9259 nan 0.1000 0.0126
## 9 0.9051 nan 0.1000 0.0061
## 10 0.8778 nan 0.1000 0.0101
## 20 0.7378 nan 0.1000 0.0033
## 40 0.6086 nan 0.1000 0.0011
## 60 0.5348 nan 0.1000 0.0006
## 80 0.4750 nan 0.1000 -0.0005
## 100 0.4220 nan 0.1000 -0.0012
## 120 0.3736 nan 0.1000 -0.0026
## 140 0.3341 nan 0.1000 -0.0013
## 160 0.3035 nan 0.1000 -0.0005
## 180 0.2767 nan 0.1000 -0.0000
## 200 0.2553 nan 0.1000 -0.0009
## 220 0.2336 nan 0.1000 -0.0004
## 240 0.2150 nan 0.1000 -0.0006
## 260 0.1996 nan 0.1000 -0.0012
## 280 0.1852 nan 0.1000 -0.0007
## 300 0.1723 nan 0.1000 -0.0004
## 320 0.1591 nan 0.1000 -0.0006
## 340 0.1470 nan 0.1000 -0.0006
## 360 0.1356 nan 0.1000 -0.0003
## 380 0.1257 nan 0.1000 -0.0002
## 400 0.1172 nan 0.1000 -0.0001
## 420 0.1082 nan 0.1000 -0.0003
## 440 0.1005 nan 0.1000 -0.0001
## 460 0.0939 nan 0.1000 -0.0002
## 480 0.0864 nan 0.1000 -0.0004
## 500 0.0809 nan 0.1000 -0.0004
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2496 nan 0.1000 0.0344
## 2 1.1873 nan 0.1000 0.0292
## 3 1.1268 nan 0.1000 0.0264
## 4 1.0743 nan 0.1000 0.0230
## 5 1.0314 nan 0.1000 0.0141
## 6 0.9872 nan 0.1000 0.0195
## 7 0.9473 nan 0.1000 0.0156
## 8 0.9158 nan 0.1000 0.0142
## 9 0.8902 nan 0.1000 0.0116
## 10 0.8628 nan 0.1000 0.0108
## 20 0.7115 nan 0.1000 0.0024
## 40 0.5594 nan 0.1000 -0.0010
## 60 0.4682 nan 0.1000 0.0003
## 80 0.3936 nan 0.1000 -0.0007
## 100 0.3369 nan 0.1000 -0.0001
## 120 0.2903 nan 0.1000 -0.0009
## 140 0.2535 nan 0.1000 -0.0001
## 160 0.2195 nan 0.1000 0.0000
## 180 0.1957 nan 0.1000 -0.0003
## 200 0.1736 nan 0.1000 0.0001
## 220 0.1559 nan 0.1000 -0.0003
## 240 0.1399 nan 0.1000 -0.0003
## 260 0.1247 nan 0.1000 -0.0002
## 280 0.1114 nan 0.1000 -0.0002
## 300 0.1009 nan 0.1000 -0.0001
## 320 0.0916 nan 0.1000 -0.0004
## 340 0.0826 nan 0.1000 -0.0004
## 360 0.0750 nan 0.1000 -0.0001
## 380 0.0683 nan 0.1000 -0.0001
## 400 0.0616 nan 0.1000 -0.0002
## 420 0.0561 nan 0.1000 -0.0001
## 440 0.0512 nan 0.1000 -0.0001
## 460 0.0468 nan 0.1000 -0.0001
## 480 0.0424 nan 0.1000 -0.0000
## 500 0.0384 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2387 nan 0.1000 0.0359
## 2 1.1728 nan 0.1000 0.0249
## 3 1.1146 nan 0.1000 0.0242
## 4 1.0527 nan 0.1000 0.0260
## 5 1.0118 nan 0.1000 0.0187
## 6 0.9754 nan 0.1000 0.0137
## 7 0.9428 nan 0.1000 0.0136
## 8 0.9074 nan 0.1000 0.0120
## 9 0.8798 nan 0.1000 0.0113
## 10 0.8564 nan 0.1000 0.0076
## 20 0.7107 nan 0.1000 0.0023
## 40 0.5675 nan 0.1000 -0.0006
## 60 0.4794 nan 0.1000 -0.0004
## 80 0.4062 nan 0.1000 -0.0004
## 100 0.3545 nan 0.1000 0.0000
## 120 0.3118 nan 0.1000 -0.0007
## 140 0.2772 nan 0.1000 -0.0007
## 160 0.2393 nan 0.1000 -0.0010
## 180 0.2105 nan 0.1000 -0.0001
## 200 0.1865 nan 0.1000 -0.0004
## 220 0.1648 nan 0.1000 0.0001
## 240 0.1468 nan 0.1000 0.0002
## 260 0.1324 nan 0.1000 -0.0006
## 280 0.1193 nan 0.1000 -0.0005
## 300 0.1067 nan 0.1000 -0.0003
## 320 0.0957 nan 0.1000 -0.0000
## 340 0.0861 nan 0.1000 -0.0002
## 360 0.0791 nan 0.1000 -0.0006
## 380 0.0712 nan 0.1000 -0.0002
## 400 0.0650 nan 0.1000 -0.0004
## 420 0.0595 nan 0.1000 -0.0002
## 440 0.0533 nan 0.1000 -0.0001
## 460 0.0481 nan 0.1000 -0.0002
## 480 0.0435 nan 0.1000 -0.0002
## 500 0.0395 nan 0.1000 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2362 nan 0.1000 0.0362
## 2 1.1636 nan 0.1000 0.0313
## 3 1.1072 nan 0.1000 0.0279
## 4 1.0562 nan 0.1000 0.0209
## 5 1.0135 nan 0.1000 0.0201
## 6 0.9726 nan 0.1000 0.0179
## 7 0.9389 nan 0.1000 0.0129
## 8 0.9093 nan 0.1000 0.0112
## 9 0.8836 nan 0.1000 0.0090
## 10 0.8561 nan 0.1000 0.0105
## 20 0.7126 nan 0.1000 0.0024
## 40 0.5733 nan 0.1000 -0.0011
## 60 0.4872 nan 0.1000 -0.0016
## 80 0.4204 nan 0.1000 -0.0015
## 100 0.3672 nan 0.1000 -0.0010
## 120 0.3198 nan 0.1000 -0.0017
## 140 0.2841 nan 0.1000 -0.0007
## 160 0.2508 nan 0.1000 -0.0003
## 180 0.2228 nan 0.1000 -0.0004
## 200 0.1984 nan 0.1000 -0.0003
## 220 0.1756 nan 0.1000 -0.0008
## 240 0.1571 nan 0.1000 -0.0009
## 260 0.1421 nan 0.1000 -0.0005
## 280 0.1258 nan 0.1000 -0.0007
## 300 0.1122 nan 0.1000 -0.0006
## 320 0.1017 nan 0.1000 -0.0003
## 340 0.0932 nan 0.1000 -0.0002
## 360 0.0853 nan 0.1000 -0.0006
## 380 0.0781 nan 0.1000 -0.0003
## 400 0.0713 nan 0.1000 -0.0002
## 420 0.0647 nan 0.1000 -0.0002
## 440 0.0601 nan 0.1000 -0.0002
## 460 0.0542 nan 0.1000 -0.0001
## 480 0.0493 nan 0.1000 -0.0002
## 500 0.0454 nan 0.1000 -0.0004
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2394 nan 0.1000 0.0370
## 2 1.1615 nan 0.1000 0.0301
## 3 1.1007 nan 0.1000 0.0252
## 4 1.0483 nan 0.1000 0.0216
## 5 1.0001 nan 0.1000 0.0192
## 6 0.9619 nan 0.1000 0.0150
## 7 0.9223 nan 0.1000 0.0153
## 8 0.8907 nan 0.1000 0.0119
## 9 0.8662 nan 0.1000 0.0077
## 10 0.8386 nan 0.1000 0.0094
## 20 0.6783 nan 0.1000 0.0010
## 40 0.5148 nan 0.1000 0.0007
## 60 0.4257 nan 0.1000 -0.0013
## 80 0.3497 nan 0.1000 0.0005
## 100 0.2922 nan 0.1000 -0.0005
## 120 0.2465 nan 0.1000 0.0000
## 140 0.2112 nan 0.1000 -0.0003
## 160 0.1811 nan 0.1000 -0.0001
## 180 0.1582 nan 0.1000 -0.0005
## 200 0.1354 nan 0.1000 -0.0004
## 220 0.1167 nan 0.1000 -0.0002
## 240 0.1024 nan 0.1000 -0.0002
## 260 0.0910 nan 0.1000 -0.0003
## 280 0.0800 nan 0.1000 0.0001
## 300 0.0710 nan 0.1000 -0.0002
## 320 0.0629 nan 0.1000 -0.0000
## 340 0.0553 nan 0.1000 0.0000
## 360 0.0493 nan 0.1000 -0.0001
## 380 0.0436 nan 0.1000 0.0001
## 400 0.0386 nan 0.1000 -0.0003
## 420 0.0337 nan 0.1000 0.0001
## 440 0.0298 nan 0.1000 -0.0000
## 460 0.0263 nan 0.1000 -0.0000
## 480 0.0233 nan 0.1000 -0.0000
## 500 0.0209 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2365 nan 0.1000 0.0422
## 2 1.1624 nan 0.1000 0.0295
## 3 1.1046 nan 0.1000 0.0227
## 4 1.0556 nan 0.1000 0.0221
## 5 1.0092 nan 0.1000 0.0192
## 6 0.9697 nan 0.1000 0.0144
## 7 0.9362 nan 0.1000 0.0136
## 8 0.9014 nan 0.1000 0.0131
## 9 0.8726 nan 0.1000 0.0103
## 10 0.8496 nan 0.1000 0.0072
## 20 0.6834 nan 0.1000 0.0003
## 40 0.5183 nan 0.1000 -0.0012
## 60 0.4203 nan 0.1000 -0.0015
## 80 0.3438 nan 0.1000 0.0004
## 100 0.2925 nan 0.1000 -0.0009
## 120 0.2534 nan 0.1000 -0.0007
## 140 0.2190 nan 0.1000 -0.0007
## 160 0.1894 nan 0.1000 -0.0006
## 180 0.1637 nan 0.1000 -0.0007
## 200 0.1437 nan 0.1000 -0.0005
## 220 0.1263 nan 0.1000 -0.0005
## 240 0.1112 nan 0.1000 -0.0002
## 260 0.0978 nan 0.1000 -0.0001
## 280 0.0854 nan 0.1000 -0.0001
## 300 0.0757 nan 0.1000 -0.0003
## 320 0.0673 nan 0.1000 -0.0002
## 340 0.0602 nan 0.1000 -0.0002
## 360 0.0537 nan 0.1000 -0.0003
## 380 0.0479 nan 0.1000 -0.0001
## 400 0.0427 nan 0.1000 -0.0000
## 420 0.0380 nan 0.1000 -0.0001
## 440 0.0338 nan 0.1000 -0.0002
## 460 0.0301 nan 0.1000 -0.0001
## 480 0.0274 nan 0.1000 -0.0000
## 500 0.0242 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2353 nan 0.1000 0.0383
## 2 1.1622 nan 0.1000 0.0338
## 3 1.0967 nan 0.1000 0.0285
## 4 1.0456 nan 0.1000 0.0238
## 5 1.0050 nan 0.1000 0.0169
## 6 0.9605 nan 0.1000 0.0174
## 7 0.9284 nan 0.1000 0.0115
## 8 0.8946 nan 0.1000 0.0092
## 9 0.8681 nan 0.1000 0.0093
## 10 0.8428 nan 0.1000 0.0089
## 20 0.6936 nan 0.1000 0.0001
## 40 0.5390 nan 0.1000 -0.0014
## 60 0.4329 nan 0.1000 -0.0001
## 80 0.3642 nan 0.1000 -0.0013
## 100 0.3084 nan 0.1000 -0.0016
## 120 0.2664 nan 0.1000 -0.0009
## 140 0.2306 nan 0.1000 -0.0008
## 160 0.1980 nan 0.1000 -0.0006
## 180 0.1711 nan 0.1000 -0.0003
## 200 0.1512 nan 0.1000 -0.0006
## 220 0.1325 nan 0.1000 -0.0006
## 240 0.1179 nan 0.1000 -0.0005
## 260 0.1046 nan 0.1000 -0.0002
## 280 0.0922 nan 0.1000 -0.0003
## 300 0.0819 nan 0.1000 -0.0000
## 320 0.0736 nan 0.1000 -0.0002
## 340 0.0653 nan 0.1000 -0.0001
## 360 0.0584 nan 0.1000 -0.0002
## 380 0.0521 nan 0.1000 -0.0003
## 400 0.0460 nan 0.1000 -0.0001
## 420 0.0412 nan 0.1000 -0.0001
## 440 0.0365 nan 0.1000 -0.0002
## 460 0.0330 nan 0.1000 -0.0001
## 480 0.0296 nan 0.1000 -0.0002
## 500 0.0267 nan 0.1000 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3195 nan 0.0010 0.0004
## 3 1.3187 nan 0.0010 0.0003
## 4 1.3179 nan 0.0010 0.0004
## 5 1.3171 nan 0.0010 0.0004
## 6 1.3162 nan 0.0010 0.0004
## 7 1.3154 nan 0.0010 0.0003
## 8 1.3145 nan 0.0010 0.0004
## 9 1.3138 nan 0.0010 0.0003
## 10 1.3130 nan 0.0010 0.0003
## 20 1.3051 nan 0.0010 0.0003
## 40 1.2892 nan 0.0010 0.0004
## 60 1.2738 nan 0.0010 0.0003
## 80 1.2588 nan 0.0010 0.0004
## 100 1.2442 nan 0.0010 0.0003
## 120 1.2304 nan 0.0010 0.0003
## 140 1.2169 nan 0.0010 0.0003
## 160 1.2039 nan 0.0010 0.0003
## 180 1.1909 nan 0.0010 0.0003
## 200 1.1786 nan 0.0010 0.0003
## 220 1.1662 nan 0.0010 0.0003
## 240 1.1541 nan 0.0010 0.0003
## 260 1.1425 nan 0.0010 0.0002
## 280 1.1315 nan 0.0010 0.0002
## 300 1.1208 nan 0.0010 0.0002
## 320 1.1105 nan 0.0010 0.0003
## 340 1.1006 nan 0.0010 0.0002
## 360 1.0910 nan 0.0010 0.0002
## 380 1.0814 nan 0.0010 0.0002
## 400 1.0723 nan 0.0010 0.0002
## 420 1.0633 nan 0.0010 0.0002
## 440 1.0544 nan 0.0010 0.0002
## 460 1.0459 nan 0.0010 0.0002
## 480 1.0377 nan 0.0010 0.0002
## 500 1.0293 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3195 nan 0.0010 0.0004
## 3 1.3187 nan 0.0010 0.0003
## 4 1.3178 nan 0.0010 0.0004
## 5 1.3170 nan 0.0010 0.0004
## 6 1.3162 nan 0.0010 0.0004
## 7 1.3153 nan 0.0010 0.0004
## 8 1.3145 nan 0.0010 0.0004
## 9 1.3136 nan 0.0010 0.0004
## 10 1.3127 nan 0.0010 0.0004
## 20 1.3044 nan 0.0010 0.0004
## 40 1.2887 nan 0.0010 0.0004
## 60 1.2730 nan 0.0010 0.0004
## 80 1.2579 nan 0.0010 0.0003
## 100 1.2433 nan 0.0010 0.0003
## 120 1.2294 nan 0.0010 0.0002
## 140 1.2155 nan 0.0010 0.0003
## 160 1.2023 nan 0.0010 0.0003
## 180 1.1893 nan 0.0010 0.0003
## 200 1.1770 nan 0.0010 0.0003
## 220 1.1652 nan 0.0010 0.0002
## 240 1.1535 nan 0.0010 0.0003
## 260 1.1422 nan 0.0010 0.0003
## 280 1.1310 nan 0.0010 0.0002
## 300 1.1203 nan 0.0010 0.0002
## 320 1.1100 nan 0.0010 0.0002
## 340 1.1001 nan 0.0010 0.0002
## 360 1.0905 nan 0.0010 0.0002
## 380 1.0809 nan 0.0010 0.0002
## 400 1.0716 nan 0.0010 0.0002
## 420 1.0624 nan 0.0010 0.0002
## 440 1.0538 nan 0.0010 0.0002
## 460 1.0451 nan 0.0010 0.0002
## 480 1.0370 nan 0.0010 0.0001
## 500 1.0289 nan 0.0010 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3204 nan 0.0010 0.0004
## 2 1.3195 nan 0.0010 0.0004
## 3 1.3186 nan 0.0010 0.0004
## 4 1.3178 nan 0.0010 0.0004
## 5 1.3169 nan 0.0010 0.0004
## 6 1.3161 nan 0.0010 0.0004
## 7 1.3153 nan 0.0010 0.0003
## 8 1.3145 nan 0.0010 0.0004
## 9 1.3137 nan 0.0010 0.0004
## 10 1.3129 nan 0.0010 0.0004
## 20 1.3047 nan 0.0010 0.0004
## 40 1.2885 nan 0.0010 0.0003
## 60 1.2730 nan 0.0010 0.0003
## 80 1.2586 nan 0.0010 0.0003
## 100 1.2438 nan 0.0010 0.0003
## 120 1.2302 nan 0.0010 0.0003
## 140 1.2170 nan 0.0010 0.0003
## 160 1.2038 nan 0.0010 0.0003
## 180 1.1909 nan 0.0010 0.0003
## 200 1.1785 nan 0.0010 0.0002
## 220 1.1664 nan 0.0010 0.0002
## 240 1.1550 nan 0.0010 0.0002
## 260 1.1439 nan 0.0010 0.0002
## 280 1.1332 nan 0.0010 0.0002
## 300 1.1228 nan 0.0010 0.0003
## 320 1.1127 nan 0.0010 0.0002
## 340 1.1027 nan 0.0010 0.0002
## 360 1.0932 nan 0.0010 0.0002
## 380 1.0836 nan 0.0010 0.0002
## 400 1.0741 nan 0.0010 0.0002
## 420 1.0652 nan 0.0010 0.0002
## 440 1.0565 nan 0.0010 0.0001
## 460 1.0483 nan 0.0010 0.0002
## 480 1.0400 nan 0.0010 0.0002
## 500 1.0317 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3202 nan 0.0010 0.0005
## 2 1.3194 nan 0.0010 0.0004
## 3 1.3185 nan 0.0010 0.0004
## 4 1.3175 nan 0.0010 0.0004
## 5 1.3167 nan 0.0010 0.0004
## 6 1.3158 nan 0.0010 0.0004
## 7 1.3149 nan 0.0010 0.0004
## 8 1.3140 nan 0.0010 0.0004
## 9 1.3132 nan 0.0010 0.0003
## 10 1.3123 nan 0.0010 0.0004
## 20 1.3037 nan 0.0010 0.0003
## 40 1.2866 nan 0.0010 0.0004
## 60 1.2704 nan 0.0010 0.0004
## 80 1.2545 nan 0.0010 0.0003
## 100 1.2385 nan 0.0010 0.0004
## 120 1.2235 nan 0.0010 0.0003
## 140 1.2089 nan 0.0010 0.0003
## 160 1.1951 nan 0.0010 0.0003
## 180 1.1818 nan 0.0010 0.0003
## 200 1.1684 nan 0.0010 0.0003
## 220 1.1557 nan 0.0010 0.0003
## 240 1.1434 nan 0.0010 0.0003
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## 300 1.1083 nan 0.0010 0.0002
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## 380 1.0662 nan 0.0010 0.0002
## 400 1.0563 nan 0.0010 0.0002
## 420 1.0469 nan 0.0010 0.0002
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## 460 1.0283 nan 0.0010 0.0002
## 480 1.0196 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3202 nan 0.0010 0.0004
## 2 1.3194 nan 0.0010 0.0004
## 3 1.3185 nan 0.0010 0.0004
## 4 1.3176 nan 0.0010 0.0004
## 5 1.3167 nan 0.0010 0.0004
## 6 1.3158 nan 0.0010 0.0004
## 7 1.3148 nan 0.0010 0.0004
## 8 1.3138 nan 0.0010 0.0004
## 9 1.3129 nan 0.0010 0.0004
## 10 1.3120 nan 0.0010 0.0004
## 20 1.3031 nan 0.0010 0.0004
## 40 1.2864 nan 0.0010 0.0004
## 60 1.2698 nan 0.0010 0.0004
## 80 1.2540 nan 0.0010 0.0003
## 100 1.2388 nan 0.0010 0.0003
## 120 1.2234 nan 0.0010 0.0003
## 140 1.2089 nan 0.0010 0.0003
## 160 1.1951 nan 0.0010 0.0003
## 180 1.1814 nan 0.0010 0.0003
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## 340 1.0878 nan 0.0010 0.0002
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## 380 1.0674 nan 0.0010 0.0002
## 400 1.0579 nan 0.0010 0.0002
## 420 1.0484 nan 0.0010 0.0002
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## 480 1.0219 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3194 nan 0.0010 0.0004
## 3 1.3185 nan 0.0010 0.0004
## 4 1.3175 nan 0.0010 0.0004
## 5 1.3166 nan 0.0010 0.0004
## 6 1.3156 nan 0.0010 0.0004
## 7 1.3146 nan 0.0010 0.0004
## 8 1.3138 nan 0.0010 0.0004
## 9 1.3129 nan 0.0010 0.0004
## 10 1.3120 nan 0.0010 0.0004
## 20 1.3031 nan 0.0010 0.0004
## 40 1.2863 nan 0.0010 0.0004
## 60 1.2699 nan 0.0010 0.0004
## 80 1.2541 nan 0.0010 0.0003
## 100 1.2389 nan 0.0010 0.0003
## 120 1.2242 nan 0.0010 0.0003
## 140 1.2102 nan 0.0010 0.0003
## 160 1.1966 nan 0.0010 0.0002
## 180 1.1833 nan 0.0010 0.0003
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## 320 1.1008 nan 0.0010 0.0002
## 340 1.0903 nan 0.0010 0.0002
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## 380 1.0701 nan 0.0010 0.0002
## 400 1.0607 nan 0.0010 0.0002
## 420 1.0512 nan 0.0010 0.0002
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## 460 1.0333 nan 0.0010 0.0002
## 480 1.0245 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3193 nan 0.0010 0.0004
## 3 1.3183 nan 0.0010 0.0005
## 4 1.3174 nan 0.0010 0.0004
## 5 1.3165 nan 0.0010 0.0004
## 6 1.3155 nan 0.0010 0.0004
## 7 1.3146 nan 0.0010 0.0004
## 8 1.3136 nan 0.0010 0.0004
## 9 1.3127 nan 0.0010 0.0004
## 10 1.3117 nan 0.0010 0.0004
## 20 1.3026 nan 0.0010 0.0004
## 40 1.2844 nan 0.0010 0.0004
## 60 1.2668 nan 0.0010 0.0004
## 80 1.2501 nan 0.0010 0.0004
## 100 1.2340 nan 0.0010 0.0004
## 120 1.2184 nan 0.0010 0.0004
## 140 1.2034 nan 0.0010 0.0003
## 160 1.1889 nan 0.0010 0.0003
## 180 1.1745 nan 0.0010 0.0003
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## 280 1.1107 nan 0.0010 0.0003
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## 320 1.0876 nan 0.0010 0.0002
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## 380 1.0557 nan 0.0010 0.0002
## 400 1.0454 nan 0.0010 0.0002
## 420 1.0353 nan 0.0010 0.0002
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## 480 1.0071 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3202 nan 0.0010 0.0004
## 2 1.3193 nan 0.0010 0.0004
## 3 1.3184 nan 0.0010 0.0004
## 4 1.3174 nan 0.0010 0.0004
## 5 1.3165 nan 0.0010 0.0004
## 6 1.3155 nan 0.0010 0.0004
## 7 1.3146 nan 0.0010 0.0004
## 8 1.3136 nan 0.0010 0.0004
## 9 1.3126 nan 0.0010 0.0004
## 10 1.3116 nan 0.0010 0.0005
## 20 1.3023 nan 0.0010 0.0004
## 40 1.2843 nan 0.0010 0.0003
## 60 1.2675 nan 0.0010 0.0004
## 80 1.2506 nan 0.0010 0.0004
## 100 1.2345 nan 0.0010 0.0004
## 120 1.2193 nan 0.0010 0.0004
## 140 1.2044 nan 0.0010 0.0003
## 160 1.1898 nan 0.0010 0.0003
## 180 1.1759 nan 0.0010 0.0003
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## 220 1.1493 nan 0.0010 0.0003
## 240 1.1364 nan 0.0010 0.0003
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## 300 1.1007 nan 0.0010 0.0002
## 320 1.0892 nan 0.0010 0.0002
## 340 1.0782 nan 0.0010 0.0003
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## 380 1.0573 nan 0.0010 0.0002
## 400 1.0472 nan 0.0010 0.0002
## 420 1.0373 nan 0.0010 0.0002
## 440 1.0278 nan 0.0010 0.0002
## 460 1.0183 nan 0.0010 0.0002
## 480 1.0093 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3193 nan 0.0010 0.0005
## 3 1.3184 nan 0.0010 0.0004
## 4 1.3175 nan 0.0010 0.0004
## 5 1.3166 nan 0.0010 0.0004
## 6 1.3156 nan 0.0010 0.0004
## 7 1.3146 nan 0.0010 0.0004
## 8 1.3137 nan 0.0010 0.0004
## 9 1.3129 nan 0.0010 0.0004
## 10 1.3119 nan 0.0010 0.0004
## 20 1.3031 nan 0.0010 0.0004
## 40 1.2853 nan 0.0010 0.0004
## 60 1.2687 nan 0.0010 0.0003
## 80 1.2524 nan 0.0010 0.0004
## 100 1.2367 nan 0.0010 0.0004
## 120 1.2211 nan 0.0010 0.0004
## 140 1.2066 nan 0.0010 0.0003
## 160 1.1922 nan 0.0010 0.0003
## 180 1.1785 nan 0.0010 0.0003
## 200 1.1652 nan 0.0010 0.0003
## 220 1.1518 nan 0.0010 0.0003
## 240 1.1392 nan 0.0010 0.0003
## 260 1.1269 nan 0.0010 0.0003
## 280 1.1150 nan 0.0010 0.0003
## 300 1.1036 nan 0.0010 0.0003
## 320 1.0925 nan 0.0010 0.0003
## 340 1.0817 nan 0.0010 0.0003
## 360 1.0714 nan 0.0010 0.0002
## 380 1.0613 nan 0.0010 0.0002
## 400 1.0515 nan 0.0010 0.0002
## 420 1.0417 nan 0.0010 0.0002
## 440 1.0322 nan 0.0010 0.0002
## 460 1.0230 nan 0.0010 0.0002
## 480 1.0137 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3125 nan 0.0100 0.0040
## 2 1.3055 nan 0.0100 0.0027
## 3 1.2973 nan 0.0100 0.0039
## 4 1.2883 nan 0.0100 0.0041
## 5 1.2806 nan 0.0100 0.0035
## 6 1.2727 nan 0.0100 0.0039
## 7 1.2650 nan 0.0100 0.0032
## 8 1.2569 nan 0.0100 0.0036
## 9 1.2498 nan 0.0100 0.0035
## 10 1.2430 nan 0.0100 0.0032
## 20 1.1746 nan 0.0100 0.0028
## 40 1.0688 nan 0.0100 0.0020
## 60 0.9916 nan 0.0100 0.0014
## 80 0.9304 nan 0.0100 0.0012
## 100 0.8786 nan 0.0100 0.0008
## 120 0.8343 nan 0.0100 0.0007
## 140 0.8001 nan 0.0100 0.0006
## 160 0.7717 nan 0.0100 0.0003
## 180 0.7463 nan 0.0100 0.0003
## 200 0.7258 nan 0.0100 0.0002
## 220 0.7066 nan 0.0100 0.0000
## 240 0.6898 nan 0.0100 0.0001
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## 320 0.6298 nan 0.0100 0.0001
## 340 0.6176 nan 0.0100 -0.0001
## 360 0.6067 nan 0.0100 0.0001
## 380 0.5954 nan 0.0100 -0.0001
## 400 0.5850 nan 0.0100 -0.0001
## 420 0.5757 nan 0.0100 -0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3124 nan 0.0100 0.0037
## 2 1.3039 nan 0.0100 0.0040
## 3 1.2951 nan 0.0100 0.0036
## 4 1.2872 nan 0.0100 0.0033
## 5 1.2798 nan 0.0100 0.0032
## 6 1.2727 nan 0.0100 0.0034
## 7 1.2658 nan 0.0100 0.0030
## 8 1.2581 nan 0.0100 0.0034
## 9 1.2507 nan 0.0100 0.0034
## 10 1.2445 nan 0.0100 0.0030
## 20 1.1785 nan 0.0100 0.0026
## 40 1.0739 nan 0.0100 0.0022
## 60 0.9939 nan 0.0100 0.0017
## 80 0.9290 nan 0.0100 0.0013
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## 140 0.8016 nan 0.0100 0.0004
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## 220 0.7072 nan 0.0100 0.0001
## 240 0.6901 nan 0.0100 0.0001
## 260 0.6753 nan 0.0100 -0.0000
## 280 0.6611 nan 0.0100 0.0001
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## 320 0.6360 nan 0.0100 0.0000
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## 380 0.6021 nan 0.0100 -0.0000
## 400 0.5922 nan 0.0100 -0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3133 nan 0.0100 0.0037
## 2 1.3048 nan 0.0100 0.0039
## 3 1.2962 nan 0.0100 0.0040
## 4 1.2882 nan 0.0100 0.0038
## 5 1.2810 nan 0.0100 0.0035
## 6 1.2741 nan 0.0100 0.0036
## 7 1.2667 nan 0.0100 0.0033
## 8 1.2587 nan 0.0100 0.0035
## 9 1.2509 nan 0.0100 0.0034
## 10 1.2432 nan 0.0100 0.0036
## 20 1.1772 nan 0.0100 0.0026
## 40 1.0734 nan 0.0100 0.0015
## 60 0.9919 nan 0.0100 0.0014
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## 140 0.8035 nan 0.0100 0.0004
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## 180 0.7506 nan 0.0100 0.0002
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## 340 0.6315 nan 0.0100 -0.0001
## 360 0.6212 nan 0.0100 0.0001
## 380 0.6114 nan 0.0100 -0.0000
## 400 0.6014 nan 0.0100 -0.0002
## 420 0.5917 nan 0.0100 -0.0001
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## 460 0.5736 nan 0.0100 -0.0001
## 480 0.5651 nan 0.0100 -0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3123 nan 0.0100 0.0042
## 2 1.3033 nan 0.0100 0.0042
## 3 1.2947 nan 0.0100 0.0039
## 4 1.2867 nan 0.0100 0.0033
## 5 1.2788 nan 0.0100 0.0035
## 6 1.2707 nan 0.0100 0.0037
## 7 1.2621 nan 0.0100 0.0034
## 8 1.2547 nan 0.0100 0.0032
## 9 1.2462 nan 0.0100 0.0036
## 10 1.2391 nan 0.0100 0.0031
## 20 1.1673 nan 0.0100 0.0027
## 40 1.0559 nan 0.0100 0.0021
## 60 0.9716 nan 0.0100 0.0014
## 80 0.9047 nan 0.0100 0.0012
## 100 0.8498 nan 0.0100 0.0009
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## 180 0.7110 nan 0.0100 0.0004
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## 320 0.5871 nan 0.0100 0.0001
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## 380 0.5496 nan 0.0100 -0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3123 nan 0.0100 0.0039
## 2 1.3043 nan 0.0100 0.0037
## 3 1.2954 nan 0.0100 0.0040
## 4 1.2874 nan 0.0100 0.0039
## 5 1.2795 nan 0.0100 0.0038
## 6 1.2707 nan 0.0100 0.0037
## 7 1.2630 nan 0.0100 0.0038
## 8 1.2549 nan 0.0100 0.0035
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## 10 1.2392 nan 0.0100 0.0039
## 20 1.1699 nan 0.0100 0.0030
## 40 1.0579 nan 0.0100 0.0019
## 60 0.9747 nan 0.0100 0.0014
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3123 nan 0.0100 0.0040
## 2 1.3029 nan 0.0100 0.0041
## 3 1.2945 nan 0.0100 0.0038
## 4 1.2856 nan 0.0100 0.0042
## 5 1.2775 nan 0.0100 0.0034
## 6 1.2694 nan 0.0100 0.0039
## 7 1.2613 nan 0.0100 0.0037
## 8 1.2533 nan 0.0100 0.0038
## 9 1.2454 nan 0.0100 0.0033
## 10 1.2374 nan 0.0100 0.0034
## 20 1.1705 nan 0.0100 0.0028
## 40 1.0593 nan 0.0100 0.0020
## 60 0.9740 nan 0.0100 0.0015
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3113 nan 0.0100 0.0047
## 2 1.3020 nan 0.0100 0.0043
## 3 1.2930 nan 0.0100 0.0041
## 4 1.2840 nan 0.0100 0.0038
## 5 1.2746 nan 0.0100 0.0042
## 6 1.2656 nan 0.0100 0.0040
## 7 1.2577 nan 0.0100 0.0034
## 8 1.2487 nan 0.0100 0.0042
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## 20 1.1578 nan 0.0100 0.0026
## 40 1.0424 nan 0.0100 0.0021
## 60 0.9541 nan 0.0100 0.0014
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3118 nan 0.0100 0.0038
## 2 1.3028 nan 0.0100 0.0037
## 3 1.2939 nan 0.0100 0.0040
## 4 1.2853 nan 0.0100 0.0040
## 5 1.2764 nan 0.0100 0.0037
## 6 1.2675 nan 0.0100 0.0040
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## 8 1.2492 nan 0.0100 0.0037
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## 40 1.0467 nan 0.0100 0.0019
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3113 nan 0.0100 0.0045
## 2 1.3021 nan 0.0100 0.0036
## 3 1.2931 nan 0.0100 0.0043
## 4 1.2847 nan 0.0100 0.0038
## 5 1.2766 nan 0.0100 0.0040
## 6 1.2684 nan 0.0100 0.0037
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## 40 1.0508 nan 0.0100 0.0021
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## 400 0.5225 nan 0.0100 -0.0000
## 420 0.5114 nan 0.0100 -0.0000
## 440 0.5006 nan 0.0100 0.0000
## 460 0.4899 nan 0.0100 -0.0001
## 480 0.4800 nan 0.0100 -0.0001
## 500 0.4708 nan 0.0100 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2391 nan 0.1000 0.0378
## 2 1.1661 nan 0.1000 0.0317
## 3 1.1201 nan 0.1000 0.0183
## 4 1.0661 nan 0.1000 0.0239
## 5 1.0215 nan 0.1000 0.0195
## 6 0.9836 nan 0.1000 0.0119
## 7 0.9523 nan 0.1000 0.0145
## 8 0.9215 nan 0.1000 0.0097
## 9 0.8941 nan 0.1000 0.0103
## 10 0.8702 nan 0.1000 0.0093
## 20 0.7247 nan 0.1000 0.0021
## 40 0.5967 nan 0.1000 -0.0002
## 60 0.5048 nan 0.1000 0.0001
## 80 0.4321 nan 0.1000 0.0008
## 100 0.3812 nan 0.1000 -0.0002
## 120 0.3426 nan 0.1000 -0.0006
## 140 0.3008 nan 0.1000 -0.0001
## 160 0.2688 nan 0.1000 -0.0000
## 180 0.2423 nan 0.1000 -0.0008
## 200 0.2190 nan 0.1000 -0.0004
## 220 0.1969 nan 0.1000 -0.0007
## 240 0.1780 nan 0.1000 -0.0001
## 260 0.1600 nan 0.1000 -0.0005
## 280 0.1453 nan 0.1000 -0.0003
## 300 0.1315 nan 0.1000 -0.0002
## 320 0.1201 nan 0.1000 -0.0002
## 340 0.1099 nan 0.1000 -0.0002
## 360 0.1015 nan 0.1000 -0.0002
## 380 0.0941 nan 0.1000 -0.0003
## 400 0.0860 nan 0.1000 0.0001
## 420 0.0790 nan 0.1000 -0.0001
## 440 0.0731 nan 0.1000 -0.0002
## 460 0.0669 nan 0.1000 -0.0002
## 480 0.0620 nan 0.1000 -0.0001
## 500 0.0571 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2277 nan 0.1000 0.0409
## 2 1.1687 nan 0.1000 0.0253
## 3 1.1082 nan 0.1000 0.0242
## 4 1.0621 nan 0.1000 0.0203
## 5 1.0195 nan 0.1000 0.0167
## 6 0.9813 nan 0.1000 0.0146
## 7 0.9514 nan 0.1000 0.0130
## 8 0.9208 nan 0.1000 0.0114
## 9 0.8956 nan 0.1000 0.0100
## 10 0.8689 nan 0.1000 0.0090
## 20 0.7322 nan 0.1000 0.0018
## 40 0.5988 nan 0.1000 -0.0007
## 60 0.5237 nan 0.1000 -0.0004
## 80 0.4528 nan 0.1000 -0.0003
## 100 0.3995 nan 0.1000 -0.0004
## 120 0.3522 nan 0.1000 -0.0000
## 140 0.3144 nan 0.1000 -0.0001
## 160 0.2863 nan 0.1000 -0.0004
## 180 0.2578 nan 0.1000 -0.0007
## 200 0.2318 nan 0.1000 -0.0007
## 220 0.2083 nan 0.1000 -0.0004
## 240 0.1898 nan 0.1000 -0.0009
## 260 0.1724 nan 0.1000 -0.0005
## 280 0.1566 nan 0.1000 -0.0001
## 300 0.1435 nan 0.1000 -0.0003
## 320 0.1316 nan 0.1000 0.0001
## 340 0.1199 nan 0.1000 -0.0003
## 360 0.1104 nan 0.1000 -0.0005
## 380 0.1009 nan 0.1000 -0.0001
## 400 0.0920 nan 0.1000 -0.0003
## 420 0.0848 nan 0.1000 -0.0003
## 440 0.0782 nan 0.1000 -0.0004
## 460 0.0719 nan 0.1000 -0.0004
## 480 0.0660 nan 0.1000 -0.0002
## 500 0.0610 nan 0.1000 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2387 nan 0.1000 0.0404
## 2 1.1760 nan 0.1000 0.0281
## 3 1.1221 nan 0.1000 0.0243
## 4 1.0716 nan 0.1000 0.0198
## 5 1.0306 nan 0.1000 0.0188
## 6 0.9997 nan 0.1000 0.0122
## 7 0.9636 nan 0.1000 0.0139
## 8 0.9346 nan 0.1000 0.0127
## 9 0.9069 nan 0.1000 0.0099
## 10 0.8815 nan 0.1000 0.0064
## 20 0.7386 nan 0.1000 0.0021
## 40 0.6165 nan 0.1000 -0.0008
## 60 0.5297 nan 0.1000 0.0004
## 80 0.4759 nan 0.1000 -0.0003
## 100 0.4244 nan 0.1000 -0.0021
## 120 0.3734 nan 0.1000 -0.0014
## 140 0.3382 nan 0.1000 -0.0009
## 160 0.3072 nan 0.1000 -0.0016
## 180 0.2755 nan 0.1000 -0.0003
## 200 0.2510 nan 0.1000 -0.0007
## 220 0.2306 nan 0.1000 -0.0007
## 240 0.2104 nan 0.1000 -0.0004
## 260 0.1930 nan 0.1000 -0.0004
## 280 0.1767 nan 0.1000 -0.0000
## 300 0.1614 nan 0.1000 -0.0000
## 320 0.1483 nan 0.1000 -0.0010
## 340 0.1364 nan 0.1000 -0.0004
## 360 0.1272 nan 0.1000 -0.0007
## 380 0.1163 nan 0.1000 -0.0002
## 400 0.1070 nan 0.1000 -0.0007
## 420 0.0982 nan 0.1000 -0.0004
## 440 0.0909 nan 0.1000 -0.0002
## 460 0.0837 nan 0.1000 -0.0004
## 480 0.0782 nan 0.1000 -0.0004
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2273 nan 0.1000 0.0399
## 2 1.1568 nan 0.1000 0.0319
## 3 1.1035 nan 0.1000 0.0249
## 4 1.0555 nan 0.1000 0.0217
## 5 1.0104 nan 0.1000 0.0196
## 6 0.9699 nan 0.1000 0.0157
## 7 0.9379 nan 0.1000 0.0131
## 8 0.9070 nan 0.1000 0.0127
## 9 0.8792 nan 0.1000 0.0106
## 10 0.8565 nan 0.1000 0.0102
## 20 0.6927 nan 0.1000 0.0023
## 40 0.5393 nan 0.1000 0.0003
## 60 0.4510 nan 0.1000 -0.0002
## 80 0.3787 nan 0.1000 0.0016
## 100 0.3228 nan 0.1000 -0.0005
## 120 0.2758 nan 0.1000 0.0000
## 140 0.2371 nan 0.1000 -0.0001
## 160 0.2103 nan 0.1000 -0.0001
## 180 0.1827 nan 0.1000 -0.0002
## 200 0.1595 nan 0.1000 -0.0003
## 220 0.1415 nan 0.1000 0.0000
## 240 0.1255 nan 0.1000 -0.0001
## 260 0.1109 nan 0.1000 -0.0003
## 280 0.0994 nan 0.1000 -0.0003
## 300 0.0880 nan 0.1000 -0.0003
## 320 0.0799 nan 0.1000 -0.0003
## 340 0.0719 nan 0.1000 -0.0003
## 360 0.0646 nan 0.1000 -0.0002
## 380 0.0581 nan 0.1000 -0.0002
## 400 0.0524 nan 0.1000 -0.0000
## 420 0.0472 nan 0.1000 -0.0001
## 440 0.0430 nan 0.1000 -0.0001
## 460 0.0387 nan 0.1000 -0.0001
## 480 0.0352 nan 0.1000 -0.0001
## 500 0.0316 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2338 nan 0.1000 0.0360
## 2 1.1604 nan 0.1000 0.0302
## 3 1.0991 nan 0.1000 0.0280
## 4 1.0456 nan 0.1000 0.0224
## 5 0.9960 nan 0.1000 0.0205
## 6 0.9615 nan 0.1000 0.0143
## 7 0.9245 nan 0.1000 0.0142
## 8 0.8935 nan 0.1000 0.0128
## 9 0.8660 nan 0.1000 0.0085
## 10 0.8449 nan 0.1000 0.0068
## 20 0.7004 nan 0.1000 0.0026
## 40 0.5568 nan 0.1000 0.0005
## 60 0.4691 nan 0.1000 0.0010
## 80 0.4006 nan 0.1000 -0.0008
## 100 0.3406 nan 0.1000 -0.0004
## 120 0.2947 nan 0.1000 -0.0003
## 140 0.2549 nan 0.1000 -0.0002
## 160 0.2220 nan 0.1000 -0.0004
## 180 0.1957 nan 0.1000 -0.0001
## 200 0.1729 nan 0.1000 -0.0006
## 220 0.1544 nan 0.1000 -0.0006
## 240 0.1371 nan 0.1000 -0.0007
## 260 0.1219 nan 0.1000 -0.0004
## 280 0.1090 nan 0.1000 -0.0007
## 300 0.0970 nan 0.1000 -0.0001
## 320 0.0852 nan 0.1000 -0.0002
## 340 0.0774 nan 0.1000 -0.0003
## 360 0.0702 nan 0.1000 -0.0002
## 380 0.0629 nan 0.1000 -0.0001
## 400 0.0572 nan 0.1000 -0.0002
## 420 0.0519 nan 0.1000 -0.0001
## 440 0.0465 nan 0.1000 -0.0001
## 460 0.0421 nan 0.1000 -0.0000
## 480 0.0383 nan 0.1000 -0.0001
## 500 0.0348 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2379 nan 0.1000 0.0365
## 2 1.1654 nan 0.1000 0.0354
## 3 1.1073 nan 0.1000 0.0235
## 4 1.0559 nan 0.1000 0.0171
## 5 1.0154 nan 0.1000 0.0177
## 6 0.9760 nan 0.1000 0.0169
## 7 0.9437 nan 0.1000 0.0149
## 8 0.9122 nan 0.1000 0.0110
## 9 0.8843 nan 0.1000 0.0113
## 10 0.8601 nan 0.1000 0.0082
## 20 0.7031 nan 0.1000 0.0018
## 40 0.5596 nan 0.1000 -0.0016
## 60 0.4727 nan 0.1000 -0.0000
## 80 0.4030 nan 0.1000 -0.0017
## 100 0.3501 nan 0.1000 -0.0014
## 120 0.3006 nan 0.1000 -0.0006
## 140 0.2666 nan 0.1000 -0.0004
## 160 0.2376 nan 0.1000 -0.0004
## 180 0.2120 nan 0.1000 -0.0005
## 200 0.1881 nan 0.1000 -0.0005
## 220 0.1686 nan 0.1000 -0.0004
## 240 0.1503 nan 0.1000 -0.0007
## 260 0.1350 nan 0.1000 -0.0004
## 280 0.1209 nan 0.1000 -0.0004
## 300 0.1085 nan 0.1000 -0.0004
## 320 0.0973 nan 0.1000 -0.0005
## 340 0.0888 nan 0.1000 -0.0003
## 360 0.0798 nan 0.1000 -0.0001
## 380 0.0714 nan 0.1000 -0.0000
## 400 0.0643 nan 0.1000 -0.0002
## 420 0.0586 nan 0.1000 -0.0002
## 440 0.0532 nan 0.1000 -0.0001
## 460 0.0479 nan 0.1000 -0.0001
## 480 0.0438 nan 0.1000 -0.0002
## 500 0.0394 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2405 nan 0.1000 0.0361
## 2 1.1596 nan 0.1000 0.0366
## 3 1.0962 nan 0.1000 0.0267
## 4 1.0431 nan 0.1000 0.0233
## 5 0.9928 nan 0.1000 0.0204
## 6 0.9538 nan 0.1000 0.0149
## 7 0.9199 nan 0.1000 0.0114
## 8 0.8890 nan 0.1000 0.0126
## 9 0.8552 nan 0.1000 0.0112
## 10 0.8280 nan 0.1000 0.0117
## 20 0.6651 nan 0.1000 -0.0008
## 40 0.4964 nan 0.1000 -0.0004
## 60 0.3997 nan 0.1000 -0.0006
## 80 0.3235 nan 0.1000 -0.0005
## 100 0.2724 nan 0.1000 -0.0004
## 120 0.2296 nan 0.1000 -0.0004
## 140 0.1952 nan 0.1000 -0.0007
## 160 0.1668 nan 0.1000 -0.0002
## 180 0.1417 nan 0.1000 -0.0000
## 200 0.1238 nan 0.1000 -0.0005
## 220 0.1054 nan 0.1000 -0.0000
## 240 0.0917 nan 0.1000 -0.0001
## 260 0.0795 nan 0.1000 0.0000
## 280 0.0688 nan 0.1000 -0.0002
## 300 0.0610 nan 0.1000 -0.0002
## 320 0.0537 nan 0.1000 -0.0001
## 340 0.0477 nan 0.1000 -0.0002
## 360 0.0421 nan 0.1000 -0.0002
## 380 0.0375 nan 0.1000 -0.0001
## 400 0.0333 nan 0.1000 -0.0000
## 420 0.0298 nan 0.1000 -0.0000
## 440 0.0264 nan 0.1000 -0.0000
## 460 0.0234 nan 0.1000 -0.0000
## 480 0.0207 nan 0.1000 -0.0001
## 500 0.0182 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2285 nan 0.1000 0.0436
## 2 1.1459 nan 0.1000 0.0354
## 3 1.0861 nan 0.1000 0.0239
## 4 1.0300 nan 0.1000 0.0258
## 5 0.9835 nan 0.1000 0.0208
## 6 0.9450 nan 0.1000 0.0146
## 7 0.9073 nan 0.1000 0.0138
## 8 0.8768 nan 0.1000 0.0110
## 9 0.8499 nan 0.1000 0.0078
## 10 0.8264 nan 0.1000 0.0063
## 20 0.6655 nan 0.1000 0.0021
## 40 0.5048 nan 0.1000 -0.0012
## 60 0.4093 nan 0.1000 -0.0017
## 80 0.3326 nan 0.1000 -0.0000
## 100 0.2759 nan 0.1000 0.0002
## 120 0.2327 nan 0.1000 -0.0006
## 140 0.1999 nan 0.1000 -0.0004
## 160 0.1711 nan 0.1000 -0.0003
## 180 0.1483 nan 0.1000 -0.0006
## 200 0.1263 nan 0.1000 -0.0004
## 220 0.1085 nan 0.1000 -0.0003
## 240 0.0948 nan 0.1000 -0.0002
## 260 0.0832 nan 0.1000 -0.0005
## 280 0.0727 nan 0.1000 -0.0001
## 300 0.0632 nan 0.1000 -0.0003
## 320 0.0554 nan 0.1000 -0.0001
## 340 0.0481 nan 0.1000 -0.0000
## 360 0.0424 nan 0.1000 -0.0001
## 380 0.0367 nan 0.1000 -0.0001
## 400 0.0324 nan 0.1000 -0.0001
## 420 0.0287 nan 0.1000 -0.0001
## 440 0.0250 nan 0.1000 -0.0001
## 460 0.0222 nan 0.1000 -0.0001
## 480 0.0197 nan 0.1000 -0.0001
## 500 0.0174 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2392 nan 0.1000 0.0370
## 2 1.1641 nan 0.1000 0.0323
## 3 1.1025 nan 0.1000 0.0240
## 4 1.0496 nan 0.1000 0.0207
## 5 0.9999 nan 0.1000 0.0211
## 6 0.9648 nan 0.1000 0.0155
## 7 0.9265 nan 0.1000 0.0127
## 8 0.8973 nan 0.1000 0.0097
## 9 0.8681 nan 0.1000 0.0112
## 10 0.8423 nan 0.1000 0.0107
## 20 0.6891 nan 0.1000 0.0007
## 40 0.5401 nan 0.1000 -0.0004
## 60 0.4396 nan 0.1000 -0.0016
## 80 0.3600 nan 0.1000 -0.0001
## 100 0.3052 nan 0.1000 -0.0002
## 120 0.2567 nan 0.1000 -0.0012
## 140 0.2217 nan 0.1000 -0.0007
## 160 0.1913 nan 0.1000 -0.0007
## 180 0.1674 nan 0.1000 -0.0007
## 200 0.1439 nan 0.1000 -0.0001
## 220 0.1263 nan 0.1000 -0.0010
## 240 0.1102 nan 0.1000 -0.0001
## 260 0.0970 nan 0.1000 -0.0002
## 280 0.0863 nan 0.1000 -0.0003
## 300 0.0757 nan 0.1000 -0.0004
## 320 0.0669 nan 0.1000 -0.0004
## 340 0.0586 nan 0.1000 -0.0002
## 360 0.0517 nan 0.1000 -0.0002
## 380 0.0457 nan 0.1000 -0.0001
## 400 0.0404 nan 0.1000 -0.0002
## 420 0.0357 nan 0.1000 -0.0001
## 440 0.0319 nan 0.1000 -0.0001
## 460 0.0281 nan 0.1000 -0.0001
## 480 0.0252 nan 0.1000 -0.0001
## 500 0.0220 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3199 nan 0.0010 0.0003
## 2 1.3191 nan 0.0010 0.0003
## 3 1.3183 nan 0.0010 0.0004
## 4 1.3175 nan 0.0010 0.0004
## 5 1.3167 nan 0.0010 0.0003
## 6 1.3158 nan 0.0010 0.0004
## 7 1.3149 nan 0.0010 0.0004
## 8 1.3142 nan 0.0010 0.0003
## 9 1.3134 nan 0.0010 0.0004
## 10 1.3126 nan 0.0010 0.0004
## 20 1.3048 nan 0.0010 0.0004
## 40 1.2894 nan 0.0010 0.0003
## 60 1.2747 nan 0.0010 0.0003
## 80 1.2607 nan 0.0010 0.0003
## 100 1.2471 nan 0.0010 0.0003
## 120 1.2337 nan 0.0010 0.0003
## 140 1.2205 nan 0.0010 0.0003
## 160 1.2074 nan 0.0010 0.0003
## 180 1.1951 nan 0.0010 0.0003
## 200 1.1831 nan 0.0010 0.0003
## 220 1.1715 nan 0.0010 0.0003
## 240 1.1604 nan 0.0010 0.0003
## 260 1.1494 nan 0.0010 0.0002
## 280 1.1388 nan 0.0010 0.0003
## 300 1.1285 nan 0.0010 0.0002
## 320 1.1181 nan 0.0010 0.0002
## 340 1.1083 nan 0.0010 0.0002
## 360 1.0985 nan 0.0010 0.0002
## 380 1.0890 nan 0.0010 0.0002
## 400 1.0798 nan 0.0010 0.0002
## 420 1.0711 nan 0.0010 0.0002
## 440 1.0622 nan 0.0010 0.0002
## 460 1.0538 nan 0.0010 0.0002
## 480 1.0455 nan 0.0010 0.0002
## 500 1.0373 nan 0.0010 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3191 nan 0.0010 0.0003
## 3 1.3182 nan 0.0010 0.0004
## 4 1.3174 nan 0.0010 0.0003
## 5 1.3166 nan 0.0010 0.0004
## 6 1.3158 nan 0.0010 0.0003
## 7 1.3150 nan 0.0010 0.0003
## 8 1.3143 nan 0.0010 0.0004
## 9 1.3136 nan 0.0010 0.0003
## 10 1.3127 nan 0.0010 0.0004
## 20 1.3049 nan 0.0010 0.0004
## 40 1.2898 nan 0.0010 0.0003
## 60 1.2747 nan 0.0010 0.0003
## 80 1.2603 nan 0.0010 0.0003
## 100 1.2461 nan 0.0010 0.0003
## 120 1.2326 nan 0.0010 0.0003
## 140 1.2198 nan 0.0010 0.0003
## 160 1.2070 nan 0.0010 0.0003
## 180 1.1946 nan 0.0010 0.0003
## 200 1.1825 nan 0.0010 0.0003
## 220 1.1708 nan 0.0010 0.0003
## 240 1.1595 nan 0.0010 0.0002
## 260 1.1486 nan 0.0010 0.0002
## 280 1.1380 nan 0.0010 0.0002
## 300 1.1275 nan 0.0010 0.0002
## 320 1.1175 nan 0.0010 0.0002
## 340 1.1077 nan 0.0010 0.0002
## 360 1.0981 nan 0.0010 0.0002
## 380 1.0888 nan 0.0010 0.0002
## 400 1.0797 nan 0.0010 0.0002
## 420 1.0709 nan 0.0010 0.0002
## 440 1.0622 nan 0.0010 0.0002
## 460 1.0538 nan 0.0010 0.0002
## 480 1.0458 nan 0.0010 0.0001
## 500 1.0379 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3199 nan 0.0010 0.0004
## 2 1.3191 nan 0.0010 0.0003
## 3 1.3183 nan 0.0010 0.0004
## 4 1.3174 nan 0.0010 0.0004
## 5 1.3166 nan 0.0010 0.0003
## 6 1.3158 nan 0.0010 0.0004
## 7 1.3151 nan 0.0010 0.0003
## 8 1.3143 nan 0.0010 0.0004
## 9 1.3135 nan 0.0010 0.0004
## 10 1.3126 nan 0.0010 0.0004
## 20 1.3049 nan 0.0010 0.0003
## 40 1.2899 nan 0.0010 0.0003
## 60 1.2750 nan 0.0010 0.0003
## 80 1.2606 nan 0.0010 0.0003
## 100 1.2469 nan 0.0010 0.0003
## 120 1.2333 nan 0.0010 0.0003
## 140 1.2205 nan 0.0010 0.0003
## 160 1.2080 nan 0.0010 0.0003
## 180 1.1958 nan 0.0010 0.0003
## 200 1.1841 nan 0.0010 0.0002
## 220 1.1726 nan 0.0010 0.0003
## 240 1.1615 nan 0.0010 0.0002
## 260 1.1504 nan 0.0010 0.0002
## 280 1.1397 nan 0.0010 0.0002
## 300 1.1296 nan 0.0010 0.0002
## 320 1.1198 nan 0.0010 0.0002
## 340 1.1100 nan 0.0010 0.0002
## 360 1.1006 nan 0.0010 0.0002
## 380 1.0911 nan 0.0010 0.0002
## 400 1.0821 nan 0.0010 0.0002
## 420 1.0734 nan 0.0010 0.0002
## 440 1.0650 nan 0.0010 0.0002
## 460 1.0566 nan 0.0010 0.0002
## 480 1.0482 nan 0.0010 0.0002
## 500 1.0404 nan 0.0010 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3180 nan 0.0010 0.0004
## 4 1.3171 nan 0.0010 0.0004
## 5 1.3163 nan 0.0010 0.0004
## 6 1.3154 nan 0.0010 0.0004
## 7 1.3146 nan 0.0010 0.0004
## 8 1.3137 nan 0.0010 0.0004
## 9 1.3129 nan 0.0010 0.0004
## 10 1.3120 nan 0.0010 0.0004
## 20 1.3034 nan 0.0010 0.0004
## 40 1.2870 nan 0.0010 0.0004
## 60 1.2711 nan 0.0010 0.0004
## 80 1.2560 nan 0.0010 0.0004
## 100 1.2412 nan 0.0010 0.0003
## 120 1.2268 nan 0.0010 0.0003
## 140 1.2128 nan 0.0010 0.0003
## 160 1.1992 nan 0.0010 0.0003
## 180 1.1862 nan 0.0010 0.0003
## 200 1.1735 nan 0.0010 0.0003
## 220 1.1612 nan 0.0010 0.0003
## 240 1.1492 nan 0.0010 0.0003
## 260 1.1374 nan 0.0010 0.0002
## 280 1.1261 nan 0.0010 0.0002
## 300 1.1148 nan 0.0010 0.0002
## 320 1.1041 nan 0.0010 0.0002
## 340 1.0936 nan 0.0010 0.0002
## 360 1.0836 nan 0.0010 0.0002
## 380 1.0738 nan 0.0010 0.0002
## 400 1.0641 nan 0.0010 0.0002
## 420 1.0547 nan 0.0010 0.0002
## 440 1.0455 nan 0.0010 0.0002
## 460 1.0366 nan 0.0010 0.0002
## 480 1.0282 nan 0.0010 0.0002
## 500 1.0199 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3180 nan 0.0010 0.0003
## 4 1.3171 nan 0.0010 0.0004
## 5 1.3162 nan 0.0010 0.0003
## 6 1.3154 nan 0.0010 0.0004
## 7 1.3145 nan 0.0010 0.0004
## 8 1.3136 nan 0.0010 0.0004
## 9 1.3128 nan 0.0010 0.0004
## 10 1.3119 nan 0.0010 0.0004
## 20 1.3036 nan 0.0010 0.0004
## 40 1.2872 nan 0.0010 0.0004
## 60 1.2715 nan 0.0010 0.0004
## 80 1.2561 nan 0.0010 0.0003
## 100 1.2410 nan 0.0010 0.0003
## 120 1.2268 nan 0.0010 0.0003
## 140 1.2130 nan 0.0010 0.0003
## 160 1.1997 nan 0.0010 0.0003
## 180 1.1869 nan 0.0010 0.0003
## 200 1.1741 nan 0.0010 0.0002
## 220 1.1619 nan 0.0010 0.0003
## 240 1.1499 nan 0.0010 0.0003
## 260 1.1382 nan 0.0010 0.0002
## 280 1.1269 nan 0.0010 0.0003
## 300 1.1159 nan 0.0010 0.0002
## 320 1.1053 nan 0.0010 0.0002
## 340 1.0950 nan 0.0010 0.0002
## 360 1.0848 nan 0.0010 0.0002
## 380 1.0748 nan 0.0010 0.0002
## 400 1.0653 nan 0.0010 0.0002
## 420 1.0561 nan 0.0010 0.0002
## 440 1.0467 nan 0.0010 0.0002
## 460 1.0379 nan 0.0010 0.0002
## 480 1.0294 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3190 nan 0.0010 0.0004
## 3 1.3182 nan 0.0010 0.0004
## 4 1.3174 nan 0.0010 0.0003
## 5 1.3164 nan 0.0010 0.0004
## 6 1.3156 nan 0.0010 0.0003
## 7 1.3148 nan 0.0010 0.0004
## 8 1.3139 nan 0.0010 0.0003
## 9 1.3132 nan 0.0010 0.0004
## 10 1.3124 nan 0.0010 0.0004
## 20 1.3042 nan 0.0010 0.0004
## 40 1.2878 nan 0.0010 0.0003
## 60 1.2724 nan 0.0010 0.0003
## 80 1.2573 nan 0.0010 0.0003
## 100 1.2425 nan 0.0010 0.0003
## 120 1.2282 nan 0.0010 0.0003
## 140 1.2145 nan 0.0010 0.0003
## 160 1.2012 nan 0.0010 0.0003
## 180 1.1881 nan 0.0010 0.0003
## 200 1.1753 nan 0.0010 0.0003
## 220 1.1627 nan 0.0010 0.0003
## 240 1.1505 nan 0.0010 0.0003
## 260 1.1392 nan 0.0010 0.0002
## 280 1.1277 nan 0.0010 0.0002
## 300 1.1168 nan 0.0010 0.0003
## 320 1.1061 nan 0.0010 0.0002
## 340 1.0960 nan 0.0010 0.0002
## 360 1.0861 nan 0.0010 0.0002
## 380 1.0764 nan 0.0010 0.0002
## 400 1.0670 nan 0.0010 0.0002
## 420 1.0576 nan 0.0010 0.0002
## 440 1.0485 nan 0.0010 0.0002
## 460 1.0396 nan 0.0010 0.0002
## 480 1.0313 nan 0.0010 0.0002
## 500 1.0229 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3187 nan 0.0010 0.0004
## 3 1.3178 nan 0.0010 0.0004
## 4 1.3168 nan 0.0010 0.0004
## 5 1.3159 nan 0.0010 0.0004
## 6 1.3150 nan 0.0010 0.0004
## 7 1.3140 nan 0.0010 0.0004
## 8 1.3132 nan 0.0010 0.0004
## 9 1.3122 nan 0.0010 0.0004
## 10 1.3114 nan 0.0010 0.0004
## 20 1.3025 nan 0.0010 0.0004
## 40 1.2850 nan 0.0010 0.0003
## 60 1.2683 nan 0.0010 0.0004
## 80 1.2520 nan 0.0010 0.0003
## 100 1.2362 nan 0.0010 0.0003
## 120 1.2211 nan 0.0010 0.0003
## 140 1.2066 nan 0.0010 0.0003
## 160 1.1926 nan 0.0010 0.0003
## 180 1.1788 nan 0.0010 0.0003
## 200 1.1653 nan 0.0010 0.0003
## 220 1.1522 nan 0.0010 0.0003
## 240 1.1399 nan 0.0010 0.0003
## 260 1.1278 nan 0.0010 0.0003
## 280 1.1157 nan 0.0010 0.0003
## 300 1.1042 nan 0.0010 0.0002
## 320 1.0929 nan 0.0010 0.0002
## 340 1.0818 nan 0.0010 0.0002
## 360 1.0711 nan 0.0010 0.0002
## 380 1.0606 nan 0.0010 0.0002
## 400 1.0506 nan 0.0010 0.0002
## 420 1.0407 nan 0.0010 0.0002
## 440 1.0313 nan 0.0010 0.0002
## 460 1.0222 nan 0.0010 0.0002
## 480 1.0133 nan 0.0010 0.0002
## 500 1.0045 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3180 nan 0.0010 0.0004
## 4 1.3171 nan 0.0010 0.0004
## 5 1.3162 nan 0.0010 0.0004
## 6 1.3153 nan 0.0010 0.0004
## 7 1.3144 nan 0.0010 0.0004
## 8 1.3135 nan 0.0010 0.0004
## 9 1.3126 nan 0.0010 0.0004
## 10 1.3117 nan 0.0010 0.0004
## 20 1.3030 nan 0.0010 0.0004
## 40 1.2860 nan 0.0010 0.0004
## 60 1.2693 nan 0.0010 0.0003
## 80 1.2531 nan 0.0010 0.0004
## 100 1.2372 nan 0.0010 0.0004
## 120 1.2222 nan 0.0010 0.0003
## 140 1.2073 nan 0.0010 0.0003
## 160 1.1931 nan 0.0010 0.0003
## 180 1.1794 nan 0.0010 0.0003
## 200 1.1662 nan 0.0010 0.0003
## 220 1.1534 nan 0.0010 0.0003
## 240 1.1410 nan 0.0010 0.0003
## 260 1.1292 nan 0.0010 0.0002
## 280 1.1174 nan 0.0010 0.0003
## 300 1.1058 nan 0.0010 0.0003
## 320 1.0947 nan 0.0010 0.0003
## 340 1.0840 nan 0.0010 0.0002
## 360 1.0733 nan 0.0010 0.0002
## 380 1.0631 nan 0.0010 0.0002
## 400 1.0533 nan 0.0010 0.0002
## 420 1.0434 nan 0.0010 0.0002
## 440 1.0340 nan 0.0010 0.0002
## 460 1.0248 nan 0.0010 0.0002
## 480 1.0159 nan 0.0010 0.0002
## 500 1.0073 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3188 nan 0.0010 0.0005
## 3 1.3180 nan 0.0010 0.0004
## 4 1.3171 nan 0.0010 0.0004
## 5 1.3162 nan 0.0010 0.0004
## 6 1.3153 nan 0.0010 0.0004
## 7 1.3144 nan 0.0010 0.0004
## 8 1.3135 nan 0.0010 0.0004
## 9 1.3127 nan 0.0010 0.0004
## 10 1.3118 nan 0.0010 0.0004
## 20 1.3032 nan 0.0010 0.0004
## 40 1.2864 nan 0.0010 0.0004
## 60 1.2701 nan 0.0010 0.0004
## 80 1.2542 nan 0.0010 0.0003
## 100 1.2388 nan 0.0010 0.0004
## 120 1.2241 nan 0.0010 0.0003
## 140 1.2097 nan 0.0010 0.0003
## 160 1.1955 nan 0.0010 0.0003
## 180 1.1821 nan 0.0010 0.0002
## 200 1.1692 nan 0.0010 0.0003
## 220 1.1564 nan 0.0010 0.0003
## 240 1.1439 nan 0.0010 0.0003
## 260 1.1321 nan 0.0010 0.0002
## 280 1.1200 nan 0.0010 0.0002
## 300 1.1089 nan 0.0010 0.0002
## 320 1.0980 nan 0.0010 0.0002
## 340 1.0873 nan 0.0010 0.0002
## 360 1.0771 nan 0.0010 0.0002
## 380 1.0669 nan 0.0010 0.0002
## 400 1.0569 nan 0.0010 0.0002
## 420 1.0472 nan 0.0010 0.0002
## 440 1.0377 nan 0.0010 0.0002
## 460 1.0287 nan 0.0010 0.0002
## 480 1.0199 nan 0.0010 0.0002
## 500 1.0112 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3122 nan 0.0100 0.0039
## 2 1.3048 nan 0.0100 0.0033
## 3 1.2977 nan 0.0100 0.0033
## 4 1.2893 nan 0.0100 0.0037
## 5 1.2820 nan 0.0100 0.0036
## 6 1.2753 nan 0.0100 0.0026
## 7 1.2692 nan 0.0100 0.0024
## 8 1.2624 nan 0.0100 0.0032
## 9 1.2555 nan 0.0100 0.0033
## 10 1.2473 nan 0.0100 0.0035
## 20 1.1856 nan 0.0100 0.0023
## 40 1.0800 nan 0.0100 0.0022
## 60 0.9989 nan 0.0100 0.0014
## 80 0.9386 nan 0.0100 0.0012
## 100 0.8885 nan 0.0100 0.0009
## 120 0.8466 nan 0.0100 0.0005
## 140 0.8122 nan 0.0100 0.0005
## 160 0.7816 nan 0.0100 0.0004
## 180 0.7561 nan 0.0100 0.0004
## 200 0.7341 nan 0.0100 0.0003
## 220 0.7159 nan 0.0100 0.0001
## 240 0.6989 nan 0.0100 0.0001
## 260 0.6830 nan 0.0100 0.0000
## 280 0.6686 nan 0.0100 0.0001
## 300 0.6545 nan 0.0100 0.0001
## 320 0.6422 nan 0.0100 -0.0000
## 340 0.6312 nan 0.0100 0.0002
## 360 0.6202 nan 0.0100 -0.0002
## 380 0.6104 nan 0.0100 0.0001
## 400 0.6010 nan 0.0100 -0.0002
## 420 0.5917 nan 0.0100 0.0000
## 440 0.5822 nan 0.0100 -0.0000
## 460 0.5736 nan 0.0100 -0.0001
## 480 0.5645 nan 0.0100 -0.0001
## 500 0.5568 nan 0.0100 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3122 nan 0.0100 0.0039
## 2 1.3044 nan 0.0100 0.0035
## 3 1.2969 nan 0.0100 0.0035
## 4 1.2897 nan 0.0100 0.0028
## 5 1.2822 nan 0.0100 0.0031
## 6 1.2753 nan 0.0100 0.0032
## 7 1.2677 nan 0.0100 0.0031
## 8 1.2606 nan 0.0100 0.0030
## 9 1.2536 nan 0.0100 0.0032
## 10 1.2466 nan 0.0100 0.0028
## 20 1.1821 nan 0.0100 0.0027
## 40 1.0803 nan 0.0100 0.0019
## 60 1.0009 nan 0.0100 0.0014
## 80 0.9373 nan 0.0100 0.0011
## 100 0.8882 nan 0.0100 0.0009
## 120 0.8481 nan 0.0100 0.0006
## 140 0.8140 nan 0.0100 0.0005
## 160 0.7853 nan 0.0100 0.0002
## 180 0.7601 nan 0.0100 0.0003
## 200 0.7395 nan 0.0100 0.0005
## 220 0.7209 nan 0.0100 0.0000
## 240 0.7038 nan 0.0100 0.0001
## 260 0.6879 nan 0.0100 0.0001
## 280 0.6731 nan 0.0100 0.0000
## 300 0.6600 nan 0.0100 0.0002
## 320 0.6470 nan 0.0100 -0.0000
## 340 0.6359 nan 0.0100 -0.0000
## 360 0.6263 nan 0.0100 0.0000
## 380 0.6162 nan 0.0100 0.0001
## 400 0.6067 nan 0.0100 0.0000
## 420 0.5966 nan 0.0100 0.0001
## 440 0.5865 nan 0.0100 0.0001
## 460 0.5768 nan 0.0100 0.0000
## 480 0.5684 nan 0.0100 -0.0002
## 500 0.5608 nan 0.0100 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3131 nan 0.0100 0.0036
## 2 1.3050 nan 0.0100 0.0037
## 3 1.2980 nan 0.0100 0.0029
## 4 1.2901 nan 0.0100 0.0033
## 5 1.2821 nan 0.0100 0.0032
## 6 1.2748 nan 0.0100 0.0033
## 7 1.2673 nan 0.0100 0.0031
## 8 1.2601 nan 0.0100 0.0033
## 9 1.2535 nan 0.0100 0.0029
## 10 1.2465 nan 0.0100 0.0031
## 20 1.1824 nan 0.0100 0.0030
## 40 1.0804 nan 0.0100 0.0020
## 60 1.0009 nan 0.0100 0.0014
## 80 0.9375 nan 0.0100 0.0012
## 100 0.8872 nan 0.0100 0.0008
## 120 0.8469 nan 0.0100 0.0007
## 140 0.8148 nan 0.0100 0.0004
## 160 0.7868 nan 0.0100 0.0004
## 180 0.7632 nan 0.0100 0.0003
## 200 0.7417 nan 0.0100 0.0003
## 220 0.7241 nan 0.0100 0.0002
## 240 0.7082 nan 0.0100 0.0001
## 260 0.6948 nan 0.0100 0.0001
## 280 0.6818 nan 0.0100 0.0002
## 300 0.6695 nan 0.0100 0.0001
## 320 0.6584 nan 0.0100 0.0000
## 340 0.6475 nan 0.0100 0.0001
## 360 0.6387 nan 0.0100 0.0000
## 380 0.6293 nan 0.0100 0.0002
## 400 0.6195 nan 0.0100 -0.0000
## 420 0.6105 nan 0.0100 -0.0003
## 440 0.6023 nan 0.0100 -0.0000
## 460 0.5936 nan 0.0100 0.0001
## 480 0.5855 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3122 nan 0.0100 0.0038
## 2 1.3035 nan 0.0100 0.0036
## 3 1.2951 nan 0.0100 0.0040
## 4 1.2868 nan 0.0100 0.0035
## 5 1.2785 nan 0.0100 0.0038
## 6 1.2705 nan 0.0100 0.0033
## 7 1.2628 nan 0.0100 0.0034
## 8 1.2555 nan 0.0100 0.0034
## 9 1.2474 nan 0.0100 0.0038
## 10 1.2397 nan 0.0100 0.0031
## 20 1.1726 nan 0.0100 0.0028
## 40 1.0634 nan 0.0100 0.0021
## 60 0.9787 nan 0.0100 0.0015
## 80 0.9137 nan 0.0100 0.0011
## 100 0.8607 nan 0.0100 0.0008
## 120 0.8175 nan 0.0100 0.0005
## 140 0.7815 nan 0.0100 0.0004
## 160 0.7507 nan 0.0100 0.0002
## 180 0.7242 nan 0.0100 0.0003
## 200 0.7015 nan 0.0100 0.0003
## 220 0.6809 nan 0.0100 0.0002
## 240 0.6623 nan 0.0100 0.0002
## 260 0.6456 nan 0.0100 0.0001
## 280 0.6300 nan 0.0100 0.0002
## 300 0.6159 nan 0.0100 -0.0002
## 320 0.6014 nan 0.0100 0.0001
## 340 0.5873 nan 0.0100 0.0001
## 360 0.5756 nan 0.0100 0.0002
## 380 0.5629 nan 0.0100 0.0001
## 400 0.5509 nan 0.0100 0.0000
## 420 0.5405 nan 0.0100 -0.0000
## 440 0.5296 nan 0.0100 0.0001
## 460 0.5201 nan 0.0100 -0.0000
## 480 0.5109 nan 0.0100 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3121 nan 0.0100 0.0037
## 2 1.3039 nan 0.0100 0.0038
## 3 1.2960 nan 0.0100 0.0042
## 4 1.2877 nan 0.0100 0.0036
## 5 1.2793 nan 0.0100 0.0035
## 6 1.2711 nan 0.0100 0.0035
## 7 1.2640 nan 0.0100 0.0029
## 8 1.2572 nan 0.0100 0.0030
## 9 1.2499 nan 0.0100 0.0035
## 10 1.2420 nan 0.0100 0.0032
## 20 1.1745 nan 0.0100 0.0027
## 40 1.0675 nan 0.0100 0.0020
## 60 0.9838 nan 0.0100 0.0016
## 80 0.9177 nan 0.0100 0.0012
## 100 0.8658 nan 0.0100 0.0010
## 120 0.8227 nan 0.0100 0.0008
## 140 0.7875 nan 0.0100 0.0004
## 160 0.7573 nan 0.0100 0.0004
## 180 0.7316 nan 0.0100 0.0002
## 200 0.7085 nan 0.0100 0.0002
## 220 0.6881 nan 0.0100 0.0000
## 240 0.6708 nan 0.0100 -0.0000
## 260 0.6543 nan 0.0100 0.0001
## 280 0.6396 nan 0.0100 0.0000
## 300 0.6248 nan 0.0100 0.0001
## 320 0.6106 nan 0.0100 -0.0002
## 340 0.5977 nan 0.0100 -0.0002
## 360 0.5859 nan 0.0100 0.0001
## 380 0.5746 nan 0.0100 -0.0000
## 400 0.5641 nan 0.0100 -0.0001
## 420 0.5537 nan 0.0100 -0.0001
## 440 0.5438 nan 0.0100 -0.0001
## 460 0.5341 nan 0.0100 -0.0000
## 480 0.5242 nan 0.0100 -0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3126 nan 0.0100 0.0035
## 2 1.3045 nan 0.0100 0.0040
## 3 1.2968 nan 0.0100 0.0038
## 4 1.2886 nan 0.0100 0.0038
## 5 1.2803 nan 0.0100 0.0036
## 6 1.2727 nan 0.0100 0.0033
## 7 1.2647 nan 0.0100 0.0037
## 8 1.2574 nan 0.0100 0.0033
## 9 1.2498 nan 0.0100 0.0036
## 10 1.2424 nan 0.0100 0.0033
## 20 1.1763 nan 0.0100 0.0027
## 40 1.0691 nan 0.0100 0.0019
## 60 0.9849 nan 0.0100 0.0013
## 80 0.9203 nan 0.0100 0.0011
## 100 0.8673 nan 0.0100 0.0008
## 120 0.8243 nan 0.0100 0.0007
## 140 0.7905 nan 0.0100 0.0006
## 160 0.7613 nan 0.0100 0.0003
## 180 0.7364 nan 0.0100 0.0003
## 200 0.7141 nan 0.0100 0.0003
## 220 0.6936 nan 0.0100 0.0001
## 240 0.6754 nan 0.0100 0.0002
## 260 0.6601 nan 0.0100 -0.0000
## 280 0.6444 nan 0.0100 0.0001
## 300 0.6308 nan 0.0100 -0.0000
## 320 0.6172 nan 0.0100 -0.0001
## 340 0.6046 nan 0.0100 0.0001
## 360 0.5929 nan 0.0100 -0.0001
## 380 0.5813 nan 0.0100 0.0000
## 400 0.5715 nan 0.0100 0.0001
## 420 0.5606 nan 0.0100 -0.0001
## 440 0.5507 nan 0.0100 0.0001
## 460 0.5415 nan 0.0100 -0.0000
## 480 0.5325 nan 0.0100 0.0001
## 500 0.5239 nan 0.0100 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3120 nan 0.0100 0.0036
## 2 1.3021 nan 0.0100 0.0042
## 3 1.2937 nan 0.0100 0.0036
## 4 1.2846 nan 0.0100 0.0040
## 5 1.2764 nan 0.0100 0.0039
## 6 1.2683 nan 0.0100 0.0038
## 7 1.2605 nan 0.0100 0.0037
## 8 1.2528 nan 0.0100 0.0034
## 9 1.2448 nan 0.0100 0.0034
## 10 1.2372 nan 0.0100 0.0035
## 20 1.1665 nan 0.0100 0.0026
## 40 1.0525 nan 0.0100 0.0017
## 60 0.9656 nan 0.0100 0.0012
## 80 0.8969 nan 0.0100 0.0010
## 100 0.8425 nan 0.0100 0.0010
## 120 0.7958 nan 0.0100 0.0006
## 140 0.7580 nan 0.0100 0.0004
## 160 0.7256 nan 0.0100 0.0001
## 180 0.6970 nan 0.0100 0.0003
## 200 0.6722 nan 0.0100 0.0003
## 220 0.6491 nan 0.0100 0.0003
## 240 0.6294 nan 0.0100 -0.0000
## 260 0.6102 nan 0.0100 -0.0001
## 280 0.5932 nan 0.0100 0.0001
## 300 0.5771 nan 0.0100 0.0001
## 320 0.5621 nan 0.0100 0.0001
## 340 0.5492 nan 0.0100 0.0000
## 360 0.5361 nan 0.0100 0.0001
## 380 0.5238 nan 0.0100 0.0001
## 400 0.5112 nan 0.0100 0.0000
## 420 0.4999 nan 0.0100 -0.0001
## 440 0.4886 nan 0.0100 0.0001
## 460 0.4781 nan 0.0100 0.0001
## 480 0.4681 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3111 nan 0.0100 0.0039
## 2 1.3019 nan 0.0100 0.0040
## 3 1.2928 nan 0.0100 0.0039
## 4 1.2842 nan 0.0100 0.0038
## 5 1.2760 nan 0.0100 0.0036
## 6 1.2670 nan 0.0100 0.0038
## 7 1.2591 nan 0.0100 0.0036
## 8 1.2508 nan 0.0100 0.0036
## 9 1.2428 nan 0.0100 0.0035
## 10 1.2354 nan 0.0100 0.0033
## 20 1.1649 nan 0.0100 0.0028
## 40 1.0511 nan 0.0100 0.0020
## 60 0.9649 nan 0.0100 0.0016
## 80 0.8984 nan 0.0100 0.0009
## 100 0.8444 nan 0.0100 0.0008
## 120 0.7990 nan 0.0100 0.0009
## 140 0.7630 nan 0.0100 0.0005
## 160 0.7324 nan 0.0100 0.0004
## 180 0.7057 nan 0.0100 0.0003
## 200 0.6813 nan 0.0100 0.0003
## 220 0.6601 nan 0.0100 0.0001
## 240 0.6403 nan 0.0100 0.0001
## 260 0.6236 nan 0.0100 0.0001
## 280 0.6059 nan 0.0100 0.0000
## 300 0.5912 nan 0.0100 -0.0000
## 320 0.5777 nan 0.0100 0.0001
## 340 0.5631 nan 0.0100 0.0001
## 360 0.5498 nan 0.0100 -0.0001
## 380 0.5373 nan 0.0100 -0.0000
## 400 0.5265 nan 0.0100 0.0000
## 420 0.5153 nan 0.0100 -0.0001
## 440 0.5043 nan 0.0100 0.0001
## 460 0.4938 nan 0.0100 -0.0001
## 480 0.4837 nan 0.0100 -0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3111 nan 0.0100 0.0042
## 2 1.3024 nan 0.0100 0.0040
## 3 1.2940 nan 0.0100 0.0037
## 4 1.2857 nan 0.0100 0.0036
## 5 1.2780 nan 0.0100 0.0038
## 6 1.2694 nan 0.0100 0.0036
## 7 1.2615 nan 0.0100 0.0034
## 8 1.2537 nan 0.0100 0.0035
## 9 1.2463 nan 0.0100 0.0031
## 10 1.2394 nan 0.0100 0.0031
## 20 1.1694 nan 0.0100 0.0024
## 40 1.0566 nan 0.0100 0.0019
## 60 0.9700 nan 0.0100 0.0013
## 80 0.9026 nan 0.0100 0.0011
## 100 0.8486 nan 0.0100 0.0010
## 120 0.8043 nan 0.0100 0.0006
## 140 0.7697 nan 0.0100 0.0005
## 160 0.7382 nan 0.0100 0.0003
## 180 0.7123 nan 0.0100 0.0001
## 200 0.6895 nan 0.0100 0.0005
## 220 0.6682 nan 0.0100 0.0002
## 240 0.6495 nan 0.0100 0.0000
## 260 0.6325 nan 0.0100 0.0000
## 280 0.6164 nan 0.0100 0.0002
## 300 0.6013 nan 0.0100 0.0001
## 320 0.5876 nan 0.0100 0.0001
## 340 0.5742 nan 0.0100 -0.0000
## 360 0.5609 nan 0.0100 -0.0001
## 380 0.5481 nan 0.0100 -0.0000
## 400 0.5373 nan 0.0100 0.0001
## 420 0.5261 nan 0.0100 -0.0001
## 440 0.5161 nan 0.0100 0.0000
## 460 0.5057 nan 0.0100 -0.0003
## 480 0.4960 nan 0.0100 0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2447 nan 0.1000 0.0378
## 2 1.1822 nan 0.1000 0.0256
## 3 1.1276 nan 0.1000 0.0184
## 4 1.0808 nan 0.1000 0.0228
## 5 1.0349 nan 0.1000 0.0188
## 6 0.9951 nan 0.1000 0.0178
## 7 0.9638 nan 0.1000 0.0097
## 8 0.9309 nan 0.1000 0.0135
## 9 0.9060 nan 0.1000 0.0104
## 10 0.8842 nan 0.1000 0.0076
## 20 0.7405 nan 0.1000 0.0011
## 40 0.6010 nan 0.1000 0.0007
## 60 0.5190 nan 0.1000 -0.0002
## 80 0.4481 nan 0.1000 -0.0003
## 100 0.3962 nan 0.1000 0.0006
## 120 0.3505 nan 0.1000 -0.0004
## 140 0.3151 nan 0.1000 -0.0004
## 160 0.2835 nan 0.1000 -0.0003
## 180 0.2567 nan 0.1000 -0.0001
## 200 0.2318 nan 0.1000 0.0000
## 220 0.2095 nan 0.1000 -0.0003
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## 320 0.1314 nan 0.1000 -0.0003
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## 380 0.1019 nan 0.1000 -0.0002
## 400 0.0951 nan 0.1000 -0.0003
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## 480 0.0691 nan 0.1000 -0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2445 nan 0.1000 0.0365
## 2 1.1807 nan 0.1000 0.0254
## 3 1.1280 nan 0.1000 0.0235
## 4 1.0749 nan 0.1000 0.0224
## 5 1.0383 nan 0.1000 0.0135
## 6 0.9984 nan 0.1000 0.0160
## 7 0.9635 nan 0.1000 0.0153
## 8 0.9347 nan 0.1000 0.0126
## 9 0.9055 nan 0.1000 0.0111
## 10 0.8798 nan 0.1000 0.0101
## 20 0.7339 nan 0.1000 0.0023
## 40 0.6039 nan 0.1000 -0.0003
## 60 0.5145 nan 0.1000 -0.0007
## 80 0.4507 nan 0.1000 -0.0007
## 100 0.4001 nan 0.1000 0.0002
## 120 0.3545 nan 0.1000 -0.0006
## 140 0.3230 nan 0.1000 -0.0002
## 160 0.2897 nan 0.1000 -0.0002
## 180 0.2589 nan 0.1000 -0.0003
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## 220 0.2175 nan 0.1000 -0.0008
## 240 0.2001 nan 0.1000 -0.0010
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## 340 0.1300 nan 0.1000 -0.0003
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## 380 0.1117 nan 0.1000 -0.0004
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## 460 0.0818 nan 0.1000 -0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2449 nan 0.1000 0.0325
## 2 1.1842 nan 0.1000 0.0288
## 3 1.1275 nan 0.1000 0.0253
## 4 1.0790 nan 0.1000 0.0195
## 5 1.0422 nan 0.1000 0.0165
## 6 1.0065 nan 0.1000 0.0164
## 7 0.9725 nan 0.1000 0.0129
## 8 0.9400 nan 0.1000 0.0117
## 9 0.9166 nan 0.1000 0.0084
## 10 0.8924 nan 0.1000 0.0089
## 20 0.7437 nan 0.1000 0.0020
## 40 0.6262 nan 0.1000 -0.0009
## 60 0.5457 nan 0.1000 0.0001
## 80 0.4854 nan 0.1000 -0.0014
## 100 0.4302 nan 0.1000 -0.0012
## 120 0.3844 nan 0.1000 -0.0009
## 140 0.3507 nan 0.1000 -0.0014
## 160 0.3188 nan 0.1000 -0.0010
## 180 0.2877 nan 0.1000 -0.0003
## 200 0.2610 nan 0.1000 -0.0006
## 220 0.2389 nan 0.1000 -0.0008
## 240 0.2196 nan 0.1000 -0.0004
## 260 0.1988 nan 0.1000 -0.0008
## 280 0.1842 nan 0.1000 -0.0010
## 300 0.1698 nan 0.1000 -0.0003
## 320 0.1573 nan 0.1000 -0.0003
## 340 0.1434 nan 0.1000 -0.0004
## 360 0.1326 nan 0.1000 -0.0006
## 380 0.1228 nan 0.1000 -0.0002
## 400 0.1120 nan 0.1000 -0.0005
## 420 0.1042 nan 0.1000 -0.0001
## 440 0.0964 nan 0.1000 -0.0003
## 460 0.0893 nan 0.1000 -0.0002
## 480 0.0827 nan 0.1000 -0.0002
## 500 0.0765 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2454 nan 0.1000 0.0347
## 2 1.1713 nan 0.1000 0.0313
## 3 1.1073 nan 0.1000 0.0284
## 4 1.0583 nan 0.1000 0.0204
## 5 1.0134 nan 0.1000 0.0212
## 6 0.9725 nan 0.1000 0.0164
## 7 0.9323 nan 0.1000 0.0148
## 8 0.9010 nan 0.1000 0.0123
## 9 0.8787 nan 0.1000 0.0079
## 10 0.8512 nan 0.1000 0.0116
## 20 0.6942 nan 0.1000 0.0013
## 40 0.5527 nan 0.1000 -0.0007
## 60 0.4664 nan 0.1000 -0.0006
## 80 0.4005 nan 0.1000 -0.0009
## 100 0.3431 nan 0.1000 -0.0006
## 120 0.2980 nan 0.1000 -0.0004
## 140 0.2612 nan 0.1000 -0.0003
## 160 0.2255 nan 0.1000 -0.0005
## 180 0.1944 nan 0.1000 -0.0000
## 200 0.1749 nan 0.1000 -0.0004
## 220 0.1534 nan 0.1000 -0.0003
## 240 0.1378 nan 0.1000 -0.0004
## 260 0.1232 nan 0.1000 0.0000
## 280 0.1097 nan 0.1000 -0.0004
## 300 0.0981 nan 0.1000 -0.0000
## 320 0.0868 nan 0.1000 -0.0002
## 340 0.0785 nan 0.1000 -0.0001
## 360 0.0709 nan 0.1000 -0.0003
## 380 0.0639 nan 0.1000 0.0001
## 400 0.0575 nan 0.1000 -0.0001
## 420 0.0520 nan 0.1000 -0.0000
## 440 0.0474 nan 0.1000 -0.0002
## 460 0.0431 nan 0.1000 -0.0001
## 480 0.0388 nan 0.1000 -0.0000
## 500 0.0352 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2328 nan 0.1000 0.0392
## 2 1.1640 nan 0.1000 0.0307
## 3 1.1044 nan 0.1000 0.0261
## 4 1.0514 nan 0.1000 0.0199
## 5 1.0102 nan 0.1000 0.0172
## 6 0.9706 nan 0.1000 0.0160
## 7 0.9341 nan 0.1000 0.0132
## 8 0.9041 nan 0.1000 0.0112
## 9 0.8800 nan 0.1000 0.0074
## 10 0.8523 nan 0.1000 0.0096
## 20 0.7001 nan 0.1000 -0.0002
## 40 0.5680 nan 0.1000 -0.0004
## 60 0.4821 nan 0.1000 -0.0006
## 80 0.4125 nan 0.1000 0.0000
## 100 0.3632 nan 0.1000 -0.0020
## 120 0.3103 nan 0.1000 -0.0012
## 140 0.2709 nan 0.1000 -0.0010
## 160 0.2377 nan 0.1000 -0.0003
## 180 0.2124 nan 0.1000 -0.0006
## 200 0.1879 nan 0.1000 -0.0008
## 220 0.1675 nan 0.1000 -0.0009
## 240 0.1494 nan 0.1000 0.0004
## 260 0.1342 nan 0.1000 0.0000
## 280 0.1200 nan 0.1000 -0.0003
## 300 0.1083 nan 0.1000 -0.0001
## 320 0.0972 nan 0.1000 -0.0000
## 340 0.0877 nan 0.1000 -0.0004
## 360 0.0793 nan 0.1000 -0.0004
## 380 0.0727 nan 0.1000 -0.0003
## 400 0.0658 nan 0.1000 -0.0002
## 420 0.0591 nan 0.1000 -0.0002
## 440 0.0535 nan 0.1000 -0.0004
## 460 0.0484 nan 0.1000 -0.0002
## 480 0.0435 nan 0.1000 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2410 nan 0.1000 0.0358
## 2 1.1691 nan 0.1000 0.0323
## 3 1.1078 nan 0.1000 0.0267
## 4 1.0599 nan 0.1000 0.0206
## 5 1.0139 nan 0.1000 0.0183
## 6 0.9787 nan 0.1000 0.0150
## 7 0.9422 nan 0.1000 0.0150
## 8 0.9113 nan 0.1000 0.0121
## 9 0.8846 nan 0.1000 0.0102
## 10 0.8629 nan 0.1000 0.0078
## 20 0.6984 nan 0.1000 0.0036
## 40 0.5699 nan 0.1000 -0.0006
## 60 0.4856 nan 0.1000 -0.0012
## 80 0.4209 nan 0.1000 0.0001
## 100 0.3607 nan 0.1000 -0.0007
## 120 0.3156 nan 0.1000 -0.0006
## 140 0.2791 nan 0.1000 -0.0016
## 160 0.2484 nan 0.1000 -0.0011
## 180 0.2217 nan 0.1000 -0.0003
## 200 0.1986 nan 0.1000 -0.0007
## 220 0.1777 nan 0.1000 -0.0006
## 240 0.1617 nan 0.1000 -0.0008
## 260 0.1441 nan 0.1000 -0.0007
## 280 0.1309 nan 0.1000 -0.0006
## 300 0.1177 nan 0.1000 -0.0005
## 320 0.1063 nan 0.1000 -0.0005
## 340 0.0960 nan 0.1000 -0.0002
## 360 0.0864 nan 0.1000 -0.0005
## 380 0.0776 nan 0.1000 -0.0003
## 400 0.0701 nan 0.1000 -0.0002
## 420 0.0638 nan 0.1000 -0.0001
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## 460 0.0519 nan 0.1000 -0.0002
## 480 0.0475 nan 0.1000 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2403 nan 0.1000 0.0366
## 2 1.1655 nan 0.1000 0.0330
## 3 1.1021 nan 0.1000 0.0267
## 4 1.0474 nan 0.1000 0.0234
## 5 0.9965 nan 0.1000 0.0231
## 6 0.9579 nan 0.1000 0.0146
## 7 0.9177 nan 0.1000 0.0150
## 8 0.8867 nan 0.1000 0.0127
## 9 0.8587 nan 0.1000 0.0101
## 10 0.8339 nan 0.1000 0.0082
## 20 0.6723 nan 0.1000 0.0021
## 40 0.5220 nan 0.1000 -0.0007
## 60 0.4266 nan 0.1000 -0.0009
## 80 0.3494 nan 0.1000 -0.0001
## 100 0.2914 nan 0.1000 0.0000
## 120 0.2527 nan 0.1000 -0.0008
## 140 0.2162 nan 0.1000 -0.0002
## 160 0.1831 nan 0.1000 -0.0011
## 180 0.1571 nan 0.1000 -0.0002
## 200 0.1345 nan 0.1000 -0.0003
## 220 0.1177 nan 0.1000 -0.0002
## 240 0.1031 nan 0.1000 -0.0002
## 260 0.0911 nan 0.1000 -0.0003
## 280 0.0804 nan 0.1000 -0.0003
## 300 0.0707 nan 0.1000 -0.0003
## 320 0.0628 nan 0.1000 -0.0003
## 340 0.0554 nan 0.1000 -0.0001
## 360 0.0483 nan 0.1000 -0.0000
## 380 0.0430 nan 0.1000 -0.0001
## 400 0.0378 nan 0.1000 -0.0001
## 420 0.0333 nan 0.1000 0.0000
## 440 0.0297 nan 0.1000 -0.0000
## 460 0.0263 nan 0.1000 -0.0001
## 480 0.0235 nan 0.1000 -0.0001
## 500 0.0212 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2443 nan 0.1000 0.0289
## 2 1.1741 nan 0.1000 0.0333
## 3 1.1095 nan 0.1000 0.0260
## 4 1.0509 nan 0.1000 0.0235
## 5 1.0031 nan 0.1000 0.0224
## 6 0.9671 nan 0.1000 0.0128
## 7 0.9309 nan 0.1000 0.0158
## 8 0.8939 nan 0.1000 0.0125
## 9 0.8638 nan 0.1000 0.0122
## 10 0.8381 nan 0.1000 0.0093
## 20 0.6806 nan 0.1000 0.0003
## 40 0.5296 nan 0.1000 0.0010
## 60 0.4321 nan 0.1000 -0.0001
## 80 0.3567 nan 0.1000 -0.0016
## 100 0.2999 nan 0.1000 -0.0005
## 120 0.2545 nan 0.1000 -0.0006
## 140 0.2169 nan 0.1000 -0.0000
## 160 0.1899 nan 0.1000 -0.0008
## 180 0.1628 nan 0.1000 -0.0010
## 200 0.1415 nan 0.1000 -0.0005
## 220 0.1219 nan 0.1000 -0.0005
## 240 0.1071 nan 0.1000 -0.0004
## 260 0.0942 nan 0.1000 -0.0002
## 280 0.0833 nan 0.1000 -0.0000
## 300 0.0731 nan 0.1000 -0.0002
## 320 0.0645 nan 0.1000 -0.0001
## 340 0.0567 nan 0.1000 -0.0001
## 360 0.0495 nan 0.1000 -0.0001
## 380 0.0440 nan 0.1000 -0.0001
## 400 0.0391 nan 0.1000 -0.0001
## 420 0.0348 nan 0.1000 -0.0000
## 440 0.0307 nan 0.1000 -0.0001
## 460 0.0274 nan 0.1000 -0.0001
## 480 0.0240 nan 0.1000 -0.0000
## 500 0.0214 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2320 nan 0.1000 0.0440
## 2 1.1611 nan 0.1000 0.0352
## 3 1.1037 nan 0.1000 0.0267
## 4 1.0492 nan 0.1000 0.0222
## 5 1.0054 nan 0.1000 0.0198
## 6 0.9642 nan 0.1000 0.0188
## 7 0.9279 nan 0.1000 0.0130
## 8 0.8977 nan 0.1000 0.0122
## 9 0.8698 nan 0.1000 0.0108
## 10 0.8452 nan 0.1000 0.0074
## 20 0.6984 nan 0.1000 -0.0003
## 40 0.5521 nan 0.1000 -0.0012
## 60 0.4518 nan 0.1000 -0.0015
## 80 0.3766 nan 0.1000 -0.0012
## 100 0.3219 nan 0.1000 -0.0004
## 120 0.2728 nan 0.1000 -0.0005
## 140 0.2308 nan 0.1000 -0.0015
## 160 0.1986 nan 0.1000 -0.0004
## 180 0.1731 nan 0.1000 -0.0005
## 200 0.1534 nan 0.1000 -0.0004
## 220 0.1331 nan 0.1000 -0.0003
## 240 0.1175 nan 0.1000 -0.0001
## 260 0.1049 nan 0.1000 -0.0006
## 280 0.0923 nan 0.1000 -0.0001
## 300 0.0820 nan 0.1000 -0.0004
## 320 0.0738 nan 0.1000 -0.0002
## 340 0.0664 nan 0.1000 -0.0002
## 360 0.0585 nan 0.1000 -0.0002
## 380 0.0524 nan 0.1000 -0.0003
## 400 0.0461 nan 0.1000 -0.0000
## 420 0.0408 nan 0.1000 -0.0002
## 440 0.0359 nan 0.1000 -0.0001
## 460 0.0323 nan 0.1000 -0.0000
## 480 0.0292 nan 0.1000 -0.0001
## 500 0.0264 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3180 nan 0.0010 0.0003
## 4 1.3171 nan 0.0010 0.0004
## 5 1.3162 nan 0.0010 0.0004
## 6 1.3153 nan 0.0010 0.0004
## 7 1.3143 nan 0.0010 0.0004
## 8 1.3134 nan 0.0010 0.0004
## 9 1.3126 nan 0.0010 0.0004
## 10 1.3117 nan 0.0010 0.0004
## 20 1.3031 nan 0.0010 0.0004
## 40 1.2867 nan 0.0010 0.0004
## 60 1.2704 nan 0.0010 0.0004
## 80 1.2549 nan 0.0010 0.0002
## 100 1.2394 nan 0.0010 0.0004
## 120 1.2245 nan 0.0010 0.0003
## 140 1.2102 nan 0.0010 0.0003
## 160 1.1963 nan 0.0010 0.0003
## 180 1.1832 nan 0.0010 0.0003
## 200 1.1704 nan 0.0010 0.0003
## 220 1.1577 nan 0.0010 0.0002
## 240 1.1456 nan 0.0010 0.0003
## 260 1.1340 nan 0.0010 0.0003
## 280 1.1222 nan 0.0010 0.0002
## 300 1.1110 nan 0.0010 0.0002
## 320 1.1005 nan 0.0010 0.0002
## 340 1.0896 nan 0.0010 0.0002
## 360 1.0793 nan 0.0010 0.0002
## 380 1.0692 nan 0.0010 0.0003
## 400 1.0593 nan 0.0010 0.0002
## 420 1.0497 nan 0.0010 0.0002
## 440 1.0407 nan 0.0010 0.0002
## 460 1.0318 nan 0.0010 0.0002
## 480 1.0231 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3188 nan 0.0010 0.0005
## 3 1.3179 nan 0.0010 0.0005
## 4 1.3169 nan 0.0010 0.0004
## 5 1.3160 nan 0.0010 0.0004
## 6 1.3151 nan 0.0010 0.0004
## 7 1.3143 nan 0.0010 0.0004
## 8 1.3134 nan 0.0010 0.0004
## 9 1.3125 nan 0.0010 0.0004
## 10 1.3117 nan 0.0010 0.0004
## 20 1.3028 nan 0.0010 0.0004
## 40 1.2862 nan 0.0010 0.0004
## 60 1.2695 nan 0.0010 0.0004
## 80 1.2537 nan 0.0010 0.0003
## 100 1.2386 nan 0.0010 0.0004
## 120 1.2236 nan 0.0010 0.0003
## 140 1.2095 nan 0.0010 0.0003
## 160 1.1959 nan 0.0010 0.0003
## 180 1.1827 nan 0.0010 0.0003
## 200 1.1700 nan 0.0010 0.0003
## 220 1.1577 nan 0.0010 0.0003
## 240 1.1455 nan 0.0010 0.0003
## 260 1.1338 nan 0.0010 0.0002
## 280 1.1224 nan 0.0010 0.0003
## 300 1.1113 nan 0.0010 0.0002
## 320 1.1005 nan 0.0010 0.0002
## 340 1.0901 nan 0.0010 0.0002
## 360 1.0800 nan 0.0010 0.0002
## 380 1.0701 nan 0.0010 0.0002
## 400 1.0604 nan 0.0010 0.0002
## 420 1.0512 nan 0.0010 0.0002
## 440 1.0421 nan 0.0010 0.0002
## 460 1.0332 nan 0.0010 0.0002
## 480 1.0244 nan 0.0010 0.0002
## 500 1.0158 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3179 nan 0.0010 0.0005
## 4 1.3171 nan 0.0010 0.0004
## 5 1.3161 nan 0.0010 0.0004
## 6 1.3153 nan 0.0010 0.0004
## 7 1.3144 nan 0.0010 0.0004
## 8 1.3135 nan 0.0010 0.0004
## 9 1.3126 nan 0.0010 0.0004
## 10 1.3117 nan 0.0010 0.0004
## 20 1.3031 nan 0.0010 0.0004
## 40 1.2863 nan 0.0010 0.0004
## 60 1.2705 nan 0.0010 0.0003
## 80 1.2548 nan 0.0010 0.0003
## 100 1.2398 nan 0.0010 0.0003
## 120 1.2252 nan 0.0010 0.0003
## 140 1.2110 nan 0.0010 0.0003
## 160 1.1974 nan 0.0010 0.0003
## 180 1.1840 nan 0.0010 0.0003
## 200 1.1710 nan 0.0010 0.0003
## 220 1.1585 nan 0.0010 0.0002
## 240 1.1464 nan 0.0010 0.0002
## 260 1.1348 nan 0.0010 0.0003
## 280 1.1235 nan 0.0010 0.0002
## 300 1.1125 nan 0.0010 0.0003
## 320 1.1016 nan 0.0010 0.0002
## 340 1.0914 nan 0.0010 0.0002
## 360 1.0815 nan 0.0010 0.0002
## 380 1.0714 nan 0.0010 0.0002
## 400 1.0620 nan 0.0010 0.0002
## 420 1.0530 nan 0.0010 0.0002
## 440 1.0439 nan 0.0010 0.0002
## 460 1.0350 nan 0.0010 0.0002
## 480 1.0265 nan 0.0010 0.0002
## 500 1.0183 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0005
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3179 nan 0.0010 0.0005
## 4 1.3170 nan 0.0010 0.0004
## 5 1.3160 nan 0.0010 0.0005
## 6 1.3150 nan 0.0010 0.0004
## 7 1.3141 nan 0.0010 0.0004
## 8 1.3131 nan 0.0010 0.0005
## 9 1.3122 nan 0.0010 0.0004
## 10 1.3112 nan 0.0010 0.0004
## 20 1.3019 nan 0.0010 0.0004
## 40 1.2842 nan 0.0010 0.0004
## 60 1.2670 nan 0.0010 0.0004
## 80 1.2506 nan 0.0010 0.0004
## 100 1.2347 nan 0.0010 0.0004
## 120 1.2189 nan 0.0010 0.0004
## 140 1.2039 nan 0.0010 0.0003
## 160 1.1898 nan 0.0010 0.0004
## 180 1.1754 nan 0.0010 0.0003
## 200 1.1618 nan 0.0010 0.0003
## 220 1.1482 nan 0.0010 0.0003
## 240 1.1354 nan 0.0010 0.0002
## 260 1.1230 nan 0.0010 0.0003
## 280 1.1108 nan 0.0010 0.0003
## 300 1.0990 nan 0.0010 0.0003
## 320 1.0874 nan 0.0010 0.0002
## 340 1.0762 nan 0.0010 0.0003
## 360 1.0653 nan 0.0010 0.0002
## 380 1.0551 nan 0.0010 0.0002
## 400 1.0448 nan 0.0010 0.0002
## 420 1.0346 nan 0.0010 0.0002
## 440 1.0251 nan 0.0010 0.0002
## 460 1.0158 nan 0.0010 0.0002
## 480 1.0067 nan 0.0010 0.0002
## 500 0.9977 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3196 nan 0.0010 0.0004
## 2 1.3186 nan 0.0010 0.0004
## 3 1.3176 nan 0.0010 0.0004
## 4 1.3167 nan 0.0010 0.0004
## 5 1.3157 nan 0.0010 0.0004
## 6 1.3148 nan 0.0010 0.0004
## 7 1.3139 nan 0.0010 0.0004
## 8 1.3129 nan 0.0010 0.0004
## 9 1.3119 nan 0.0010 0.0004
## 10 1.3111 nan 0.0010 0.0004
## 20 1.3019 nan 0.0010 0.0004
## 40 1.2837 nan 0.0010 0.0004
## 60 1.2669 nan 0.0010 0.0004
## 80 1.2504 nan 0.0010 0.0004
## 100 1.2342 nan 0.0010 0.0004
## 120 1.2189 nan 0.0010 0.0004
## 140 1.2034 nan 0.0010 0.0003
## 160 1.1888 nan 0.0010 0.0003
## 180 1.1747 nan 0.0010 0.0003
## 200 1.1615 nan 0.0010 0.0003
## 220 1.1482 nan 0.0010 0.0003
## 240 1.1352 nan 0.0010 0.0003
## 260 1.1228 nan 0.0010 0.0002
## 280 1.1106 nan 0.0010 0.0002
## 300 1.0988 nan 0.0010 0.0003
## 320 1.0875 nan 0.0010 0.0003
## 340 1.0763 nan 0.0010 0.0002
## 360 1.0658 nan 0.0010 0.0002
## 380 1.0552 nan 0.0010 0.0002
## 400 1.0451 nan 0.0010 0.0002
## 420 1.0354 nan 0.0010 0.0002
## 440 1.0258 nan 0.0010 0.0002
## 460 1.0162 nan 0.0010 0.0002
## 480 1.0070 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3180 nan 0.0010 0.0004
## 4 1.3171 nan 0.0010 0.0005
## 5 1.3161 nan 0.0010 0.0004
## 6 1.3151 nan 0.0010 0.0004
## 7 1.3141 nan 0.0010 0.0004
## 8 1.3133 nan 0.0010 0.0004
## 9 1.3124 nan 0.0010 0.0005
## 10 1.3114 nan 0.0010 0.0005
## 20 1.3023 nan 0.0010 0.0004
## 40 1.2850 nan 0.0010 0.0004
## 60 1.2677 nan 0.0010 0.0004
## 80 1.2514 nan 0.0010 0.0004
## 100 1.2359 nan 0.0010 0.0003
## 120 1.2205 nan 0.0010 0.0003
## 140 1.2055 nan 0.0010 0.0003
## 160 1.1912 nan 0.0010 0.0003
## 180 1.1772 nan 0.0010 0.0003
## 200 1.1637 nan 0.0010 0.0003
## 220 1.1506 nan 0.0010 0.0003
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## 260 1.1251 nan 0.0010 0.0003
## 280 1.1133 nan 0.0010 0.0003
## 300 1.1016 nan 0.0010 0.0002
## 320 1.0902 nan 0.0010 0.0003
## 340 1.0794 nan 0.0010 0.0002
## 360 1.0687 nan 0.0010 0.0002
## 380 1.0585 nan 0.0010 0.0002
## 400 1.0484 nan 0.0010 0.0002
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## 440 1.0290 nan 0.0010 0.0002
## 460 1.0196 nan 0.0010 0.0002
## 480 1.0107 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3187 nan 0.0010 0.0005
## 3 1.3177 nan 0.0010 0.0004
## 4 1.3167 nan 0.0010 0.0005
## 5 1.3156 nan 0.0010 0.0005
## 6 1.3147 nan 0.0010 0.0004
## 7 1.3138 nan 0.0010 0.0004
## 8 1.3129 nan 0.0010 0.0005
## 9 1.3120 nan 0.0010 0.0004
## 10 1.3111 nan 0.0010 0.0004
## 20 1.3015 nan 0.0010 0.0004
## 40 1.2827 nan 0.0010 0.0004
## 60 1.2646 nan 0.0010 0.0004
## 80 1.2470 nan 0.0010 0.0004
## 100 1.2300 nan 0.0010 0.0004
## 120 1.2137 nan 0.0010 0.0004
## 140 1.1982 nan 0.0010 0.0003
## 160 1.1831 nan 0.0010 0.0004
## 180 1.1683 nan 0.0010 0.0003
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## 320 1.0776 nan 0.0010 0.0003
## 340 1.0661 nan 0.0010 0.0002
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## 380 1.0437 nan 0.0010 0.0002
## 400 1.0330 nan 0.0010 0.0002
## 420 1.0226 nan 0.0010 0.0002
## 440 1.0125 nan 0.0010 0.0002
## 460 1.0027 nan 0.0010 0.0002
## 480 0.9930 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3187 nan 0.0010 0.0004
## 3 1.3178 nan 0.0010 0.0005
## 4 1.3169 nan 0.0010 0.0004
## 5 1.3159 nan 0.0010 0.0005
## 6 1.3149 nan 0.0010 0.0005
## 7 1.3140 nan 0.0010 0.0004
## 8 1.3130 nan 0.0010 0.0004
## 9 1.3122 nan 0.0010 0.0004
## 10 1.3112 nan 0.0010 0.0004
## 20 1.3015 nan 0.0010 0.0004
## 40 1.2830 nan 0.0010 0.0004
## 60 1.2652 nan 0.0010 0.0004
## 80 1.2478 nan 0.0010 0.0004
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## 120 1.2151 nan 0.0010 0.0003
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## 500 0.9866 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0005
## 2 1.3187 nan 0.0010 0.0004
## 3 1.3178 nan 0.0010 0.0004
## 4 1.3168 nan 0.0010 0.0005
## 5 1.3159 nan 0.0010 0.0005
## 6 1.3149 nan 0.0010 0.0004
## 7 1.3139 nan 0.0010 0.0005
## 8 1.3129 nan 0.0010 0.0004
## 9 1.3120 nan 0.0010 0.0004
## 10 1.3110 nan 0.0010 0.0004
## 20 1.3017 nan 0.0010 0.0004
## 40 1.2837 nan 0.0010 0.0005
## 60 1.2661 nan 0.0010 0.0004
## 80 1.2489 nan 0.0010 0.0003
## 100 1.2325 nan 0.0010 0.0004
## 120 1.2167 nan 0.0010 0.0003
## 140 1.2015 nan 0.0010 0.0003
## 160 1.1866 nan 0.0010 0.0003
## 180 1.1723 nan 0.0010 0.0003
## 200 1.1583 nan 0.0010 0.0003
## 220 1.1448 nan 0.0010 0.0003
## 240 1.1318 nan 0.0010 0.0003
## 260 1.1191 nan 0.0010 0.0003
## 280 1.1067 nan 0.0010 0.0002
## 300 1.0945 nan 0.0010 0.0003
## 320 1.0830 nan 0.0010 0.0002
## 340 1.0715 nan 0.0010 0.0003
## 360 1.0605 nan 0.0010 0.0002
## 380 1.0497 nan 0.0010 0.0002
## 400 1.0392 nan 0.0010 0.0002
## 420 1.0290 nan 0.0010 0.0002
## 440 1.0192 nan 0.0010 0.0002
## 460 1.0099 nan 0.0010 0.0002
## 480 1.0005 nan 0.0010 0.0002
## 500 0.9913 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3110 nan 0.0100 0.0041
## 2 1.3017 nan 0.0100 0.0041
## 3 1.2933 nan 0.0100 0.0040
## 4 1.2844 nan 0.0100 0.0043
## 5 1.2765 nan 0.0100 0.0032
## 6 1.2675 nan 0.0100 0.0038
## 7 1.2606 nan 0.0100 0.0034
## 8 1.2536 nan 0.0100 0.0028
## 9 1.2465 nan 0.0100 0.0033
## 10 1.2395 nan 0.0100 0.0031
## 20 1.1687 nan 0.0100 0.0030
## 40 1.0613 nan 0.0100 0.0022
## 60 0.9758 nan 0.0100 0.0015
## 80 0.9116 nan 0.0100 0.0011
## 100 0.8591 nan 0.0100 0.0009
## 120 0.8164 nan 0.0100 0.0008
## 140 0.7823 nan 0.0100 0.0003
## 160 0.7524 nan 0.0100 0.0004
## 180 0.7274 nan 0.0100 0.0003
## 200 0.7041 nan 0.0100 0.0002
## 220 0.6844 nan 0.0100 0.0002
## 240 0.6666 nan 0.0100 0.0002
## 260 0.6513 nan 0.0100 0.0001
## 280 0.6366 nan 0.0100 -0.0001
## 300 0.6224 nan 0.0100 0.0001
## 320 0.6100 nan 0.0100 0.0000
## 340 0.5968 nan 0.0100 0.0002
## 360 0.5860 nan 0.0100 0.0000
## 380 0.5753 nan 0.0100 0.0002
## 400 0.5649 nan 0.0100 -0.0001
## 420 0.5543 nan 0.0100 0.0001
## 440 0.5455 nan 0.0100 0.0000
## 460 0.5374 nan 0.0100 0.0000
## 480 0.5288 nan 0.0100 -0.0001
## 500 0.5209 nan 0.0100 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3120 nan 0.0100 0.0041
## 2 1.3036 nan 0.0100 0.0039
## 3 1.2956 nan 0.0100 0.0035
## 4 1.2872 nan 0.0100 0.0042
## 5 1.2796 nan 0.0100 0.0033
## 6 1.2706 nan 0.0100 0.0043
## 7 1.2623 nan 0.0100 0.0040
## 8 1.2542 nan 0.0100 0.0038
## 9 1.2468 nan 0.0100 0.0033
## 10 1.2402 nan 0.0100 0.0029
## 20 1.1726 nan 0.0100 0.0027
## 40 1.0637 nan 0.0100 0.0020
## 60 0.9799 nan 0.0100 0.0016
## 80 0.9118 nan 0.0100 0.0011
## 100 0.8572 nan 0.0100 0.0009
## 120 0.8143 nan 0.0100 0.0007
## 140 0.7795 nan 0.0100 0.0004
## 160 0.7498 nan 0.0100 0.0003
## 180 0.7246 nan 0.0100 0.0004
## 200 0.7048 nan 0.0100 0.0002
## 220 0.6851 nan 0.0100 0.0001
## 240 0.6675 nan 0.0100 0.0002
## 260 0.6525 nan 0.0100 0.0001
## 280 0.6383 nan 0.0100 0.0000
## 300 0.6254 nan 0.0100 -0.0000
## 320 0.6128 nan 0.0100 -0.0000
## 340 0.6021 nan 0.0100 0.0001
## 360 0.5920 nan 0.0100 0.0001
## 380 0.5817 nan 0.0100 -0.0001
## 400 0.5712 nan 0.0100 0.0000
## 420 0.5620 nan 0.0100 0.0001
## 440 0.5531 nan 0.0100 -0.0002
## 460 0.5450 nan 0.0100 -0.0001
## 480 0.5367 nan 0.0100 -0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3117 nan 0.0100 0.0041
## 2 1.3024 nan 0.0100 0.0041
## 3 1.2944 nan 0.0100 0.0040
## 4 1.2859 nan 0.0100 0.0039
## 5 1.2781 nan 0.0100 0.0037
## 6 1.2705 nan 0.0100 0.0035
## 7 1.2629 nan 0.0100 0.0034
## 8 1.2553 nan 0.0100 0.0039
## 9 1.2472 nan 0.0100 0.0036
## 10 1.2395 nan 0.0100 0.0037
## 20 1.1707 nan 0.0100 0.0026
## 40 1.0607 nan 0.0100 0.0018
## 60 0.9794 nan 0.0100 0.0014
## 80 0.9123 nan 0.0100 0.0011
## 100 0.8599 nan 0.0100 0.0008
## 120 0.8173 nan 0.0100 0.0004
## 140 0.7842 nan 0.0100 0.0004
## 160 0.7543 nan 0.0100 0.0004
## 180 0.7297 nan 0.0100 0.0002
## 200 0.7078 nan 0.0100 0.0002
## 220 0.6898 nan 0.0100 0.0002
## 240 0.6735 nan 0.0100 0.0003
## 260 0.6591 nan 0.0100 0.0001
## 280 0.6457 nan 0.0100 0.0001
## 300 0.6333 nan 0.0100 0.0000
## 320 0.6219 nan 0.0100 0.0001
## 340 0.6108 nan 0.0100 0.0000
## 360 0.6007 nan 0.0100 0.0001
## 380 0.5908 nan 0.0100 0.0000
## 400 0.5809 nan 0.0100 -0.0000
## 420 0.5717 nan 0.0100 0.0000
## 440 0.5631 nan 0.0100 -0.0000
## 460 0.5553 nan 0.0100 0.0000
## 480 0.5470 nan 0.0100 -0.0000
## 500 0.5395 nan 0.0100 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3126 nan 0.0100 0.0038
## 2 1.3033 nan 0.0100 0.0043
## 3 1.2935 nan 0.0100 0.0044
## 4 1.2838 nan 0.0100 0.0042
## 5 1.2760 nan 0.0100 0.0036
## 6 1.2670 nan 0.0100 0.0039
## 7 1.2579 nan 0.0100 0.0037
## 8 1.2495 nan 0.0100 0.0037
## 9 1.2411 nan 0.0100 0.0038
## 10 1.2335 nan 0.0100 0.0033
## 20 1.1602 nan 0.0100 0.0034
## 40 1.0433 nan 0.0100 0.0023
## 60 0.9563 nan 0.0100 0.0012
## 80 0.8846 nan 0.0100 0.0010
## 100 0.8296 nan 0.0100 0.0013
## 120 0.7852 nan 0.0100 0.0008
## 140 0.7483 nan 0.0100 0.0002
## 160 0.7176 nan 0.0100 0.0005
## 180 0.6923 nan 0.0100 0.0003
## 200 0.6686 nan 0.0100 0.0001
## 220 0.6464 nan 0.0100 0.0002
## 240 0.6275 nan 0.0100 0.0002
## 260 0.6100 nan 0.0100 0.0001
## 280 0.5932 nan 0.0100 0.0001
## 300 0.5790 nan 0.0100 -0.0001
## 320 0.5644 nan 0.0100 0.0001
## 340 0.5521 nan 0.0100 -0.0002
## 360 0.5398 nan 0.0100 0.0001
## 380 0.5283 nan 0.0100 -0.0001
## 400 0.5157 nan 0.0100 -0.0000
## 420 0.5050 nan 0.0100 -0.0001
## 440 0.4955 nan 0.0100 0.0001
## 460 0.4855 nan 0.0100 -0.0001
## 480 0.4762 nan 0.0100 -0.0001
## 500 0.4674 nan 0.0100 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3108 nan 0.0100 0.0044
## 2 1.3018 nan 0.0100 0.0036
## 3 1.2922 nan 0.0100 0.0042
## 4 1.2833 nan 0.0100 0.0037
## 5 1.2753 nan 0.0100 0.0036
## 6 1.2670 nan 0.0100 0.0037
## 7 1.2592 nan 0.0100 0.0033
## 8 1.2509 nan 0.0100 0.0039
## 9 1.2421 nan 0.0100 0.0039
## 10 1.2335 nan 0.0100 0.0035
## 20 1.1636 nan 0.0100 0.0028
## 40 1.0487 nan 0.0100 0.0020
## 60 0.9613 nan 0.0100 0.0013
## 80 0.8921 nan 0.0100 0.0012
## 100 0.8384 nan 0.0100 0.0009
## 120 0.7937 nan 0.0100 0.0009
## 140 0.7561 nan 0.0100 0.0003
## 160 0.7239 nan 0.0100 0.0003
## 180 0.6975 nan 0.0100 0.0003
## 200 0.6738 nan 0.0100 0.0001
## 220 0.6537 nan 0.0100 0.0001
## 240 0.6342 nan 0.0100 0.0001
## 260 0.6168 nan 0.0100 -0.0001
## 280 0.6011 nan 0.0100 0.0000
## 300 0.5866 nan 0.0100 0.0001
## 320 0.5737 nan 0.0100 0.0001
## 340 0.5608 nan 0.0100 0.0000
## 360 0.5479 nan 0.0100 -0.0001
## 380 0.5372 nan 0.0100 -0.0000
## 400 0.5268 nan 0.0100 -0.0000
## 420 0.5161 nan 0.0100 -0.0000
## 440 0.5071 nan 0.0100 -0.0001
## 460 0.4969 nan 0.0100 0.0000
## 480 0.4882 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3115 nan 0.0100 0.0038
## 2 1.3021 nan 0.0100 0.0043
## 3 1.2928 nan 0.0100 0.0043
## 4 1.2840 nan 0.0100 0.0042
## 5 1.2750 nan 0.0100 0.0038
## 6 1.2663 nan 0.0100 0.0041
## 7 1.2581 nan 0.0100 0.0033
## 8 1.2507 nan 0.0100 0.0033
## 9 1.2424 nan 0.0100 0.0037
## 10 1.2342 nan 0.0100 0.0039
## 20 1.1608 nan 0.0100 0.0030
## 40 1.0475 nan 0.0100 0.0020
## 60 0.9612 nan 0.0100 0.0017
## 80 0.8909 nan 0.0100 0.0012
## 100 0.8385 nan 0.0100 0.0009
## 120 0.7952 nan 0.0100 0.0006
## 140 0.7591 nan 0.0100 0.0005
## 160 0.7287 nan 0.0100 0.0003
## 180 0.7023 nan 0.0100 0.0002
## 200 0.6796 nan 0.0100 0.0003
## 220 0.6596 nan 0.0100 0.0001
## 240 0.6423 nan 0.0100 0.0000
## 260 0.6261 nan 0.0100 0.0002
## 280 0.6121 nan 0.0100 -0.0000
## 300 0.5978 nan 0.0100 -0.0001
## 320 0.5849 nan 0.0100 -0.0000
## 340 0.5728 nan 0.0100 -0.0000
## 360 0.5617 nan 0.0100 0.0000
## 380 0.5501 nan 0.0100 -0.0001
## 400 0.5392 nan 0.0100 0.0000
## 420 0.5298 nan 0.0100 -0.0001
## 440 0.5206 nan 0.0100 -0.0001
## 460 0.5105 nan 0.0100 -0.0001
## 480 0.5013 nan 0.0100 -0.0000
## 500 0.4925 nan 0.0100 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3104 nan 0.0100 0.0045
## 2 1.3004 nan 0.0100 0.0046
## 3 1.2911 nan 0.0100 0.0043
## 4 1.2815 nan 0.0100 0.0044
## 5 1.2729 nan 0.0100 0.0039
## 6 1.2644 nan 0.0100 0.0039
## 7 1.2549 nan 0.0100 0.0043
## 8 1.2467 nan 0.0100 0.0037
## 9 1.2377 nan 0.0100 0.0043
## 10 1.2290 nan 0.0100 0.0036
## 20 1.1518 nan 0.0100 0.0032
## 40 1.0306 nan 0.0100 0.0019
## 60 0.9393 nan 0.0100 0.0016
## 80 0.8682 nan 0.0100 0.0011
## 100 0.8121 nan 0.0100 0.0009
## 120 0.7648 nan 0.0100 0.0006
## 140 0.7246 nan 0.0100 0.0005
## 160 0.6926 nan 0.0100 0.0005
## 180 0.6649 nan 0.0100 0.0001
## 200 0.6404 nan 0.0100 0.0003
## 220 0.6172 nan 0.0100 -0.0000
## 240 0.5968 nan 0.0100 0.0001
## 260 0.5779 nan 0.0100 0.0001
## 280 0.5609 nan 0.0100 0.0002
## 300 0.5455 nan 0.0100 0.0001
## 320 0.5309 nan 0.0100 -0.0001
## 340 0.5166 nan 0.0100 0.0000
## 360 0.5037 nan 0.0100 0.0000
## 380 0.4908 nan 0.0100 -0.0002
## 400 0.4788 nan 0.0100 -0.0000
## 420 0.4674 nan 0.0100 0.0001
## 440 0.4563 nan 0.0100 -0.0000
## 460 0.4452 nan 0.0100 -0.0002
## 480 0.4347 nan 0.0100 -0.0001
## 500 0.4254 nan 0.0100 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3099 nan 0.0100 0.0049
## 2 1.3013 nan 0.0100 0.0040
## 3 1.2926 nan 0.0100 0.0038
## 4 1.2833 nan 0.0100 0.0040
## 5 1.2742 nan 0.0100 0.0043
## 6 1.2655 nan 0.0100 0.0037
## 7 1.2569 nan 0.0100 0.0039
## 8 1.2483 nan 0.0100 0.0040
## 9 1.2400 nan 0.0100 0.0039
## 10 1.2315 nan 0.0100 0.0040
## 20 1.1550 nan 0.0100 0.0033
## 40 1.0333 nan 0.0100 0.0020
## 60 0.9420 nan 0.0100 0.0016
## 80 0.8706 nan 0.0100 0.0013
## 100 0.8122 nan 0.0100 0.0009
## 120 0.7684 nan 0.0100 0.0005
## 140 0.7293 nan 0.0100 0.0006
## 160 0.6964 nan 0.0100 0.0005
## 180 0.6682 nan 0.0100 0.0005
## 200 0.6442 nan 0.0100 0.0000
## 220 0.6227 nan 0.0100 0.0003
## 240 0.6028 nan 0.0100 0.0003
## 260 0.5844 nan 0.0100 0.0001
## 280 0.5685 nan 0.0100 -0.0000
## 300 0.5529 nan 0.0100 0.0001
## 320 0.5386 nan 0.0100 0.0001
## 340 0.5251 nan 0.0100 -0.0000
## 360 0.5119 nan 0.0100 -0.0001
## 380 0.4997 nan 0.0100 -0.0001
## 400 0.4886 nan 0.0100 0.0000
## 420 0.4779 nan 0.0100 0.0001
## 440 0.4672 nan 0.0100 -0.0001
## 460 0.4565 nan 0.0100 0.0000
## 480 0.4466 nan 0.0100 0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3098 nan 0.0100 0.0046
## 2 1.2997 nan 0.0100 0.0049
## 3 1.2918 nan 0.0100 0.0034
## 4 1.2832 nan 0.0100 0.0039
## 5 1.2732 nan 0.0100 0.0044
## 6 1.2650 nan 0.0100 0.0035
## 7 1.2562 nan 0.0100 0.0041
## 8 1.2479 nan 0.0100 0.0036
## 9 1.2395 nan 0.0100 0.0038
## 10 1.2315 nan 0.0100 0.0035
## 20 1.1594 nan 0.0100 0.0033
## 40 1.0399 nan 0.0100 0.0024
## 60 0.9481 nan 0.0100 0.0017
## 80 0.8771 nan 0.0100 0.0011
## 100 0.8226 nan 0.0100 0.0010
## 120 0.7780 nan 0.0100 0.0007
## 140 0.7403 nan 0.0100 0.0004
## 160 0.7082 nan 0.0100 0.0004
## 180 0.6797 nan 0.0100 0.0004
## 200 0.6568 nan 0.0100 0.0002
## 220 0.6354 nan 0.0100 0.0000
## 240 0.6166 nan 0.0100 0.0001
## 260 0.6004 nan 0.0100 -0.0002
## 280 0.5838 nan 0.0100 -0.0001
## 300 0.5689 nan 0.0100 0.0001
## 320 0.5549 nan 0.0100 -0.0001
## 340 0.5403 nan 0.0100 -0.0000
## 360 0.5278 nan 0.0100 0.0000
## 380 0.5161 nan 0.0100 0.0001
## 400 0.5054 nan 0.0100 -0.0000
## 420 0.4937 nan 0.0100 -0.0002
## 440 0.4825 nan 0.0100 -0.0001
## 460 0.4728 nan 0.0100 -0.0000
## 480 0.4630 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2328 nan 0.1000 0.0408
## 2 1.1660 nan 0.1000 0.0300
## 3 1.1001 nan 0.1000 0.0275
## 4 1.0456 nan 0.1000 0.0216
## 5 1.0005 nan 0.1000 0.0166
## 6 0.9660 nan 0.1000 0.0144
## 7 0.9329 nan 0.1000 0.0118
## 8 0.9005 nan 0.1000 0.0144
## 9 0.8763 nan 0.1000 0.0114
## 10 0.8529 nan 0.1000 0.0075
## 20 0.7005 nan 0.1000 0.0036
## 40 0.5721 nan 0.1000 -0.0002
## 60 0.4840 nan 0.1000 -0.0017
## 80 0.4261 nan 0.1000 -0.0008
## 100 0.3813 nan 0.1000 -0.0008
## 120 0.3308 nan 0.1000 -0.0005
## 140 0.2943 nan 0.1000 -0.0002
## 160 0.2635 nan 0.1000 -0.0005
## 180 0.2389 nan 0.1000 -0.0004
## 200 0.2161 nan 0.1000 0.0000
## 220 0.1952 nan 0.1000 -0.0002
## 240 0.1752 nan 0.1000 -0.0004
## 260 0.1591 nan 0.1000 -0.0004
## 280 0.1435 nan 0.1000 -0.0004
## 300 0.1322 nan 0.1000 -0.0006
## 320 0.1218 nan 0.1000 -0.0001
## 340 0.1104 nan 0.1000 -0.0002
## 360 0.1006 nan 0.1000 -0.0001
## 380 0.0921 nan 0.1000 -0.0001
## 400 0.0851 nan 0.1000 0.0000
## 420 0.0790 nan 0.1000 -0.0003
## 440 0.0724 nan 0.1000 -0.0002
## 460 0.0673 nan 0.1000 -0.0003
## 480 0.0619 nan 0.1000 -0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2348 nan 0.1000 0.0406
## 2 1.1695 nan 0.1000 0.0286
## 3 1.1119 nan 0.1000 0.0244
## 4 1.0572 nan 0.1000 0.0246
## 5 1.0078 nan 0.1000 0.0236
## 6 0.9701 nan 0.1000 0.0157
## 7 0.9364 nan 0.1000 0.0133
## 8 0.9032 nan 0.1000 0.0113
## 9 0.8738 nan 0.1000 0.0100
## 10 0.8444 nan 0.1000 0.0122
## 20 0.7007 nan 0.1000 0.0022
## 40 0.5764 nan 0.1000 -0.0007
## 60 0.4957 nan 0.1000 -0.0014
## 80 0.4310 nan 0.1000 -0.0001
## 100 0.3822 nan 0.1000 0.0003
## 120 0.3372 nan 0.1000 -0.0007
## 140 0.3013 nan 0.1000 -0.0005
## 160 0.2728 nan 0.1000 -0.0006
## 180 0.2486 nan 0.1000 -0.0003
## 200 0.2242 nan 0.1000 -0.0014
## 220 0.2013 nan 0.1000 -0.0008
## 240 0.1806 nan 0.1000 -0.0004
## 260 0.1662 nan 0.1000 -0.0002
## 280 0.1541 nan 0.1000 -0.0006
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## 320 0.1293 nan 0.1000 -0.0001
## 340 0.1188 nan 0.1000 -0.0003
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## 480 0.0688 nan 0.1000 -0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2353 nan 0.1000 0.0386
## 2 1.1638 nan 0.1000 0.0321
## 3 1.1038 nan 0.1000 0.0252
## 4 1.0540 nan 0.1000 0.0192
## 5 1.0129 nan 0.1000 0.0173
## 6 0.9681 nan 0.1000 0.0189
## 7 0.9338 nan 0.1000 0.0163
## 8 0.9038 nan 0.1000 0.0149
## 9 0.8784 nan 0.1000 0.0092
## 10 0.8547 nan 0.1000 0.0098
## 20 0.7055 nan 0.1000 0.0048
## 40 0.5862 nan 0.1000 0.0001
## 60 0.5104 nan 0.1000 -0.0004
## 80 0.4554 nan 0.1000 -0.0011
## 100 0.4058 nan 0.1000 -0.0002
## 120 0.3648 nan 0.1000 -0.0008
## 140 0.3302 nan 0.1000 -0.0002
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## 180 0.2653 nan 0.1000 -0.0001
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## 220 0.2172 nan 0.1000 -0.0008
## 240 0.1992 nan 0.1000 -0.0006
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## 280 0.1654 nan 0.1000 -0.0003
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## 340 0.1281 nan 0.1000 -0.0002
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## 380 0.1086 nan 0.1000 -0.0003
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2358 nan 0.1000 0.0398
## 2 1.1656 nan 0.1000 0.0285
## 3 1.1089 nan 0.1000 0.0229
## 4 1.0461 nan 0.1000 0.0266
## 5 0.9991 nan 0.1000 0.0205
## 6 0.9572 nan 0.1000 0.0159
## 7 0.9212 nan 0.1000 0.0146
## 8 0.8797 nan 0.1000 0.0146
## 9 0.8492 nan 0.1000 0.0101
## 10 0.8277 nan 0.1000 0.0048
## 20 0.6697 nan 0.1000 0.0034
## 40 0.5286 nan 0.1000 -0.0003
## 60 0.4327 nan 0.1000 -0.0005
## 80 0.3633 nan 0.1000 0.0002
## 100 0.3117 nan 0.1000 -0.0005
## 120 0.2727 nan 0.1000 -0.0012
## 140 0.2385 nan 0.1000 -0.0003
## 160 0.2063 nan 0.1000 -0.0007
## 180 0.1782 nan 0.1000 -0.0001
## 200 0.1584 nan 0.1000 -0.0004
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## 280 0.0993 nan 0.1000 -0.0005
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## 320 0.0788 nan 0.1000 -0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2315 nan 0.1000 0.0415
## 2 1.1550 nan 0.1000 0.0314
## 3 1.0971 nan 0.1000 0.0223
## 4 1.0462 nan 0.1000 0.0220
## 5 1.0048 nan 0.1000 0.0195
## 6 0.9576 nan 0.1000 0.0204
## 7 0.9168 nan 0.1000 0.0180
## 8 0.8856 nan 0.1000 0.0128
## 9 0.8572 nan 0.1000 0.0129
## 10 0.8286 nan 0.1000 0.0094
## 20 0.6789 nan 0.1000 0.0025
## 40 0.5419 nan 0.1000 -0.0015
## 60 0.4533 nan 0.1000 -0.0012
## 80 0.3828 nan 0.1000 -0.0005
## 100 0.3252 nan 0.1000 -0.0002
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## 140 0.2459 nan 0.1000 -0.0010
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## 180 0.1909 nan 0.1000 -0.0009
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## 240 0.1346 nan 0.1000 -0.0005
## 260 0.1189 nan 0.1000 -0.0002
## 280 0.1077 nan 0.1000 -0.0003
## 300 0.0974 nan 0.1000 -0.0002
## 320 0.0885 nan 0.1000 -0.0004
## 340 0.0794 nan 0.1000 -0.0005
## 360 0.0719 nan 0.1000 -0.0004
## 380 0.0644 nan 0.1000 -0.0004
## 400 0.0575 nan 0.1000 -0.0001
## 420 0.0519 nan 0.1000 -0.0001
## 440 0.0470 nan 0.1000 -0.0001
## 460 0.0422 nan 0.1000 -0.0001
## 480 0.0379 nan 0.1000 -0.0002
## 500 0.0346 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2231 nan 0.1000 0.0407
## 2 1.1473 nan 0.1000 0.0323
## 3 1.0870 nan 0.1000 0.0279
## 4 1.0345 nan 0.1000 0.0240
## 5 0.9764 nan 0.1000 0.0220
## 6 0.9393 nan 0.1000 0.0176
## 7 0.9049 nan 0.1000 0.0132
## 8 0.8746 nan 0.1000 0.0113
## 9 0.8463 nan 0.1000 0.0107
## 10 0.8229 nan 0.1000 0.0095
## 20 0.6721 nan 0.1000 0.0024
## 40 0.5339 nan 0.1000 -0.0005
## 60 0.4523 nan 0.1000 -0.0009
## 80 0.3861 nan 0.1000 -0.0019
## 100 0.3333 nan 0.1000 -0.0006
## 120 0.2895 nan 0.1000 -0.0014
## 140 0.2570 nan 0.1000 -0.0011
## 160 0.2229 nan 0.1000 -0.0003
## 180 0.1966 nan 0.1000 -0.0009
## 200 0.1756 nan 0.1000 -0.0007
## 220 0.1562 nan 0.1000 -0.0009
## 240 0.1428 nan 0.1000 -0.0002
## 260 0.1286 nan 0.1000 -0.0004
## 280 0.1168 nan 0.1000 -0.0005
## 300 0.1045 nan 0.1000 -0.0002
## 320 0.0945 nan 0.1000 -0.0001
## 340 0.0852 nan 0.1000 -0.0007
## 360 0.0768 nan 0.1000 -0.0003
## 380 0.0702 nan 0.1000 -0.0004
## 400 0.0634 nan 0.1000 -0.0001
## 420 0.0576 nan 0.1000 -0.0003
## 440 0.0513 nan 0.1000 -0.0003
## 460 0.0460 nan 0.1000 -0.0002
## 480 0.0415 nan 0.1000 -0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2284 nan 0.1000 0.0422
## 2 1.1509 nan 0.1000 0.0326
## 3 1.0852 nan 0.1000 0.0289
## 4 1.0293 nan 0.1000 0.0255
## 5 0.9766 nan 0.1000 0.0185
## 6 0.9329 nan 0.1000 0.0168
## 7 0.8957 nan 0.1000 0.0168
## 8 0.8626 nan 0.1000 0.0112
## 9 0.8298 nan 0.1000 0.0137
## 10 0.8031 nan 0.1000 0.0104
## 20 0.6397 nan 0.1000 0.0035
## 40 0.4810 nan 0.1000 0.0008
## 60 0.3841 nan 0.1000 0.0007
## 80 0.3177 nan 0.1000 -0.0008
## 100 0.2658 nan 0.1000 -0.0008
## 120 0.2263 nan 0.1000 0.0001
## 140 0.1894 nan 0.1000 -0.0003
## 160 0.1627 nan 0.1000 -0.0004
## 180 0.1388 nan 0.1000 0.0002
## 200 0.1208 nan 0.1000 -0.0004
## 220 0.1042 nan 0.1000 0.0000
## 240 0.0900 nan 0.1000 -0.0002
## 260 0.0788 nan 0.1000 -0.0004
## 280 0.0693 nan 0.1000 -0.0001
## 300 0.0605 nan 0.1000 -0.0002
## 320 0.0537 nan 0.1000 -0.0000
## 340 0.0471 nan 0.1000 -0.0002
## 360 0.0411 nan 0.1000 -0.0001
## 380 0.0362 nan 0.1000 0.0000
## 400 0.0324 nan 0.1000 -0.0001
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## 460 0.0223 nan 0.1000 -0.0001
## 480 0.0199 nan 0.1000 -0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2286 nan 0.1000 0.0466
## 2 1.1516 nan 0.1000 0.0371
## 3 1.0864 nan 0.1000 0.0264
## 4 1.0274 nan 0.1000 0.0259
## 5 0.9793 nan 0.1000 0.0215
## 6 0.9409 nan 0.1000 0.0128
## 7 0.9052 nan 0.1000 0.0140
## 8 0.8734 nan 0.1000 0.0127
## 9 0.8448 nan 0.1000 0.0109
## 10 0.8176 nan 0.1000 0.0082
## 20 0.6502 nan 0.1000 0.0031
## 40 0.4996 nan 0.1000 -0.0003
## 60 0.4036 nan 0.1000 0.0003
## 80 0.3271 nan 0.1000 -0.0004
## 100 0.2768 nan 0.1000 -0.0008
## 120 0.2322 nan 0.1000 -0.0008
## 140 0.1966 nan 0.1000 -0.0004
## 160 0.1694 nan 0.1000 -0.0003
## 180 0.1465 nan 0.1000 -0.0002
## 200 0.1264 nan 0.1000 -0.0000
## 220 0.1082 nan 0.1000 0.0002
## 240 0.0947 nan 0.1000 -0.0003
## 260 0.0831 nan 0.1000 -0.0004
## 280 0.0738 nan 0.1000 -0.0002
## 300 0.0651 nan 0.1000 -0.0003
## 320 0.0572 nan 0.1000 -0.0004
## 340 0.0504 nan 0.1000 -0.0002
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## 380 0.0400 nan 0.1000 -0.0003
## 400 0.0350 nan 0.1000 -0.0001
## 420 0.0310 nan 0.1000 -0.0000
## 440 0.0275 nan 0.1000 -0.0001
## 460 0.0245 nan 0.1000 -0.0001
## 480 0.0213 nan 0.1000 -0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2315 nan 0.1000 0.0422
## 2 1.1601 nan 0.1000 0.0308
## 3 1.0905 nan 0.1000 0.0299
## 4 1.0383 nan 0.1000 0.0242
## 5 0.9904 nan 0.1000 0.0215
## 6 0.9467 nan 0.1000 0.0169
## 7 0.9093 nan 0.1000 0.0144
## 8 0.8721 nan 0.1000 0.0157
## 9 0.8446 nan 0.1000 0.0100
## 10 0.8199 nan 0.1000 0.0082
## 20 0.6573 nan 0.1000 0.0022
## 40 0.5216 nan 0.1000 -0.0001
## 60 0.4237 nan 0.1000 -0.0005
## 80 0.3539 nan 0.1000 -0.0015
## 100 0.2961 nan 0.1000 -0.0008
## 120 0.2528 nan 0.1000 -0.0010
## 140 0.2183 nan 0.1000 -0.0010
## 160 0.1867 nan 0.1000 -0.0004
## 180 0.1595 nan 0.1000 -0.0005
## 200 0.1397 nan 0.1000 -0.0005
## 220 0.1210 nan 0.1000 -0.0003
## 240 0.1063 nan 0.1000 -0.0007
## 260 0.0935 nan 0.1000 -0.0002
## 280 0.0823 nan 0.1000 -0.0005
## 300 0.0718 nan 0.1000 -0.0002
## 320 0.0637 nan 0.1000 -0.0003
## 340 0.0566 nan 0.1000 -0.0002
## 360 0.0503 nan 0.1000 -0.0001
## 380 0.0442 nan 0.1000 -0.0001
## 400 0.0394 nan 0.1000 -0.0002
## 420 0.0351 nan 0.1000 -0.0001
## 440 0.0315 nan 0.1000 -0.0001
## 460 0.0279 nan 0.1000 -0.0001
## 480 0.0248 nan 0.1000 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3199 nan 0.0010 0.0003
## 2 1.3190 nan 0.0010 0.0004
## 3 1.3183 nan 0.0010 0.0003
## 4 1.3175 nan 0.0010 0.0004
## 5 1.3166 nan 0.0010 0.0004
## 6 1.3158 nan 0.0010 0.0004
## 7 1.3149 nan 0.0010 0.0004
## 8 1.3141 nan 0.0010 0.0004
## 9 1.3133 nan 0.0010 0.0003
## 10 1.3125 nan 0.0010 0.0004
## 20 1.3046 nan 0.0010 0.0003
## 40 1.2891 nan 0.0010 0.0004
## 60 1.2743 nan 0.0010 0.0003
## 80 1.2596 nan 0.0010 0.0003
## 100 1.2455 nan 0.0010 0.0003
## 120 1.2317 nan 0.0010 0.0003
## 140 1.2182 nan 0.0010 0.0003
## 160 1.2055 nan 0.0010 0.0003
## 180 1.1928 nan 0.0010 0.0003
## 200 1.1806 nan 0.0010 0.0002
## 220 1.1684 nan 0.0010 0.0003
## 240 1.1570 nan 0.0010 0.0003
## 260 1.1458 nan 0.0010 0.0002
## 280 1.1353 nan 0.0010 0.0002
## 300 1.1247 nan 0.0010 0.0002
## 320 1.1146 nan 0.0010 0.0002
## 340 1.1045 nan 0.0010 0.0002
## 360 1.0949 nan 0.0010 0.0002
## 380 1.0852 nan 0.0010 0.0002
## 400 1.0760 nan 0.0010 0.0002
## 420 1.0671 nan 0.0010 0.0001
## 440 1.0582 nan 0.0010 0.0002
## 460 1.0495 nan 0.0010 0.0002
## 480 1.0412 nan 0.0010 0.0002
## 500 1.0332 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3199 nan 0.0010 0.0004
## 2 1.3191 nan 0.0010 0.0003
## 3 1.3184 nan 0.0010 0.0003
## 4 1.3175 nan 0.0010 0.0004
## 5 1.3168 nan 0.0010 0.0003
## 6 1.3160 nan 0.0010 0.0004
## 7 1.3151 nan 0.0010 0.0004
## 8 1.3142 nan 0.0010 0.0004
## 9 1.3135 nan 0.0010 0.0003
## 10 1.3127 nan 0.0010 0.0004
## 20 1.3047 nan 0.0010 0.0004
## 40 1.2892 nan 0.0010 0.0003
## 60 1.2741 nan 0.0010 0.0003
## 80 1.2594 nan 0.0010 0.0004
## 100 1.2453 nan 0.0010 0.0003
## 120 1.2314 nan 0.0010 0.0003
## 140 1.2178 nan 0.0010 0.0003
## 160 1.2051 nan 0.0010 0.0003
## 180 1.1926 nan 0.0010 0.0003
## 200 1.1808 nan 0.0010 0.0002
## 220 1.1689 nan 0.0010 0.0002
## 240 1.1578 nan 0.0010 0.0002
## 260 1.1466 nan 0.0010 0.0002
## 280 1.1360 nan 0.0010 0.0002
## 300 1.1253 nan 0.0010 0.0002
## 320 1.1151 nan 0.0010 0.0002
## 340 1.1050 nan 0.0010 0.0002
## 360 1.0953 nan 0.0010 0.0002
## 380 1.0857 nan 0.0010 0.0002
## 400 1.0764 nan 0.0010 0.0002
## 420 1.0676 nan 0.0010 0.0002
## 440 1.0589 nan 0.0010 0.0002
## 460 1.0504 nan 0.0010 0.0002
## 480 1.0424 nan 0.0010 0.0001
## 500 1.0343 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3181 nan 0.0010 0.0004
## 4 1.3173 nan 0.0010 0.0003
## 5 1.3166 nan 0.0010 0.0004
## 6 1.3158 nan 0.0010 0.0004
## 7 1.3151 nan 0.0010 0.0003
## 8 1.3143 nan 0.0010 0.0004
## 9 1.3135 nan 0.0010 0.0004
## 10 1.3127 nan 0.0010 0.0004
## 20 1.3050 nan 0.0010 0.0003
## 40 1.2895 nan 0.0010 0.0004
## 60 1.2741 nan 0.0010 0.0004
## 80 1.2600 nan 0.0010 0.0003
## 100 1.2462 nan 0.0010 0.0002
## 120 1.2326 nan 0.0010 0.0003
## 140 1.2192 nan 0.0010 0.0003
## 160 1.2065 nan 0.0010 0.0003
## 180 1.1941 nan 0.0010 0.0003
## 200 1.1818 nan 0.0010 0.0002
## 220 1.1703 nan 0.0010 0.0002
## 240 1.1589 nan 0.0010 0.0002
## 260 1.1479 nan 0.0010 0.0002
## 280 1.1372 nan 0.0010 0.0002
## 300 1.1267 nan 0.0010 0.0002
## 320 1.1163 nan 0.0010 0.0002
## 340 1.1063 nan 0.0010 0.0002
## 360 1.0965 nan 0.0010 0.0002
## 380 1.0874 nan 0.0010 0.0002
## 400 1.0780 nan 0.0010 0.0002
## 420 1.0690 nan 0.0010 0.0002
## 440 1.0602 nan 0.0010 0.0002
## 460 1.0518 nan 0.0010 0.0002
## 480 1.0435 nan 0.0010 0.0002
## 500 1.0355 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3180 nan 0.0010 0.0004
## 4 1.3172 nan 0.0010 0.0004
## 5 1.3163 nan 0.0010 0.0004
## 6 1.3154 nan 0.0010 0.0004
## 7 1.3146 nan 0.0010 0.0004
## 8 1.3137 nan 0.0010 0.0004
## 9 1.3128 nan 0.0010 0.0004
## 10 1.3121 nan 0.0010 0.0003
## 20 1.3034 nan 0.0010 0.0003
## 40 1.2867 nan 0.0010 0.0003
## 60 1.2707 nan 0.0010 0.0003
## 80 1.2552 nan 0.0010 0.0004
## 100 1.2402 nan 0.0010 0.0004
## 120 1.2254 nan 0.0010 0.0003
## 140 1.2115 nan 0.0010 0.0003
## 160 1.1977 nan 0.0010 0.0003
## 180 1.1845 nan 0.0010 0.0003
## 200 1.1716 nan 0.0010 0.0002
## 220 1.1592 nan 0.0010 0.0003
## 240 1.1473 nan 0.0010 0.0003
## 260 1.1355 nan 0.0010 0.0002
## 280 1.1243 nan 0.0010 0.0002
## 300 1.1130 nan 0.0010 0.0002
## 320 1.1019 nan 0.0010 0.0002
## 340 1.0914 nan 0.0010 0.0002
## 360 1.0811 nan 0.0010 0.0002
## 380 1.0711 nan 0.0010 0.0002
## 400 1.0614 nan 0.0010 0.0002
## 420 1.0519 nan 0.0010 0.0002
## 440 1.0426 nan 0.0010 0.0002
## 460 1.0334 nan 0.0010 0.0001
## 480 1.0248 nan 0.0010 0.0002
## 500 1.0161 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3180 nan 0.0010 0.0004
## 4 1.3171 nan 0.0010 0.0004
## 5 1.3162 nan 0.0010 0.0003
## 6 1.3154 nan 0.0010 0.0004
## 7 1.3144 nan 0.0010 0.0004
## 8 1.3136 nan 0.0010 0.0003
## 9 1.3128 nan 0.0010 0.0004
## 10 1.3120 nan 0.0010 0.0004
## 20 1.3037 nan 0.0010 0.0003
## 40 1.2872 nan 0.0010 0.0003
## 60 1.2711 nan 0.0010 0.0003
## 80 1.2555 nan 0.0010 0.0003
## 100 1.2405 nan 0.0010 0.0003
## 120 1.2259 nan 0.0010 0.0003
## 140 1.2117 nan 0.0010 0.0003
## 160 1.1979 nan 0.0010 0.0003
## 180 1.1849 nan 0.0010 0.0003
## 200 1.1720 nan 0.0010 0.0003
## 220 1.1596 nan 0.0010 0.0002
## 240 1.1478 nan 0.0010 0.0003
## 260 1.1361 nan 0.0010 0.0003
## 280 1.1247 nan 0.0010 0.0002
## 300 1.1134 nan 0.0010 0.0002
## 320 1.1028 nan 0.0010 0.0002
## 340 1.0923 nan 0.0010 0.0002
## 360 1.0821 nan 0.0010 0.0002
## 380 1.0720 nan 0.0010 0.0002
## 400 1.0623 nan 0.0010 0.0002
## 420 1.0527 nan 0.0010 0.0002
## 440 1.0438 nan 0.0010 0.0002
## 460 1.0348 nan 0.0010 0.0002
## 480 1.0262 nan 0.0010 0.0002
## 500 1.0177 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3180 nan 0.0010 0.0004
## 4 1.3172 nan 0.0010 0.0004
## 5 1.3163 nan 0.0010 0.0004
## 6 1.3155 nan 0.0010 0.0004
## 7 1.3146 nan 0.0010 0.0004
## 8 1.3137 nan 0.0010 0.0004
## 9 1.3129 nan 0.0010 0.0004
## 10 1.3120 nan 0.0010 0.0004
## 20 1.3037 nan 0.0010 0.0004
## 40 1.2874 nan 0.0010 0.0003
## 60 1.2716 nan 0.0010 0.0004
## 80 1.2561 nan 0.0010 0.0003
## 100 1.2413 nan 0.0010 0.0003
## 120 1.2269 nan 0.0010 0.0003
## 140 1.2128 nan 0.0010 0.0003
## 160 1.1992 nan 0.0010 0.0003
## 180 1.1862 nan 0.0010 0.0003
## 200 1.1733 nan 0.0010 0.0003
## 220 1.1611 nan 0.0010 0.0003
## 240 1.1490 nan 0.0010 0.0002
## 260 1.1374 nan 0.0010 0.0003
## 280 1.1264 nan 0.0010 0.0002
## 300 1.1152 nan 0.0010 0.0002
## 320 1.1047 nan 0.0010 0.0002
## 340 1.0941 nan 0.0010 0.0002
## 360 1.0840 nan 0.0010 0.0002
## 380 1.0742 nan 0.0010 0.0002
## 400 1.0644 nan 0.0010 0.0002
## 420 1.0551 nan 0.0010 0.0002
## 440 1.0463 nan 0.0010 0.0001
## 460 1.0373 nan 0.0010 0.0002
## 480 1.0287 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0003
## 3 1.3180 nan 0.0010 0.0004
## 4 1.3171 nan 0.0010 0.0004
## 5 1.3162 nan 0.0010 0.0004
## 6 1.3153 nan 0.0010 0.0004
## 7 1.3144 nan 0.0010 0.0004
## 8 1.3135 nan 0.0010 0.0004
## 9 1.3126 nan 0.0010 0.0004
## 10 1.3117 nan 0.0010 0.0004
## 20 1.3027 nan 0.0010 0.0004
## 40 1.2855 nan 0.0010 0.0004
## 60 1.2686 nan 0.0010 0.0004
## 80 1.2522 nan 0.0010 0.0004
## 100 1.2366 nan 0.0010 0.0003
## 120 1.2214 nan 0.0010 0.0003
## 140 1.2064 nan 0.0010 0.0003
## 160 1.1922 nan 0.0010 0.0003
## 180 1.1784 nan 0.0010 0.0003
## 200 1.1650 nan 0.0010 0.0003
## 220 1.1523 nan 0.0010 0.0002
## 240 1.1398 nan 0.0010 0.0003
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## 280 1.1156 nan 0.0010 0.0003
## 300 1.1042 nan 0.0010 0.0002
## 320 1.0927 nan 0.0010 0.0002
## 340 1.0820 nan 0.0010 0.0002
## 360 1.0714 nan 0.0010 0.0002
## 380 1.0610 nan 0.0010 0.0002
## 400 1.0508 nan 0.0010 0.0002
## 420 1.0410 nan 0.0010 0.0002
## 440 1.0314 nan 0.0010 0.0001
## 460 1.0221 nan 0.0010 0.0002
## 480 1.0129 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3179 nan 0.0010 0.0004
## 4 1.3169 nan 0.0010 0.0004
## 5 1.3160 nan 0.0010 0.0004
## 6 1.3152 nan 0.0010 0.0004
## 7 1.3143 nan 0.0010 0.0003
## 8 1.3133 nan 0.0010 0.0005
## 9 1.3124 nan 0.0010 0.0004
## 10 1.3115 nan 0.0010 0.0004
## 20 1.3027 nan 0.0010 0.0004
## 40 1.2855 nan 0.0010 0.0004
## 60 1.2687 nan 0.0010 0.0004
## 80 1.2523 nan 0.0010 0.0004
## 100 1.2369 nan 0.0010 0.0003
## 120 1.2216 nan 0.0010 0.0004
## 140 1.2069 nan 0.0010 0.0003
## 160 1.1926 nan 0.0010 0.0003
## 180 1.1786 nan 0.0010 0.0003
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## 340 1.0820 nan 0.0010 0.0002
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## 380 1.0611 nan 0.0010 0.0002
## 400 1.0509 nan 0.0010 0.0002
## 420 1.0409 nan 0.0010 0.0002
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## 460 1.0217 nan 0.0010 0.0002
## 480 1.0127 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3190 nan 0.0010 0.0004
## 3 1.3181 nan 0.0010 0.0003
## 4 1.3173 nan 0.0010 0.0004
## 5 1.3164 nan 0.0010 0.0004
## 6 1.3155 nan 0.0010 0.0004
## 7 1.3146 nan 0.0010 0.0003
## 8 1.3138 nan 0.0010 0.0004
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## 10 1.3119 nan 0.0010 0.0004
## 20 1.3031 nan 0.0010 0.0003
## 40 1.2863 nan 0.0010 0.0004
## 60 1.2697 nan 0.0010 0.0003
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## 380 1.0652 nan 0.0010 0.0002
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## 480 1.0182 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3123 nan 0.0100 0.0040
## 2 1.3041 nan 0.0100 0.0031
## 3 1.2962 nan 0.0100 0.0038
## 4 1.2881 nan 0.0100 0.0034
## 5 1.2808 nan 0.0100 0.0033
## 6 1.2731 nan 0.0100 0.0033
## 7 1.2661 nan 0.0100 0.0031
## 8 1.2586 nan 0.0100 0.0033
## 9 1.2511 nan 0.0100 0.0033
## 10 1.2441 nan 0.0100 0.0028
## 20 1.1780 nan 0.0100 0.0028
## 40 1.0724 nan 0.0100 0.0021
## 60 0.9930 nan 0.0100 0.0018
## 80 0.9314 nan 0.0100 0.0010
## 100 0.8818 nan 0.0100 0.0008
## 120 0.8418 nan 0.0100 0.0005
## 140 0.8087 nan 0.0100 0.0007
## 160 0.7800 nan 0.0100 0.0004
## 180 0.7550 nan 0.0100 0.0003
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## 400 0.6013 nan 0.0100 -0.0001
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## 460 0.5733 nan 0.0100 -0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3123 nan 0.0100 0.0037
## 2 1.3047 nan 0.0100 0.0034
## 3 1.2963 nan 0.0100 0.0035
## 4 1.2884 nan 0.0100 0.0035
## 5 1.2808 nan 0.0100 0.0034
## 6 1.2733 nan 0.0100 0.0032
## 7 1.2662 nan 0.0100 0.0033
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## 20 1.1800 nan 0.0100 0.0023
## 40 1.0777 nan 0.0100 0.0020
## 60 0.9976 nan 0.0100 0.0013
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## 100 0.8816 nan 0.0100 0.0009
## 120 0.8410 nan 0.0100 0.0005
## 140 0.8079 nan 0.0100 0.0002
## 160 0.7795 nan 0.0100 0.0005
## 180 0.7555 nan 0.0100 0.0004
## 200 0.7343 nan 0.0100 0.0003
## 220 0.7146 nan 0.0100 0.0002
## 240 0.6971 nan 0.0100 0.0002
## 260 0.6827 nan 0.0100 -0.0002
## 280 0.6683 nan 0.0100 0.0001
## 300 0.6553 nan 0.0100 0.0001
## 320 0.6432 nan 0.0100 -0.0000
## 340 0.6317 nan 0.0100 -0.0001
## 360 0.6213 nan 0.0100 -0.0000
## 380 0.6111 nan 0.0100 0.0001
## 400 0.6015 nan 0.0100 -0.0000
## 420 0.5915 nan 0.0100 0.0001
## 440 0.5830 nan 0.0100 0.0001
## 460 0.5742 nan 0.0100 0.0000
## 480 0.5659 nan 0.0100 0.0000
## 500 0.5578 nan 0.0100 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3122 nan 0.0100 0.0041
## 2 1.3050 nan 0.0100 0.0033
## 3 1.2976 nan 0.0100 0.0035
## 4 1.2899 nan 0.0100 0.0035
## 5 1.2827 nan 0.0100 0.0035
## 6 1.2759 nan 0.0100 0.0031
## 7 1.2675 nan 0.0100 0.0040
## 8 1.2602 nan 0.0100 0.0034
## 9 1.2534 nan 0.0100 0.0030
## 10 1.2473 nan 0.0100 0.0031
## 20 1.1799 nan 0.0100 0.0030
## 40 1.0755 nan 0.0100 0.0019
## 60 0.9958 nan 0.0100 0.0014
## 80 0.9341 nan 0.0100 0.0009
## 100 0.8845 nan 0.0100 0.0009
## 120 0.8414 nan 0.0100 0.0007
## 140 0.8077 nan 0.0100 0.0005
## 160 0.7792 nan 0.0100 0.0002
## 180 0.7561 nan 0.0100 0.0004
## 200 0.7352 nan 0.0100 0.0003
## 220 0.7168 nan 0.0100 0.0003
## 240 0.7006 nan 0.0100 0.0000
## 260 0.6863 nan 0.0100 0.0000
## 280 0.6730 nan 0.0100 0.0001
## 300 0.6604 nan 0.0100 0.0001
## 320 0.6482 nan 0.0100 0.0002
## 340 0.6369 nan 0.0100 0.0001
## 360 0.6262 nan 0.0100 0.0000
## 380 0.6157 nan 0.0100 0.0000
## 400 0.6058 nan 0.0100 0.0000
## 420 0.5977 nan 0.0100 -0.0002
## 440 0.5898 nan 0.0100 -0.0001
## 460 0.5811 nan 0.0100 -0.0001
## 480 0.5727 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3120 nan 0.0100 0.0036
## 2 1.3031 nan 0.0100 0.0042
## 3 1.2950 nan 0.0100 0.0031
## 4 1.2863 nan 0.0100 0.0037
## 5 1.2784 nan 0.0100 0.0035
## 6 1.2706 nan 0.0100 0.0033
## 7 1.2621 nan 0.0100 0.0038
## 8 1.2551 nan 0.0100 0.0028
## 9 1.2472 nan 0.0100 0.0033
## 10 1.2394 nan 0.0100 0.0030
## 20 1.1693 nan 0.0100 0.0031
## 40 1.0596 nan 0.0100 0.0020
## 60 0.9754 nan 0.0100 0.0015
## 80 0.9080 nan 0.0100 0.0011
## 100 0.8548 nan 0.0100 0.0008
## 120 0.8102 nan 0.0100 0.0008
## 140 0.7729 nan 0.0100 0.0003
## 160 0.7413 nan 0.0100 0.0004
## 180 0.7145 nan 0.0100 0.0001
## 200 0.6917 nan 0.0100 0.0002
## 220 0.6710 nan 0.0100 -0.0001
## 240 0.6521 nan 0.0100 -0.0000
## 260 0.6351 nan 0.0100 0.0001
## 280 0.6202 nan 0.0100 -0.0001
## 300 0.6063 nan 0.0100 -0.0001
## 320 0.5927 nan 0.0100 -0.0001
## 340 0.5789 nan 0.0100 0.0000
## 360 0.5674 nan 0.0100 0.0002
## 380 0.5567 nan 0.0100 -0.0001
## 400 0.5457 nan 0.0100 -0.0001
## 420 0.5351 nan 0.0100 -0.0000
## 440 0.5256 nan 0.0100 -0.0001
## 460 0.5159 nan 0.0100 -0.0001
## 480 0.5069 nan 0.0100 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3122 nan 0.0100 0.0035
## 2 1.3033 nan 0.0100 0.0039
## 3 1.2952 nan 0.0100 0.0038
## 4 1.2867 nan 0.0100 0.0041
## 5 1.2789 nan 0.0100 0.0030
## 6 1.2713 nan 0.0100 0.0034
## 7 1.2629 nan 0.0100 0.0036
## 8 1.2552 nan 0.0100 0.0036
## 9 1.2473 nan 0.0100 0.0033
## 10 1.2394 nan 0.0100 0.0033
## 20 1.1713 nan 0.0100 0.0029
## 40 1.0586 nan 0.0100 0.0016
## 60 0.9771 nan 0.0100 0.0013
## 80 0.9107 nan 0.0100 0.0011
## 100 0.8582 nan 0.0100 0.0010
## 120 0.8159 nan 0.0100 0.0006
## 140 0.7802 nan 0.0100 0.0005
## 160 0.7497 nan 0.0100 0.0005
## 180 0.7241 nan 0.0100 0.0002
## 200 0.7002 nan 0.0100 0.0004
## 220 0.6802 nan 0.0100 0.0002
## 240 0.6623 nan 0.0100 0.0001
## 260 0.6453 nan 0.0100 0.0000
## 280 0.6298 nan 0.0100 0.0000
## 300 0.6163 nan 0.0100 -0.0000
## 320 0.6034 nan 0.0100 -0.0000
## 340 0.5898 nan 0.0100 -0.0001
## 360 0.5785 nan 0.0100 -0.0001
## 380 0.5677 nan 0.0100 -0.0000
## 400 0.5570 nan 0.0100 -0.0001
## 420 0.5472 nan 0.0100 -0.0001
## 440 0.5374 nan 0.0100 0.0001
## 460 0.5277 nan 0.0100 -0.0000
## 480 0.5188 nan 0.0100 -0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3122 nan 0.0100 0.0041
## 2 1.3038 nan 0.0100 0.0039
## 3 1.2954 nan 0.0100 0.0036
## 4 1.2866 nan 0.0100 0.0037
## 5 1.2787 nan 0.0100 0.0039
## 6 1.2701 nan 0.0100 0.0036
## 7 1.2631 nan 0.0100 0.0029
## 8 1.2550 nan 0.0100 0.0036
## 9 1.2476 nan 0.0100 0.0033
## 10 1.2405 nan 0.0100 0.0028
## 20 1.1732 nan 0.0100 0.0028
## 40 1.0661 nan 0.0100 0.0022
## 60 0.9811 nan 0.0100 0.0013
## 80 0.9147 nan 0.0100 0.0013
## 100 0.8642 nan 0.0100 0.0008
## 120 0.8209 nan 0.0100 0.0007
## 140 0.7854 nan 0.0100 0.0005
## 160 0.7556 nan 0.0100 0.0002
## 180 0.7310 nan 0.0100 0.0004
## 200 0.7078 nan 0.0100 0.0002
## 220 0.6893 nan 0.0100 0.0001
## 240 0.6720 nan 0.0100 0.0002
## 260 0.6575 nan 0.0100 0.0002
## 280 0.6422 nan 0.0100 0.0000
## 300 0.6274 nan 0.0100 -0.0001
## 320 0.6145 nan 0.0100 0.0001
## 340 0.6034 nan 0.0100 -0.0002
## 360 0.5923 nan 0.0100 0.0001
## 380 0.5801 nan 0.0100 0.0001
## 400 0.5694 nan 0.0100 -0.0001
## 420 0.5594 nan 0.0100 -0.0000
## 440 0.5506 nan 0.0100 -0.0001
## 460 0.5404 nan 0.0100 -0.0001
## 480 0.5304 nan 0.0100 -0.0001
## 500 0.5215 nan 0.0100 0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3117 nan 0.0100 0.0044
## 2 1.3020 nan 0.0100 0.0040
## 3 1.2930 nan 0.0100 0.0040
## 4 1.2851 nan 0.0100 0.0037
## 5 1.2766 nan 0.0100 0.0038
## 6 1.2682 nan 0.0100 0.0039
## 7 1.2604 nan 0.0100 0.0034
## 8 1.2528 nan 0.0100 0.0032
## 9 1.2442 nan 0.0100 0.0038
## 10 1.2361 nan 0.0100 0.0036
## 20 1.1648 nan 0.0100 0.0031
## 40 1.0497 nan 0.0100 0.0024
## 60 0.9620 nan 0.0100 0.0019
## 80 0.8916 nan 0.0100 0.0014
## 100 0.8348 nan 0.0100 0.0010
## 120 0.7905 nan 0.0100 0.0007
## 140 0.7518 nan 0.0100 0.0006
## 160 0.7184 nan 0.0100 0.0005
## 180 0.6894 nan 0.0100 0.0003
## 200 0.6654 nan 0.0100 0.0002
## 220 0.6445 nan 0.0100 0.0003
## 240 0.6234 nan 0.0100 0.0001
## 260 0.6055 nan 0.0100 -0.0001
## 280 0.5873 nan 0.0100 0.0001
## 300 0.5720 nan 0.0100 0.0001
## 320 0.5574 nan 0.0100 0.0001
## 340 0.5427 nan 0.0100 0.0001
## 360 0.5295 nan 0.0100 0.0001
## 380 0.5171 nan 0.0100 0.0002
## 400 0.5057 nan 0.0100 -0.0002
## 420 0.4954 nan 0.0100 0.0000
## 440 0.4851 nan 0.0100 0.0000
## 460 0.4748 nan 0.0100 -0.0000
## 480 0.4645 nan 0.0100 0.0001
## 500 0.4546 nan 0.0100 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3124 nan 0.0100 0.0037
## 2 1.3027 nan 0.0100 0.0042
## 3 1.2940 nan 0.0100 0.0037
## 4 1.2855 nan 0.0100 0.0040
## 5 1.2770 nan 0.0100 0.0035
## 6 1.2684 nan 0.0100 0.0040
## 7 1.2601 nan 0.0100 0.0038
## 8 1.2518 nan 0.0100 0.0034
## 9 1.2444 nan 0.0100 0.0037
## 10 1.2364 nan 0.0100 0.0037
## 20 1.1652 nan 0.0100 0.0029
## 40 1.0507 nan 0.0100 0.0019
## 60 0.9644 nan 0.0100 0.0016
## 80 0.8942 nan 0.0100 0.0012
## 100 0.8391 nan 0.0100 0.0008
## 120 0.7949 nan 0.0100 0.0004
## 140 0.7563 nan 0.0100 0.0004
## 160 0.7243 nan 0.0100 0.0004
## 180 0.6977 nan 0.0100 0.0004
## 200 0.6741 nan 0.0100 0.0003
## 220 0.6532 nan 0.0100 0.0000
## 240 0.6330 nan 0.0100 0.0003
## 260 0.6155 nan 0.0100 0.0001
## 280 0.5989 nan 0.0100 -0.0000
## 300 0.5837 nan 0.0100 -0.0000
## 320 0.5704 nan 0.0100 -0.0000
## 340 0.5564 nan 0.0100 -0.0000
## 360 0.5445 nan 0.0100 -0.0002
## 380 0.5321 nan 0.0100 -0.0000
## 400 0.5207 nan 0.0100 0.0000
## 420 0.5085 nan 0.0100 -0.0000
## 440 0.4973 nan 0.0100 -0.0002
## 460 0.4875 nan 0.0100 -0.0001
## 480 0.4767 nan 0.0100 0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3119 nan 0.0100 0.0036
## 2 1.3034 nan 0.0100 0.0035
## 3 1.2953 nan 0.0100 0.0036
## 4 1.2866 nan 0.0100 0.0040
## 5 1.2780 nan 0.0100 0.0039
## 6 1.2706 nan 0.0100 0.0032
## 7 1.2621 nan 0.0100 0.0036
## 8 1.2547 nan 0.0100 0.0030
## 9 1.2467 nan 0.0100 0.0034
## 10 1.2382 nan 0.0100 0.0036
## 20 1.1689 nan 0.0100 0.0030
## 40 1.0543 nan 0.0100 0.0019
## 60 0.9676 nan 0.0100 0.0017
## 80 0.9012 nan 0.0100 0.0009
## 100 0.8470 nan 0.0100 0.0007
## 120 0.8009 nan 0.0100 0.0007
## 140 0.7642 nan 0.0100 0.0004
## 160 0.7334 nan 0.0100 0.0005
## 180 0.7063 nan 0.0100 0.0002
## 200 0.6829 nan 0.0100 0.0001
## 220 0.6623 nan 0.0100 0.0002
## 240 0.6416 nan 0.0100 0.0001
## 260 0.6242 nan 0.0100 -0.0001
## 280 0.6089 nan 0.0100 -0.0001
## 300 0.5935 nan 0.0100 -0.0000
## 320 0.5791 nan 0.0100 0.0000
## 340 0.5655 nan 0.0100 0.0001
## 360 0.5539 nan 0.0100 -0.0002
## 380 0.5420 nan 0.0100 0.0000
## 400 0.5311 nan 0.0100 0.0000
## 420 0.5219 nan 0.0100 -0.0001
## 440 0.5111 nan 0.0100 -0.0002
## 460 0.4996 nan 0.0100 0.0001
## 480 0.4902 nan 0.0100 0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2456 nan 0.1000 0.0316
## 2 1.1810 nan 0.1000 0.0310
## 3 1.1266 nan 0.1000 0.0263
## 4 1.0780 nan 0.1000 0.0202
## 5 1.0322 nan 0.1000 0.0180
## 6 0.9898 nan 0.1000 0.0167
## 7 0.9560 nan 0.1000 0.0129
## 8 0.9260 nan 0.1000 0.0107
## 9 0.8982 nan 0.1000 0.0115
## 10 0.8767 nan 0.1000 0.0081
## 20 0.7281 nan 0.1000 -0.0005
## 40 0.6015 nan 0.1000 -0.0006
## 60 0.5156 nan 0.1000 0.0005
## 80 0.4527 nan 0.1000 -0.0006
## 100 0.4009 nan 0.1000 0.0005
## 120 0.3569 nan 0.1000 -0.0005
## 140 0.3182 nan 0.1000 -0.0010
## 160 0.2871 nan 0.1000 -0.0008
## 180 0.2599 nan 0.1000 -0.0005
## 200 0.2371 nan 0.1000 -0.0003
## 220 0.2151 nan 0.1000 -0.0009
## 240 0.1941 nan 0.1000 -0.0004
## 260 0.1747 nan 0.1000 -0.0004
## 280 0.1587 nan 0.1000 -0.0006
## 300 0.1448 nan 0.1000 -0.0000
## 320 0.1316 nan 0.1000 -0.0001
## 340 0.1214 nan 0.1000 -0.0002
## 360 0.1119 nan 0.1000 -0.0004
## 380 0.1027 nan 0.1000 -0.0003
## 400 0.0949 nan 0.1000 -0.0001
## 420 0.0869 nan 0.1000 -0.0000
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## 460 0.0743 nan 0.1000 -0.0001
## 480 0.0695 nan 0.1000 -0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2418 nan 0.1000 0.0391
## 2 1.1779 nan 0.1000 0.0298
## 3 1.1207 nan 0.1000 0.0245
## 4 1.0717 nan 0.1000 0.0198
## 5 1.0295 nan 0.1000 0.0164
## 6 0.9918 nan 0.1000 0.0160
## 7 0.9560 nan 0.1000 0.0130
## 8 0.9259 nan 0.1000 0.0130
## 9 0.8995 nan 0.1000 0.0112
## 10 0.8787 nan 0.1000 0.0070
## 20 0.7310 nan 0.1000 0.0014
## 40 0.6090 nan 0.1000 -0.0001
## 60 0.5306 nan 0.1000 -0.0005
## 80 0.4639 nan 0.1000 0.0001
## 100 0.4089 nan 0.1000 -0.0009
## 120 0.3681 nan 0.1000 -0.0000
## 140 0.3270 nan 0.1000 -0.0017
## 160 0.2972 nan 0.1000 0.0000
## 180 0.2686 nan 0.1000 -0.0006
## 200 0.2474 nan 0.1000 -0.0008
## 220 0.2241 nan 0.1000 -0.0006
## 240 0.2056 nan 0.1000 -0.0008
## 260 0.1898 nan 0.1000 -0.0004
## 280 0.1756 nan 0.1000 -0.0002
## 300 0.1613 nan 0.1000 -0.0004
## 320 0.1491 nan 0.1000 -0.0004
## 340 0.1373 nan 0.1000 -0.0005
## 360 0.1274 nan 0.1000 -0.0005
## 380 0.1181 nan 0.1000 -0.0002
## 400 0.1104 nan 0.1000 -0.0002
## 420 0.1015 nan 0.1000 -0.0008
## 440 0.0940 nan 0.1000 -0.0002
## 460 0.0871 nan 0.1000 -0.0005
## 480 0.0807 nan 0.1000 -0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2434 nan 0.1000 0.0371
## 2 1.1731 nan 0.1000 0.0338
## 3 1.1214 nan 0.1000 0.0259
## 4 1.0758 nan 0.1000 0.0182
## 5 1.0328 nan 0.1000 0.0177
## 6 0.9950 nan 0.1000 0.0176
## 7 0.9632 nan 0.1000 0.0114
## 8 0.9366 nan 0.1000 0.0099
## 9 0.9084 nan 0.1000 0.0098
## 10 0.8871 nan 0.1000 0.0082
## 20 0.7435 nan 0.1000 0.0022
## 40 0.6234 nan 0.1000 -0.0003
## 60 0.5416 nan 0.1000 -0.0007
## 80 0.4831 nan 0.1000 0.0006
## 100 0.4295 nan 0.1000 -0.0004
## 120 0.3875 nan 0.1000 -0.0002
## 140 0.3506 nan 0.1000 -0.0014
## 160 0.3186 nan 0.1000 -0.0020
## 180 0.2925 nan 0.1000 -0.0012
## 200 0.2688 nan 0.1000 -0.0005
## 220 0.2449 nan 0.1000 -0.0005
## 240 0.2238 nan 0.1000 -0.0015
## 260 0.2067 nan 0.1000 -0.0010
## 280 0.1906 nan 0.1000 -0.0002
## 300 0.1754 nan 0.1000 -0.0004
## 320 0.1610 nan 0.1000 -0.0003
## 340 0.1487 nan 0.1000 -0.0004
## 360 0.1371 nan 0.1000 -0.0002
## 380 0.1275 nan 0.1000 -0.0004
## 400 0.1196 nan 0.1000 -0.0005
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## 440 0.1020 nan 0.1000 -0.0003
## 460 0.0945 nan 0.1000 -0.0002
## 480 0.0877 nan 0.1000 -0.0004
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2345 nan 0.1000 0.0360
## 2 1.1595 nan 0.1000 0.0307
## 3 1.1004 nan 0.1000 0.0239
## 4 1.0533 nan 0.1000 0.0206
## 5 1.0109 nan 0.1000 0.0166
## 6 0.9692 nan 0.1000 0.0165
## 7 0.9340 nan 0.1000 0.0145
## 8 0.9020 nan 0.1000 0.0127
## 9 0.8771 nan 0.1000 0.0100
## 10 0.8545 nan 0.1000 0.0068
## 20 0.6986 nan 0.1000 0.0014
## 40 0.5347 nan 0.1000 -0.0017
## 60 0.4510 nan 0.1000 -0.0013
## 80 0.3799 nan 0.1000 -0.0006
## 100 0.3290 nan 0.1000 -0.0001
## 120 0.2911 nan 0.1000 -0.0007
## 140 0.2570 nan 0.1000 -0.0008
## 160 0.2259 nan 0.1000 -0.0006
## 180 0.1992 nan 0.1000 -0.0001
## 200 0.1756 nan 0.1000 -0.0002
## 220 0.1561 nan 0.1000 -0.0000
## 240 0.1396 nan 0.1000 -0.0003
## 260 0.1256 nan 0.1000 -0.0004
## 280 0.1128 nan 0.1000 -0.0005
## 300 0.1019 nan 0.1000 -0.0001
## 320 0.0913 nan 0.1000 0.0000
## 340 0.0823 nan 0.1000 -0.0002
## 360 0.0749 nan 0.1000 -0.0003
## 380 0.0674 nan 0.1000 -0.0001
## 400 0.0612 nan 0.1000 -0.0003
## 420 0.0554 nan 0.1000 -0.0002
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## 480 0.0416 nan 0.1000 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2346 nan 0.1000 0.0395
## 2 1.1680 nan 0.1000 0.0289
## 3 1.1009 nan 0.1000 0.0282
## 4 1.0494 nan 0.1000 0.0220
## 5 1.0032 nan 0.1000 0.0209
## 6 0.9688 nan 0.1000 0.0114
## 7 0.9327 nan 0.1000 0.0153
## 8 0.8994 nan 0.1000 0.0131
## 9 0.8727 nan 0.1000 0.0097
## 10 0.8474 nan 0.1000 0.0095
## 20 0.7014 nan 0.1000 0.0005
## 40 0.5626 nan 0.1000 -0.0016
## 60 0.4769 nan 0.1000 0.0011
## 80 0.4091 nan 0.1000 0.0001
## 100 0.3533 nan 0.1000 -0.0007
## 120 0.3019 nan 0.1000 -0.0011
## 140 0.2676 nan 0.1000 -0.0013
## 160 0.2363 nan 0.1000 -0.0007
## 180 0.2063 nan 0.1000 -0.0003
## 200 0.1845 nan 0.1000 -0.0003
## 220 0.1633 nan 0.1000 -0.0002
## 240 0.1450 nan 0.1000 -0.0001
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## 280 0.1172 nan 0.1000 -0.0008
## 300 0.1046 nan 0.1000 -0.0004
## 320 0.0939 nan 0.1000 0.0000
## 340 0.0852 nan 0.1000 -0.0002
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## 380 0.0702 nan 0.1000 -0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2428 nan 0.1000 0.0401
## 2 1.1729 nan 0.1000 0.0333
## 3 1.1079 nan 0.1000 0.0252
## 4 1.0589 nan 0.1000 0.0186
## 5 1.0165 nan 0.1000 0.0183
## 6 0.9751 nan 0.1000 0.0168
## 7 0.9420 nan 0.1000 0.0114
## 8 0.9083 nan 0.1000 0.0132
## 9 0.8812 nan 0.1000 0.0113
## 10 0.8562 nan 0.1000 0.0106
## 20 0.7081 nan 0.1000 0.0031
## 40 0.5608 nan 0.1000 -0.0009
## 60 0.4785 nan 0.1000 -0.0021
## 80 0.4112 nan 0.1000 -0.0006
## 100 0.3574 nan 0.1000 0.0002
## 120 0.3149 nan 0.1000 -0.0001
## 140 0.2733 nan 0.1000 -0.0003
## 160 0.2402 nan 0.1000 -0.0007
## 180 0.2127 nan 0.1000 -0.0008
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2364 nan 0.1000 0.0389
## 2 1.1560 nan 0.1000 0.0370
## 3 1.0984 nan 0.1000 0.0245
## 4 1.0479 nan 0.1000 0.0195
## 5 1.0030 nan 0.1000 0.0190
## 6 0.9632 nan 0.1000 0.0144
## 7 0.9257 nan 0.1000 0.0133
## 8 0.8906 nan 0.1000 0.0141
## 9 0.8603 nan 0.1000 0.0094
## 10 0.8354 nan 0.1000 0.0104
## 20 0.6746 nan 0.1000 0.0004
## 40 0.5164 nan 0.1000 -0.0006
## 60 0.4215 nan 0.1000 -0.0005
## 80 0.3463 nan 0.1000 -0.0009
## 100 0.2864 nan 0.1000 0.0003
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## 180 0.1563 nan 0.1000 -0.0004
## 200 0.1370 nan 0.1000 -0.0007
## 220 0.1192 nan 0.1000 -0.0002
## 240 0.1050 nan 0.1000 0.0000
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## 320 0.0626 nan 0.1000 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2403 nan 0.1000 0.0325
## 2 1.1646 nan 0.1000 0.0346
## 3 1.1061 nan 0.1000 0.0261
## 4 1.0580 nan 0.1000 0.0196
## 5 1.0115 nan 0.1000 0.0191
## 6 0.9752 nan 0.1000 0.0151
## 7 0.9355 nan 0.1000 0.0143
## 8 0.9056 nan 0.1000 0.0117
## 9 0.8756 nan 0.1000 0.0118
## 10 0.8509 nan 0.1000 0.0092
## 20 0.6878 nan 0.1000 0.0039
## 40 0.5271 nan 0.1000 0.0007
## 60 0.4276 nan 0.1000 -0.0017
## 80 0.3572 nan 0.1000 -0.0010
## 100 0.3020 nan 0.1000 -0.0007
## 120 0.2516 nan 0.1000 0.0009
## 140 0.2124 nan 0.1000 0.0000
## 160 0.1820 nan 0.1000 -0.0007
## 180 0.1583 nan 0.1000 -0.0001
## 200 0.1398 nan 0.1000 -0.0005
## 220 0.1210 nan 0.1000 -0.0003
## 240 0.1061 nan 0.1000 -0.0003
## 260 0.0925 nan 0.1000 -0.0006
## 280 0.0811 nan 0.1000 -0.0003
## 300 0.0715 nan 0.1000 -0.0003
## 320 0.0634 nan 0.1000 -0.0002
## 340 0.0560 nan 0.1000 -0.0003
## 360 0.0501 nan 0.1000 -0.0001
## 380 0.0444 nan 0.1000 -0.0002
## 400 0.0393 nan 0.1000 -0.0002
## 420 0.0347 nan 0.1000 -0.0000
## 440 0.0310 nan 0.1000 -0.0001
## 460 0.0276 nan 0.1000 -0.0001
## 480 0.0248 nan 0.1000 -0.0001
## 500 0.0219 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2318 nan 0.1000 0.0401
## 2 1.1584 nan 0.1000 0.0338
## 3 1.1019 nan 0.1000 0.0237
## 4 1.0516 nan 0.1000 0.0230
## 5 1.0042 nan 0.1000 0.0188
## 6 0.9672 nan 0.1000 0.0166
## 7 0.9353 nan 0.1000 0.0120
## 8 0.9007 nan 0.1000 0.0134
## 9 0.8710 nan 0.1000 0.0098
## 10 0.8457 nan 0.1000 0.0094
## 20 0.6841 nan 0.1000 0.0046
## 40 0.5418 nan 0.1000 -0.0010
## 60 0.4428 nan 0.1000 -0.0006
## 80 0.3716 nan 0.1000 -0.0004
## 100 0.3168 nan 0.1000 -0.0014
## 120 0.2746 nan 0.1000 -0.0008
## 140 0.2364 nan 0.1000 -0.0010
## 160 0.2067 nan 0.1000 -0.0004
## 180 0.1816 nan 0.1000 -0.0011
## 200 0.1590 nan 0.1000 -0.0007
## 220 0.1413 nan 0.1000 -0.0006
## 240 0.1245 nan 0.1000 -0.0005
## 260 0.1102 nan 0.1000 -0.0004
## 280 0.0971 nan 0.1000 -0.0005
## 300 0.0857 nan 0.1000 -0.0004
## 320 0.0754 nan 0.1000 -0.0001
## 340 0.0669 nan 0.1000 -0.0002
## 360 0.0598 nan 0.1000 -0.0002
## 380 0.0536 nan 0.1000 -0.0001
## 400 0.0477 nan 0.1000 -0.0001
## 420 0.0433 nan 0.1000 -0.0002
## 440 0.0385 nan 0.1000 -0.0002
## 460 0.0349 nan 0.1000 -0.0003
## 480 0.0313 nan 0.1000 -0.0001
## 500 0.0282 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3204 nan 0.0010 0.0004
## 2 1.3195 nan 0.0010 0.0004
## 3 1.3187 nan 0.0010 0.0003
## 4 1.3179 nan 0.0010 0.0004
## 5 1.3171 nan 0.0010 0.0004
## 6 1.3162 nan 0.0010 0.0004
## 7 1.3154 nan 0.0010 0.0003
## 8 1.3147 nan 0.0010 0.0003
## 9 1.3140 nan 0.0010 0.0003
## 10 1.3131 nan 0.0010 0.0004
## 20 1.3052 nan 0.0010 0.0004
## 40 1.2896 nan 0.0010 0.0004
## 60 1.2745 nan 0.0010 0.0003
## 80 1.2598 nan 0.0010 0.0003
## 100 1.2458 nan 0.0010 0.0003
## 120 1.2321 nan 0.0010 0.0003
## 140 1.2187 nan 0.0010 0.0003
## 160 1.2057 nan 0.0010 0.0003
## 180 1.1933 nan 0.0010 0.0002
## 200 1.1814 nan 0.0010 0.0003
## 220 1.1698 nan 0.0010 0.0002
## 240 1.1586 nan 0.0010 0.0003
## 260 1.1474 nan 0.0010 0.0002
## 280 1.1367 nan 0.0010 0.0002
## 300 1.1262 nan 0.0010 0.0002
## 320 1.1161 nan 0.0010 0.0002
## 340 1.1060 nan 0.0010 0.0002
## 360 1.0964 nan 0.0010 0.0002
## 380 1.0868 nan 0.0010 0.0002
## 400 1.0777 nan 0.0010 0.0002
## 420 1.0690 nan 0.0010 0.0002
## 440 1.0604 nan 0.0010 0.0002
## 460 1.0519 nan 0.0010 0.0002
## 480 1.0436 nan 0.0010 0.0002
## 500 1.0355 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3204 nan 0.0010 0.0004
## 2 1.3195 nan 0.0010 0.0004
## 3 1.3187 nan 0.0010 0.0004
## 4 1.3179 nan 0.0010 0.0004
## 5 1.3170 nan 0.0010 0.0004
## 6 1.3163 nan 0.0010 0.0003
## 7 1.3155 nan 0.0010 0.0004
## 8 1.3148 nan 0.0010 0.0003
## 9 1.3140 nan 0.0010 0.0004
## 10 1.3132 nan 0.0010 0.0004
## 20 1.3051 nan 0.0010 0.0004
## 40 1.2898 nan 0.0010 0.0004
## 60 1.2747 nan 0.0010 0.0003
## 80 1.2601 nan 0.0010 0.0003
## 100 1.2462 nan 0.0010 0.0003
## 120 1.2325 nan 0.0010 0.0003
## 140 1.2192 nan 0.0010 0.0003
## 160 1.2062 nan 0.0010 0.0003
## 180 1.1937 nan 0.0010 0.0003
## 200 1.1815 nan 0.0010 0.0003
## 220 1.1698 nan 0.0010 0.0003
## 240 1.1587 nan 0.0010 0.0003
## 260 1.1477 nan 0.0010 0.0003
## 280 1.1373 nan 0.0010 0.0002
## 300 1.1269 nan 0.0010 0.0002
## 320 1.1169 nan 0.0010 0.0002
## 340 1.1072 nan 0.0010 0.0002
## 360 1.0976 nan 0.0010 0.0002
## 380 1.0885 nan 0.0010 0.0002
## 400 1.0794 nan 0.0010 0.0002
## 420 1.0706 nan 0.0010 0.0002
## 440 1.0622 nan 0.0010 0.0002
## 460 1.0537 nan 0.0010 0.0001
## 480 1.0455 nan 0.0010 0.0002
## 500 1.0376 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3204 nan 0.0010 0.0004
## 2 1.3195 nan 0.0010 0.0004
## 3 1.3187 nan 0.0010 0.0004
## 4 1.3180 nan 0.0010 0.0003
## 5 1.3172 nan 0.0010 0.0004
## 6 1.3163 nan 0.0010 0.0004
## 7 1.3155 nan 0.0010 0.0003
## 8 1.3146 nan 0.0010 0.0004
## 9 1.3138 nan 0.0010 0.0004
## 10 1.3130 nan 0.0010 0.0004
## 20 1.3052 nan 0.0010 0.0003
## 40 1.2896 nan 0.0010 0.0003
## 60 1.2746 nan 0.0010 0.0003
## 80 1.2603 nan 0.0010 0.0003
## 100 1.2465 nan 0.0010 0.0003
## 120 1.2327 nan 0.0010 0.0003
## 140 1.2199 nan 0.0010 0.0003
## 160 1.2073 nan 0.0010 0.0003
## 180 1.1950 nan 0.0010 0.0003
## 200 1.1832 nan 0.0010 0.0002
## 220 1.1716 nan 0.0010 0.0002
## 240 1.1602 nan 0.0010 0.0002
## 260 1.1493 nan 0.0010 0.0002
## 280 1.1388 nan 0.0010 0.0002
## 300 1.1285 nan 0.0010 0.0002
## 320 1.1186 nan 0.0010 0.0002
## 340 1.1091 nan 0.0010 0.0002
## 360 1.0997 nan 0.0010 0.0002
## 380 1.0904 nan 0.0010 0.0002
## 400 1.0815 nan 0.0010 0.0002
## 420 1.0728 nan 0.0010 0.0002
## 440 1.0642 nan 0.0010 0.0002
## 460 1.0559 nan 0.0010 0.0002
## 480 1.0477 nan 0.0010 0.0002
## 500 1.0396 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3194 nan 0.0010 0.0004
## 3 1.3186 nan 0.0010 0.0004
## 4 1.3177 nan 0.0010 0.0004
## 5 1.3168 nan 0.0010 0.0004
## 6 1.3159 nan 0.0010 0.0004
## 7 1.3150 nan 0.0010 0.0004
## 8 1.3141 nan 0.0010 0.0004
## 9 1.3133 nan 0.0010 0.0004
## 10 1.3125 nan 0.0010 0.0004
## 20 1.3039 nan 0.0010 0.0004
## 40 1.2873 nan 0.0010 0.0004
## 60 1.2717 nan 0.0010 0.0004
## 80 1.2561 nan 0.0010 0.0003
## 100 1.2411 nan 0.0010 0.0003
## 120 1.2268 nan 0.0010 0.0003
## 140 1.2133 nan 0.0010 0.0003
## 160 1.1998 nan 0.0010 0.0003
## 180 1.1868 nan 0.0010 0.0003
## 200 1.1738 nan 0.0010 0.0003
## 220 1.1613 nan 0.0010 0.0002
## 240 1.1495 nan 0.0010 0.0003
## 260 1.1379 nan 0.0010 0.0002
## 280 1.1263 nan 0.0010 0.0002
## 300 1.1153 nan 0.0010 0.0002
## 320 1.1044 nan 0.0010 0.0002
## 340 1.0938 nan 0.0010 0.0002
## 360 1.0838 nan 0.0010 0.0002
## 380 1.0739 nan 0.0010 0.0002
## 400 1.0641 nan 0.0010 0.0002
## 420 1.0548 nan 0.0010 0.0002
## 440 1.0456 nan 0.0010 0.0002
## 460 1.0364 nan 0.0010 0.0002
## 480 1.0277 nan 0.0010 0.0002
## 500 1.0194 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3195 nan 0.0010 0.0004
## 3 1.3186 nan 0.0010 0.0004
## 4 1.3178 nan 0.0010 0.0004
## 5 1.3170 nan 0.0010 0.0003
## 6 1.3160 nan 0.0010 0.0004
## 7 1.3152 nan 0.0010 0.0004
## 8 1.3144 nan 0.0010 0.0003
## 9 1.3135 nan 0.0010 0.0004
## 10 1.3126 nan 0.0010 0.0004
## 20 1.3040 nan 0.0010 0.0004
## 40 1.2877 nan 0.0010 0.0004
## 60 1.2717 nan 0.0010 0.0004
## 80 1.2565 nan 0.0010 0.0004
## 100 1.2418 nan 0.0010 0.0003
## 120 1.2276 nan 0.0010 0.0003
## 140 1.2135 nan 0.0010 0.0003
## 160 1.1998 nan 0.0010 0.0003
## 180 1.1867 nan 0.0010 0.0002
## 200 1.1739 nan 0.0010 0.0003
## 220 1.1613 nan 0.0010 0.0003
## 240 1.1492 nan 0.0010 0.0002
## 260 1.1376 nan 0.0010 0.0002
## 280 1.1265 nan 0.0010 0.0002
## 300 1.1156 nan 0.0010 0.0002
## 320 1.1048 nan 0.0010 0.0002
## 340 1.0943 nan 0.0010 0.0002
## 360 1.0840 nan 0.0010 0.0002
## 380 1.0741 nan 0.0010 0.0001
## 400 1.0645 nan 0.0010 0.0002
## 420 1.0551 nan 0.0010 0.0002
## 440 1.0461 nan 0.0010 0.0002
## 460 1.0371 nan 0.0010 0.0002
## 480 1.0286 nan 0.0010 0.0002
## 500 1.0201 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3194 nan 0.0010 0.0004
## 3 1.3186 nan 0.0010 0.0003
## 4 1.3177 nan 0.0010 0.0004
## 5 1.3170 nan 0.0010 0.0004
## 6 1.3161 nan 0.0010 0.0004
## 7 1.3152 nan 0.0010 0.0004
## 8 1.3144 nan 0.0010 0.0004
## 9 1.3137 nan 0.0010 0.0003
## 10 1.3128 nan 0.0010 0.0004
## 20 1.3045 nan 0.0010 0.0004
## 40 1.2885 nan 0.0010 0.0003
## 60 1.2725 nan 0.0010 0.0004
## 80 1.2575 nan 0.0010 0.0003
## 100 1.2427 nan 0.0010 0.0003
## 120 1.2286 nan 0.0010 0.0003
## 140 1.2149 nan 0.0010 0.0003
## 160 1.2014 nan 0.0010 0.0003
## 180 1.1883 nan 0.0010 0.0003
## 200 1.1756 nan 0.0010 0.0003
## 220 1.1635 nan 0.0010 0.0003
## 240 1.1516 nan 0.0010 0.0003
## 260 1.1401 nan 0.0010 0.0002
## 280 1.1289 nan 0.0010 0.0002
## 300 1.1182 nan 0.0010 0.0002
## 320 1.1077 nan 0.0010 0.0002
## 340 1.0973 nan 0.0010 0.0002
## 360 1.0873 nan 0.0010 0.0002
## 380 1.0775 nan 0.0010 0.0002
## 400 1.0679 nan 0.0010 0.0002
## 420 1.0589 nan 0.0010 0.0002
## 440 1.0500 nan 0.0010 0.0002
## 460 1.0412 nan 0.0010 0.0002
## 480 1.0325 nan 0.0010 0.0002
## 500 1.0244 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3202 nan 0.0010 0.0004
## 2 1.3193 nan 0.0010 0.0004
## 3 1.3184 nan 0.0010 0.0004
## 4 1.3176 nan 0.0010 0.0004
## 5 1.3166 nan 0.0010 0.0004
## 6 1.3156 nan 0.0010 0.0004
## 7 1.3148 nan 0.0010 0.0004
## 8 1.3138 nan 0.0010 0.0004
## 9 1.3129 nan 0.0010 0.0004
## 10 1.3120 nan 0.0010 0.0004
## 20 1.3031 nan 0.0010 0.0004
## 40 1.2854 nan 0.0010 0.0004
## 60 1.2685 nan 0.0010 0.0003
## 80 1.2524 nan 0.0010 0.0004
## 100 1.2369 nan 0.0010 0.0002
## 120 1.2219 nan 0.0010 0.0003
## 140 1.2071 nan 0.0010 0.0004
## 160 1.1929 nan 0.0010 0.0003
## 180 1.1791 nan 0.0010 0.0003
## 200 1.1654 nan 0.0010 0.0003
## 220 1.1524 nan 0.0010 0.0003
## 240 1.1399 nan 0.0010 0.0002
## 260 1.1277 nan 0.0010 0.0003
## 280 1.1158 nan 0.0010 0.0003
## 300 1.1043 nan 0.0010 0.0002
## 320 1.0932 nan 0.0010 0.0002
## 340 1.0823 nan 0.0010 0.0002
## 360 1.0716 nan 0.0010 0.0002
## 380 1.0614 nan 0.0010 0.0002
## 400 1.0516 nan 0.0010 0.0002
## 420 1.0420 nan 0.0010 0.0002
## 440 1.0325 nan 0.0010 0.0002
## 460 1.0233 nan 0.0010 0.0002
## 480 1.0144 nan 0.0010 0.0002
## 500 1.0056 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3193 nan 0.0010 0.0004
## 3 1.3184 nan 0.0010 0.0004
## 4 1.3175 nan 0.0010 0.0004
## 5 1.3167 nan 0.0010 0.0004
## 6 1.3158 nan 0.0010 0.0004
## 7 1.3149 nan 0.0010 0.0004
## 8 1.3140 nan 0.0010 0.0004
## 9 1.3131 nan 0.0010 0.0004
## 10 1.3123 nan 0.0010 0.0004
## 20 1.3037 nan 0.0010 0.0004
## 40 1.2866 nan 0.0010 0.0004
## 60 1.2701 nan 0.0010 0.0003
## 80 1.2543 nan 0.0010 0.0004
## 100 1.2387 nan 0.0010 0.0004
## 120 1.2236 nan 0.0010 0.0003
## 140 1.2093 nan 0.0010 0.0003
## 160 1.1953 nan 0.0010 0.0003
## 180 1.1819 nan 0.0010 0.0003
## 200 1.1685 nan 0.0010 0.0003
## 220 1.1558 nan 0.0010 0.0003
## 240 1.1435 nan 0.0010 0.0002
## 260 1.1313 nan 0.0010 0.0003
## 280 1.1193 nan 0.0010 0.0002
## 300 1.1079 nan 0.0010 0.0003
## 320 1.0966 nan 0.0010 0.0002
## 340 1.0857 nan 0.0010 0.0002
## 360 1.0751 nan 0.0010 0.0002
## 380 1.0646 nan 0.0010 0.0002
## 400 1.0545 nan 0.0010 0.0002
## 420 1.0449 nan 0.0010 0.0002
## 440 1.0356 nan 0.0010 0.0002
## 460 1.0265 nan 0.0010 0.0002
## 480 1.0174 nan 0.0010 0.0002
## 500 1.0089 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3194 nan 0.0010 0.0004
## 3 1.3185 nan 0.0010 0.0004
## 4 1.3176 nan 0.0010 0.0004
## 5 1.3168 nan 0.0010 0.0003
## 6 1.3158 nan 0.0010 0.0004
## 7 1.3150 nan 0.0010 0.0004
## 8 1.3141 nan 0.0010 0.0004
## 9 1.3132 nan 0.0010 0.0004
## 10 1.3123 nan 0.0010 0.0004
## 20 1.3036 nan 0.0010 0.0004
## 40 1.2866 nan 0.0010 0.0003
## 60 1.2705 nan 0.0010 0.0003
## 80 1.2547 nan 0.0010 0.0004
## 100 1.2391 nan 0.0010 0.0003
## 120 1.2245 nan 0.0010 0.0003
## 140 1.2102 nan 0.0010 0.0003
## 160 1.1963 nan 0.0010 0.0003
## 180 1.1826 nan 0.0010 0.0003
## 200 1.1697 nan 0.0010 0.0003
## 220 1.1569 nan 0.0010 0.0003
## 240 1.1447 nan 0.0010 0.0003
## 260 1.1327 nan 0.0010 0.0002
## 280 1.1210 nan 0.0010 0.0002
## 300 1.1097 nan 0.0010 0.0002
## 320 1.0983 nan 0.0010 0.0002
## 340 1.0879 nan 0.0010 0.0002
## 360 1.0774 nan 0.0010 0.0002
## 380 1.0674 nan 0.0010 0.0002
## 400 1.0577 nan 0.0010 0.0002
## 420 1.0482 nan 0.0010 0.0002
## 440 1.0388 nan 0.0010 0.0002
## 460 1.0298 nan 0.0010 0.0002
## 480 1.0209 nan 0.0010 0.0002
## 500 1.0122 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3131 nan 0.0100 0.0040
## 2 1.3058 nan 0.0100 0.0035
## 3 1.2981 nan 0.0100 0.0036
## 4 1.2909 nan 0.0100 0.0035
## 5 1.2828 nan 0.0100 0.0038
## 6 1.2751 nan 0.0100 0.0036
## 7 1.2676 nan 0.0100 0.0032
## 8 1.2602 nan 0.0100 0.0031
## 9 1.2529 nan 0.0100 0.0032
## 10 1.2461 nan 0.0100 0.0034
## 20 1.1825 nan 0.0100 0.0027
## 40 1.0784 nan 0.0100 0.0020
## 60 0.9985 nan 0.0100 0.0009
## 80 0.9356 nan 0.0100 0.0013
## 100 0.8828 nan 0.0100 0.0009
## 120 0.8422 nan 0.0100 0.0008
## 140 0.8077 nan 0.0100 0.0004
## 160 0.7789 nan 0.0100 0.0005
## 180 0.7543 nan 0.0100 0.0004
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## 220 0.7119 nan 0.0100 0.0002
## 240 0.6945 nan 0.0100 0.0001
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## 280 0.6655 nan 0.0100 0.0001
## 300 0.6531 nan 0.0100 0.0001
## 320 0.6415 nan 0.0100 0.0001
## 340 0.6306 nan 0.0100 0.0000
## 360 0.6201 nan 0.0100 -0.0001
## 380 0.6095 nan 0.0100 0.0000
## 400 0.5996 nan 0.0100 -0.0001
## 420 0.5908 nan 0.0100 -0.0000
## 440 0.5823 nan 0.0100 -0.0001
## 460 0.5723 nan 0.0100 0.0002
## 480 0.5633 nan 0.0100 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3128 nan 0.0100 0.0041
## 2 1.3045 nan 0.0100 0.0035
## 3 1.2963 nan 0.0100 0.0038
## 4 1.2883 nan 0.0100 0.0032
## 5 1.2803 nan 0.0100 0.0035
## 6 1.2726 nan 0.0100 0.0031
## 7 1.2654 nan 0.0100 0.0031
## 8 1.2576 nan 0.0100 0.0036
## 9 1.2504 nan 0.0100 0.0033
## 10 1.2431 nan 0.0100 0.0033
## 20 1.1802 nan 0.0100 0.0025
## 40 1.0787 nan 0.0100 0.0020
## 60 1.0009 nan 0.0100 0.0017
## 80 0.9394 nan 0.0100 0.0012
## 100 0.8887 nan 0.0100 0.0006
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## 140 0.8131 nan 0.0100 0.0004
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## 180 0.7592 nan 0.0100 0.0003
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## 240 0.6999 nan 0.0100 0.0001
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## 280 0.6705 nan 0.0100 -0.0000
## 300 0.6588 nan 0.0100 0.0000
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## 340 0.6344 nan 0.0100 0.0001
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## 380 0.6133 nan 0.0100 -0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3131 nan 0.0100 0.0035
## 2 1.3058 nan 0.0100 0.0033
## 3 1.2978 nan 0.0100 0.0035
## 4 1.2899 nan 0.0100 0.0036
## 5 1.2822 nan 0.0100 0.0034
## 6 1.2747 nan 0.0100 0.0032
## 7 1.2675 nan 0.0100 0.0033
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## 20 1.1835 nan 0.0100 0.0027
## 40 1.0820 nan 0.0100 0.0019
## 60 1.0030 nan 0.0100 0.0014
## 80 0.9397 nan 0.0100 0.0010
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## 140 0.8151 nan 0.0100 0.0003
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## 180 0.7612 nan 0.0100 0.0002
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## 220 0.7234 nan 0.0100 -0.0000
## 240 0.7079 nan 0.0100 0.0001
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## 280 0.6804 nan 0.0100 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3120 nan 0.0100 0.0042
## 2 1.3033 nan 0.0100 0.0037
## 3 1.2941 nan 0.0100 0.0039
## 4 1.2850 nan 0.0100 0.0038
## 5 1.2770 nan 0.0100 0.0033
## 6 1.2689 nan 0.0100 0.0036
## 7 1.2610 nan 0.0100 0.0034
## 8 1.2536 nan 0.0100 0.0037
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## 10 1.2390 nan 0.0100 0.0030
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## 40 1.0632 nan 0.0100 0.0015
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## 220 0.6793 nan 0.0100 -0.0000
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## 480 0.5059 nan 0.0100 0.0001
## 500 0.4965 nan 0.0100 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3119 nan 0.0100 0.0042
## 2 1.3030 nan 0.0100 0.0040
## 3 1.2949 nan 0.0100 0.0037
## 4 1.2873 nan 0.0100 0.0036
## 5 1.2792 nan 0.0100 0.0035
## 6 1.2708 nan 0.0100 0.0036
## 7 1.2634 nan 0.0100 0.0035
## 8 1.2550 nan 0.0100 0.0035
## 9 1.2471 nan 0.0100 0.0032
## 10 1.2397 nan 0.0100 0.0030
## 20 1.1713 nan 0.0100 0.0020
## 40 1.0618 nan 0.0100 0.0020
## 60 0.9801 nan 0.0100 0.0015
## 80 0.9142 nan 0.0100 0.0012
## 100 0.8624 nan 0.0100 0.0008
## 120 0.8204 nan 0.0100 0.0006
## 140 0.7852 nan 0.0100 0.0006
## 160 0.7553 nan 0.0100 0.0004
## 180 0.7309 nan 0.0100 0.0003
## 200 0.7076 nan 0.0100 0.0004
## 220 0.6884 nan 0.0100 0.0002
## 240 0.6692 nan 0.0100 0.0002
## 260 0.6520 nan 0.0100 0.0002
## 280 0.6371 nan 0.0100 0.0000
## 300 0.6227 nan 0.0100 -0.0001
## 320 0.6081 nan 0.0100 -0.0002
## 340 0.5960 nan 0.0100 0.0001
## 360 0.5845 nan 0.0100 0.0000
## 380 0.5729 nan 0.0100 0.0002
## 400 0.5618 nan 0.0100 0.0001
## 420 0.5509 nan 0.0100 0.0001
## 440 0.5405 nan 0.0100 0.0001
## 460 0.5311 nan 0.0100 0.0000
## 480 0.5220 nan 0.0100 -0.0001
## 500 0.5127 nan 0.0100 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3132 nan 0.0100 0.0035
## 2 1.3047 nan 0.0100 0.0036
## 3 1.2964 nan 0.0100 0.0038
## 4 1.2882 nan 0.0100 0.0036
## 5 1.2800 nan 0.0100 0.0036
## 6 1.2723 nan 0.0100 0.0034
## 7 1.2646 nan 0.0100 0.0035
## 8 1.2568 nan 0.0100 0.0035
## 9 1.2499 nan 0.0100 0.0028
## 10 1.2424 nan 0.0100 0.0032
## 20 1.1744 nan 0.0100 0.0030
## 40 1.0676 nan 0.0100 0.0022
## 60 0.9836 nan 0.0100 0.0014
## 80 0.9198 nan 0.0100 0.0013
## 100 0.8679 nan 0.0100 0.0010
## 120 0.8255 nan 0.0100 0.0004
## 140 0.7913 nan 0.0100 0.0003
## 160 0.7611 nan 0.0100 0.0004
## 180 0.7357 nan 0.0100 0.0001
## 200 0.7131 nan 0.0100 0.0002
## 220 0.6925 nan 0.0100 0.0003
## 240 0.6745 nan 0.0100 0.0002
## 260 0.6594 nan 0.0100 -0.0001
## 280 0.6441 nan 0.0100 0.0001
## 300 0.6302 nan 0.0100 -0.0001
## 320 0.6166 nan 0.0100 0.0002
## 340 0.6037 nan 0.0100 -0.0001
## 360 0.5904 nan 0.0100 0.0000
## 380 0.5788 nan 0.0100 -0.0003
## 400 0.5686 nan 0.0100 0.0000
## 420 0.5590 nan 0.0100 0.0000
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## 460 0.5387 nan 0.0100 -0.0001
## 480 0.5288 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3124 nan 0.0100 0.0041
## 2 1.3036 nan 0.0100 0.0041
## 3 1.2943 nan 0.0100 0.0043
## 4 1.2855 nan 0.0100 0.0044
## 5 1.2761 nan 0.0100 0.0038
## 6 1.2674 nan 0.0100 0.0037
## 7 1.2595 nan 0.0100 0.0033
## 8 1.2515 nan 0.0100 0.0033
## 9 1.2437 nan 0.0100 0.0033
## 10 1.2358 nan 0.0100 0.0036
## 20 1.1634 nan 0.0100 0.0030
## 40 1.0494 nan 0.0100 0.0023
## 60 0.9638 nan 0.0100 0.0017
## 80 0.8956 nan 0.0100 0.0009
## 100 0.8408 nan 0.0100 0.0008
## 120 0.7944 nan 0.0100 0.0009
## 140 0.7577 nan 0.0100 0.0006
## 160 0.7251 nan 0.0100 0.0008
## 180 0.6966 nan 0.0100 0.0003
## 200 0.6715 nan 0.0100 0.0003
## 220 0.6488 nan 0.0100 0.0003
## 240 0.6307 nan 0.0100 0.0001
## 260 0.6124 nan 0.0100 0.0001
## 280 0.5952 nan 0.0100 -0.0001
## 300 0.5800 nan 0.0100 0.0002
## 320 0.5646 nan 0.0100 -0.0001
## 340 0.5496 nan 0.0100 0.0001
## 360 0.5361 nan 0.0100 -0.0000
## 380 0.5224 nan 0.0100 0.0001
## 400 0.5110 nan 0.0100 0.0001
## 420 0.4992 nan 0.0100 -0.0002
## 440 0.4881 nan 0.0100 0.0000
## 460 0.4776 nan 0.0100 -0.0001
## 480 0.4678 nan 0.0100 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3128 nan 0.0100 0.0040
## 2 1.3035 nan 0.0100 0.0040
## 3 1.2947 nan 0.0100 0.0040
## 4 1.2862 nan 0.0100 0.0040
## 5 1.2782 nan 0.0100 0.0035
## 6 1.2701 nan 0.0100 0.0036
## 7 1.2616 nan 0.0100 0.0034
## 8 1.2542 nan 0.0100 0.0032
## 9 1.2469 nan 0.0100 0.0030
## 10 1.2394 nan 0.0100 0.0031
## 20 1.1689 nan 0.0100 0.0027
## 40 1.0568 nan 0.0100 0.0022
## 60 0.9702 nan 0.0100 0.0017
## 80 0.9017 nan 0.0100 0.0013
## 100 0.8465 nan 0.0100 0.0010
## 120 0.8015 nan 0.0100 0.0006
## 140 0.7646 nan 0.0100 0.0004
## 160 0.7331 nan 0.0100 0.0005
## 180 0.7055 nan 0.0100 0.0002
## 200 0.6823 nan 0.0100 0.0000
## 220 0.6604 nan 0.0100 0.0001
## 240 0.6413 nan 0.0100 0.0001
## 260 0.6225 nan 0.0100 0.0001
## 280 0.6053 nan 0.0100 0.0002
## 300 0.5880 nan 0.0100 0.0001
## 320 0.5734 nan 0.0100 0.0001
## 340 0.5601 nan 0.0100 0.0001
## 360 0.5478 nan 0.0100 0.0000
## 380 0.5342 nan 0.0100 -0.0001
## 400 0.5215 nan 0.0100 0.0000
## 420 0.5094 nan 0.0100 -0.0000
## 440 0.4975 nan 0.0100 -0.0001
## 460 0.4873 nan 0.0100 0.0000
## 480 0.4769 nan 0.0100 -0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3126 nan 0.0100 0.0039
## 2 1.3035 nan 0.0100 0.0043
## 3 1.2945 nan 0.0100 0.0037
## 4 1.2863 nan 0.0100 0.0038
## 5 1.2782 nan 0.0100 0.0033
## 6 1.2707 nan 0.0100 0.0038
## 7 1.2628 nan 0.0100 0.0036
## 8 1.2552 nan 0.0100 0.0035
## 9 1.2472 nan 0.0100 0.0036
## 10 1.2396 nan 0.0100 0.0032
## 20 1.1694 nan 0.0100 0.0029
## 40 1.0572 nan 0.0100 0.0019
## 60 0.9715 nan 0.0100 0.0015
## 80 0.9052 nan 0.0100 0.0011
## 100 0.8506 nan 0.0100 0.0010
## 120 0.8042 nan 0.0100 0.0006
## 140 0.7674 nan 0.0100 0.0004
## 160 0.7362 nan 0.0100 0.0004
## 180 0.7091 nan 0.0100 0.0004
## 200 0.6844 nan 0.0100 0.0003
## 220 0.6650 nan 0.0100 0.0001
## 240 0.6458 nan 0.0100 0.0002
## 260 0.6283 nan 0.0100 0.0001
## 280 0.6113 nan 0.0100 0.0001
## 300 0.5962 nan 0.0100 0.0001
## 320 0.5818 nan 0.0100 -0.0001
## 340 0.5685 nan 0.0100 -0.0001
## 360 0.5560 nan 0.0100 -0.0000
## 380 0.5429 nan 0.0100 0.0001
## 400 0.5310 nan 0.0100 0.0001
## 420 0.5194 nan 0.0100 -0.0001
## 440 0.5087 nan 0.0100 -0.0000
## 460 0.4983 nan 0.0100 0.0000
## 480 0.4879 nan 0.0100 0.0000
## 500 0.4791 nan 0.0100 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2430 nan 0.1000 0.0322
## 2 1.1783 nan 0.1000 0.0294
## 3 1.1196 nan 0.1000 0.0234
## 4 1.0714 nan 0.1000 0.0181
## 5 1.0270 nan 0.1000 0.0167
## 6 0.9869 nan 0.1000 0.0154
## 7 0.9533 nan 0.1000 0.0133
## 8 0.9255 nan 0.1000 0.0124
## 9 0.8998 nan 0.1000 0.0093
## 10 0.8721 nan 0.1000 0.0087
## 20 0.7289 nan 0.1000 0.0011
## 40 0.5906 nan 0.1000 -0.0008
## 60 0.5087 nan 0.1000 -0.0030
## 80 0.4387 nan 0.1000 -0.0005
## 100 0.3835 nan 0.1000 -0.0002
## 120 0.3392 nan 0.1000 -0.0002
## 140 0.3014 nan 0.1000 0.0004
## 160 0.2698 nan 0.1000 -0.0003
## 180 0.2442 nan 0.1000 -0.0000
## 200 0.2232 nan 0.1000 -0.0005
## 220 0.2035 nan 0.1000 -0.0011
## 240 0.1853 nan 0.1000 -0.0008
## 260 0.1693 nan 0.1000 0.0002
## 280 0.1526 nan 0.1000 -0.0002
## 300 0.1387 nan 0.1000 -0.0002
## 320 0.1267 nan 0.1000 -0.0002
## 340 0.1172 nan 0.1000 -0.0003
## 360 0.1074 nan 0.1000 -0.0003
## 380 0.0987 nan 0.1000 -0.0003
## 400 0.0903 nan 0.1000 -0.0002
## 420 0.0834 nan 0.1000 -0.0002
## 440 0.0774 nan 0.1000 -0.0003
## 460 0.0716 nan 0.1000 -0.0000
## 480 0.0662 nan 0.1000 -0.0002
## 500 0.0608 nan 0.1000 -0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2474 nan 0.1000 0.0311
## 2 1.1756 nan 0.1000 0.0311
## 3 1.1216 nan 0.1000 0.0232
## 4 1.0760 nan 0.1000 0.0181
## 5 1.0338 nan 0.1000 0.0176
## 6 0.9951 nan 0.1000 0.0178
## 7 0.9656 nan 0.1000 0.0124
## 8 0.9384 nan 0.1000 0.0118
## 9 0.9130 nan 0.1000 0.0099
## 10 0.8900 nan 0.1000 0.0091
## 20 0.7386 nan 0.1000 0.0001
## 40 0.6007 nan 0.1000 0.0000
## 60 0.5187 nan 0.1000 0.0020
## 80 0.4509 nan 0.1000 -0.0021
## 100 0.3968 nan 0.1000 -0.0011
## 120 0.3519 nan 0.1000 -0.0013
## 140 0.3131 nan 0.1000 -0.0012
## 160 0.2819 nan 0.1000 -0.0003
## 180 0.2556 nan 0.1000 -0.0005
## 200 0.2329 nan 0.1000 -0.0010
## 220 0.2119 nan 0.1000 -0.0008
## 240 0.1937 nan 0.1000 -0.0006
## 260 0.1787 nan 0.1000 -0.0004
## 280 0.1632 nan 0.1000 -0.0004
## 300 0.1502 nan 0.1000 0.0000
## 320 0.1368 nan 0.1000 -0.0004
## 340 0.1269 nan 0.1000 -0.0005
## 360 0.1171 nan 0.1000 -0.0001
## 380 0.1076 nan 0.1000 -0.0003
## 400 0.0993 nan 0.1000 -0.0003
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## 440 0.0846 nan 0.1000 -0.0002
## 460 0.0785 nan 0.1000 -0.0003
## 480 0.0729 nan 0.1000 -0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2404 nan 0.1000 0.0375
## 2 1.1835 nan 0.1000 0.0263
## 3 1.1284 nan 0.1000 0.0250
## 4 1.0808 nan 0.1000 0.0212
## 5 1.0359 nan 0.1000 0.0188
## 6 1.0014 nan 0.1000 0.0146
## 7 0.9659 nan 0.1000 0.0130
## 8 0.9344 nan 0.1000 0.0115
## 9 0.9069 nan 0.1000 0.0099
## 10 0.8842 nan 0.1000 0.0098
## 20 0.7494 nan 0.1000 0.0009
## 40 0.6168 nan 0.1000 -0.0008
## 60 0.5394 nan 0.1000 -0.0010
## 80 0.4764 nan 0.1000 0.0006
## 100 0.4186 nan 0.1000 0.0002
## 120 0.3735 nan 0.1000 -0.0018
## 140 0.3385 nan 0.1000 -0.0011
## 160 0.3087 nan 0.1000 -0.0014
## 180 0.2773 nan 0.1000 -0.0007
## 200 0.2525 nan 0.1000 -0.0007
## 220 0.2312 nan 0.1000 -0.0012
## 240 0.2100 nan 0.1000 -0.0005
## 260 0.1912 nan 0.1000 -0.0003
## 280 0.1783 nan 0.1000 -0.0001
## 300 0.1640 nan 0.1000 -0.0005
## 320 0.1515 nan 0.1000 -0.0005
## 340 0.1401 nan 0.1000 -0.0003
## 360 0.1293 nan 0.1000 -0.0004
## 380 0.1199 nan 0.1000 -0.0003
## 400 0.1119 nan 0.1000 -0.0006
## 420 0.1042 nan 0.1000 -0.0003
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## 460 0.0904 nan 0.1000 -0.0003
## 480 0.0839 nan 0.1000 -0.0004
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2329 nan 0.1000 0.0402
## 2 1.1694 nan 0.1000 0.0277
## 3 1.1071 nan 0.1000 0.0289
## 4 1.0575 nan 0.1000 0.0192
## 5 1.0135 nan 0.1000 0.0185
## 6 0.9778 nan 0.1000 0.0151
## 7 0.9469 nan 0.1000 0.0133
## 8 0.9123 nan 0.1000 0.0142
## 9 0.8875 nan 0.1000 0.0106
## 10 0.8642 nan 0.1000 0.0070
## 20 0.7020 nan 0.1000 0.0035
## 40 0.5641 nan 0.1000 -0.0018
## 60 0.4714 nan 0.1000 -0.0004
## 80 0.3974 nan 0.1000 0.0009
## 100 0.3357 nan 0.1000 -0.0011
## 120 0.2888 nan 0.1000 -0.0008
## 140 0.2523 nan 0.1000 0.0002
## 160 0.2221 nan 0.1000 0.0002
## 180 0.1926 nan 0.1000 -0.0002
## 200 0.1729 nan 0.1000 -0.0004
## 220 0.1534 nan 0.1000 -0.0002
## 240 0.1351 nan 0.1000 -0.0003
## 260 0.1203 nan 0.1000 -0.0002
## 280 0.1072 nan 0.1000 -0.0001
## 300 0.0968 nan 0.1000 -0.0004
## 320 0.0873 nan 0.1000 -0.0002
## 340 0.0787 nan 0.1000 -0.0003
## 360 0.0711 nan 0.1000 -0.0001
## 380 0.0638 nan 0.1000 -0.0002
## 400 0.0567 nan 0.1000 -0.0000
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## 460 0.0422 nan 0.1000 -0.0001
## 480 0.0381 nan 0.1000 0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2448 nan 0.1000 0.0325
## 2 1.1704 nan 0.1000 0.0300
## 3 1.1201 nan 0.1000 0.0194
## 4 1.0692 nan 0.1000 0.0216
## 5 1.0304 nan 0.1000 0.0162
## 6 0.9903 nan 0.1000 0.0131
## 7 0.9538 nan 0.1000 0.0167
## 8 0.9197 nan 0.1000 0.0160
## 9 0.8927 nan 0.1000 0.0103
## 10 0.8669 nan 0.1000 0.0094
## 20 0.7106 nan 0.1000 0.0008
## 40 0.5655 nan 0.1000 -0.0001
## 60 0.4657 nan 0.1000 -0.0007
## 80 0.3965 nan 0.1000 -0.0002
## 100 0.3370 nan 0.1000 -0.0006
## 120 0.2972 nan 0.1000 -0.0006
## 140 0.2626 nan 0.1000 -0.0005
## 160 0.2303 nan 0.1000 -0.0006
## 180 0.2024 nan 0.1000 -0.0004
## 200 0.1777 nan 0.1000 -0.0005
## 220 0.1572 nan 0.1000 -0.0005
## 240 0.1403 nan 0.1000 -0.0003
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## 280 0.1129 nan 0.1000 0.0002
## 300 0.1008 nan 0.1000 -0.0002
## 320 0.0901 nan 0.1000 -0.0002
## 340 0.0803 nan 0.1000 -0.0001
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## 380 0.0659 nan 0.1000 -0.0001
## 400 0.0597 nan 0.1000 -0.0003
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2386 nan 0.1000 0.0349
## 2 1.1708 nan 0.1000 0.0269
## 3 1.1139 nan 0.1000 0.0246
## 4 1.0664 nan 0.1000 0.0206
## 5 1.0261 nan 0.1000 0.0157
## 6 0.9880 nan 0.1000 0.0166
## 7 0.9530 nan 0.1000 0.0113
## 8 0.9240 nan 0.1000 0.0113
## 9 0.8994 nan 0.1000 0.0082
## 10 0.8723 nan 0.1000 0.0098
## 20 0.7191 nan 0.1000 0.0027
## 40 0.5788 nan 0.1000 -0.0023
## 60 0.4854 nan 0.1000 0.0000
## 80 0.4140 nan 0.1000 -0.0003
## 100 0.3602 nan 0.1000 -0.0008
## 120 0.3159 nan 0.1000 -0.0010
## 140 0.2772 nan 0.1000 -0.0002
## 160 0.2470 nan 0.1000 -0.0006
## 180 0.2216 nan 0.1000 -0.0006
## 200 0.1973 nan 0.1000 -0.0011
## 220 0.1744 nan 0.1000 -0.0005
## 240 0.1562 nan 0.1000 -0.0006
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## 280 0.1262 nan 0.1000 -0.0002
## 300 0.1133 nan 0.1000 -0.0003
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## 340 0.0909 nan 0.1000 -0.0004
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## 380 0.0749 nan 0.1000 -0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2319 nan 0.1000 0.0381
## 2 1.1542 nan 0.1000 0.0324
## 3 1.0940 nan 0.1000 0.0264
## 4 1.0400 nan 0.1000 0.0220
## 5 0.9941 nan 0.1000 0.0208
## 6 0.9589 nan 0.1000 0.0134
## 7 0.9242 nan 0.1000 0.0104
## 8 0.8892 nan 0.1000 0.0120
## 9 0.8623 nan 0.1000 0.0116
## 10 0.8344 nan 0.1000 0.0104
## 20 0.6655 nan 0.1000 0.0045
## 40 0.5015 nan 0.1000 -0.0001
## 60 0.4072 nan 0.1000 -0.0001
## 80 0.3404 nan 0.1000 0.0002
## 100 0.2878 nan 0.1000 -0.0016
## 120 0.2409 nan 0.1000 -0.0009
## 140 0.2027 nan 0.1000 -0.0008
## 160 0.1721 nan 0.1000 -0.0003
## 180 0.1495 nan 0.1000 -0.0002
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## 220 0.1117 nan 0.1000 -0.0002
## 240 0.0982 nan 0.1000 -0.0002
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## 300 0.0653 nan 0.1000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2287 nan 0.1000 0.0386
## 2 1.1565 nan 0.1000 0.0321
## 3 1.0975 nan 0.1000 0.0233
## 4 1.0476 nan 0.1000 0.0205
## 5 0.9977 nan 0.1000 0.0218
## 6 0.9607 nan 0.1000 0.0158
## 7 0.9241 nan 0.1000 0.0161
## 8 0.8938 nan 0.1000 0.0098
## 9 0.8648 nan 0.1000 0.0105
## 10 0.8407 nan 0.1000 0.0087
## 20 0.6769 nan 0.1000 0.0010
## 40 0.5284 nan 0.1000 -0.0007
## 60 0.4306 nan 0.1000 -0.0011
## 80 0.3524 nan 0.1000 -0.0004
## 100 0.3006 nan 0.1000 -0.0011
## 120 0.2496 nan 0.1000 -0.0007
## 140 0.2133 nan 0.1000 -0.0005
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## 180 0.1603 nan 0.1000 -0.0005
## 200 0.1386 nan 0.1000 -0.0003
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## 240 0.1051 nan 0.1000 -0.0003
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## 280 0.0791 nan 0.1000 -0.0003
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## 320 0.0613 nan 0.1000 -0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2481 nan 0.1000 0.0321
## 2 1.1782 nan 0.1000 0.0301
## 3 1.1144 nan 0.1000 0.0300
## 4 1.0608 nan 0.1000 0.0248
## 5 1.0125 nan 0.1000 0.0202
## 6 0.9754 nan 0.1000 0.0142
## 7 0.9393 nan 0.1000 0.0144
## 8 0.9074 nan 0.1000 0.0114
## 9 0.8798 nan 0.1000 0.0108
## 10 0.8564 nan 0.1000 0.0083
## 20 0.6899 nan 0.1000 0.0027
## 40 0.5384 nan 0.1000 -0.0006
## 60 0.4447 nan 0.1000 0.0003
## 80 0.3695 nan 0.1000 -0.0011
## 100 0.3129 nan 0.1000 -0.0006
## 120 0.2667 nan 0.1000 -0.0003
## 140 0.2294 nan 0.1000 -0.0010
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## 180 0.1712 nan 0.1000 -0.0005
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## 280 0.0896 nan 0.1000 -0.0004
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3190 nan 0.0010 0.0003
## 3 1.3183 nan 0.0010 0.0003
## 4 1.3175 nan 0.0010 0.0004
## 5 1.3167 nan 0.0010 0.0003
## 6 1.3159 nan 0.0010 0.0003
## 7 1.3151 nan 0.0010 0.0004
## 8 1.3143 nan 0.0010 0.0004
## 9 1.3136 nan 0.0010 0.0003
## 10 1.3128 nan 0.0010 0.0003
## 20 1.3052 nan 0.0010 0.0003
## 40 1.2903 nan 0.0010 0.0003
## 60 1.2756 nan 0.0010 0.0003
## 80 1.2616 nan 0.0010 0.0003
## 100 1.2479 nan 0.0010 0.0003
## 120 1.2347 nan 0.0010 0.0003
## 140 1.2215 nan 0.0010 0.0003
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## 180 1.1968 nan 0.0010 0.0003
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3191 nan 0.0010 0.0003
## 3 1.3183 nan 0.0010 0.0003
## 4 1.3175 nan 0.0010 0.0003
## 5 1.3167 nan 0.0010 0.0003
## 6 1.3160 nan 0.0010 0.0004
## 7 1.3151 nan 0.0010 0.0004
## 8 1.3143 nan 0.0010 0.0004
## 9 1.3135 nan 0.0010 0.0003
## 10 1.3128 nan 0.0010 0.0003
## 20 1.3049 nan 0.0010 0.0003
## 40 1.2899 nan 0.0010 0.0003
## 60 1.2753 nan 0.0010 0.0003
## 80 1.2612 nan 0.0010 0.0003
## 100 1.2474 nan 0.0010 0.0003
## 120 1.2342 nan 0.0010 0.0002
## 140 1.2215 nan 0.0010 0.0003
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3190 nan 0.0010 0.0003
## 3 1.3183 nan 0.0010 0.0003
## 4 1.3175 nan 0.0010 0.0004
## 5 1.3167 nan 0.0010 0.0004
## 6 1.3159 nan 0.0010 0.0004
## 7 1.3151 nan 0.0010 0.0003
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## 10 1.3128 nan 0.0010 0.0004
## 20 1.3051 nan 0.0010 0.0003
## 40 1.2904 nan 0.0010 0.0003
## 60 1.2758 nan 0.0010 0.0003
## 80 1.2619 nan 0.0010 0.0003
## 100 1.2487 nan 0.0010 0.0003
## 120 1.2354 nan 0.0010 0.0002
## 140 1.2226 nan 0.0010 0.0003
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0003
## 3 1.3181 nan 0.0010 0.0004
## 4 1.3173 nan 0.0010 0.0004
## 5 1.3164 nan 0.0010 0.0004
## 6 1.3156 nan 0.0010 0.0003
## 7 1.3148 nan 0.0010 0.0004
## 8 1.3139 nan 0.0010 0.0004
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## 10 1.3123 nan 0.0010 0.0004
## 20 1.3041 nan 0.0010 0.0004
## 40 1.2875 nan 0.0010 0.0003
## 60 1.2719 nan 0.0010 0.0004
## 80 1.2566 nan 0.0010 0.0004
## 100 1.2421 nan 0.0010 0.0003
## 120 1.2278 nan 0.0010 0.0003
## 140 1.2140 nan 0.0010 0.0003
## 160 1.2006 nan 0.0010 0.0003
## 180 1.1875 nan 0.0010 0.0003
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## 380 1.0758 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3190 nan 0.0010 0.0003
## 3 1.3182 nan 0.0010 0.0003
## 4 1.3174 nan 0.0010 0.0004
## 5 1.3165 nan 0.0010 0.0004
## 6 1.3156 nan 0.0010 0.0004
## 7 1.3148 nan 0.0010 0.0004
## 8 1.3140 nan 0.0010 0.0003
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## 10 1.3123 nan 0.0010 0.0005
## 20 1.3038 nan 0.0010 0.0004
## 40 1.2875 nan 0.0010 0.0004
## 60 1.2718 nan 0.0010 0.0004
## 80 1.2566 nan 0.0010 0.0003
## 100 1.2421 nan 0.0010 0.0003
## 120 1.2279 nan 0.0010 0.0003
## 140 1.2141 nan 0.0010 0.0003
## 160 1.2006 nan 0.0010 0.0003
## 180 1.1873 nan 0.0010 0.0003
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## 400 1.0654 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3179 nan 0.0010 0.0004
## 4 1.3171 nan 0.0010 0.0004
## 5 1.3162 nan 0.0010 0.0004
## 6 1.3153 nan 0.0010 0.0004
## 7 1.3145 nan 0.0010 0.0004
## 8 1.3136 nan 0.0010 0.0004
## 9 1.3128 nan 0.0010 0.0004
## 10 1.3119 nan 0.0010 0.0004
## 20 1.3036 nan 0.0010 0.0004
## 40 1.2874 nan 0.0010 0.0003
## 60 1.2719 nan 0.0010 0.0003
## 80 1.2566 nan 0.0010 0.0003
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## 120 1.2278 nan 0.0010 0.0003
## 140 1.2138 nan 0.0010 0.0003
## 160 1.2004 nan 0.0010 0.0003
## 180 1.1877 nan 0.0010 0.0003
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## 380 1.0774 nan 0.0010 0.0002
## 400 1.0679 nan 0.0010 0.0002
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## 480 1.0321 nan 0.0010 0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3179 nan 0.0010 0.0004
## 4 1.3170 nan 0.0010 0.0004
## 5 1.3161 nan 0.0010 0.0004
## 6 1.3152 nan 0.0010 0.0004
## 7 1.3143 nan 0.0010 0.0004
## 8 1.3133 nan 0.0010 0.0004
## 9 1.3124 nan 0.0010 0.0004
## 10 1.3115 nan 0.0010 0.0004
## 20 1.3028 nan 0.0010 0.0004
## 40 1.2856 nan 0.0010 0.0003
## 60 1.2689 nan 0.0010 0.0004
## 80 1.2529 nan 0.0010 0.0003
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## 120 1.2229 nan 0.0010 0.0003
## 140 1.2082 nan 0.0010 0.0003
## 160 1.1942 nan 0.0010 0.0003
## 180 1.1803 nan 0.0010 0.0003
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## 220 1.1538 nan 0.0010 0.0002
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## 300 1.1060 nan 0.0010 0.0003
## 320 1.0949 nan 0.0010 0.0002
## 340 1.0842 nan 0.0010 0.0002
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## 380 1.0631 nan 0.0010 0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3179 nan 0.0010 0.0004
## 4 1.3170 nan 0.0010 0.0004
## 5 1.3161 nan 0.0010 0.0004
## 6 1.3153 nan 0.0010 0.0004
## 7 1.3143 nan 0.0010 0.0004
## 8 1.3135 nan 0.0010 0.0003
## 9 1.3126 nan 0.0010 0.0004
## 10 1.3117 nan 0.0010 0.0004
## 20 1.3031 nan 0.0010 0.0004
## 40 1.2860 nan 0.0010 0.0004
## 60 1.2693 nan 0.0010 0.0004
## 80 1.2534 nan 0.0010 0.0004
## 100 1.2376 nan 0.0010 0.0003
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## 140 1.2080 nan 0.0010 0.0003
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## 180 1.1805 nan 0.0010 0.0003
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## 380 1.0643 nan 0.0010 0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3180 nan 0.0010 0.0004
## 4 1.3172 nan 0.0010 0.0004
## 5 1.3163 nan 0.0010 0.0004
## 6 1.3154 nan 0.0010 0.0004
## 7 1.3145 nan 0.0010 0.0004
## 8 1.3137 nan 0.0010 0.0004
## 9 1.3128 nan 0.0010 0.0004
## 10 1.3119 nan 0.0010 0.0004
## 20 1.3032 nan 0.0010 0.0004
## 40 1.2865 nan 0.0010 0.0003
## 60 1.2699 nan 0.0010 0.0004
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## 140 1.2098 nan 0.0010 0.0003
## 160 1.1960 nan 0.0010 0.0002
## 180 1.1825 nan 0.0010 0.0003
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## 380 1.0664 nan 0.0010 0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3129 nan 0.0100 0.0037
## 2 1.3053 nan 0.0100 0.0038
## 3 1.2982 nan 0.0100 0.0032
## 4 1.2912 nan 0.0100 0.0026
## 5 1.2842 nan 0.0100 0.0031
## 6 1.2769 nan 0.0100 0.0033
## 7 1.2698 nan 0.0100 0.0031
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## 10 1.2492 nan 0.0100 0.0031
## 20 1.1872 nan 0.0100 0.0022
## 40 1.0851 nan 0.0100 0.0019
## 60 1.0041 nan 0.0100 0.0013
## 80 0.9406 nan 0.0100 0.0010
## 100 0.8868 nan 0.0100 0.0008
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## 140 0.8083 nan 0.0100 0.0004
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## 320 0.6404 nan 0.0100 0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3123 nan 0.0100 0.0037
## 2 1.3049 nan 0.0100 0.0033
## 3 1.2977 nan 0.0100 0.0030
## 4 1.2904 nan 0.0100 0.0032
## 5 1.2835 nan 0.0100 0.0030
## 6 1.2763 nan 0.0100 0.0033
## 7 1.2697 nan 0.0100 0.0028
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## 40 1.0848 nan 0.0100 0.0020
## 60 1.0054 nan 0.0100 0.0014
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## 340 0.6345 nan 0.0100 -0.0001
## 360 0.6241 nan 0.0100 -0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3131 nan 0.0100 0.0033
## 2 1.3050 nan 0.0100 0.0034
## 3 1.2982 nan 0.0100 0.0028
## 4 1.2907 nan 0.0100 0.0031
## 5 1.2830 nan 0.0100 0.0033
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## 9 1.2547 nan 0.0100 0.0034
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## 20 1.1845 nan 0.0100 0.0023
## 40 1.0842 nan 0.0100 0.0019
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3127 nan 0.0100 0.0032
## 2 1.3046 nan 0.0100 0.0033
## 3 1.2960 nan 0.0100 0.0040
## 4 1.2881 nan 0.0100 0.0037
## 5 1.2798 nan 0.0100 0.0038
## 6 1.2720 nan 0.0100 0.0031
## 7 1.2644 nan 0.0100 0.0033
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## 40 1.0676 nan 0.0100 0.0022
## 60 0.9835 nan 0.0100 0.0014
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3121 nan 0.0100 0.0040
## 2 1.3035 nan 0.0100 0.0040
## 3 1.2961 nan 0.0100 0.0030
## 4 1.2877 nan 0.0100 0.0036
## 5 1.2796 nan 0.0100 0.0040
## 6 1.2719 nan 0.0100 0.0034
## 7 1.2645 nan 0.0100 0.0031
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3122 nan 0.0100 0.0036
## 2 1.3039 nan 0.0100 0.0036
## 3 1.2965 nan 0.0100 0.0029
## 4 1.2886 nan 0.0100 0.0034
## 5 1.2811 nan 0.0100 0.0034
## 6 1.2731 nan 0.0100 0.0035
## 7 1.2661 nan 0.0100 0.0033
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## 40 1.0707 nan 0.0100 0.0016
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3123 nan 0.0100 0.0037
## 2 1.3033 nan 0.0100 0.0042
## 3 1.2944 nan 0.0100 0.0039
## 4 1.2865 nan 0.0100 0.0035
## 5 1.2782 nan 0.0100 0.0037
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## 140 0.7580 nan 0.0100 0.0003
## 160 0.7254 nan 0.0100 0.0005
## 180 0.6962 nan 0.0100 0.0002
## 200 0.6707 nan 0.0100 0.0005
## 220 0.6478 nan 0.0100 0.0002
## 240 0.6275 nan 0.0100 0.0001
## 260 0.6095 nan 0.0100 -0.0001
## 280 0.5925 nan 0.0100 0.0002
## 300 0.5766 nan 0.0100 0.0001
## 320 0.5617 nan 0.0100 0.0001
## 340 0.5458 nan 0.0100 0.0000
## 360 0.5333 nan 0.0100 0.0001
## 380 0.5212 nan 0.0100 -0.0000
## 400 0.5104 nan 0.0100 0.0000
## 420 0.4990 nan 0.0100 -0.0001
## 440 0.4877 nan 0.0100 0.0000
## 460 0.4775 nan 0.0100 -0.0000
## 480 0.4673 nan 0.0100 -0.0000
## 500 0.4572 nan 0.0100 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3120 nan 0.0100 0.0038
## 2 1.3028 nan 0.0100 0.0041
## 3 1.2937 nan 0.0100 0.0042
## 4 1.2855 nan 0.0100 0.0034
## 5 1.2771 nan 0.0100 0.0039
## 6 1.2689 nan 0.0100 0.0037
## 7 1.2606 nan 0.0100 0.0036
## 8 1.2535 nan 0.0100 0.0031
## 9 1.2456 nan 0.0100 0.0036
## 10 1.2382 nan 0.0100 0.0035
## 20 1.1663 nan 0.0100 0.0029
## 40 1.0557 nan 0.0100 0.0014
## 60 0.9686 nan 0.0100 0.0015
## 80 0.9006 nan 0.0100 0.0014
## 100 0.8449 nan 0.0100 0.0010
## 120 0.7993 nan 0.0100 0.0005
## 140 0.7619 nan 0.0100 0.0006
## 160 0.7305 nan 0.0100 0.0003
## 180 0.7020 nan 0.0100 0.0004
## 200 0.6758 nan 0.0100 0.0002
## 220 0.6537 nan 0.0100 0.0002
## 240 0.6331 nan 0.0100 0.0001
## 260 0.6146 nan 0.0100 0.0001
## 280 0.5989 nan 0.0100 -0.0001
## 300 0.5829 nan 0.0100 0.0000
## 320 0.5680 nan 0.0100 0.0001
## 340 0.5541 nan 0.0100 0.0000
## 360 0.5408 nan 0.0100 0.0000
## 380 0.5290 nan 0.0100 0.0001
## 400 0.5169 nan 0.0100 0.0002
## 420 0.5059 nan 0.0100 -0.0000
## 440 0.4956 nan 0.0100 0.0000
## 460 0.4854 nan 0.0100 -0.0001
## 480 0.4752 nan 0.0100 0.0001
## 500 0.4656 nan 0.0100 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3122 nan 0.0100 0.0041
## 2 1.3030 nan 0.0100 0.0039
## 3 1.2944 nan 0.0100 0.0038
## 4 1.2860 nan 0.0100 0.0039
## 5 1.2776 nan 0.0100 0.0036
## 6 1.2689 nan 0.0100 0.0037
## 7 1.2605 nan 0.0100 0.0033
## 8 1.2530 nan 0.0100 0.0034
## 9 1.2451 nan 0.0100 0.0033
## 10 1.2373 nan 0.0100 0.0034
## 20 1.1676 nan 0.0100 0.0031
## 40 1.0567 nan 0.0100 0.0020
## 60 0.9705 nan 0.0100 0.0016
## 80 0.9034 nan 0.0100 0.0011
## 100 0.8482 nan 0.0100 0.0007
## 120 0.8034 nan 0.0100 0.0009
## 140 0.7671 nan 0.0100 0.0003
## 160 0.7354 nan 0.0100 0.0003
## 180 0.7082 nan 0.0100 0.0005
## 200 0.6845 nan 0.0100 0.0002
## 220 0.6642 nan 0.0100 -0.0000
## 240 0.6454 nan 0.0100 -0.0001
## 260 0.6260 nan 0.0100 0.0001
## 280 0.6097 nan 0.0100 0.0001
## 300 0.5952 nan 0.0100 -0.0000
## 320 0.5815 nan 0.0100 -0.0000
## 340 0.5681 nan 0.0100 -0.0001
## 360 0.5554 nan 0.0100 0.0001
## 380 0.5430 nan 0.0100 0.0001
## 400 0.5317 nan 0.0100 -0.0001
## 420 0.5211 nan 0.0100 -0.0001
## 440 0.5103 nan 0.0100 0.0001
## 460 0.4997 nan 0.0100 -0.0001
## 480 0.4901 nan 0.0100 0.0001
## 500 0.4802 nan 0.0100 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2466 nan 0.1000 0.0354
## 2 1.1854 nan 0.1000 0.0283
## 3 1.1298 nan 0.1000 0.0227
## 4 1.0807 nan 0.1000 0.0188
## 5 1.0386 nan 0.1000 0.0189
## 6 1.0004 nan 0.1000 0.0167
## 7 0.9692 nan 0.1000 0.0128
## 8 0.9418 nan 0.1000 0.0094
## 9 0.9145 nan 0.1000 0.0120
## 10 0.8903 nan 0.1000 0.0087
## 20 0.7412 nan 0.1000 0.0024
## 40 0.5997 nan 0.1000 0.0006
## 60 0.5150 nan 0.1000 -0.0003
## 80 0.4588 nan 0.1000 -0.0011
## 100 0.4001 nan 0.1000 -0.0000
## 120 0.3543 nan 0.1000 -0.0002
## 140 0.3152 nan 0.1000 -0.0004
## 160 0.2859 nan 0.1000 -0.0002
## 180 0.2607 nan 0.1000 -0.0008
## 200 0.2362 nan 0.1000 -0.0004
## 220 0.2141 nan 0.1000 -0.0009
## 240 0.1956 nan 0.1000 -0.0003
## 260 0.1780 nan 0.1000 -0.0003
## 280 0.1638 nan 0.1000 -0.0000
## 300 0.1496 nan 0.1000 -0.0005
## 320 0.1382 nan 0.1000 -0.0002
## 340 0.1269 nan 0.1000 -0.0001
## 360 0.1157 nan 0.1000 -0.0000
## 380 0.1071 nan 0.1000 -0.0002
## 400 0.0984 nan 0.1000 -0.0002
## 420 0.0917 nan 0.1000 -0.0003
## 440 0.0840 nan 0.1000 -0.0001
## 460 0.0773 nan 0.1000 -0.0002
## 480 0.0705 nan 0.1000 -0.0001
## 500 0.0652 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2556 nan 0.1000 0.0277
## 2 1.1898 nan 0.1000 0.0282
## 3 1.1409 nan 0.1000 0.0228
## 4 1.0985 nan 0.1000 0.0157
## 5 1.0546 nan 0.1000 0.0185
## 6 1.0180 nan 0.1000 0.0154
## 7 0.9787 nan 0.1000 0.0171
## 8 0.9477 nan 0.1000 0.0086
## 9 0.9202 nan 0.1000 0.0080
## 10 0.8959 nan 0.1000 0.0098
## 20 0.7462 nan 0.1000 0.0042
## 40 0.6104 nan 0.1000 -0.0005
## 60 0.5308 nan 0.1000 -0.0004
## 80 0.4773 nan 0.1000 0.0002
## 100 0.4216 nan 0.1000 -0.0012
## 120 0.3800 nan 0.1000 -0.0008
## 140 0.3397 nan 0.1000 -0.0013
## 160 0.3038 nan 0.1000 -0.0012
## 180 0.2720 nan 0.1000 -0.0000
## 200 0.2502 nan 0.1000 -0.0002
## 220 0.2297 nan 0.1000 -0.0011
## 240 0.2094 nan 0.1000 -0.0004
## 260 0.1903 nan 0.1000 -0.0002
## 280 0.1720 nan 0.1000 -0.0003
## 300 0.1563 nan 0.1000 -0.0004
## 320 0.1444 nan 0.1000 -0.0001
## 340 0.1326 nan 0.1000 -0.0004
## 360 0.1218 nan 0.1000 -0.0006
## 380 0.1125 nan 0.1000 -0.0003
## 400 0.1035 nan 0.1000 -0.0002
## 420 0.0956 nan 0.1000 -0.0004
## 440 0.0878 nan 0.1000 -0.0002
## 460 0.0814 nan 0.1000 -0.0002
## 480 0.0750 nan 0.1000 -0.0001
## 500 0.0697 nan 0.1000 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2424 nan 0.1000 0.0343
## 2 1.1825 nan 0.1000 0.0304
## 3 1.1262 nan 0.1000 0.0263
## 4 1.0788 nan 0.1000 0.0204
## 5 1.0351 nan 0.1000 0.0196
## 6 0.9972 nan 0.1000 0.0147
## 7 0.9648 nan 0.1000 0.0136
## 8 0.9364 nan 0.1000 0.0105
## 9 0.9098 nan 0.1000 0.0096
## 10 0.8807 nan 0.1000 0.0104
## 20 0.7405 nan 0.1000 0.0048
## 40 0.6121 nan 0.1000 0.0004
## 60 0.5384 nan 0.1000 -0.0012
## 80 0.4721 nan 0.1000 -0.0003
## 100 0.4261 nan 0.1000 -0.0013
## 120 0.3812 nan 0.1000 -0.0013
## 140 0.3509 nan 0.1000 -0.0011
## 160 0.3155 nan 0.1000 -0.0008
## 180 0.2883 nan 0.1000 -0.0007
## 200 0.2603 nan 0.1000 -0.0005
## 220 0.2358 nan 0.1000 -0.0007
## 240 0.2173 nan 0.1000 -0.0008
## 260 0.1980 nan 0.1000 -0.0001
## 280 0.1831 nan 0.1000 -0.0007
## 300 0.1684 nan 0.1000 -0.0007
## 320 0.1543 nan 0.1000 -0.0002
## 340 0.1431 nan 0.1000 -0.0005
## 360 0.1320 nan 0.1000 -0.0003
## 380 0.1229 nan 0.1000 -0.0006
## 400 0.1128 nan 0.1000 -0.0004
## 420 0.1043 nan 0.1000 -0.0003
## 440 0.0961 nan 0.1000 -0.0005
## 460 0.0893 nan 0.1000 -0.0002
## 480 0.0826 nan 0.1000 -0.0004
## 500 0.0771 nan 0.1000 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2355 nan 0.1000 0.0382
## 2 1.1671 nan 0.1000 0.0307
## 3 1.1082 nan 0.1000 0.0237
## 4 1.0607 nan 0.1000 0.0198
## 5 1.0169 nan 0.1000 0.0172
## 6 0.9773 nan 0.1000 0.0151
## 7 0.9423 nan 0.1000 0.0157
## 8 0.9121 nan 0.1000 0.0103
## 9 0.8836 nan 0.1000 0.0103
## 10 0.8611 nan 0.1000 0.0098
## 20 0.7021 nan 0.1000 0.0009
## 40 0.5599 nan 0.1000 -0.0002
## 60 0.4664 nan 0.1000 -0.0023
## 80 0.3991 nan 0.1000 -0.0007
## 100 0.3429 nan 0.1000 -0.0004
## 120 0.2982 nan 0.1000 -0.0000
## 140 0.2574 nan 0.1000 -0.0008
## 160 0.2247 nan 0.1000 -0.0004
## 180 0.2000 nan 0.1000 -0.0010
## 200 0.1790 nan 0.1000 -0.0004
## 220 0.1599 nan 0.1000 0.0001
## 240 0.1416 nan 0.1000 -0.0003
## 260 0.1261 nan 0.1000 -0.0003
## 280 0.1117 nan 0.1000 -0.0003
## 300 0.1011 nan 0.1000 -0.0005
## 320 0.0919 nan 0.1000 -0.0002
## 340 0.0834 nan 0.1000 -0.0002
## 360 0.0746 nan 0.1000 -0.0003
## 380 0.0676 nan 0.1000 -0.0003
## 400 0.0610 nan 0.1000 0.0000
## 420 0.0550 nan 0.1000 -0.0001
## 440 0.0502 nan 0.1000 -0.0001
## 460 0.0455 nan 0.1000 -0.0001
## 480 0.0414 nan 0.1000 -0.0002
## 500 0.0376 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2318 nan 0.1000 0.0375
## 2 1.1608 nan 0.1000 0.0297
## 3 1.1008 nan 0.1000 0.0243
## 4 1.0490 nan 0.1000 0.0226
## 5 1.0059 nan 0.1000 0.0179
## 6 0.9702 nan 0.1000 0.0154
## 7 0.9412 nan 0.1000 0.0123
## 8 0.9125 nan 0.1000 0.0142
## 9 0.8845 nan 0.1000 0.0112
## 10 0.8614 nan 0.1000 0.0072
## 20 0.7047 nan 0.1000 0.0025
## 40 0.5586 nan 0.1000 0.0002
## 60 0.4706 nan 0.1000 -0.0004
## 80 0.4039 nan 0.1000 -0.0010
## 100 0.3519 nan 0.1000 -0.0015
## 120 0.3087 nan 0.1000 -0.0003
## 140 0.2719 nan 0.1000 -0.0004
## 160 0.2410 nan 0.1000 -0.0013
## 180 0.2130 nan 0.1000 -0.0002
## 200 0.1914 nan 0.1000 -0.0009
## 220 0.1696 nan 0.1000 -0.0001
## 240 0.1518 nan 0.1000 -0.0007
## 260 0.1357 nan 0.1000 -0.0008
## 280 0.1224 nan 0.1000 -0.0002
## 300 0.1095 nan 0.1000 -0.0003
## 320 0.0993 nan 0.1000 -0.0004
## 340 0.0903 nan 0.1000 -0.0005
## 360 0.0806 nan 0.1000 -0.0004
## 380 0.0727 nan 0.1000 -0.0002
## 400 0.0659 nan 0.1000 -0.0002
## 420 0.0593 nan 0.1000 -0.0001
## 440 0.0537 nan 0.1000 -0.0002
## 460 0.0485 nan 0.1000 -0.0001
## 480 0.0441 nan 0.1000 -0.0001
## 500 0.0401 nan 0.1000 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2324 nan 0.1000 0.0383
## 2 1.1712 nan 0.1000 0.0279
## 3 1.1169 nan 0.1000 0.0223
## 4 1.0650 nan 0.1000 0.0211
## 5 1.0209 nan 0.1000 0.0171
## 6 0.9858 nan 0.1000 0.0141
## 7 0.9496 nan 0.1000 0.0136
## 8 0.9202 nan 0.1000 0.0116
## 9 0.8960 nan 0.1000 0.0089
## 10 0.8674 nan 0.1000 0.0111
## 20 0.7167 nan 0.1000 -0.0005
## 40 0.5779 nan 0.1000 0.0004
## 60 0.4841 nan 0.1000 0.0011
## 80 0.4197 nan 0.1000 -0.0008
## 100 0.3628 nan 0.1000 -0.0007
## 120 0.3223 nan 0.1000 -0.0006
## 140 0.2823 nan 0.1000 -0.0005
## 160 0.2516 nan 0.1000 -0.0002
## 180 0.2248 nan 0.1000 -0.0005
## 200 0.2020 nan 0.1000 -0.0003
## 220 0.1812 nan 0.1000 -0.0014
## 240 0.1638 nan 0.1000 -0.0007
## 260 0.1471 nan 0.1000 -0.0010
## 280 0.1334 nan 0.1000 -0.0003
## 300 0.1198 nan 0.1000 0.0000
## 320 0.1079 nan 0.1000 -0.0002
## 340 0.0976 nan 0.1000 -0.0004
## 360 0.0877 nan 0.1000 -0.0003
## 380 0.0789 nan 0.1000 -0.0002
## 400 0.0720 nan 0.1000 -0.0003
## 420 0.0654 nan 0.1000 -0.0001
## 440 0.0605 nan 0.1000 -0.0002
## 460 0.0545 nan 0.1000 -0.0004
## 480 0.0496 nan 0.1000 -0.0003
## 500 0.0444 nan 0.1000 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2363 nan 0.1000 0.0409
## 2 1.1652 nan 0.1000 0.0327
## 3 1.0972 nan 0.1000 0.0271
## 4 1.0427 nan 0.1000 0.0212
## 5 1.0008 nan 0.1000 0.0158
## 6 0.9568 nan 0.1000 0.0188
## 7 0.9223 nan 0.1000 0.0129
## 8 0.8917 nan 0.1000 0.0136
## 9 0.8598 nan 0.1000 0.0124
## 10 0.8317 nan 0.1000 0.0112
## 20 0.6667 nan 0.1000 -0.0006
## 40 0.5094 nan 0.1000 0.0014
## 60 0.4112 nan 0.1000 -0.0011
## 80 0.3439 nan 0.1000 -0.0025
## 100 0.2856 nan 0.1000 0.0004
## 120 0.2406 nan 0.1000 -0.0008
## 140 0.2030 nan 0.1000 -0.0002
## 160 0.1754 nan 0.1000 -0.0003
## 180 0.1502 nan 0.1000 -0.0002
## 200 0.1330 nan 0.1000 -0.0003
## 220 0.1155 nan 0.1000 -0.0001
## 240 0.1019 nan 0.1000 -0.0004
## 260 0.0896 nan 0.1000 -0.0002
## 280 0.0793 nan 0.1000 -0.0001
## 300 0.0695 nan 0.1000 -0.0001
## 320 0.0610 nan 0.1000 -0.0002
## 340 0.0553 nan 0.1000 -0.0003
## 360 0.0493 nan 0.1000 -0.0001
## 380 0.0437 nan 0.1000 -0.0000
## 400 0.0384 nan 0.1000 0.0000
## 420 0.0340 nan 0.1000 -0.0001
## 440 0.0301 nan 0.1000 -0.0001
## 460 0.0269 nan 0.1000 -0.0000
## 480 0.0238 nan 0.1000 -0.0001
## 500 0.0216 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2364 nan 0.1000 0.0374
## 2 1.1637 nan 0.1000 0.0330
## 3 1.0991 nan 0.1000 0.0264
## 4 1.0469 nan 0.1000 0.0227
## 5 1.0071 nan 0.1000 0.0168
## 6 0.9695 nan 0.1000 0.0161
## 7 0.9288 nan 0.1000 0.0179
## 8 0.8990 nan 0.1000 0.0133
## 9 0.8727 nan 0.1000 0.0077
## 10 0.8459 nan 0.1000 0.0094
## 20 0.6770 nan 0.1000 0.0000
## 40 0.5317 nan 0.1000 0.0004
## 60 0.4317 nan 0.1000 -0.0002
## 80 0.3538 nan 0.1000 -0.0002
## 100 0.2961 nan 0.1000 0.0003
## 120 0.2544 nan 0.1000 -0.0012
## 140 0.2168 nan 0.1000 -0.0009
## 160 0.1869 nan 0.1000 -0.0005
## 180 0.1601 nan 0.1000 -0.0009
## 200 0.1385 nan 0.1000 -0.0005
## 220 0.1231 nan 0.1000 -0.0003
## 240 0.1085 nan 0.1000 -0.0003
## 260 0.0950 nan 0.1000 -0.0002
## 280 0.0818 nan 0.1000 -0.0001
## 300 0.0728 nan 0.1000 -0.0002
## 320 0.0638 nan 0.1000 0.0000
## 340 0.0571 nan 0.1000 -0.0000
## 360 0.0501 nan 0.1000 -0.0002
## 380 0.0446 nan 0.1000 -0.0002
## 400 0.0399 nan 0.1000 -0.0002
## 420 0.0353 nan 0.1000 -0.0000
## 440 0.0311 nan 0.1000 -0.0000
## 460 0.0280 nan 0.1000 0.0000
## 480 0.0249 nan 0.1000 -0.0000
## 500 0.0220 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2326 nan 0.1000 0.0356
## 2 1.1624 nan 0.1000 0.0300
## 3 1.1075 nan 0.1000 0.0259
## 4 1.0542 nan 0.1000 0.0231
## 5 1.0083 nan 0.1000 0.0189
## 6 0.9701 nan 0.1000 0.0160
## 7 0.9377 nan 0.1000 0.0113
## 8 0.9090 nan 0.1000 0.0097
## 9 0.8790 nan 0.1000 0.0110
## 10 0.8520 nan 0.1000 0.0081
## 20 0.6776 nan 0.1000 0.0007
## 40 0.5346 nan 0.1000 -0.0004
## 60 0.4455 nan 0.1000 -0.0002
## 80 0.3646 nan 0.1000 -0.0006
## 100 0.3100 nan 0.1000 -0.0007
## 120 0.2655 nan 0.1000 -0.0006
## 140 0.2278 nan 0.1000 -0.0016
## 160 0.1966 nan 0.1000 -0.0003
## 180 0.1710 nan 0.1000 -0.0004
## 200 0.1521 nan 0.1000 0.0002
## 220 0.1343 nan 0.1000 -0.0002
## 240 0.1197 nan 0.1000 -0.0001
## 260 0.1056 nan 0.1000 -0.0002
## 280 0.0931 nan 0.1000 -0.0003
## 300 0.0823 nan 0.1000 -0.0002
## 320 0.0721 nan 0.1000 -0.0001
## 340 0.0642 nan 0.1000 -0.0002
## 360 0.0568 nan 0.1000 -0.0002
## 380 0.0505 nan 0.1000 -0.0001
## 400 0.0454 nan 0.1000 -0.0002
## 420 0.0407 nan 0.1000 -0.0002
## 440 0.0366 nan 0.1000 -0.0001
## 460 0.0327 nan 0.1000 -0.0002
## 480 0.0291 nan 0.1000 -0.0002
## 500 0.0259 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3193 nan 0.0010 0.0004
## 3 1.3185 nan 0.0010 0.0004
## 4 1.3177 nan 0.0010 0.0004
## 5 1.3170 nan 0.0010 0.0004
## 6 1.3162 nan 0.0010 0.0003
## 7 1.3155 nan 0.0010 0.0003
## 8 1.3146 nan 0.0010 0.0004
## 9 1.3137 nan 0.0010 0.0004
## 10 1.3129 nan 0.0010 0.0004
## 20 1.3049 nan 0.0010 0.0004
## 40 1.2891 nan 0.0010 0.0004
## 60 1.2738 nan 0.0010 0.0003
## 80 1.2589 nan 0.0010 0.0004
## 100 1.2442 nan 0.0010 0.0003
## 120 1.2301 nan 0.0010 0.0003
## 140 1.2165 nan 0.0010 0.0003
## 160 1.2033 nan 0.0010 0.0003
## 180 1.1904 nan 0.0010 0.0003
## 200 1.1779 nan 0.0010 0.0003
## 220 1.1660 nan 0.0010 0.0003
## 240 1.1544 nan 0.0010 0.0002
## 260 1.1429 nan 0.0010 0.0002
## 280 1.1318 nan 0.0010 0.0002
## 300 1.1208 nan 0.0010 0.0002
## 320 1.1102 nan 0.0010 0.0002
## 340 1.1004 nan 0.0010 0.0003
## 360 1.0903 nan 0.0010 0.0002
## 380 1.0806 nan 0.0010 0.0002
## 400 1.0710 nan 0.0010 0.0002
## 420 1.0620 nan 0.0010 0.0002
## 440 1.0531 nan 0.0010 0.0002
## 460 1.0445 nan 0.0010 0.0002
## 480 1.0360 nan 0.0010 0.0001
## 500 1.0275 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3195 nan 0.0010 0.0003
## 3 1.3187 nan 0.0010 0.0004
## 4 1.3178 nan 0.0010 0.0003
## 5 1.3170 nan 0.0010 0.0004
## 6 1.3162 nan 0.0010 0.0004
## 7 1.3154 nan 0.0010 0.0003
## 8 1.3146 nan 0.0010 0.0004
## 9 1.3137 nan 0.0010 0.0004
## 10 1.3129 nan 0.0010 0.0003
## 20 1.3049 nan 0.0010 0.0004
## 40 1.2889 nan 0.0010 0.0004
## 60 1.2732 nan 0.0010 0.0003
## 80 1.2584 nan 0.0010 0.0003
## 100 1.2441 nan 0.0010 0.0003
## 120 1.2301 nan 0.0010 0.0003
## 140 1.2167 nan 0.0010 0.0003
## 160 1.2035 nan 0.0010 0.0003
## 180 1.1906 nan 0.0010 0.0003
## 200 1.1785 nan 0.0010 0.0003
## 220 1.1666 nan 0.0010 0.0003
## 240 1.1546 nan 0.0010 0.0002
## 260 1.1433 nan 0.0010 0.0002
## 280 1.1322 nan 0.0010 0.0002
## 300 1.1214 nan 0.0010 0.0002
## 320 1.1109 nan 0.0010 0.0002
## 340 1.1011 nan 0.0010 0.0002
## 360 1.0911 nan 0.0010 0.0003
## 380 1.0813 nan 0.0010 0.0002
## 400 1.0717 nan 0.0010 0.0002
## 420 1.0627 nan 0.0010 0.0002
## 440 1.0537 nan 0.0010 0.0002
## 460 1.0449 nan 0.0010 0.0002
## 480 1.0366 nan 0.0010 0.0002
## 500 1.0285 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0003
## 2 1.3194 nan 0.0010 0.0004
## 3 1.3186 nan 0.0010 0.0003
## 4 1.3178 nan 0.0010 0.0004
## 5 1.3169 nan 0.0010 0.0004
## 6 1.3161 nan 0.0010 0.0004
## 7 1.3153 nan 0.0010 0.0004
## 8 1.3145 nan 0.0010 0.0004
## 9 1.3137 nan 0.0010 0.0004
## 10 1.3129 nan 0.0010 0.0004
## 20 1.3049 nan 0.0010 0.0003
## 40 1.2895 nan 0.0010 0.0004
## 60 1.2746 nan 0.0010 0.0003
## 80 1.2598 nan 0.0010 0.0003
## 100 1.2459 nan 0.0010 0.0003
## 120 1.2320 nan 0.0010 0.0003
## 140 1.2183 nan 0.0010 0.0003
## 160 1.2053 nan 0.0010 0.0003
## 180 1.1925 nan 0.0010 0.0003
## 200 1.1801 nan 0.0010 0.0003
## 220 1.1683 nan 0.0010 0.0002
## 240 1.1567 nan 0.0010 0.0003
## 260 1.1452 nan 0.0010 0.0003
## 280 1.1343 nan 0.0010 0.0003
## 300 1.1233 nan 0.0010 0.0002
## 320 1.1129 nan 0.0010 0.0002
## 340 1.1028 nan 0.0010 0.0002
## 360 1.0927 nan 0.0010 0.0002
## 380 1.0833 nan 0.0010 0.0002
## 400 1.0736 nan 0.0010 0.0002
## 420 1.0645 nan 0.0010 0.0002
## 440 1.0555 nan 0.0010 0.0002
## 460 1.0472 nan 0.0010 0.0002
## 480 1.0388 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3195 nan 0.0010 0.0004
## 3 1.3186 nan 0.0010 0.0004
## 4 1.3178 nan 0.0010 0.0004
## 5 1.3169 nan 0.0010 0.0004
## 6 1.3160 nan 0.0010 0.0004
## 7 1.3152 nan 0.0010 0.0004
## 8 1.3144 nan 0.0010 0.0003
## 9 1.3134 nan 0.0010 0.0004
## 10 1.3126 nan 0.0010 0.0004
## 20 1.3038 nan 0.0010 0.0003
## 40 1.2870 nan 0.0010 0.0004
## 60 1.2702 nan 0.0010 0.0004
## 80 1.2541 nan 0.0010 0.0003
## 100 1.2386 nan 0.0010 0.0004
## 120 1.2233 nan 0.0010 0.0003
## 140 1.2091 nan 0.0010 0.0003
## 160 1.1954 nan 0.0010 0.0003
## 180 1.1819 nan 0.0010 0.0003
## 200 1.1688 nan 0.0010 0.0003
## 220 1.1561 nan 0.0010 0.0002
## 240 1.1437 nan 0.0010 0.0002
## 260 1.1317 nan 0.0010 0.0003
## 280 1.1201 nan 0.0010 0.0002
## 300 1.1087 nan 0.0010 0.0002
## 320 1.0977 nan 0.0010 0.0002
## 340 1.0867 nan 0.0010 0.0002
## 360 1.0760 nan 0.0010 0.0002
## 380 1.0659 nan 0.0010 0.0002
## 400 1.0561 nan 0.0010 0.0002
## 420 1.0464 nan 0.0010 0.0002
## 440 1.0370 nan 0.0010 0.0002
## 460 1.0276 nan 0.0010 0.0002
## 480 1.0186 nan 0.0010 0.0002
## 500 1.0100 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3204 nan 0.0010 0.0004
## 2 1.3195 nan 0.0010 0.0004
## 3 1.3187 nan 0.0010 0.0004
## 4 1.3177 nan 0.0010 0.0004
## 5 1.3169 nan 0.0010 0.0004
## 6 1.3161 nan 0.0010 0.0004
## 7 1.3152 nan 0.0010 0.0004
## 8 1.3143 nan 0.0010 0.0004
## 9 1.3133 nan 0.0010 0.0004
## 10 1.3124 nan 0.0010 0.0004
## 20 1.3037 nan 0.0010 0.0004
## 40 1.2872 nan 0.0010 0.0004
## 60 1.2711 nan 0.0010 0.0004
## 80 1.2553 nan 0.0010 0.0003
## 100 1.2396 nan 0.0010 0.0003
## 120 1.2250 nan 0.0010 0.0003
## 140 1.2108 nan 0.0010 0.0003
## 160 1.1970 nan 0.0010 0.0003
## 180 1.1836 nan 0.0010 0.0003
## 200 1.1704 nan 0.0010 0.0003
## 220 1.1577 nan 0.0010 0.0003
## 240 1.1454 nan 0.0010 0.0003
## 260 1.1336 nan 0.0010 0.0003
## 280 1.1221 nan 0.0010 0.0002
## 300 1.1107 nan 0.0010 0.0002
## 320 1.0995 nan 0.0010 0.0003
## 340 1.0886 nan 0.0010 0.0002
## 360 1.0780 nan 0.0010 0.0002
## 380 1.0679 nan 0.0010 0.0002
## 400 1.0582 nan 0.0010 0.0002
## 420 1.0486 nan 0.0010 0.0002
## 440 1.0390 nan 0.0010 0.0002
## 460 1.0296 nan 0.0010 0.0002
## 480 1.0208 nan 0.0010 0.0002
## 500 1.0122 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3195 nan 0.0010 0.0004
## 3 1.3186 nan 0.0010 0.0004
## 4 1.3178 nan 0.0010 0.0004
## 5 1.3169 nan 0.0010 0.0004
## 6 1.3161 nan 0.0010 0.0004
## 7 1.3151 nan 0.0010 0.0004
## 8 1.3142 nan 0.0010 0.0004
## 9 1.3133 nan 0.0010 0.0004
## 10 1.3124 nan 0.0010 0.0004
## 20 1.3040 nan 0.0010 0.0004
## 40 1.2873 nan 0.0010 0.0004
## 60 1.2716 nan 0.0010 0.0004
## 80 1.2558 nan 0.0010 0.0004
## 100 1.2409 nan 0.0010 0.0003
## 120 1.2265 nan 0.0010 0.0003
## 140 1.2122 nan 0.0010 0.0003
## 160 1.1984 nan 0.0010 0.0003
## 180 1.1849 nan 0.0010 0.0003
## 200 1.1719 nan 0.0010 0.0003
## 220 1.1593 nan 0.0010 0.0002
## 240 1.1470 nan 0.0010 0.0002
## 260 1.1354 nan 0.0010 0.0003
## 280 1.1237 nan 0.0010 0.0003
## 300 1.1127 nan 0.0010 0.0003
## 320 1.1018 nan 0.0010 0.0002
## 340 1.0911 nan 0.0010 0.0002
## 360 1.0807 nan 0.0010 0.0002
## 380 1.0707 nan 0.0010 0.0002
## 400 1.0611 nan 0.0010 0.0002
## 420 1.0516 nan 0.0010 0.0002
## 440 1.0424 nan 0.0010 0.0002
## 460 1.0331 nan 0.0010 0.0002
## 480 1.0244 nan 0.0010 0.0002
## 500 1.0157 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3202 nan 0.0010 0.0005
## 2 1.3194 nan 0.0010 0.0004
## 3 1.3184 nan 0.0010 0.0004
## 4 1.3175 nan 0.0010 0.0004
## 5 1.3166 nan 0.0010 0.0004
## 6 1.3156 nan 0.0010 0.0005
## 7 1.3148 nan 0.0010 0.0004
## 8 1.3138 nan 0.0010 0.0004
## 9 1.3128 nan 0.0010 0.0004
## 10 1.3119 nan 0.0010 0.0004
## 20 1.3028 nan 0.0010 0.0004
## 40 1.2852 nan 0.0010 0.0004
## 60 1.2682 nan 0.0010 0.0003
## 80 1.2514 nan 0.0010 0.0004
## 100 1.2350 nan 0.0010 0.0003
## 120 1.2196 nan 0.0010 0.0003
## 140 1.2045 nan 0.0010 0.0003
## 160 1.1900 nan 0.0010 0.0003
## 180 1.1757 nan 0.0010 0.0003
## 200 1.1617 nan 0.0010 0.0003
## 220 1.1485 nan 0.0010 0.0003
## 240 1.1356 nan 0.0010 0.0002
## 260 1.1232 nan 0.0010 0.0003
## 280 1.1112 nan 0.0010 0.0003
## 300 1.0995 nan 0.0010 0.0003
## 320 1.0881 nan 0.0010 0.0002
## 340 1.0766 nan 0.0010 0.0002
## 360 1.0657 nan 0.0010 0.0002
## 380 1.0549 nan 0.0010 0.0002
## 400 1.0446 nan 0.0010 0.0002
## 420 1.0343 nan 0.0010 0.0002
## 440 1.0245 nan 0.0010 0.0002
## 460 1.0148 nan 0.0010 0.0002
## 480 1.0054 nan 0.0010 0.0002
## 500 0.9965 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0005
## 2 1.3194 nan 0.0010 0.0004
## 3 1.3185 nan 0.0010 0.0004
## 4 1.3175 nan 0.0010 0.0004
## 5 1.3166 nan 0.0010 0.0004
## 6 1.3157 nan 0.0010 0.0004
## 7 1.3147 nan 0.0010 0.0004
## 8 1.3138 nan 0.0010 0.0004
## 9 1.3129 nan 0.0010 0.0004
## 10 1.3120 nan 0.0010 0.0005
## 20 1.3030 nan 0.0010 0.0004
## 40 1.2856 nan 0.0010 0.0004
## 60 1.2685 nan 0.0010 0.0004
## 80 1.2524 nan 0.0010 0.0003
## 100 1.2362 nan 0.0010 0.0003
## 120 1.2205 nan 0.0010 0.0003
## 140 1.2054 nan 0.0010 0.0003
## 160 1.1911 nan 0.0010 0.0003
## 180 1.1771 nan 0.0010 0.0003
## 200 1.1633 nan 0.0010 0.0002
## 220 1.1500 nan 0.0010 0.0003
## 240 1.1372 nan 0.0010 0.0002
## 260 1.1248 nan 0.0010 0.0003
## 280 1.1125 nan 0.0010 0.0003
## 300 1.1007 nan 0.0010 0.0002
## 320 1.0894 nan 0.0010 0.0003
## 340 1.0785 nan 0.0010 0.0002
## 360 1.0679 nan 0.0010 0.0002
## 380 1.0574 nan 0.0010 0.0002
## 400 1.0470 nan 0.0010 0.0002
## 420 1.0372 nan 0.0010 0.0002
## 440 1.0273 nan 0.0010 0.0002
## 460 1.0179 nan 0.0010 0.0002
## 480 1.0087 nan 0.0010 0.0002
## 500 0.9999 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3202 nan 0.0010 0.0004
## 2 1.3193 nan 0.0010 0.0004
## 3 1.3184 nan 0.0010 0.0004
## 4 1.3175 nan 0.0010 0.0004
## 5 1.3166 nan 0.0010 0.0004
## 6 1.3157 nan 0.0010 0.0004
## 7 1.3147 nan 0.0010 0.0004
## 8 1.3138 nan 0.0010 0.0004
## 9 1.3129 nan 0.0010 0.0004
## 10 1.3120 nan 0.0010 0.0004
## 20 1.3031 nan 0.0010 0.0004
## 40 1.2856 nan 0.0010 0.0004
## 60 1.2691 nan 0.0010 0.0004
## 80 1.2530 nan 0.0010 0.0004
## 100 1.2373 nan 0.0010 0.0003
## 120 1.2222 nan 0.0010 0.0003
## 140 1.2073 nan 0.0010 0.0003
## 160 1.1931 nan 0.0010 0.0003
## 180 1.1792 nan 0.0010 0.0003
## 200 1.1659 nan 0.0010 0.0003
## 220 1.1527 nan 0.0010 0.0003
## 240 1.1399 nan 0.0010 0.0003
## 260 1.1278 nan 0.0010 0.0003
## 280 1.1157 nan 0.0010 0.0003
## 300 1.1040 nan 0.0010 0.0003
## 320 1.0927 nan 0.0010 0.0003
## 340 1.0818 nan 0.0010 0.0003
## 360 1.0710 nan 0.0010 0.0002
## 380 1.0605 nan 0.0010 0.0002
## 400 1.0502 nan 0.0010 0.0002
## 420 1.0407 nan 0.0010 0.0002
## 440 1.0313 nan 0.0010 0.0002
## 460 1.0219 nan 0.0010 0.0002
## 480 1.0128 nan 0.0010 0.0002
## 500 1.0040 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3116 nan 0.0100 0.0043
## 2 1.3038 nan 0.0100 0.0032
## 3 1.2960 nan 0.0100 0.0037
## 4 1.2882 nan 0.0100 0.0037
## 5 1.2800 nan 0.0100 0.0038
## 6 1.2712 nan 0.0100 0.0037
## 7 1.2642 nan 0.0100 0.0030
## 8 1.2566 nan 0.0100 0.0034
## 9 1.2489 nan 0.0100 0.0035
## 10 1.2414 nan 0.0100 0.0031
## 20 1.1747 nan 0.0100 0.0030
## 40 1.0680 nan 0.0100 0.0021
## 60 0.9886 nan 0.0100 0.0013
## 80 0.9256 nan 0.0100 0.0012
## 100 0.8735 nan 0.0100 0.0006
## 120 0.8312 nan 0.0100 0.0007
## 140 0.7958 nan 0.0100 0.0006
## 160 0.7659 nan 0.0100 0.0005
## 180 0.7409 nan 0.0100 0.0002
## 200 0.7194 nan 0.0100 0.0002
## 220 0.6994 nan 0.0100 0.0001
## 240 0.6818 nan 0.0100 0.0002
## 260 0.6650 nan 0.0100 0.0001
## 280 0.6507 nan 0.0100 0.0000
## 300 0.6362 nan 0.0100 0.0000
## 320 0.6234 nan 0.0100 -0.0001
## 340 0.6116 nan 0.0100 -0.0001
## 360 0.6006 nan 0.0100 0.0000
## 380 0.5902 nan 0.0100 -0.0001
## 400 0.5809 nan 0.0100 -0.0000
## 420 0.5711 nan 0.0100 -0.0000
## 440 0.5619 nan 0.0100 -0.0000
## 460 0.5532 nan 0.0100 0.0000
## 480 0.5444 nan 0.0100 -0.0001
## 500 0.5359 nan 0.0100 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3142 nan 0.0100 0.0032
## 2 1.3058 nan 0.0100 0.0042
## 3 1.2976 nan 0.0100 0.0039
## 4 1.2897 nan 0.0100 0.0034
## 5 1.2819 nan 0.0100 0.0034
## 6 1.2739 nan 0.0100 0.0038
## 7 1.2668 nan 0.0100 0.0032
## 8 1.2599 nan 0.0100 0.0030
## 9 1.2528 nan 0.0100 0.0032
## 10 1.2459 nan 0.0100 0.0031
## 20 1.1825 nan 0.0100 0.0027
## 40 1.0741 nan 0.0100 0.0023
## 60 0.9923 nan 0.0100 0.0013
## 80 0.9289 nan 0.0100 0.0009
## 100 0.8753 nan 0.0100 0.0011
## 120 0.8324 nan 0.0100 0.0006
## 140 0.7982 nan 0.0100 0.0004
## 160 0.7689 nan 0.0100 0.0003
## 180 0.7443 nan 0.0100 0.0002
## 200 0.7225 nan 0.0100 0.0002
## 220 0.7038 nan 0.0100 0.0002
## 240 0.6865 nan 0.0100 0.0002
## 260 0.6703 nan 0.0100 -0.0001
## 280 0.6570 nan 0.0100 -0.0001
## 300 0.6439 nan 0.0100 0.0001
## 320 0.6324 nan 0.0100 -0.0001
## 340 0.6216 nan 0.0100 -0.0002
## 360 0.6104 nan 0.0100 0.0000
## 380 0.6000 nan 0.0100 -0.0001
## 400 0.5910 nan 0.0100 -0.0001
## 420 0.5825 nan 0.0100 -0.0000
## 440 0.5737 nan 0.0100 -0.0000
## 460 0.5642 nan 0.0100 -0.0000
## 480 0.5568 nan 0.0100 0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3138 nan 0.0100 0.0035
## 2 1.3058 nan 0.0100 0.0034
## 3 1.2981 nan 0.0100 0.0037
## 4 1.2911 nan 0.0100 0.0031
## 5 1.2837 nan 0.0100 0.0036
## 6 1.2762 nan 0.0100 0.0036
## 7 1.2688 nan 0.0100 0.0033
## 8 1.2620 nan 0.0100 0.0032
## 9 1.2549 nan 0.0100 0.0033
## 10 1.2475 nan 0.0100 0.0034
## 20 1.1830 nan 0.0100 0.0026
## 40 1.0737 nan 0.0100 0.0023
## 60 0.9912 nan 0.0100 0.0016
## 80 0.9276 nan 0.0100 0.0010
## 100 0.8743 nan 0.0100 0.0010
## 120 0.8325 nan 0.0100 0.0006
## 140 0.7985 nan 0.0100 0.0005
## 160 0.7699 nan 0.0100 0.0003
## 180 0.7451 nan 0.0100 0.0003
## 200 0.7242 nan 0.0100 0.0001
## 220 0.7056 nan 0.0100 0.0001
## 240 0.6879 nan 0.0100 0.0000
## 260 0.6730 nan 0.0100 0.0001
## 280 0.6592 nan 0.0100 -0.0001
## 300 0.6462 nan 0.0100 0.0001
## 320 0.6341 nan 0.0100 0.0001
## 340 0.6232 nan 0.0100 -0.0001
## 360 0.6134 nan 0.0100 -0.0001
## 380 0.6031 nan 0.0100 0.0000
## 400 0.5951 nan 0.0100 -0.0001
## 420 0.5861 nan 0.0100 -0.0000
## 440 0.5777 nan 0.0100 0.0000
## 460 0.5699 nan 0.0100 -0.0000
## 480 0.5630 nan 0.0100 -0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3121 nan 0.0100 0.0042
## 2 1.3032 nan 0.0100 0.0040
## 3 1.2948 nan 0.0100 0.0034
## 4 1.2861 nan 0.0100 0.0041
## 5 1.2780 nan 0.0100 0.0036
## 6 1.2700 nan 0.0100 0.0032
## 7 1.2617 nan 0.0100 0.0038
## 8 1.2539 nan 0.0100 0.0035
## 9 1.2463 nan 0.0100 0.0031
## 10 1.2390 nan 0.0100 0.0029
## 20 1.1687 nan 0.0100 0.0027
## 40 1.0539 nan 0.0100 0.0021
## 60 0.9694 nan 0.0100 0.0016
## 80 0.9014 nan 0.0100 0.0013
## 100 0.8477 nan 0.0100 0.0010
## 120 0.8017 nan 0.0100 0.0007
## 140 0.7655 nan 0.0100 0.0004
## 160 0.7349 nan 0.0100 0.0003
## 180 0.7088 nan 0.0100 0.0004
## 200 0.6848 nan 0.0100 0.0003
## 220 0.6638 nan 0.0100 0.0001
## 240 0.6451 nan 0.0100 0.0000
## 260 0.6270 nan 0.0100 0.0003
## 280 0.6114 nan 0.0100 0.0001
## 300 0.5973 nan 0.0100 -0.0000
## 320 0.5822 nan 0.0100 0.0000
## 340 0.5687 nan 0.0100 -0.0000
## 360 0.5561 nan 0.0100 -0.0000
## 380 0.5443 nan 0.0100 -0.0002
## 400 0.5336 nan 0.0100 -0.0000
## 420 0.5233 nan 0.0100 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3122 nan 0.0100 0.0040
## 2 1.3033 nan 0.0100 0.0040
## 3 1.2951 nan 0.0100 0.0036
## 4 1.2876 nan 0.0100 0.0034
## 5 1.2791 nan 0.0100 0.0038
## 6 1.2714 nan 0.0100 0.0035
## 7 1.2636 nan 0.0100 0.0033
## 8 1.2556 nan 0.0100 0.0035
## 9 1.2476 nan 0.0100 0.0033
## 10 1.2397 nan 0.0100 0.0036
## 20 1.1702 nan 0.0100 0.0027
## 40 1.0581 nan 0.0100 0.0018
## 60 0.9735 nan 0.0100 0.0015
## 80 0.9068 nan 0.0100 0.0010
## 100 0.8526 nan 0.0100 0.0008
## 120 0.8078 nan 0.0100 0.0008
## 140 0.7710 nan 0.0100 0.0006
## 160 0.7404 nan 0.0100 0.0004
## 180 0.7143 nan 0.0100 0.0004
## 200 0.6920 nan 0.0100 0.0002
## 220 0.6720 nan 0.0100 0.0003
## 240 0.6539 nan 0.0100 0.0001
## 260 0.6367 nan 0.0100 0.0000
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## 320 0.5941 nan 0.0100 0.0001
## 340 0.5813 nan 0.0100 0.0000
## 360 0.5705 nan 0.0100 0.0000
## 380 0.5586 nan 0.0100 0.0001
## 400 0.5474 nan 0.0100 -0.0000
## 420 0.5372 nan 0.0100 -0.0000
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## 460 0.5174 nan 0.0100 -0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3120 nan 0.0100 0.0040
## 2 1.3035 nan 0.0100 0.0037
## 3 1.2951 nan 0.0100 0.0034
## 4 1.2873 nan 0.0100 0.0037
## 5 1.2795 nan 0.0100 0.0035
## 6 1.2724 nan 0.0100 0.0031
## 7 1.2645 nan 0.0100 0.0036
## 8 1.2566 nan 0.0100 0.0035
## 9 1.2485 nan 0.0100 0.0038
## 10 1.2407 nan 0.0100 0.0035
## 20 1.1713 nan 0.0100 0.0030
## 40 1.0605 nan 0.0100 0.0021
## 60 0.9725 nan 0.0100 0.0017
## 80 0.9070 nan 0.0100 0.0011
## 100 0.8535 nan 0.0100 0.0008
## 120 0.8103 nan 0.0100 0.0008
## 140 0.7723 nan 0.0100 0.0005
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## 180 0.7172 nan 0.0100 0.0002
## 200 0.6944 nan 0.0100 0.0002
## 220 0.6758 nan 0.0100 0.0001
## 240 0.6588 nan 0.0100 -0.0000
## 260 0.6425 nan 0.0100 0.0001
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## 320 0.6012 nan 0.0100 0.0000
## 340 0.5897 nan 0.0100 0.0001
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## 380 0.5672 nan 0.0100 -0.0000
## 400 0.5569 nan 0.0100 -0.0001
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## 480 0.5204 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3114 nan 0.0100 0.0044
## 2 1.3016 nan 0.0100 0.0045
## 3 1.2932 nan 0.0100 0.0038
## 4 1.2849 nan 0.0100 0.0037
## 5 1.2757 nan 0.0100 0.0036
## 6 1.2675 nan 0.0100 0.0038
## 7 1.2588 nan 0.0100 0.0038
## 8 1.2503 nan 0.0100 0.0037
## 9 1.2423 nan 0.0100 0.0035
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## 20 1.1620 nan 0.0100 0.0034
## 40 1.0457 nan 0.0100 0.0018
## 60 0.9544 nan 0.0100 0.0018
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## 100 0.8264 nan 0.0100 0.0006
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## 280 0.5743 nan 0.0100 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3121 nan 0.0100 0.0037
## 2 1.3027 nan 0.0100 0.0043
## 3 1.2931 nan 0.0100 0.0042
## 4 1.2842 nan 0.0100 0.0037
## 5 1.2756 nan 0.0100 0.0041
## 6 1.2672 nan 0.0100 0.0034
## 7 1.2584 nan 0.0100 0.0037
## 8 1.2498 nan 0.0100 0.0041
## 9 1.2423 nan 0.0100 0.0033
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## 40 1.0444 nan 0.0100 0.0021
## 60 0.9572 nan 0.0100 0.0018
## 80 0.8890 nan 0.0100 0.0014
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## 180 0.6921 nan 0.0100 0.0003
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## 380 0.5181 nan 0.0100 -0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3120 nan 0.0100 0.0041
## 2 1.3031 nan 0.0100 0.0038
## 3 1.2938 nan 0.0100 0.0044
## 4 1.2851 nan 0.0100 0.0038
## 5 1.2770 nan 0.0100 0.0036
## 6 1.2685 nan 0.0100 0.0039
## 7 1.2603 nan 0.0100 0.0034
## 8 1.2515 nan 0.0100 0.0041
## 9 1.2433 nan 0.0100 0.0037
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## 20 1.1622 nan 0.0100 0.0028
## 40 1.0461 nan 0.0100 0.0021
## 60 0.9589 nan 0.0100 0.0013
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## 280 0.5967 nan 0.0100 -0.0000
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## 500 0.4683 nan 0.0100 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2432 nan 0.1000 0.0336
## 2 1.1832 nan 0.1000 0.0260
## 3 1.1225 nan 0.1000 0.0251
## 4 1.0738 nan 0.1000 0.0222
## 5 1.0251 nan 0.1000 0.0237
## 6 0.9873 nan 0.1000 0.0149
## 7 0.9565 nan 0.1000 0.0127
## 8 0.9301 nan 0.1000 0.0082
## 9 0.9022 nan 0.1000 0.0113
## 10 0.8770 nan 0.1000 0.0093
## 20 0.7236 nan 0.1000 0.0047
## 40 0.5820 nan 0.1000 0.0002
## 60 0.5024 nan 0.1000 -0.0009
## 80 0.4433 nan 0.1000 -0.0008
## 100 0.3918 nan 0.1000 -0.0006
## 120 0.3489 nan 0.1000 -0.0005
## 140 0.3145 nan 0.1000 -0.0002
## 160 0.2823 nan 0.1000 -0.0003
## 180 0.2515 nan 0.1000 -0.0001
## 200 0.2259 nan 0.1000 -0.0002
## 220 0.2030 nan 0.1000 -0.0001
## 240 0.1849 nan 0.1000 -0.0002
## 260 0.1668 nan 0.1000 -0.0002
## 280 0.1520 nan 0.1000 -0.0006
## 300 0.1375 nan 0.1000 -0.0003
## 320 0.1265 nan 0.1000 -0.0002
## 340 0.1171 nan 0.1000 -0.0005
## 360 0.1084 nan 0.1000 -0.0004
## 380 0.0995 nan 0.1000 -0.0004
## 400 0.0920 nan 0.1000 -0.0002
## 420 0.0852 nan 0.1000 -0.0005
## 440 0.0783 nan 0.1000 -0.0002
## 460 0.0714 nan 0.1000 -0.0001
## 480 0.0653 nan 0.1000 0.0001
## 500 0.0604 nan 0.1000 -0.0003
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2448 nan 0.1000 0.0335
## 2 1.1728 nan 0.1000 0.0304
## 3 1.1139 nan 0.1000 0.0289
## 4 1.0616 nan 0.1000 0.0232
## 5 1.0202 nan 0.1000 0.0155
## 6 0.9832 nan 0.1000 0.0165
## 7 0.9481 nan 0.1000 0.0152
## 8 0.9187 nan 0.1000 0.0128
## 9 0.8900 nan 0.1000 0.0126
## 10 0.8715 nan 0.1000 0.0064
## 20 0.7191 nan 0.1000 0.0032
## 40 0.6004 nan 0.1000 0.0004
## 60 0.5260 nan 0.1000 0.0006
## 80 0.4579 nan 0.1000 -0.0017
## 100 0.4086 nan 0.1000 -0.0001
## 120 0.3613 nan 0.1000 -0.0010
## 140 0.3241 nan 0.1000 -0.0018
## 160 0.2930 nan 0.1000 -0.0007
## 180 0.2645 nan 0.1000 -0.0008
## 200 0.2371 nan 0.1000 -0.0002
## 220 0.2178 nan 0.1000 -0.0009
## 240 0.1964 nan 0.1000 -0.0008
## 260 0.1775 nan 0.1000 -0.0002
## 280 0.1616 nan 0.1000 0.0001
## 300 0.1482 nan 0.1000 -0.0003
## 320 0.1357 nan 0.1000 -0.0003
## 340 0.1250 nan 0.1000 -0.0002
## 360 0.1155 nan 0.1000 -0.0004
## 380 0.1050 nan 0.1000 -0.0003
## 400 0.0967 nan 0.1000 -0.0003
## 420 0.0886 nan 0.1000 -0.0003
## 440 0.0810 nan 0.1000 -0.0003
## 460 0.0745 nan 0.1000 -0.0002
## 480 0.0688 nan 0.1000 -0.0002
## 500 0.0639 nan 0.1000 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2391 nan 0.1000 0.0330
## 2 1.1805 nan 0.1000 0.0259
## 3 1.1283 nan 0.1000 0.0234
## 4 1.0777 nan 0.1000 0.0195
## 5 1.0300 nan 0.1000 0.0220
## 6 0.9886 nan 0.1000 0.0173
## 7 0.9548 nan 0.1000 0.0136
## 8 0.9266 nan 0.1000 0.0101
## 9 0.9003 nan 0.1000 0.0116
## 10 0.8749 nan 0.1000 0.0113
## 20 0.7291 nan 0.1000 0.0008
## 40 0.6100 nan 0.1000 -0.0009
## 60 0.5254 nan 0.1000 -0.0003
## 80 0.4722 nan 0.1000 -0.0013
## 100 0.4222 nan 0.1000 -0.0004
## 120 0.3757 nan 0.1000 -0.0015
## 140 0.3434 nan 0.1000 -0.0009
## 160 0.3103 nan 0.1000 -0.0003
## 180 0.2817 nan 0.1000 -0.0007
## 200 0.2546 nan 0.1000 0.0001
## 220 0.2308 nan 0.1000 -0.0004
## 240 0.2109 nan 0.1000 -0.0005
## 260 0.1930 nan 0.1000 -0.0005
## 280 0.1780 nan 0.1000 -0.0006
## 300 0.1630 nan 0.1000 -0.0002
## 320 0.1502 nan 0.1000 -0.0002
## 340 0.1388 nan 0.1000 -0.0006
## 360 0.1278 nan 0.1000 -0.0005
## 380 0.1192 nan 0.1000 -0.0005
## 400 0.1099 nan 0.1000 -0.0002
## 420 0.1009 nan 0.1000 -0.0002
## 440 0.0934 nan 0.1000 -0.0001
## 460 0.0861 nan 0.1000 -0.0004
## 480 0.0796 nan 0.1000 -0.0003
## 500 0.0741 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2333 nan 0.1000 0.0386
## 2 1.1692 nan 0.1000 0.0276
## 3 1.1075 nan 0.1000 0.0260
## 4 1.0580 nan 0.1000 0.0213
## 5 1.0118 nan 0.1000 0.0209
## 6 0.9712 nan 0.1000 0.0167
## 7 0.9383 nan 0.1000 0.0113
## 8 0.9082 nan 0.1000 0.0100
## 9 0.8795 nan 0.1000 0.0111
## 10 0.8524 nan 0.1000 0.0116
## 20 0.6876 nan 0.1000 0.0010
## 40 0.5497 nan 0.1000 -0.0014
## 60 0.4523 nan 0.1000 -0.0001
## 80 0.3826 nan 0.1000 0.0003
## 100 0.3275 nan 0.1000 -0.0008
## 120 0.2811 nan 0.1000 -0.0003
## 140 0.2452 nan 0.1000 0.0000
## 160 0.2144 nan 0.1000 0.0000
## 180 0.1864 nan 0.1000 -0.0000
## 200 0.1649 nan 0.1000 -0.0007
## 220 0.1475 nan 0.1000 -0.0002
## 240 0.1314 nan 0.1000 -0.0003
## 260 0.1165 nan 0.1000 -0.0003
## 280 0.1040 nan 0.1000 -0.0001
## 300 0.0935 nan 0.1000 -0.0003
## 320 0.0837 nan 0.1000 0.0000
## 340 0.0752 nan 0.1000 -0.0001
## 360 0.0672 nan 0.1000 -0.0001
## 380 0.0603 nan 0.1000 -0.0002
## 400 0.0553 nan 0.1000 -0.0001
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## 460 0.0404 nan 0.1000 -0.0002
## 480 0.0364 nan 0.1000 -0.0001
## 500 0.0330 nan 0.1000 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2345 nan 0.1000 0.0346
## 2 1.1623 nan 0.1000 0.0303
## 3 1.1022 nan 0.1000 0.0260
## 4 1.0435 nan 0.1000 0.0229
## 5 0.9938 nan 0.1000 0.0222
## 6 0.9524 nan 0.1000 0.0166
## 7 0.9193 nan 0.1000 0.0130
## 8 0.8871 nan 0.1000 0.0134
## 9 0.8600 nan 0.1000 0.0077
## 10 0.8371 nan 0.1000 0.0077
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## 40 0.5428 nan 0.1000 0.0000
## 60 0.4538 nan 0.1000 -0.0006
## 80 0.3785 nan 0.1000 -0.0008
## 100 0.3270 nan 0.1000 -0.0004
## 120 0.2917 nan 0.1000 -0.0012
## 140 0.2557 nan 0.1000 -0.0011
## 160 0.2242 nan 0.1000 -0.0003
## 180 0.1996 nan 0.1000 -0.0014
## 200 0.1765 nan 0.1000 -0.0006
## 220 0.1589 nan 0.1000 -0.0004
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## 260 0.1262 nan 0.1000 -0.0003
## 280 0.1122 nan 0.1000 -0.0006
## 300 0.0997 nan 0.1000 -0.0002
## 320 0.0889 nan 0.1000 -0.0005
## 340 0.0807 nan 0.1000 0.0000
## 360 0.0723 nan 0.1000 -0.0003
## 380 0.0657 nan 0.1000 -0.0001
## 400 0.0588 nan 0.1000 -0.0001
## 420 0.0533 nan 0.1000 -0.0002
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## 460 0.0439 nan 0.1000 -0.0000
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## 500 0.0365 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2475 nan 0.1000 0.0341
## 2 1.1685 nan 0.1000 0.0345
## 3 1.1105 nan 0.1000 0.0268
## 4 1.0597 nan 0.1000 0.0208
## 5 1.0166 nan 0.1000 0.0209
## 6 0.9775 nan 0.1000 0.0155
## 7 0.9476 nan 0.1000 0.0102
## 8 0.9175 nan 0.1000 0.0107
## 9 0.8897 nan 0.1000 0.0100
## 10 0.8653 nan 0.1000 0.0100
## 20 0.7041 nan 0.1000 0.0028
## 40 0.5632 nan 0.1000 0.0006
## 60 0.4672 nan 0.1000 -0.0010
## 80 0.4073 nan 0.1000 0.0002
## 100 0.3586 nan 0.1000 -0.0007
## 120 0.3133 nan 0.1000 -0.0007
## 140 0.2735 nan 0.1000 -0.0008
## 160 0.2416 nan 0.1000 -0.0003
## 180 0.2166 nan 0.1000 -0.0007
## 200 0.1915 nan 0.1000 -0.0004
## 220 0.1689 nan 0.1000 -0.0006
## 240 0.1514 nan 0.1000 -0.0002
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## 280 0.1228 nan 0.1000 -0.0004
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## 320 0.0990 nan 0.1000 -0.0005
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## 360 0.0805 nan 0.1000 -0.0002
## 380 0.0725 nan 0.1000 -0.0003
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## 460 0.0486 nan 0.1000 -0.0001
## 480 0.0443 nan 0.1000 -0.0001
## 500 0.0403 nan 0.1000 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2381 nan 0.1000 0.0410
## 2 1.1672 nan 0.1000 0.0317
## 3 1.1027 nan 0.1000 0.0291
## 4 1.0442 nan 0.1000 0.0248
## 5 0.9980 nan 0.1000 0.0229
## 6 0.9570 nan 0.1000 0.0136
## 7 0.9175 nan 0.1000 0.0149
## 8 0.8893 nan 0.1000 0.0105
## 9 0.8597 nan 0.1000 0.0115
## 10 0.8329 nan 0.1000 0.0074
## 20 0.6609 nan 0.1000 0.0033
## 40 0.4933 nan 0.1000 -0.0002
## 60 0.3999 nan 0.1000 -0.0004
## 80 0.3292 nan 0.1000 0.0007
## 100 0.2711 nan 0.1000 -0.0006
## 120 0.2291 nan 0.1000 -0.0009
## 140 0.1957 nan 0.1000 -0.0005
## 160 0.1684 nan 0.1000 -0.0002
## 180 0.1439 nan 0.1000 0.0001
## 200 0.1264 nan 0.1000 -0.0003
## 220 0.1092 nan 0.1000 -0.0002
## 240 0.0954 nan 0.1000 -0.0003
## 260 0.0838 nan 0.1000 -0.0002
## 280 0.0748 nan 0.1000 0.0001
## 300 0.0662 nan 0.1000 -0.0002
## 320 0.0571 nan 0.1000 -0.0002
## 340 0.0508 nan 0.1000 0.0001
## 360 0.0452 nan 0.1000 -0.0000
## 380 0.0396 nan 0.1000 -0.0000
## 400 0.0351 nan 0.1000 0.0000
## 420 0.0314 nan 0.1000 -0.0001
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## 460 0.0246 nan 0.1000 -0.0001
## 480 0.0221 nan 0.1000 0.0000
## 500 0.0197 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2324 nan 0.1000 0.0405
## 2 1.1554 nan 0.1000 0.0331
## 3 1.0897 nan 0.1000 0.0298
## 4 1.0326 nan 0.1000 0.0245
## 5 0.9879 nan 0.1000 0.0189
## 6 0.9449 nan 0.1000 0.0177
## 7 0.9059 nan 0.1000 0.0158
## 8 0.8751 nan 0.1000 0.0148
## 9 0.8435 nan 0.1000 0.0112
## 10 0.8181 nan 0.1000 0.0097
## 20 0.6571 nan 0.1000 0.0044
## 40 0.5023 nan 0.1000 -0.0012
## 60 0.4126 nan 0.1000 -0.0007
## 80 0.3326 nan 0.1000 -0.0009
## 100 0.2784 nan 0.1000 -0.0000
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## 140 0.1981 nan 0.1000 -0.0004
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## 500 0.0189 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2340 nan 0.1000 0.0379
## 2 1.1660 nan 0.1000 0.0339
## 3 1.1029 nan 0.1000 0.0279
## 4 1.0487 nan 0.1000 0.0230
## 5 0.9986 nan 0.1000 0.0190
## 6 0.9566 nan 0.1000 0.0193
## 7 0.9207 nan 0.1000 0.0134
## 8 0.8892 nan 0.1000 0.0104
## 9 0.8597 nan 0.1000 0.0114
## 10 0.8305 nan 0.1000 0.0110
## 20 0.6697 nan 0.1000 0.0026
## 40 0.5193 nan 0.1000 0.0002
## 60 0.4286 nan 0.1000 -0.0018
## 80 0.3600 nan 0.1000 -0.0019
## 100 0.3020 nan 0.1000 -0.0013
## 120 0.2533 nan 0.1000 -0.0007
## 140 0.2152 nan 0.1000 -0.0001
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## 180 0.1631 nan 0.1000 -0.0004
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## 220 0.1222 nan 0.1000 -0.0004
## 240 0.1071 nan 0.1000 -0.0003
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## 460 0.0278 nan 0.1000 -0.0000
## 480 0.0249 nan 0.1000 -0.0001
## 500 0.0225 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2390 nan 0.1000 0.0358
## 2 1.1744 nan 0.1000 0.0271
## 3 1.1110 nan 0.1000 0.0288
## 4 1.0590 nan 0.1000 0.0239
## 5 1.0149 nan 0.1000 0.0193
## 6 0.9817 nan 0.1000 0.0133
## 7 0.9503 nan 0.1000 0.0135
## 8 0.9206 nan 0.1000 0.0134
## 9 0.8915 nan 0.1000 0.0109
## 10 0.8643 nan 0.1000 0.0113
## 20 0.7128 nan 0.1000 0.0021
## 40 0.5738 nan 0.1000 0.0002
## 60 0.4796 nan 0.1000 -0.0001
## 80 0.4069 nan 0.1000 0.0007
## 100 0.3495 nan 0.1000 -0.0002
## 120 0.3062 nan 0.1000 -0.0005
## 140 0.2678 nan 0.1000 -0.0003
## 160 0.2363 nan 0.1000 -0.0004
## 180 0.2097 nan 0.1000 -0.0001
## 200 0.1871 nan 0.1000 -0.0004
## 220 0.1651 nan 0.1000 -0.0000
## 240 0.1501 nan 0.1000 -0.0003
## 260 0.1321 nan 0.1000 -0.0002
## 280 0.1185 nan 0.1000 -0.0001
## 300 0.1062 nan 0.1000 -0.0003
## 320 0.0968 nan 0.1000 0.0000
## 340 0.0878 nan 0.1000 -0.0001
## 360 0.0795 nan 0.1000 -0.0001
## 380 0.0722 nan 0.1000 0.0000
## 400 0.0656 nan 0.1000 -0.0001
## 420 0.0600 nan 0.1000 -0.0001
## 440 0.0545 nan 0.1000 -0.0001
## 460 0.0495 nan 0.1000 0.0000
## 480 0.0456 nan 0.1000 -0.0002
## 500 0.0410 nan 0.1000 0.0000##################################
# Reporting the cross-validation results
# for the train set
##################################
MBS_GBM_Tune## Stochastic Gradient Boosting
##
## 912 samples
## 6 predictor
## 2 classes: 'B', 'M'
##
## No pre-processing
## Resampling: Cross-Validated (5 fold, repeated 5 times)
## Summary of sample sizes: 729, 729, 730, 730, 730, 730, ...
## Resampling results across tuning parameters:
##
## shrinkage interaction.depth n.minobsinnode ROC Sens Spec
## 0.001 4 5 0.8855789 0.9562868 0.4994118
## 0.001 4 10 0.8856134 0.9583799 0.4976471
## 0.001 4 15 0.8847856 0.9576751 0.5035294
## 0.001 5 5 0.8905689 0.9555850 0.5411765
## 0.001 5 10 0.8896630 0.9583768 0.5341176
## 0.001 5 15 0.8897455 0.9583799 0.5347059
## 0.001 6 5 0.8949840 0.9580320 0.5570588
## 0.001 6 10 0.8937728 0.9587216 0.5547059
## 0.001 6 15 0.8924258 0.9587307 0.5470588
## 0.010 4 5 0.9123163 0.9006865 0.7588235
## 0.010 4 10 0.9103749 0.8978978 0.7541176
## 0.010 4 15 0.9096421 0.8964882 0.7547059
## 0.010 5 5 0.9191473 0.8985843 0.7676471
## 0.010 5 10 0.9173787 0.9027887 0.7670588
## 0.010 5 15 0.9148426 0.9010313 0.7694118
## 0.010 6 5 0.9256190 0.9066270 0.7835294
## 0.010 6 10 0.9232580 0.9062853 0.7811765
## 0.010 6 15 0.9205954 0.9101236 0.7752941
## 0.100 4 5 0.9611713 0.9457818 0.9011765
## 0.100 4 10 0.9614875 0.9485889 0.9011765
## 0.100 4 15 0.9585759 0.9506758 0.9011765
## 0.100 5 5 0.9647306 0.9492784 0.8964706
## 0.100 5 10 0.9623021 0.9503219 0.9000000
## 0.100 5 15 0.9599509 0.9471915 0.9011765
## 0.100 6 5 0.9644697 0.9468284 0.9041176
## 0.100 6 10 0.9638927 0.9489214 0.9000000
## 0.100 6 15 0.9622481 0.9499771 0.8964706
##
## Tuning parameter 'n.trees' was held constant at a value of 500
## ROC was used to select the optimal model using the largest value.
## The final values used for the model were n.trees = 500, interaction.depth =
## 5, shrinkage = 0.1 and n.minobsinnode = 5.MBS_GBM_Tune$finalModel## A gradient boosted model with bernoulli loss function.
## 500 iterations were performed.
## There were 6 predictors of which 6 had non-zero influence.MBS_GBM_Tune$results## shrinkage interaction.depth n.minobsinnode n.trees ROC Sens
## 1 0.001 4 5 500 0.8855789 0.9562868
## 2 0.001 4 10 500 0.8856134 0.9583799
## 3 0.001 4 15 500 0.8847856 0.9576751
## 10 0.010 4 5 500 0.9123163 0.9006865
## 11 0.010 4 10 500 0.9103749 0.8978978
## 12 0.010 4 15 500 0.9096421 0.8964882
## 19 0.100 4 5 500 0.9611713 0.9457818
## 20 0.100 4 10 500 0.9614875 0.9485889
## 21 0.100 4 15 500 0.9585759 0.9506758
## 4 0.001 5 5 500 0.8905689 0.9555850
## 5 0.001 5 10 500 0.8896630 0.9583768
## 6 0.001 5 15 500 0.8897455 0.9583799
## 13 0.010 5 5 500 0.9191473 0.8985843
## 14 0.010 5 10 500 0.9173787 0.9027887
## 15 0.010 5 15 500 0.9148426 0.9010313
## 22 0.100 5 5 500 0.9647306 0.9492784
## 23 0.100 5 10 500 0.9623021 0.9503219
## 24 0.100 5 15 500 0.9599509 0.9471915
## 7 0.001 6 5 500 0.8949840 0.9580320
## 8 0.001 6 10 500 0.8937728 0.9587216
## 9 0.001 6 15 500 0.8924258 0.9587307
## 16 0.010 6 5 500 0.9256190 0.9066270
## 17 0.010 6 10 500 0.9232580 0.9062853
## 18 0.010 6 15 500 0.9205954 0.9101236
## 25 0.100 6 5 500 0.9644697 0.9468284
## 26 0.100 6 10 500 0.9638927 0.9489214
## 27 0.100 6 15 500 0.9622481 0.9499771
## Spec ROCSD SensSD SpecSD
## 1 0.4994118 0.02405694 0.01921378 0.07346566
## 2 0.4976471 0.02439127 0.01843250 0.06880404
## 3 0.5035294 0.02463188 0.01817604 0.07429503
## 10 0.7588235 0.02111331 0.02157003 0.05111925
## 11 0.7541176 0.02115259 0.01996058 0.05378548
## 12 0.7547059 0.02078841 0.02241857 0.04853684
## 19 0.9011765 0.02144100 0.02674577 0.04038990
## 20 0.9011765 0.02063435 0.02529413 0.04016618
## 21 0.9011765 0.02002030 0.02834439 0.03689194
## 4 0.5411765 0.02375077 0.01854122 0.07039905
## 5 0.5341176 0.02388615 0.01809084 0.06278338
## 6 0.5347059 0.02371382 0.01807513 0.05895818
## 13 0.7676471 0.02037398 0.01977190 0.05233862
## 14 0.7670588 0.02138825 0.02131783 0.05211086
## 15 0.7694118 0.02096681 0.01922948 0.04426446
## 22 0.8964706 0.01848443 0.02802019 0.03760798
## 23 0.9000000 0.01999341 0.02767224 0.04093944
## 24 0.9011765 0.02014616 0.02444462 0.04061238
## 7 0.5570588 0.02361444 0.01674995 0.07183338
## 8 0.5547059 0.02400943 0.01668693 0.06514999
## 9 0.5470588 0.02430751 0.01664892 0.06576671
## 16 0.7835294 0.02103619 0.01924743 0.04905391
## 17 0.7811765 0.02057186 0.01993281 0.05099924
## 18 0.7752941 0.02101536 0.01799566 0.05173601
## 25 0.9041176 0.02010398 0.03064104 0.04393754
## 26 0.9000000 0.02091672 0.02910325 0.03867582
## 27 0.8964706 0.02050969 0.02508434 0.04169837(MBS_GBM_Train_AUROC <- MBS_GBM_Tune$results[MBS_GBM_Tune$results$n.trees==MBS_GBM_Tune$bestTune$n.trees &
MBS_GBM_Tune$results$shrinkage==MBS_GBM_Tune$bestTune$shrinkage &
MBS_GBM_Tune$results$n.minobsinnode==MBS_GBM_Tune$bestTune$n.minobsinnode &
MBS_GBM_Tune$results$interaction.depth==MBS_GBM_Tune$bestTune$interaction.depth,
c("ROC")])## [1] 0.9647306##################################
# Identifying and plotting the
# best model predictors
##################################
MBS_GBM_VarImp <- varImp(MBS_GBM_Tune, scale = TRUE)
plot(MBS_GBM_VarImp,
top=6,
scales=list(y=list(cex = .95)),
main="Ranked VariGBMle Importance : Stochastic Gradient Boosting",
xlGBM="Scaled Variable Importance Metrics",
ylGBM="Predictors",
cex=2,
origin=0,
alpha=0.45)
##################################
# Independently evaluating the model
# on the test set
##################################
MBS_GBM_Test <- data.frame(MBS_GBM_Test_Observed = MA_Test$diagnosis,
MBS_GBM_Test_Predicted = predict(MBS_GBM_Tune,
MA_Test[,!names(MA_Test) %in% c("diagnosis")],
type = "prob"))
##################################
# Reporting the independent evaluation results
# for the test set
##################################
MBS_GBM_Test_ROC <- roc(response = MBS_GBM_Test$MBS_GBM_Test_Observed,
predictor = MBS_GBM_Test$MBS_GBM_Test_Predicted.M,
levels = rev(levels(MBS_GBM_Test$MBS_GBM_Test_Observed)))
(MBS_GBM_Test_AUROC <- auc(MBS_GBM_Test_ROC)[1])## [1] 0.98306511.5.3 Extreme Gradient Boosting (MBS_XGB)
Extreme Gradient Boosting is an optimized and scalable version of the stochastic gradient boosting developed to overcome its limitations. The algorithm introduced enhancements including regularization terms to control overfitting and a second-order gradient approximation to improve convergence speed. Parallel processing is also implemented, making the computations faster than traditional GBM for large datasets.
[A] The extreme gradient boosting model was implemented through the xgboost package.
[B] The model contains 1 hyperparameter:
[B.1] nrounds = maximum number of iterations held constant at a value equal to 500
[B.2] max_depth = maximum depth of the trees made to vary across a range of values equal to 4 to 6
[B.3] eta = step size or learning rate of each boosting step made to vary across a range of values equal to 0.2 to 0.4
[B.4] gamma = minimum loss reduction required to make a further partition on a leaf node of the tree made to vary across a range of values equal to 0.001 to 0.1
[B.5] colsample_bytree = subsample ratio of columns when constructing each tree held constant at a value equal to 1
[B.6] min_child_weight = minimum sum of instance weight (hessian) needed in a child held constant at a value equal to 1
[B.7] subsample = subsample ratio of the training instance held constant at a value equal to 1
[C] The 5-cycle repeated 5-fold cross-validated model performance of the final model is summarized as follows:
[C.1] Final model configuration involves nrounds=500, max_depth=6, eta=0.3, gamma=0.001, colsample_bytree=1, min_child_weight=1 and subsample=1
[C.2] AUROC = 0.96403
[D] The model allows for ranking of predictors in terms of variable importance. The top-performing predictors in the model are as follows:
[D.1] texture_mean (numeric)
[D.2] symmetry_worst (numeric)
[D.3] smoothness_mean (numeric)
[D.4] texture_worst (numeric)
[D.5] smoothness_worst (numeric)
[E] The independent test model performance of the final model is summarized as follows:
[E.1] AUROC = 0.98972
Code Chunk | Output
##################################
# Setting the conditions
# for hyperparameter tuning
##################################
XGB_Grid = expand.grid(nrounds = 500,
max_depth = c(4,5,6),
eta = c(0.2,0.3,0.4),
gamma = c(0.1,0.01,0.001),
colsample_bytree = 1,
min_child_weight = 1,
subsample = 1)
##################################
# Running the extreme gradient boosting model
# by setting the caret method to 'xgbTree'
##################################
set.seed(12345678)
MBS_XGB_Tune <- train(x = MA_Train[,!names(MA_Train) %in% c("diagnosis")],
y = MA_Train$diagnosis,
method = "xgbTree",
tuneGrid = XGB_Grid,
metric = "ROC",
trControl = RKFold_Control)
##################################
# Reporting the cross-validation results
# for the train set
##################################
MBS_XGB_Tune## eXtreme Gradient Boosting
##
## 912 samples
## 6 predictor
## 2 classes: 'B', 'M'
##
## No pre-processing
## Resampling: Cross-Validated (5 fold, repeated 5 times)
## Summary of sample sizes: 729, 729, 730, 730, 730, 730, ...
## Resampling results across tuning parameters:
##
## eta max_depth gamma ROC Sens Spec
## 0.2 4 0.001 0.9626835 0.9527841 0.9005882
## 0.2 4 0.010 0.9633867 0.9527841 0.9023529
## 0.2 4 0.100 0.9596240 0.9503402 0.9088235
## 0.2 5 0.001 0.9626556 0.9520732 0.8982353
## 0.2 5 0.010 0.9629735 0.9527750 0.9029412
## 0.2 5 0.100 0.9611275 0.9534859 0.9011765
## 0.2 6 0.001 0.9634468 0.9503432 0.9000000
## 0.2 6 0.010 0.9633502 0.9489336 0.9000000
## 0.2 6 0.100 0.9605459 0.9499924 0.8988235
## 0.3 4 0.001 0.9629143 0.9545294 0.8958824
## 0.3 4 0.010 0.9615315 0.9520793 0.8994118
## 0.3 4 0.100 0.9582784 0.9520763 0.9011765
## 0.3 5 0.001 0.9619608 0.9534798 0.9005882
## 0.3 5 0.010 0.9613663 0.9531350 0.9029412
## 0.3 5 0.100 0.9610368 0.9513959 0.9029412
## 0.3 6 0.001 0.9640349 0.9562777 0.9000000
## 0.3 6 0.010 0.9630643 0.9520915 0.9023529
## 0.3 6 0.100 0.9599690 0.9496506 0.8994118
## 0.4 4 0.001 0.9623312 0.9545385 0.8929412
## 0.4 4 0.010 0.9614739 0.9548772 0.8982353
## 0.4 4 0.100 0.9605573 0.9531228 0.8958824
## 0.4 5 0.001 0.9627857 0.9534706 0.8976471
## 0.4 5 0.010 0.9616459 0.9510359 0.8976471
## 0.4 5 0.100 0.9599004 0.9496415 0.9011765
## 0.4 6 0.001 0.9634008 0.9559298 0.8923529
## 0.4 6 0.010 0.9635828 0.9545294 0.8982353
## 0.4 6 0.100 0.9604719 0.9538337 0.8958824
##
## Tuning parameter 'nrounds' was held constant at a value of 500
## Tuning
##
## Tuning parameter 'min_child_weight' was held constant at a value of 1
##
## Tuning parameter 'subsample' was held constant at a value of 1
## ROC was used to select the optimal model using the largest value.
## The final values used for the model were nrounds = 500, max_depth = 6, eta
## = 0.3, gamma = 0.001, colsample_bytree = 1, min_child_weight = 1 and
## subsample = 1.MBS_XGB_Tune$finalModel## ##### xgb.Booster
## raw: 538.2 Kb
## call:
## xgboost::xgb.train(params = list(eta = param$eta, max_depth = param$max_depth,
## gamma = param$gamma, colsample_bytree = param$colsample_bytree,
## min_child_weight = param$min_child_weight, subsample = param$subsample),
## data = x, nrounds = param$nrounds, objective = "binary:logistic")
## params (as set within xgb.train):
## eta = "0.3", max_depth = "6", gamma = "0.001", colsample_bytree = "1", min_child_weight = "1", subsample = "1", objective = "binary:logistic", validate_parameters = "TRUE"
## xgb.attributes:
## niter
## callbacks:
## cb.print.evaluation(period = print_every_n)
## # of features: 6
## niter: 500
## nfeatures : 6
## xNames : texture_mean smoothness_mean compactness_se texture_worst smoothness_worst symmetry_worst
## problemType : Classification
## tuneValue :
## nrounds max_depth eta gamma colsample_bytree min_child_weight subsample
## 16 500 6 0.3 0.001 1 1 1
## obsLevels : B M
## param :
## list()MBS_XGB_Tune$results## eta max_depth gamma colsample_bytree min_child_weight subsample nrounds
## 1 0.2 4 0.001 1 1 1 500
## 2 0.2 4 0.010 1 1 1 500
## 3 0.2 4 0.100 1 1 1 500
## 10 0.3 4 0.001 1 1 1 500
## 11 0.3 4 0.010 1 1 1 500
## 12 0.3 4 0.100 1 1 1 500
## 19 0.4 4 0.001 1 1 1 500
## 20 0.4 4 0.010 1 1 1 500
## 21 0.4 4 0.100 1 1 1 500
## 4 0.2 5 0.001 1 1 1 500
## 5 0.2 5 0.010 1 1 1 500
## 6 0.2 5 0.100 1 1 1 500
## 13 0.3 5 0.001 1 1 1 500
## 14 0.3 5 0.010 1 1 1 500
## 15 0.3 5 0.100 1 1 1 500
## 22 0.4 5 0.001 1 1 1 500
## 23 0.4 5 0.010 1 1 1 500
## 24 0.4 5 0.100 1 1 1 500
## 7 0.2 6 0.001 1 1 1 500
## 8 0.2 6 0.010 1 1 1 500
## 9 0.2 6 0.100 1 1 1 500
## 16 0.3 6 0.001 1 1 1 500
## 17 0.3 6 0.010 1 1 1 500
## 18 0.3 6 0.100 1 1 1 500
## 25 0.4 6 0.001 1 1 1 500
## 26 0.4 6 0.010 1 1 1 500
## 27 0.4 6 0.100 1 1 1 500
## ROC Sens Spec ROCSD SensSD SpecSD
## 1 0.9626835 0.9527841 0.9005882 0.01990016 0.02550560 0.03943261
## 2 0.9633867 0.9527841 0.9023529 0.02079769 0.02622359 0.04388829
## 3 0.9596240 0.9503402 0.9088235 0.02060821 0.02884956 0.04004935
## 10 0.9629143 0.9545294 0.8958824 0.01934490 0.02694649 0.04264706
## 11 0.9615315 0.9520793 0.8994118 0.01941356 0.02743655 0.04020206
## 12 0.9582784 0.9520763 0.9011765 0.02142355 0.02636969 0.04038990
## 19 0.9623312 0.9545385 0.8929412 0.02124940 0.02497991 0.03832945
## 20 0.9614739 0.9548772 0.8982353 0.02214990 0.02546892 0.03498640
## 21 0.9605573 0.9531228 0.8958824 0.02067730 0.02897600 0.04047904
## 4 0.9626556 0.9520732 0.8982353 0.02161392 0.03009695 0.03957859
## 5 0.9629735 0.9527750 0.9029412 0.02098700 0.02881183 0.03844213
## 6 0.9611275 0.9534859 0.9011765 0.02202231 0.02675918 0.03925856
## 13 0.9619608 0.9534798 0.9005882 0.02232639 0.02702379 0.03874100
## 14 0.9613663 0.9531350 0.9029412 0.02225453 0.02750147 0.03890811
## 15 0.9610368 0.9513959 0.9029412 0.02131813 0.02534249 0.04308430
## 22 0.9627857 0.9534706 0.8976471 0.02058136 0.02729771 0.04148171
## 23 0.9616459 0.9510359 0.8976471 0.02039345 0.03068864 0.03993218
## 24 0.9599004 0.9496415 0.9011765 0.02195891 0.03038183 0.04277365
## 7 0.9634468 0.9503432 0.9000000 0.02089150 0.02937984 0.03749279
## 8 0.9633502 0.9489336 0.9000000 0.01942530 0.02897697 0.03773236
## 9 0.9605459 0.9499924 0.8988235 0.02224698 0.02723485 0.04100982
## 16 0.9640349 0.9562777 0.9000000 0.02173075 0.02449281 0.03474864
## 17 0.9630643 0.9520915 0.9023529 0.02155671 0.02787412 0.03392993
## 18 0.9599690 0.9496506 0.8994118 0.02134556 0.02661137 0.04064787
## 25 0.9634008 0.9559298 0.8923529 0.02148545 0.02504785 0.03388741
## 26 0.9635828 0.9545294 0.8982353 0.02175795 0.02789006 0.03723232
## 27 0.9604719 0.9538337 0.8958824 0.02372122 0.02640844 0.04092183(MBS_XGB_Train_AUROC <- MBS_XGB_Tune$results[MBS_XGB_Tune$results$nrounds==MBS_XGB_Tune$bestTune$nrounds &
MBS_XGB_Tune$results$max_depth==MBS_XGB_Tune$bestTune$max_depth &
MBS_XGB_Tune$results$eta==MBS_XGB_Tune$bestTune$eta &
MBS_XGB_Tune$results$gamma==MBS_XGB_Tune$bestTune$gamma &
MBS_XGB_Tune$results$colsample_bytree==MBS_XGB_Tune$bestTune$colsample_bytree &
MBS_XGB_Tune$results$min_child_weight==MBS_XGB_Tune$bestTune$min_child_weight &
MBS_XGB_Tune$results$subsample==MBS_XGB_Tune$bestTune$subsample,
c("ROC")])## [1] 0.9640349##################################
# Identifying and plotting the
# best model predictors
##################################
MBS_XGB_VarImp <- varImp(MBS_XGB_Tune, scale = TRUE)
plot(MBS_XGB_VarImp,
top=6,
scales=list(y=list(cex = .95)),
main="Ranked VariXGBle Importance : Extreme Gradient Boosting",
xlXGB="Scaled Variable Importance Metrics",
ylXGB="Predictors",
cex=2,
origin=0,
alpha=0.45)
##################################
# Independently evaluating the model
# on the test set
##################################
MBS_XGB_Test <- data.frame(MBS_XGB_Test_Observed = MA_Test$diagnosis,
MBS_XGB_Test_Predicted = predict(MBS_XGB_Tune,
MA_Test[,!names(MA_Test) %in% c("diagnosis")],
type = "prob"))
##################################
# Reporting the independent evaluation results
# for the test set
##################################
MBS_XGB_Test_ROC <- roc(response = MBS_XGB_Test$MBS_XGB_Test_Observed,
predictor = MBS_XGB_Test$MBS_XGB_Test_Predicted.M,
levels = rev(levels(MBS_XGB_Test$MBS_XGB_Test_Observed)))
(MBS_XGB_Test_AUROC <- auc(MBS_XGB_Test_ROC)[1])## [1] 0.9897721.6 Model Bagging
Model Bagging, also known as bootstrap aggregation, is an ensemble learning approach that combines the benefits of bootstrapping and aggregation to yield a stable model and improve the prediction performance of a statistical learning method. In bagging, equal-sized subsets are sampled from a dataset with bootstrapping (repeated subsampling of the rows of the original data with replacement) and models trained on each of the subsets independently and in parallel. Aggregation involves combining the results from each model by averaging or voting to get a final result. The bagging process reduces the variance of the statistical learning method after consolidation. The final model tends to have sufficiently low variance by increasing robustness to noise in the data.
1.6.1 Random Forest (MBG_RF)
Random Forest is an ensemble learning method made up of a large set of small decision trees called estimators, with each producing its own prediction. The random forest model aggregates the predictions of the estimators to produce a more accurate prediction. The algorithm involves bootstrap aggregating (where smaller subsets of the training data are repeatedly subsampled with replacement), random subspacing (where a subset of features are sampled and used to train each individual estimator), estimator training (where unpruned decision trees are formulated for each estimator) and inference by aggregating the predictions of all estimators.
[A] The random forest model from the randomForest package was implemented through the caret package.
[B] The model contains 1 hyperparameter:
[B.1] mtry = number of randomly selected predictors made to vary across a range of values equal to 2 to 5
[C] The 5-cycle repeated 5-fold cross-validated model performance of the final model is summarized as follows:
[C.1] Final model configuration involves mtry=2
[C.2] AUROC = 0.97000
[D] The model allows for ranking of predictors in terms of variable importance. The top-performing predictors in the model are as follows:
[D.1] texture_worst (numeric)
[D.2] texture_mean (numeric)
[D.3] symmetry_worst (numeric)
[D.4] smoothness_worst (numeric)
[D.5] smoothness_mean (numeric)
[E] The independent test model performance of the final model is summarized as follows:
[E.1] AUROC = 0.99195
Code Chunk | Output
##################################
# Setting the conditions
# for hyperparameter tuning
##################################
RF_Grid = data.frame(mtry = c(2,3,4,5))
##################################
# Running the random forest model
# by setting the caret method to 'rf'
##################################
set.seed(12345678)
MBG_RF_Tune <- train(x = MA_Train[,!names(MA_Train) %in% c("diagnosis")],
y = MA_Train$diagnosis,
method = "rf",
tuneGrid = RF_Grid,
metric = "ROC",
trControl = RKFold_Control)
##################################
# Reporting the cross-validation results
# for the train set
##################################
MBG_RF_Tune## Random Forest
##
## 912 samples
## 6 predictor
## 2 classes: 'B', 'M'
##
## No pre-processing
## Resampling: Cross-Validated (5 fold, repeated 5 times)
## Summary of sample sizes: 729, 729, 730, 730, 730, 730, ...
## Resampling results across tuning parameters:
##
## mtry ROC Sens Spec
## 2 0.9700081 0.9580351 0.8970588
## 3 0.9686273 0.9562929 0.9000000
## 4 0.9677953 0.9548955 0.8952941
## 5 0.9663847 0.9538459 0.8988235
##
## ROC was used to select the optimal model using the largest value.
## The final value used for the model was mtry = 2.MBG_RF_Tune$finalModel##
## Call:
## randomForest(x = x, y = y, mtry = param$mtry)
## Type of random forest: classification
## Number of trees: 500
## No. of variables tried at each split: 2
##
## OOB estimate of error rate: 3.18%
## Confusion matrix:
## B M class.error
## B 561 11 0.01923077
## M 18 322 0.05294118MBG_RF_Tune$results## mtry ROC Sens Spec ROCSD SensSD SpecSD
## 1 2 0.9700081 0.9580351 0.8970588 0.01778857 0.02017469 0.04746303
## 2 3 0.9686273 0.9562929 0.9000000 0.01882807 0.02196796 0.04572245
## 3 4 0.9677953 0.9548955 0.8952941 0.01958256 0.02104960 0.04637213
## 4 5 0.9663847 0.9538459 0.8988235 0.02050067 0.02117526 0.04273149(MBG_RF_Train_AUROC <- MBG_RF_Tune$results[MBG_RF_Tune$results$mtry==MBG_RF_Tune$bestTune$mtry,
c("ROC")])## [1] 0.9700081##################################
# Identifying and plotting the
# best model predictors
##################################
MBG_RF_VarImp <- varImp(MBG_RF_Tune, scale = TRUE)
plot(MBG_RF_VarImp,
top=6,
scales=list(y=list(cex = .95)),
main="Ranked Variable Importance : Random Forest",
xlab="Scaled Variable Importance Metrics",
ylab="Predictors",
cex=2,
origin=0,
alpha=0.45)
##################################
# Independently evaluating the model
# on the test set
##################################
MBG_RF_Test <- data.frame(MBG_RF_Test_Observed = MA_Test$diagnosis,
MBG_RF_Test_Predicted = predict(MBG_RF_Tune,
MA_Test[,!names(MA_Test) %in% c("diagnosis")],
type = "prob"))
##################################
# Reporting the independent evaluation results
# for the test set
##################################
MBG_RF_Test_ROC <- roc(response = MBG_RF_Test$MBG_RF_Test_Observed,
predictor = MBG_RF_Test$MBG_RF_Test_Predicted.M,
levels = rev(levels(MBG_RF_Test$MBG_RF_Test_Observed)))
(MBG_RF_Test_AUROC <- auc(MBG_RF_Test_ROC)[1])## [1] 0.99195171.6.2 Bagged Classification and Regression Trees (MBG_BCART)
Bagged Classification and Regression Trees combine bootstrapping and decision trees to construct an ensemble. The modeling process involves generating bootstrap samples of the original data, training an unpruned decision tree for each bootstrap subset of the data and implementing an ensemble voting for all the individual decision tree predictions to formulate the final prediction. The bootstrap aggregation (bagging) mechanism improves the model performance by reducing variance. Although the individual decision trees in the model are identically distributed, they are not necessarily independent and share similar structure. This similarity, known as tree correlation, is an essential factor that prevents further reduction of variance.
[A] The bagged CART model from the ipred, plyr and e1071 packages was implemented through the caret package.
[B] The model does not contain any hyperparameter.
[C] The 5-cycle repeated 5-fold cross-validated model performance of the final model is summarized as follows:
[C.1] Final model configuration is fixed due to the absence of a hyperparameter
[C.2] AUROC = 0.96444
[D] The model allows for ranking of predictors in terms of variable importance. The top-performing predictors in the model are as follows:
[D.1] smoothness_worst (numeric)
[D.2] symmetry_worst (numeric)
[D.3] texture_worst (numeric)
[D.4] smoothness_mean (numeric)
[D.5] compactness_se (numeric)
[E] The independent test model performance of the final model is summarized as follows:
[E.1] AUROC = 0.98583
Code Chunk | Output
##################################
# Setting the conditions
# for hyperparameter tuning
##################################
# No hyperparameter tuning process required
##################################
# Running the bagged CART model
# by setting the caret method to 'treebag'
##################################
set.seed(12345678)
MBG_BCART_Tune <- train(x = MA_Train[,!names(MA_Train) %in% c("diagnosis")],
y = MA_Train$diagnosis,
method = "treebag",
nbagg = 50,
metric = "ROC",
trControl = RKFold_Control)
##################################
# Reporting the cross-validation results
# for the train set
##################################
MBG_BCART_Tune## Bagged CART
##
## 912 samples
## 6 predictor
## 2 classes: 'B', 'M'
##
## No pre-processing
## Resampling: Cross-Validated (5 fold, repeated 5 times)
## Summary of sample sizes: 729, 729, 730, 730, 730, 730, ...
## Resampling results:
##
## ROC Sens Spec
## 0.9644432 0.9541998 0.9MBG_BCART_Tune$finalModel##
## Bagging classification trees with 50 bootstrap replicationsMBG_BCART_Tune$results## parameter ROC Sens Spec ROCSD SensSD SpecSD
## 1 none 0.9644432 0.9541998 0.9 0.02096563 0.02170402 0.04452427(MBG_BCART_Train_AUROC <- MBG_BCART_Tune$results$ROC)## [1] 0.9644432##################################
# Identifying and plotting the
# best model predictors
##################################
MBG_BCART_VarImp <- varImp(MBG_BCART_Tune, scale = TRUE)
plot(MBG_BCART_VarImp,
top=6,
scales=list(y=list(cex = .95)),
main="Ranked Variable Importance : Bagged Classification and Regression Trees",
xlab="Scaled Variable Importance Metrics",
ylab="Predictors",
cex=2,
origin=0,
alpha=0.45)
##################################
# Independently evaluating the model
# on the test set
##################################
MBG_BCART_Test <- data.frame(MBG_BCART_Test_Observed = MA_Test$diagnosis,
MBG_BCART_Test_Predicted = predict(MBG_BCART_Tune,
MA_Test[,!names(MA_Test) %in% c("diagnosis")],
type = "prob"))
##################################
# Reporting the independent evaluation results
# for the test set
##################################
MBG_BCART_Test_ROC <- roc(response = MBG_BCART_Test$MBG_BCART_Test_Observed,
predictor = MBG_BCART_Test$MBG_BCART_Test_Predicted.M,
levels = rev(levels(MBG_BCART_Test$MBG_BCART_Test_Observed)))
(MBG_BCART_Test_AUROC <- auc(MBG_BCART_Test_ROC)[1])## [1] 0.98583171.7 Model Stacking
Model Stacking, also known as stacked generalization, is an ensemble approach which involves creating a variety of base learners and using them to create intermediate predictions, one for each learned model. A meta-model is incorporated that gains knowledge of the same target from intermediate predictions. Unlike bagging, in stacking, the models are typically different (e.g. not all decision trees) and fit on the same dataset (e.g. instead of samples of the training dataset). Unlike boosting, in stacking, a single model is used to learn how to best combine the predictions from the contributing models (e.g. instead of a sequence of models that correct the predictions of prior models). Stacking is appropriate when the predictions made by the base learners or the errors in predictions made by the models have minimal correlation. Achieving an improvement in performance is dependent upon the choice of base learners and whether they are sufficiently skillful in their predictions.
1.7.1 Base Learner Model Development using Linear Discriminant Analysis (BAL_LDA)
Linear Discriminant Analysis finds a linear combination of features that best separates the classes in a data set by projecting the data onto a lower-dimensional space that maximizes the separation between the classes. The algorithm searches for a set of linear discriminants that maximize the ratio of between-class variance to within-class variance by evaluating directions in the feature space that best separate the different classes of data. LDA assumes that the data has a Gaussian distribution and that the covariance matrices of the different classes are equal, in addition to the data being linearly separable by the presence of a linear decision boundary can accurately classify the different classes.
[A] The linear discriminant analysis model from the MASS package was implemented through the caret package.
[B] The model does not contain any hyperparameter.
[C] The 5-cycle repeated 5-fold cross-validated model performance of the final model is summarized as follows:
[C.1] Final model configuration is fixed due to the absence of a hyperparameter
[C.2] AUROC = 0.87628
[D] The model does not allow for ranking of predictors in terms of variable importance.
[E] The independent test model performance of the final model is summarized as follows:
[E.1] AUROC = 0.88833
Code Chunk | Output
##################################
# Setting the conditions
# for hyperparameter tuning
##################################
# No hyperparameter tuning process required
##################################
# Running the linear discriminant analysis model
# by setting the caret method to 'lda'
##################################
set.seed(12345678)
BAL_LDA_Tune <- train(x = MA_Train[,!names(MA_Train) %in% c("diagnosis")],
y = MA_Train$diagnosis,
method = "lda",
preProc = c("center","scale"),
metric = "ROC",
trControl = RKFold_Control)
##################################
# Reporting the cross-validation results
# for the train set
##################################
BAL_LDA_Tune## Linear Discriminant Analysis
##
## 912 samples
## 6 predictor
## 2 classes: 'B', 'M'
##
## Pre-processing: centered (6), scaled (6)
## Resampling: Cross-Validated (5 fold, repeated 5 times)
## Summary of sample sizes: 729, 729, 730, 730, 730, 730, ...
## Resampling results:
##
## ROC Sens Spec
## 0.8762815 0.8720214 0.7105882BAL_LDA_Tune$finalModel## Call:
## lda(x, y)
##
## Prior probabilities of groups:
## B M
## 0.627193 0.372807
##
## Group means:
## texture_mean smoothness_mean compactness_se texture_worst smoothness_worst
## B -0.3194390 -0.2990245 -0.2777306 -0.3459548 -0.3280838
## M 0.5374091 0.5030648 0.4672410 0.5820181 0.5519528
## symmetry_worst
## B -0.2957632
## M 0.4975782
##
## Coefficients of linear discriminants:
## LD1
## texture_mean 0.4986787
## smoothness_mean 0.3218366
## compactness_se 0.2495221
## texture_worst 0.2741160
## smoothness_worst 0.2408554
## symmetry_worst 0.3784915BAL_LDA_Tune$results## parameter ROC Sens Spec ROCSD SensSD SpecSD
## 1 none 0.8762815 0.8720214 0.7105882 0.02587517 0.02739545 0.05331432(BAL_LDA_Train_AUROC <- BAL_LDA_Tune$results$ROC)## [1] 0.8762815##################################
# Identifying and plotting the
# best model predictors
##################################
# model does not support variable importance measurement
##################################
# Independently evaluating the model
# on the test set
##################################
BAL_LDA_Test <- data.frame(BAL_LDA_Test_Observed = MA_Test$diagnosis,
BAL_LDA_Test_Predicted = predict(BAL_LDA_Tune,
MA_Test[,!names(MA_Test) %in% c("diagnosis")],
type = "prob"))
BAL_LDA_Test## BAL_LDA_Test_Observed BAL_LDA_Test_Predicted.B BAL_LDA_Test_Predicted.M
## 4 M 0.006126311 0.993873689
## 5 M 0.942711747 0.057288253
## 14 M 0.579064544 0.420935456
## 19 M 0.385557673 0.614442327
## 24 M 0.407580856 0.592419144
## 29 M 0.038467533 0.961532467
## 31 M 0.084012467 0.915987533
## 33 M 0.059833498 0.940166502
## 34 M 0.104825602 0.895174398
## 36 M 0.123134528 0.876865472
## 37 M 0.213046186 0.786953814
## 41 M 0.762194634 0.237805366
## 44 M 0.243102433 0.756897567
## 50 B 0.633616180 0.366383820
## 51 B 0.883649952 0.116350048
## 60 B 0.941211680 0.058788320
## 63 M 0.152736238 0.847263762
## 66 M 0.081701843 0.918298157
## 70 B 0.956197471 0.043802529
## 72 B 0.926760828 0.073239172
## 76 M 0.675879310 0.324120690
## 77 B 0.938615062 0.061384938
## 98 B 0.859180598 0.140819402
## 101 M 0.412386407 0.587613593
## 107 B 0.348515684 0.651484316
## 122 M 0.615196888 0.384803112
## 124 B 0.968617292 0.031382708
## 136 M 0.496541534 0.503458466
## 143 B 0.686963614 0.313036386
## 145 B 0.981924747 0.018075253
## 151 B 0.492150246 0.507849754
## 153 B 0.610903722 0.389096278
## 157 M 0.413945858 0.586054142
## 167 B 0.997185897 0.002814103
## 170 B 0.904311554 0.095688446
## 173 M 0.812030361 0.187969639
## 174 B 0.978730977 0.021269023
## 181 M 0.241659587 0.758340413
## 185 M 0.524089965 0.475910035
## 186 B 0.878625062 0.121374938
## 195 M 0.231580068 0.768419932
## 199 M 0.348140696 0.651859304
## 213 M 0.947602810 0.052397190
## 215 M 0.056860324 0.943139676
## 219 M 0.393506696 0.606493304
## 221 B 0.973705501 0.026294499
## 227 B 0.921367896 0.078632104
## 231 M 0.330572265 0.669427735
## 237 M 0.134627362 0.865372638
## 238 M 0.805720619 0.194279381
## 247 B 0.929093207 0.070906793
## 252 B 0.876231913 0.123768087
## 260 M 0.008999501 0.991000499
## 261 M 0.114163242 0.885836758
## 262 M 0.728155795 0.271844205
## 265 M 0.364679966 0.635320034
## 271 B 0.995874752 0.004125248
## 280 B 0.900993219 0.099006781
## 305 B 0.924318983 0.075681017
## 311 B 0.736500069 0.263499931
## 315 B 0.633755327 0.366244673
## 317 B 0.996050405 0.003949595
## 327 B 0.987789680 0.012210320
## 328 B 0.984770455 0.015229545
## 330 M 0.290667258 0.709332742
## 334 B 0.968849268 0.031150732
## 338 M 0.174995596 0.825004404
## 356 B 0.903045873 0.096954127
## 357 B 0.430524826 0.569475174
## 364 B 0.811698009 0.188301991
## 368 B 0.727507423 0.272492577
## 373 M 0.872668128 0.127331872
## 388 B 0.988414548 0.011585452
## 389 B 0.957745027 0.042254973
## 391 B 0.962310108 0.037689892
## 393 M 0.177431936 0.822568064
## 396 B 0.948444928 0.051555072
## 397 B 0.583968627 0.416031373
## 404 B 0.824463807 0.175536193
## 420 B 0.563842969 0.436157031
## 422 B 0.842474215 0.157525785
## 427 B 0.779936684 0.220063316
## 429 B 0.983858264 0.016141736
## 430 B 0.978101034 0.021898966
## 435 B 0.925778429 0.074221571
## 442 M 0.325620184 0.674379816
## 454 B 0.936216623 0.063783377
## 458 B 0.633531078 0.366468922
## 463 B 0.810336471 0.189663529
## 470 B 0.356359884 0.643640116
## 474 B 0.706685284 0.293314716
## 478 B 0.981238254 0.018761746
## 479 B 0.821103847 0.178896153
## 484 B 0.902055587 0.097944413
## 486 B 0.677747460 0.322252540
## 489 B 0.744881334 0.255118666
## 490 M 0.681658672 0.318341328
## 491 B 0.606617961 0.393382039
## 492 B 0.998066340 0.001933660
## 499 M 0.735157269 0.264842731
## 501 B 0.943572720 0.056427280
## 503 B 0.598182906 0.401817094
## 507 B 0.534944505 0.465055495
## 509 B 0.958952980 0.041047020
## 514 B 0.956897407 0.043102593
## 518 M 0.588851055 0.411148945
## 523 B 0.920707025 0.079292975
## 534 M 0.563458379 0.436541621
## 535 B 0.756220900 0.243779100
## 536 M 0.446628256 0.553371744
## 542 B 0.214561414 0.785438586
## 545 B 0.789312389 0.210687611
## 549 B 0.885565165 0.114434835
## 550 B 0.510647176 0.489352824
## 561 B 0.382288558 0.617711442
## 563 M 0.016016932 0.983983068
## 566 M 0.247083120 0.752916880
## 569 B 0.965394152 0.034605848
## 574 M 0.942711747 0.057288253
## 581 M 0.356014840 0.643985160
## 582 M 0.215119777 0.784880223
## 594 M 0.113214377 0.886785623
## 596 M 0.091384833 0.908615167
## 597 M 0.702993860 0.297006140
## 598 M 0.038467533 0.961532467
## 599 M 0.878500112 0.121499888
## 600 M 0.084012467 0.915987533
## 612 M 0.057472228 0.942527772
## 616 B 0.855211064 0.144788936
## 617 M 0.148575243 0.851424757
## 626 M 0.290264105 0.709735895
## 634 M 0.064339370 0.935660630
## 639 B 0.956197471 0.043802529
## 641 B 0.926760828 0.073239172
## 650 B 0.320740216 0.679259784
## 665 M 0.259719596 0.740280404
## 666 B 0.925463665 0.074536335
## 673 B 0.557022744 0.442977256
## 676 B 0.348515684 0.651484316
## 684 B 0.620718568 0.379281432
## 688 M 0.068214661 0.931785339
## 691 M 0.615196888 0.384803112
## 696 M 0.222808227 0.777191773
## 709 B 0.943771833 0.056228167
## 710 B 0.987245490 0.012754510
## 713 B 0.830292869 0.169707131
## 727 B 0.921633095 0.078366905
## 730 B 0.382668083 0.617331917
## 732 M 0.333612109 0.666387891
## 740 B 0.961858377 0.038141623
## 741 M 0.681062354 0.318937646
## 763 M 0.040915331 0.959084669
## 769 M 0.177021698 0.822978302
## 788 M 0.393506696 0.606493304
## 790 B 0.973705501 0.026294499
## 791 B 0.870821564 0.129178436
## 792 B 0.682622616 0.317377384
## 793 M 0.182399687 0.817600313
## 801 B 0.726087033 0.273912967
## 803 M 0.354643905 0.645356095
## 806 M 0.134627362 0.865372638
## 810 B 0.944706021 0.055293979
## 812 B 0.393474697 0.606525303
## 813 B 0.779393668 0.220606332
## 821 B 0.876231913 0.123768087
## 836 B 0.658099084 0.341900916
## 842 M 0.448926362 0.551073638
## 843 B 0.901788723 0.098211277
## 848 B 0.973498488 0.026501512
## 853 M 0.528763024 0.471236976
## 862 B 0.711409516 0.288590484
## 865 B 0.979123666 0.020876334
## 879 B 0.996373712 0.003626288
## 890 B 0.741480170 0.258519830
## 891 M 0.878337490 0.121662510
## 897 B 0.984770455 0.015229545
## 902 B 0.499343912 0.500656088
## 904 B 0.892320617 0.107679383
## 905 M 0.361244898 0.638755102
## 906 B 0.966801278 0.033198722
## 916 B 0.823396789 0.176603211
## 919 B 0.783263908 0.216736092
## 920 B 0.979880096 0.020119904
## 930 B 0.990855172 0.009144828
## 940 M 0.079097793 0.920902207
## 945 B 0.846430270 0.153569730
## 947 B 0.594187576 0.405812424
## 948 B 0.872995937 0.127004063
## 951 B 0.941410746 0.058589254
## 961 B 0.801844856 0.198155144
## 966 B 0.583968627 0.416031373
## 969 B 0.832591332 0.167408668
## 979 B 0.638449388 0.361550612
## 985 B 0.374275674 0.625724326
## 991 B 0.842474215 0.157525785
## 996 B 0.779936684 0.220063316
## 998 B 0.983858264 0.016141736
## 999 B 0.978101034 0.021898966
## 1005 M 0.305786852 0.694213148
## 1006 B 0.708477812 0.291522188
## 1007 B 0.942459340 0.057540660
## 1009 B 0.990361755 0.009638245
## 1015 B 0.367056365 0.632943635
## 1021 M 0.374776492 0.625223508
## 1027 B 0.633531078 0.366468922
## 1033 B 0.755897965 0.244102035
## 1042 B 0.973607166 0.026392834
## 1044 B 0.862842688 0.137157312
## 1049 M 0.359535242 0.640464758
## 1059 M 0.681658672 0.318341328
## 1060 B 0.606617961 0.393382039
## 1063 B 0.997138711 0.002861289
## 1075 B 0.556668408 0.443331592
## 1087 M 0.588851055 0.411148945
## 1089 B 0.652990898 0.347009102
## 1095 B 0.868301964 0.131698036
## 1098 B 0.929532127 0.070467873
## 1099 B 0.925197595 0.074802405
## 1102 B 0.934244281 0.065755719
## 1106 M 0.302156084 0.697843916
## 1108 B 0.561058807 0.438941193
## 1109 B 0.180044308 0.819955692
## 1113 B 0.517216264 0.482783736
## 1114 B 0.789312389 0.210687611
## 1119 B 0.510647176 0.489352824
## 1133 M 0.147249898 0.852750102##################################
# Reporting the independent evaluation results
# for the test set
##################################
BAL_LDA_Test_ROC <- roc(response = BAL_LDA_Test$BAL_LDA_Test_Observed,
predictor = BAL_LDA_Test$BAL_LDA_Test_Predicted.M,
levels = rev(levels(BAL_LDA_Test$BAL_LDA_Test_Observed)))
(BAL_LDA_Test_AUROC <- auc(BAL_LDA_Test_ROC)[1])## [1] 0.888331.7.2 Base Learner Model Development using Classification and Regression Trees (BAL_CART)
Classification and Regression Trees construct binary trees for both both nominal and continuous input attributes using Gini Index as its splitting criteria. The algorithm handles missing values by surrogating tests to approximate outcomes. In the pruning phase, CART uses pre-pruning technique called Cost-Complexity pruning to remove redundant branches from the decision tree to improve the accuracy. In the first stage, a sequence of increasingly smaller trees are built on the training data. In the second stage, one of these tree is chosen as the pruned tree, based on its classification accuracy on a pruning set, adopting a cross-validated method in its pruning technique.
[A] The classification and regression trees model from the rpart package was implemented through the caret package.
[B] The model contains 1 hyperparameter:
[B.1] cp = complexity parameter threshold made to vary across a range of values equal to 0.001 to 0.020
[C] The 5-cycle repeated 5-fold cross-validated model performance of the final model is summarized as follows:
[C.1] Final model configuration involves cp=0.001
[C.2] AUROC = 0.86145
[D] The model allows for ranking of predictors in terms of variable importance. The top-performing predictors in the model are as follows:
[D.1] smoothness_worst (numeric)
[D.2] symmetry_worst (numeric)
[D.3] texture_worst (numeric)
[D.4] compactness_se (numeric)
[D.5] smoothness_mean (numeric)
[E] The independent test model performance of the final model is summarized as follows:
[E.1] AUROC = 0.92106
Code Chunk | Output
##################################
# Setting the conditions
# for hyperparameter tuning
##################################
CART_Grid = data.frame(cp = c(0.001, 0.005, 0.010, 0.015, 0.020))
##################################
# Running the classification and regression tree model
# by setting the caret method to 'rpart'
##################################
set.seed(12345678)
BAL_CART_Tune <- train(x = MA_Train[,!names(MA_Train) %in% c("diagnosis")],
y = MA_Train$diagnosis,
method = "rpart",
tuneGrid = CART_Grid,
metric = "ROC",
trControl = RKFold_Control)
##################################
# Reporting the cross-validation results
# for the train set
##################################
BAL_CART_Tune## CART
##
## 912 samples
## 6 predictor
## 2 classes: 'B', 'M'
##
## No pre-processing
## Resampling: Cross-Validated (5 fold, repeated 5 times)
## Summary of sample sizes: 729, 729, 730, 730, 730, 730, ...
## Resampling results across tuning parameters:
##
## cp ROC Sens Spec
## 0.001 0.8614523 0.8503158 0.7482353
## 0.005 0.8512807 0.8640061 0.7452941
## 0.010 0.8297650 0.8702944 0.7211765
## 0.015 0.8139619 0.8608299 0.7170588
## 0.020 0.8094478 0.8552677 0.7164706
##
## ROC was used to select the optimal model using the largest value.
## The final value used for the model was cp = 0.001.BAL_CART_Tune$finalModel## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 340 B (0.62719298 0.37280702)
## 2) texture_mean< 2.927988 437 67 B (0.84668192 0.15331808)
## 4) symmetry_worst< -1.34686 398 38 B (0.90452261 0.09547739)
## 8) smoothness_mean< -2.074653 390 33 B (0.91538462 0.08461538)
## 16) texture_mean< 2.711046 134 0 B (1.00000000 0.00000000) *
## 17) texture_mean>=2.711046 256 33 B (0.87109375 0.12890625)
## 34) symmetry_worst< -1.427209 247 28 B (0.88663968 0.11336032)
## 68) smoothness_mean< -2.468758 62 0 B (1.00000000 0.00000000) *
## 69) smoothness_mean>=-2.468758 185 28 B (0.84864865 0.15135135)
## 138) smoothness_mean>=-2.28574 59 3 B (0.94915254 0.05084746) *
## 139) smoothness_mean< -2.28574 126 25 B (0.80158730 0.19841270)
## 278) compactness_se< -4.691273 24 0 B (1.00000000 0.00000000) *
## 279) compactness_se>=-4.691273 102 25 B (0.75490196 0.24509804)
## 558) smoothness_mean< -2.296604 94 20 B (0.78723404 0.21276596)
## 1116) compactness_se>=-4.479607 81 14 B (0.82716049 0.17283951)
## 2232) smoothness_worst< -1.472892 73 10 B (0.86301370 0.13698630)
## 4464) symmetry_worst>=-1.749307 28 0 B (1.00000000 0.00000000) *
## 4465) symmetry_worst< -1.749307 45 10 B (0.77777778 0.22222222)
## 8930) symmetry_worst< -1.841614 34 2 B (0.94117647 0.05882353) *
## 8931) symmetry_worst>=-1.841614 11 3 M (0.27272727 0.72727273) *
## 2233) smoothness_worst>=-1.472892 8 4 B (0.50000000 0.50000000) *
## 1117) compactness_se< -4.479607 13 6 B (0.53846154 0.46153846) *
## 559) smoothness_mean>=-2.296604 8 3 M (0.37500000 0.62500000) *
## 35) symmetry_worst>=-1.427209 9 4 M (0.44444444 0.55555556) *
## 9) smoothness_mean>=-2.074653 8 3 M (0.37500000 0.62500000) *
## 5) symmetry_worst>=-1.34686 39 10 M (0.25641026 0.74358974)
## 10) smoothness_mean< -2.32364 7 1 B (0.85714286 0.14285714) *
## 11) smoothness_mean>=-2.32364 32 4 M (0.12500000 0.87500000) *
## 3) texture_mean>=2.927988 475 202 M (0.42526316 0.57473684)
## 6) smoothness_mean< -2.425205 140 29 B (0.79285714 0.20714286)
## 12) symmetry_worst< -1.496954 133 23 B (0.82706767 0.17293233)
## 24) smoothness_worst< -1.60101 85 8 B (0.90588235 0.09411765)
## 48) texture_mean>=2.980363 64 3 B (0.95312500 0.04687500) *
## 49) texture_mean< 2.980363 21 5 B (0.76190476 0.23809524)
## 98) symmetry_worst< -1.919875 12 0 B (1.00000000 0.00000000) *
## 99) symmetry_worst>=-1.919875 9 4 M (0.44444444 0.55555556) *
## 25) smoothness_worst>=-1.60101 48 15 B (0.68750000 0.31250000)
## 50) texture_mean< 3.108829 21 1 B (0.95238095 0.04761905) *
## 51) texture_mean>=3.108829 27 13 M (0.48148148 0.51851852)
## 102) texture_mean>=3.176386 20 7 B (0.65000000 0.35000000)
## 204) compactness_se< -3.643388 13 2 B (0.84615385 0.15384615) *
## 205) compactness_se>=-3.643388 7 2 M (0.28571429 0.71428571) *
## 103) texture_mean< 3.176386 7 0 M (0.00000000 1.00000000) *
## 13) symmetry_worst>=-1.496954 7 1 M (0.14285714 0.85714286) *
## 7) smoothness_mean>=-2.425205 335 91 M (0.27164179 0.72835821)
## 14) texture_worst< 4.411908 18 1 B (0.94444444 0.05555556) *
## 15) texture_worst>=4.411908 317 74 M (0.23343849 0.76656151)
## 30) symmetry_worst< -1.776275 102 44 M (0.43137255 0.56862745)
## 60) compactness_se< -3.02233 89 44 M (0.49438202 0.50561798)
## 120) texture_worst< 4.897936 54 20 B (0.62962963 0.37037037)
## 240) texture_worst>=4.751011 13 0 B (1.00000000 0.00000000) *
## 241) texture_worst< 4.751011 41 20 B (0.51219512 0.48780488)
## 482) texture_mean< 3.07522 26 9 B (0.65384615 0.34615385)
## 964) smoothness_mean>=-2.347868 12 1 B (0.91666667 0.08333333) *
## 965) smoothness_mean< -2.347868 14 6 M (0.42857143 0.57142857) *
## 483) texture_mean>=3.07522 15 4 M (0.26666667 0.73333333) *
## 121) texture_worst>=4.897936 35 10 M (0.28571429 0.71428571)
## 242) symmetry_worst< -2.207988 9 2 B (0.77777778 0.22222222) *
## 243) symmetry_worst>=-2.207988 26 3 M (0.11538462 0.88461538) *
## 61) compactness_se>=-3.02233 13 0 M (0.00000000 1.00000000) *
## 31) symmetry_worst>=-1.776275 215 30 M (0.13953488 0.86046512)
## 62) compactness_se< -4.040144 38 16 M (0.42105263 0.57894737)
## 124) smoothness_mean>=-2.294648 15 2 B (0.86666667 0.13333333) *
## 125) smoothness_mean< -2.294648 23 3 M (0.13043478 0.86956522) *
## 63) compactness_se>=-4.040144 177 14 M (0.07909605 0.92090395)
## 126) smoothness_mean< -2.32432 37 9 M (0.24324324 0.75675676) *
## 127) smoothness_mean>=-2.32432 140 5 M (0.03571429 0.96428571)
## 254) texture_worst< 4.824228 54 5 M (0.09259259 0.90740741)
## 508) compactness_se< -3.447524 24 5 M (0.20833333 0.79166667)
## 1016) texture_worst>=4.608306 8 3 B (0.62500000 0.37500000) *
## 1017) texture_worst< 4.608306 16 0 M (0.00000000 1.00000000) *
## 509) compactness_se>=-3.447524 30 0 M (0.00000000 1.00000000) *
## 255) texture_worst>=4.824228 86 0 M (0.00000000 1.00000000) *BAL_CART_Tune$results## cp ROC Sens Spec ROCSD SensSD SpecSD
## 1 0.001 0.8614523 0.8503158 0.7482353 0.03685507 0.04170440 0.06635597
## 2 0.005 0.8512807 0.8640061 0.7452941 0.03662151 0.02767361 0.05816420
## 3 0.010 0.8297650 0.8702944 0.7211765 0.04245561 0.03675966 0.07705751
## 4 0.015 0.8139619 0.8608299 0.7170588 0.04009017 0.04021240 0.07056270
## 5 0.020 0.8094478 0.8552677 0.7164706 0.04150844 0.03315895 0.06891397(BAL_CART_Train_AUROC <- BAL_CART_Tune$results[BAL_CART_Tune$results$cp==BAL_CART_Tune$bestTune$cp,
c("ROC")])## [1] 0.8614523##################################
# Identifying and plotting the
# best model predictors
##################################
BAL_CART_VarImp <- varImp(BAL_CART_Tune, scale = TRUE)
plot(BAL_CART_VarImp,
top=6,
scales=list(y=list(cex = .95)),
main="Ranked Variable Importance : Classification and Regression Trees",
xlab="Scaled Variable Importance Metrics",
ylab="Predictors",
cex=2,
origin=0,
alpha=0.45)
##################################
# Independently evaluating the model
# on the test set
##################################
BAL_CART_Test <- data.frame(BAL_CART_Test_Observed = MA_Test$diagnosis,
BAL_CART_Test_Predicted = predict(BAL_CART_Tune,
MA_Test[,!names(MA_Test) %in% c("diagnosis")],
type = "prob"))
BAL_CART_Test## BAL_CART_Test_Observed BAL_CART_Test_Predicted.B BAL_CART_Test_Predicted.M
## 4 M 0.0000000 1.00000000
## 5 M 1.0000000 0.00000000
## 14 M 0.0000000 1.00000000
## 19 M 0.1153846 0.88461538
## 24 M 0.1304348 0.86956522
## 29 M 0.0000000 1.00000000
## 31 M 0.0000000 1.00000000
## 33 M 0.0000000 1.00000000
## 34 M 0.2432432 0.75675676
## 36 M 0.2432432 0.75675676
## 37 M 0.0000000 1.00000000
## 41 M 0.9531250 0.04687500
## 44 M 0.6250000 0.37500000
## 50 B 0.9523810 0.04761905
## 51 B 0.9531250 0.04687500
## 60 B 1.0000000 0.00000000
## 63 M 0.0000000 1.00000000
## 66 M 0.0000000 1.00000000
## 70 B 1.0000000 0.00000000
## 72 B 1.0000000 0.00000000
## 76 M 0.4285714 0.57142857
## 77 B 0.3750000 0.62500000
## 98 B 0.9166667 0.08333333
## 101 M 0.1153846 0.88461538
## 107 B 0.9491525 0.05084746
## 122 M 0.9491525 0.05084746
## 124 B 1.0000000 0.00000000
## 136 M 0.1304348 0.86956522
## 143 B 0.9491525 0.05084746
## 145 B 1.0000000 0.00000000
## 151 B 0.8666667 0.13333333
## 153 B 0.9491525 0.05084746
## 157 M 0.0000000 1.00000000
## 167 B 1.0000000 0.00000000
## 170 B 0.9411765 0.05882353
## 173 M 0.3750000 0.62500000
## 174 B 1.0000000 0.00000000
## 181 M 0.0000000 1.00000000
## 185 M 0.1304348 0.86956522
## 186 B 0.5000000 0.50000000
## 195 M 0.0000000 1.00000000
## 199 M 0.0000000 1.00000000
## 213 M 0.9491525 0.05084746
## 215 M 0.2432432 0.75675676
## 219 M 0.2432432 0.75675676
## 221 B 1.0000000 0.00000000
## 227 B 0.9491525 0.05084746
## 231 M 0.0000000 1.00000000
## 237 M 0.2432432 0.75675676
## 238 M 0.9523810 0.04761905
## 247 B 1.0000000 0.00000000
## 252 B 0.5384615 0.46153846
## 260 M 0.0000000 1.00000000
## 261 M 0.1304348 0.86956522
## 262 M 0.0000000 1.00000000
## 265 M 0.1304348 0.86956522
## 271 B 1.0000000 0.00000000
## 280 B 1.0000000 0.00000000
## 305 B 0.9411765 0.05882353
## 311 B 0.1428571 0.85714286
## 315 B 0.9491525 0.05084746
## 317 B 1.0000000 0.00000000
## 327 B 1.0000000 0.00000000
## 328 B 1.0000000 0.00000000
## 330 M 0.2666667 0.73333333
## 334 B 1.0000000 0.00000000
## 338 M 0.2432432 0.75675676
## 356 B 1.0000000 0.00000000
## 357 B 0.9491525 0.05084746
## 364 B 0.9411765 0.05882353
## 368 B 1.0000000 0.00000000
## 373 M 0.2727273 0.72727273
## 388 B 1.0000000 0.00000000
## 389 B 1.0000000 0.00000000
## 391 B 1.0000000 0.00000000
## 393 M 0.0000000 1.00000000
## 396 B 1.0000000 0.00000000
## 397 B 0.9166667 0.08333333
## 404 B 1.0000000 0.00000000
## 420 B 0.8666667 0.13333333
## 422 B 1.0000000 0.00000000
## 427 B 1.0000000 0.00000000
## 429 B 1.0000000 0.00000000
## 430 B 1.0000000 0.00000000
## 435 B 0.9411765 0.05882353
## 442 M 0.2857143 0.71428571
## 454 B 1.0000000 0.00000000
## 458 B 0.8461538 0.15384615
## 463 B 0.9531250 0.04687500
## 470 B 0.9491525 0.05084746
## 474 B 0.9531250 0.04687500
## 478 B 1.0000000 0.00000000
## 479 B 1.0000000 0.00000000
## 484 B 1.0000000 0.00000000
## 486 B 1.0000000 0.00000000
## 489 B 0.9491525 0.05084746
## 490 M 0.1428571 0.85714286
## 491 B 0.0000000 1.00000000
## 492 B 1.0000000 0.00000000
## 499 M 0.3750000 0.62500000
## 501 B 0.9411765 0.05882353
## 503 B 0.9491525 0.05084746
## 507 B 0.9166667 0.08333333
## 509 B 0.9411765 0.05882353
## 514 B 1.0000000 0.00000000
## 518 M 0.9166667 0.08333333
## 523 B 0.9531250 0.04687500
## 534 M 0.2432432 0.75675676
## 535 B 0.9411765 0.05882353
## 536 M 0.0000000 1.00000000
## 542 B 0.2857143 0.71428571
## 545 B 0.9166667 0.08333333
## 549 B 0.9523810 0.04761905
## 550 B 0.8461538 0.15384615
## 561 B 0.7777778 0.22222222
## 563 M 0.0000000 1.00000000
## 566 M 0.1153846 0.88461538
## 569 B 0.9531250 0.04687500
## 574 M 1.0000000 0.00000000
## 581 M 0.8571429 0.14285714
## 582 M 0.2432432 0.75675676
## 594 M 0.0000000 1.00000000
## 596 M 0.0000000 1.00000000
## 597 M 0.4285714 0.57142857
## 598 M 0.0000000 1.00000000
## 599 M 0.2727273 0.72727273
## 600 M 0.0000000 1.00000000
## 612 M 0.2432432 0.75675676
## 616 B 1.0000000 0.00000000
## 617 M 0.1250000 0.87500000
## 626 M 0.4444444 0.55555556
## 634 M 0.0000000 1.00000000
## 639 B 1.0000000 0.00000000
## 641 B 1.0000000 0.00000000
## 650 B 0.1153846 0.88461538
## 665 M 0.2432432 0.75675676
## 666 B 0.9491525 0.05084746
## 673 B 0.9166667 0.08333333
## 676 B 0.9491525 0.05084746
## 684 B 0.9491525 0.05084746
## 688 M 0.0000000 1.00000000
## 691 M 0.9491525 0.05084746
## 696 M 0.1304348 0.86956522
## 709 B 1.0000000 0.00000000
## 710 B 1.0000000 0.00000000
## 713 B 0.4444444 0.55555556
## 727 B 1.0000000 0.00000000
## 730 B 0.6250000 0.37500000
## 732 M 0.1250000 0.87500000
## 740 B 1.0000000 0.00000000
## 741 M 0.1304348 0.86956522
## 763 M 0.0000000 1.00000000
## 769 M 0.1304348 0.86956522
## 788 M 0.2432432 0.75675676
## 790 B 1.0000000 0.00000000
## 791 B 1.0000000 0.00000000
## 792 B 0.9491525 0.05084746
## 793 M 0.0000000 1.00000000
## 801 B 0.9531250 0.04687500
## 803 M 0.1153846 0.88461538
## 806 M 0.2432432 0.75675676
## 810 B 0.9411765 0.05882353
## 812 B 1.0000000 0.00000000
## 813 B 0.9531250 0.04687500
## 821 B 0.5384615 0.46153846
## 836 B 0.9444444 0.05555556
## 842 M 0.2432432 0.75675676
## 843 B 0.5384615 0.46153846
## 848 B 1.0000000 0.00000000
## 853 M 0.9166667 0.08333333
## 862 B 0.5000000 0.50000000
## 865 B 1.0000000 0.00000000
## 879 B 1.0000000 0.00000000
## 890 B 0.9491525 0.05084746
## 891 M 0.4444444 0.55555556
## 897 B 1.0000000 0.00000000
## 902 B 0.8666667 0.13333333
## 904 B 0.9523810 0.04761905
## 905 M 0.1153846 0.88461538
## 906 B 1.0000000 0.00000000
## 916 B 0.9523810 0.04761905
## 919 B 1.0000000 0.00000000
## 920 B 1.0000000 0.00000000
## 930 B 1.0000000 0.00000000
## 940 M 0.2432432 0.75675676
## 945 B 1.0000000 0.00000000
## 947 B 0.9531250 0.04687500
## 948 B 1.0000000 0.00000000
## 951 B 1.0000000 0.00000000
## 961 B 0.9491525 0.05084746
## 966 B 0.9166667 0.08333333
## 969 B 0.2727273 0.72727273
## 979 B 0.4444444 0.55555556
## 985 B 0.2432432 0.75675676
## 991 B 1.0000000 0.00000000
## 996 B 1.0000000 0.00000000
## 998 B 1.0000000 0.00000000
## 999 B 1.0000000 0.00000000
## 1005 M 0.0000000 1.00000000
## 1006 B 0.1304348 0.86956522
## 1007 B 1.0000000 0.00000000
## 1009 B 1.0000000 0.00000000
## 1015 B 1.0000000 0.00000000
## 1021 M 0.1153846 0.88461538
## 1027 B 0.8461538 0.15384615
## 1033 B 1.0000000 0.00000000
## 1042 B 1.0000000 0.00000000
## 1044 B 0.3750000 0.62500000
## 1049 M 0.9444444 0.05555556
## 1059 M 0.1428571 0.85714286
## 1060 B 0.0000000 1.00000000
## 1063 B 1.0000000 0.00000000
## 1075 B 1.0000000 0.00000000
## 1087 M 0.9166667 0.08333333
## 1089 B 0.9491525 0.05084746
## 1095 B 1.0000000 0.00000000
## 1098 B 1.0000000 0.00000000
## 1099 B 1.0000000 0.00000000
## 1102 B 1.0000000 0.00000000
## 1106 M 0.1153846 0.88461538
## 1108 B 0.8461538 0.15384615
## 1109 B 0.2857143 0.71428571
## 1113 B 0.9531250 0.04687500
## 1114 B 0.9166667 0.08333333
## 1119 B 0.8461538 0.15384615
## 1133 M 0.0000000 1.00000000##################################
# Reporting the independent evaluation results
# for the test set
##################################
BAL_CART_Test_ROC <- roc(response = BAL_CART_Test$BAL_CART_Test_Observed,
predictor = BAL_CART_Test$BAL_CART_Test_Predicted.M,
levels = rev(levels(BAL_CART_Test$BAL_CART_Test_Observed)))
(BAL_CART_Test_AUROC <- auc(BAL_CART_Test_ROC)[1])## [1] 0.92106811.7.3 Base Learner Model Development using Support Vector Machine - Radial Basis Function Kernel (BAL_SVM_R)
Support Vector Machine plots each observation in an N-dimensional space corresponding to the number of features in the data set and finds a hyperplane that maximally separates the different classes by a maximally large margin (which is defined as the distance between the hyperplane and the closest data points from each class). The algorithm applies kernel transformation by mapping non-linearly separable data using the similarities between the points in a high-dimensional feature space for improved discrimination.
[A] The support vector machine (radial basis function kernel) model from the kernlab package was implemented through the caret package.
[B] The model contains 2 hyperparameters:
[B.1] sigma = sigma held constant at a value of 0.21332
[B.2] C = cost made to vary across a range of 14 default values
[C] The 5-cycle repeated 5-fold cross-validated model performance of the final model is summarized as follows:
[C.1] Final model configuration involves sigma=0.17905 and C=2048
[C.2] AUROC = 0.90977
[D] The model does not allow for ranking of predictors in terms of variable importance.
[E] The independent test model performance of the final model is summarized as follows:
[E.1] AUROC = 0.93762
Code Chunk | Output
##################################
# Setting the conditions
# for hyperparameter tuning
##################################
# used a range of default values
##################################
# Running the support vector machine model
# by setting the caret method to 'svmRadial'
##################################
set.seed(12345678)
BAL_SVM_R_Tune <- train(x = MA_Train[,!names(MA_Train) %in% c("diagnosis")],
y = MA_Train$diagnosis,
method = "svmRadial",
preProc = c("center", "scale"),
tuneLength = 14,
metric = "ROC",
trControl = RKFold_Control)
##################################
# Reporting the cross-validation results
# for the train set
##################################
BAL_SVM_R_Tune## Support Vector Machines with Radial Basis Function Kernel
##
## 912 samples
## 6 predictor
## 2 classes: 'B', 'M'
##
## Pre-processing: centered (6), scaled (6)
## Resampling: Cross-Validated (5 fold, repeated 5 times)
## Summary of sample sizes: 729, 729, 730, 730, 730, 730, ...
## Resampling results across tuning parameters:
##
## C ROC Sens Spec
## 0.25 0.8792747 0.8964943 0.7005882
## 0.50 0.8828520 0.8964851 0.7064706
## 1.00 0.8853296 0.8961373 0.7064706
## 2.00 0.8879393 0.8958017 0.7164706
## 4.00 0.8909259 0.8937056 0.7241176
## 8.00 0.8960321 0.9003417 0.7300000
## 16.00 0.8967388 0.9052235 0.7264706
## 32.00 0.8982511 0.9059283 0.7347059
## 64.00 0.9005909 0.9135957 0.7482353
## 128.00 0.9039633 0.9233837 0.7552941
## 256.00 0.9090738 0.9374005 0.7652941
## 512.00 0.9090150 0.9412387 0.7782353
## 1024.00 0.9077637 0.9387643 0.7900000
## 2048.00 0.9097712 0.9436857 0.7976471
##
## Tuning parameter 'sigma' was held constant at a value of 0.1790538
## ROC was used to select the optimal model using the largest value.
## The final values used for the model were sigma = 0.1790538 and C = 2048.BAL_SVM_R_Tune$finalModel## Support Vector Machine object of class "ksvm"
##
## SV type: C-svc (classification)
## parameter : cost C = 2048
##
## Gaussian Radial Basis kernel function.
## Hyperparameter : sigma = 0.179053781320727
##
## Number of Support Vectors : 302
##
## Objective Function Value : -123343.3
## Training error : 0.013158
## Probability model included.BAL_SVM_R_Tune$results## sigma C ROC Sens Spec ROCSD SensSD
## 1 0.1790538 0.25 0.8792747 0.8964943 0.7005882 0.02548830 0.02322464
## 2 0.1790538 0.50 0.8828520 0.8964851 0.7064706 0.02410960 0.02143187
## 3 0.1790538 1.00 0.8853296 0.8961373 0.7064706 0.02490232 0.02237023
## 4 0.1790538 2.00 0.8879393 0.8958017 0.7164706 0.02417564 0.02120909
## 5 0.1790538 4.00 0.8909259 0.8937056 0.7241176 0.02144916 0.02499684
## 6 0.1790538 8.00 0.8960321 0.9003417 0.7300000 0.02035456 0.02715491
## 7 0.1790538 16.00 0.8967388 0.9052235 0.7264706 0.01882154 0.02634028
## 8 0.1790538 32.00 0.8982511 0.9059283 0.7347059 0.01957743 0.02468638
## 9 0.1790538 64.00 0.9005909 0.9135957 0.7482353 0.02224693 0.02746024
## 10 0.1790538 128.00 0.9039633 0.9233837 0.7552941 0.02309492 0.02966385
## 11 0.1790538 256.00 0.9090738 0.9374005 0.7652941 0.02327350 0.03080847
## 12 0.1790538 512.00 0.9090150 0.9412387 0.7782353 0.02271246 0.02797556
## 13 0.1790538 1024.00 0.9077637 0.9387643 0.7900000 0.02374909 0.03058368
## 14 0.1790538 2048.00 0.9097712 0.9436857 0.7976471 0.02420238 0.02639300
## SpecSD
## 1 0.04800669
## 2 0.05031619
## 3 0.05085061
## 4 0.04319292
## 5 0.04864069
## 6 0.04838064
## 7 0.04727280
## 8 0.05766006
## 9 0.05474856
## 10 0.04762981
## 11 0.04793155
## 12 0.04706648
## 13 0.04083366
## 14 0.04272306(BAL_SVM_R_Train_AUROC <- BAL_SVM_R_Tune$results[BAL_SVM_R_Tune$results$C==BAL_SVM_R_Tune$bestTune$C,
c("ROC")])## [1] 0.9097712##################################
# Identifying and plotting the
# best model predictors
##################################
# model does not support variable importance measurement
##################################
# Independently evaluating the model
# on the test set
##################################
BAL_SVM_R_Test <- data.frame(BAL_SVM_R_Test_Observed = MA_Test$diagnosis,
BAL_SVM_R_Test_Predicted = predict(BAL_SVM_R_Tune,
MA_Test[,!names(MA_Test) %in% c("diagnosis")],
type = "prob"))
BAL_SVM_R_Test## BAL_SVM_R_Test_Observed BAL_SVM_R_Test_Predicted.B
## 1 M 0.44662437
## 2 M 0.96681608
## 3 M 0.44658845
## 4 M 0.49596629
## 5 M 0.11150143
## 6 M 0.01569566
## 7 M 0.31379620
## 8 M 0.34778723
## 9 M 0.44660688
## 10 M 0.02901136
## 11 M 0.40709619
## 12 M 0.44656374
## 13 M 0.29240511
## 14 B 0.63760184
## 15 B 0.83664724
## 16 B 0.99997498
## 17 M 0.18805776
## 18 M 0.44661459
## 19 B 0.98590876
## 20 B 0.88515682
## 21 M 0.44663097
## 22 B 0.83142716
## 23 B 0.76187257
## 24 M 0.31943128
## 25 B 0.39732776
## 26 M 0.48486454
## 27 B 0.99999887
## 28 M 0.16435496
## 29 B 0.64793620
## 30 B 0.99903564
## 31 B 0.96526165
## 32 B 0.97945396
## 33 M 0.44659843
## 34 B 0.99989690
## 35 B 0.76853900
## 36 M 0.44661322
## 37 B 0.99278361
## 38 M 0.28446231
## 39 M 0.25385324
## 40 B 0.74652191
## 41 M 0.18837573
## 42 M 0.44657322
## 43 M 0.44660513
## 44 M 0.01181226
## 45 M 0.43513162
## 46 B 0.99791285
## 47 B 0.99736932
## 48 M 0.40247234
## 49 M 0.75056262
## 50 M 0.44663490
## 51 B 0.94428161
## 52 B 0.69498110
## 53 M 0.25963825
## 54 M 0.44662583
## 55 M 0.44658866
## 56 M 0.23194048
## 57 B 0.99979818
## 58 B 0.87311077
## 59 B 0.81813510
## 60 B 0.63012226
## 61 B 0.95423496
## 62 B 0.99996983
## 63 B 0.99936958
## 64 B 0.99994043
## 65 M 0.22183777
## 66 B 0.67864195
## 67 M 0.05280415
## 68 B 0.78464884
## 69 B 0.63010395
## 70 B 0.63010153
## 71 B 0.63010836
## 72 M 0.44657233
## 73 B 0.99782870
## 74 B 0.91971262
## 75 B 0.99999643
## 76 M 0.31889360
## 77 B 0.89735021
## 78 B 0.68136752
## 79 B 0.98731832
## 80 B 0.88269311
## 81 B 0.44297446
## 82 B 0.58223340
## 83 B 0.99975744
## 84 B 0.99912945
## 85 B 0.63008456
## 86 M 0.44662362
## 87 B 0.99996040
## 88 B 0.31981095
## 89 B 0.96210734
## 90 B 0.63009806
## 91 B 0.91531929
## 92 B 0.95207073
## 93 B 0.63007930
## 94 B 0.94340184
## 95 B 0.97479915
## 96 B 0.81672158
## 97 M 0.85442999
## 98 B 0.68987415
## 99 B 0.99952286
## 100 M 0.69757647
## 101 B 0.84281820
## 102 B 0.86154082
## 103 B 0.63009434
## 104 B 0.98927084
## 105 B 0.99013721
## 106 M 0.62321388
## 107 B 0.97686343
## 108 M 0.39672259
## 109 B 0.63005392
## 110 M 0.44666425
## 111 B 0.63008359
## 112 B 0.50601663
## 113 B 0.63010998
## 114 B 0.80018102
## 115 B 0.70965320
## 116 M 0.05996713
## 117 M 0.25816221
## 118 B 0.91915162
## 119 M 0.96681608
## 120 M 0.44659653
## 121 M 0.37879946
## 122 M 0.14365041
## 123 M 0.01429934
## 124 M 0.45018460
## 125 M 0.01569566
## 126 M 0.44660385
## 127 M 0.31379620
## 128 M 0.44665740
## 129 B 0.63002533
## 130 M 0.04854398
## 131 M 0.36356965
## 132 M 0.07119308
## 133 B 0.98590876
## 134 B 0.88515682
## 135 B 0.63009259
## 136 M 0.39144637
## 137 B 0.97776648
## 138 B 0.68430267
## 139 B 0.39732776
## 140 B 0.63008428
## 141 M 0.13525971
## 142 M 0.48486454
## 143 M 0.39703097
## 144 B 0.99876888
## 145 B 0.99999993
## 146 B 0.98148681
## 147 B 0.75366862
## 148 B 0.52900974
## 149 M 0.18003087
## 150 B 0.99999816
## 151 M 0.36986063
## 152 M 0.01615978
## 153 M 0.01179846
## 154 M 0.43513162
## 155 B 0.99791285
## 156 B 0.98708004
## 157 B 0.87622783
## 158 M 0.06918595
## 159 B 0.85767588
## 160 M 0.44656950
## 161 M 0.75056262
## 162 B 0.92288555
## 163 B 0.63009247
## 164 B 0.63004151
## 165 B 0.69498110
## 166 B 0.63006726
## 167 M 0.37933654
## 168 B 0.63007755
## 169 B 0.99838210
## 170 M 0.58769029
## 171 B 0.94781870
## 172 B 0.99996600
## 173 B 0.99990480
## 174 B 0.71567239
## 175 M 0.11516391
## 176 B 0.99994043
## 177 B 0.63004753
## 178 B 0.77813933
## 179 M 0.44656839
## 180 B 0.75846635
## 181 B 0.63003696
## 182 B 0.99014081
## 183 B 0.99624884
## 184 B 0.99999605
## 185 M 0.44661287
## 186 B 0.99400435
## 187 B 0.83290603
## 188 B 0.91218616
## 189 B 0.96651099
## 190 B 0.63007931
## 191 B 0.68136752
## 192 B 0.63006207
## 193 B 0.78872709
## 194 B 0.45225906
## 195 B 0.44297446
## 196 B 0.58223340
## 197 B 0.99975744
## 198 B 0.99912945
## 199 M 0.48573932
## 200 B 0.63006243
## 201 B 0.63009567
## 202 B 0.99988961
## 203 B 0.63002311
## 204 M 0.44656566
## 205 B 0.31981095
## 206 B 0.63875182
## 207 B 0.88256443
## 208 B 0.63011291
## 209 M 0.44659950
## 210 M 0.85442999
## 211 B 0.68987415
## 212 B 0.99992615
## 213 B 0.63005153
## 214 M 0.62321388
## 215 B 0.89393457
## 216 B 0.78611843
## 217 B 0.99888634
## 218 B 0.99998287
## 219 B 0.94387725
## 220 M 0.09677441
## 221 B 0.63002120
## 222 B 0.84509788
## 223 B 0.63007770
## 224 B 0.50601663
## 225 B 0.80018102
## 226 M 0.16459484
## BAL_SVM_R_Test_Predicted.M
## 1 5.533756e-01
## 2 3.318392e-02
## 3 5.534116e-01
## 4 5.040337e-01
## 5 8.884986e-01
## 6 9.843043e-01
## 7 6.862038e-01
## 8 6.522128e-01
## 9 5.533931e-01
## 10 9.709886e-01
## 11 5.929038e-01
## 12 5.534363e-01
## 13 7.075949e-01
## 14 3.623982e-01
## 15 1.633528e-01
## 16 2.501836e-05
## 17 8.119422e-01
## 18 5.533854e-01
## 19 1.409124e-02
## 20 1.148432e-01
## 21 5.533690e-01
## 22 1.685728e-01
## 23 2.381274e-01
## 24 6.805687e-01
## 25 6.026722e-01
## 26 5.151355e-01
## 27 1.134718e-06
## 28 8.356450e-01
## 29 3.520638e-01
## 30 9.643595e-04
## 31 3.473835e-02
## 32 2.054604e-02
## 33 5.534016e-01
## 34 1.030988e-04
## 35 2.314610e-01
## 36 5.533868e-01
## 37 7.216388e-03
## 38 7.155377e-01
## 39 7.461468e-01
## 40 2.534781e-01
## 41 8.116243e-01
## 42 5.534268e-01
## 43 5.533949e-01
## 44 9.881877e-01
## 45 5.648684e-01
## 46 2.087152e-03
## 47 2.630682e-03
## 48 5.975277e-01
## 49 2.494374e-01
## 50 5.533651e-01
## 51 5.571839e-02
## 52 3.050189e-01
## 53 7.403618e-01
## 54 5.533742e-01
## 55 5.534113e-01
## 56 7.680595e-01
## 57 2.018189e-04
## 58 1.268892e-01
## 59 1.818649e-01
## 60 3.698777e-01
## 61 4.576504e-02
## 62 3.016689e-05
## 63 6.304212e-04
## 64 5.956627e-05
## 65 7.781622e-01
## 66 3.213580e-01
## 67 9.471958e-01
## 68 2.153512e-01
## 69 3.698960e-01
## 70 3.698985e-01
## 71 3.698916e-01
## 72 5.534277e-01
## 73 2.171299e-03
## 74 8.028738e-02
## 75 3.566212e-06
## 76 6.811064e-01
## 77 1.026498e-01
## 78 3.186325e-01
## 79 1.268168e-02
## 80 1.173069e-01
## 81 5.570255e-01
## 82 4.177666e-01
## 83 2.425552e-04
## 84 8.705529e-04
## 85 3.699154e-01
## 86 5.533764e-01
## 87 3.960191e-05
## 88 6.801890e-01
## 89 3.789266e-02
## 90 3.699019e-01
## 91 8.468071e-02
## 92 4.792927e-02
## 93 3.699207e-01
## 94 5.659816e-02
## 95 2.520085e-02
## 96 1.832784e-01
## 97 1.455700e-01
## 98 3.101259e-01
## 99 4.771412e-04
## 100 3.024235e-01
## 101 1.571818e-01
## 102 1.384592e-01
## 103 3.699057e-01
## 104 1.072916e-02
## 105 9.862787e-03
## 106 3.767861e-01
## 107 2.313657e-02
## 108 6.032774e-01
## 109 3.699461e-01
## 110 5.533357e-01
## 111 3.699164e-01
## 112 4.939834e-01
## 113 3.698900e-01
## 114 1.998190e-01
## 115 2.903468e-01
## 116 9.400329e-01
## 117 7.418378e-01
## 118 8.084838e-02
## 119 3.318392e-02
## 120 5.534035e-01
## 121 6.212005e-01
## 122 8.563496e-01
## 123 9.857007e-01
## 124 5.498154e-01
## 125 9.843043e-01
## 126 5.533962e-01
## 127 6.862038e-01
## 128 5.533426e-01
## 129 3.699747e-01
## 130 9.514560e-01
## 131 6.364303e-01
## 132 9.288069e-01
## 133 1.409124e-02
## 134 1.148432e-01
## 135 3.699074e-01
## 136 6.085536e-01
## 137 2.223352e-02
## 138 3.156973e-01
## 139 6.026722e-01
## 140 3.699157e-01
## 141 8.647403e-01
## 142 5.151355e-01
## 143 6.029690e-01
## 144 1.231116e-03
## 145 7.017823e-08
## 146 1.851319e-02
## 147 2.463314e-01
## 148 4.709903e-01
## 149 8.199691e-01
## 150 1.843389e-06
## 151 6.301394e-01
## 152 9.838402e-01
## 153 9.882015e-01
## 154 5.648684e-01
## 155 2.087152e-03
## 156 1.291996e-02
## 157 1.237722e-01
## 158 9.308140e-01
## 159 1.423241e-01
## 160 5.534305e-01
## 161 2.494374e-01
## 162 7.711445e-02
## 163 3.699075e-01
## 164 3.699585e-01
## 165 3.050189e-01
## 166 3.699327e-01
## 167 6.206635e-01
## 168 3.699224e-01
## 169 1.617901e-03
## 170 4.123097e-01
## 171 5.218130e-02
## 172 3.400486e-05
## 173 9.519997e-05
## 174 2.843276e-01
## 175 8.848361e-01
## 176 5.956627e-05
## 177 3.699525e-01
## 178 2.218607e-01
## 179 5.534316e-01
## 180 2.415337e-01
## 181 3.699630e-01
## 182 9.859192e-03
## 183 3.751161e-03
## 184 3.949477e-06
## 185 5.533871e-01
## 186 5.995654e-03
## 187 1.670940e-01
## 188 8.781384e-02
## 189 3.348901e-02
## 190 3.699207e-01
## 191 3.186325e-01
## 192 3.699379e-01
## 193 2.112729e-01
## 194 5.477409e-01
## 195 5.570255e-01
## 196 4.177666e-01
## 197 2.425552e-04
## 198 8.705529e-04
## 199 5.142607e-01
## 200 3.699376e-01
## 201 3.699043e-01
## 202 1.103856e-04
## 203 3.699769e-01
## 204 5.534343e-01
## 205 6.801890e-01
## 206 3.612482e-01
## 207 1.174356e-01
## 208 3.698871e-01
## 209 5.534005e-01
## 210 1.455700e-01
## 211 3.101259e-01
## 212 7.384536e-05
## 213 3.699485e-01
## 214 3.767861e-01
## 215 1.060654e-01
## 216 2.138816e-01
## 217 1.113662e-03
## 218 1.712896e-05
## 219 5.612275e-02
## 220 9.032256e-01
## 221 3.699788e-01
## 222 1.549021e-01
## 223 3.699223e-01
## 224 4.939834e-01
## 225 1.998190e-01
## 226 8.354052e-01##################################
# Reporting the independent evaluation results
# for the test set
##################################
BAL_SVM_R_Test_ROC <- roc(response = BAL_SVM_R_Test$BAL_SVM_R_Test_Observed,
predictor = BAL_SVM_R_Test$BAL_SVM_R_Test_Predicted.M,
levels = rev(levels(BAL_SVM_R_Test$BAL_SVM_R_Test_Observed)))
(BAL_SVM_R_Test_AUROC <- auc(BAL_SVM_R_Test_ROC)[1])## [1] 0.93762581.7.4 Base Learner Model Development using K-Nearest Neighbors (BAL_KNN)
K-Nearest Neighbors use proximity to make predictions about the class grouping of an individual data point. The algorithm examines the labels of a chosen number of data points surrounding a target data point given a distance-based similarity metric, in order to make a prediction about the class that the data point falls into. The process involves setting a value for the chosen number of neighbors, calculating the distance between the target point across all instances, sorting the calculated distances, obtaining the labels of the top entries and returning the prediction for the target point.
[A] The k-nearest neighbors model was implemented through the caret package.
[B] The model contains 1 hyperparameter:
[B.1] k = number of neighbors made to vary across a range of values equal to 1 to 15
[C] The 5-cycle repeated 5-fold cross-validated model performance of the final model is summarized as follows:
[C.1] Final model configuration involves k=1
[C.2] AUROC = 0.90764
[D] The model does not allow for ranking of predictors in terms of variable importance.
[E] The independent test model performance of the final model is summarized as follows:
[E.1] AUROC = 0.93611
Code Chunk | Output
##################################
# Setting the conditions
# for hyperparameter tuning
##################################
KNN_Grid = data.frame(k = 1:15)
##################################
# Running the k-nearest neighbors model
# by setting the caret method to 'knn'
##################################
set.seed(12345678)
BAL_KNN_Tune <- train(x = MA_Train[,!names(MA_Train) %in% c("diagnosis")],
y = MA_Train$diagnosis,
method = "knn",
preProc = c("center", "scale"),
tuneGrid = KNN_Grid,
metric = "ROC",
trControl = RKFold_Control)
##################################
# Reporting the cross-validation results
# for the train set
##################################
BAL_KNN_Tune## k-Nearest Neighbors
##
## 912 samples
## 6 predictor
## 2 classes: 'B', 'M'
##
## Pre-processing: centered (6), scaled (6)
## Resampling: Cross-Validated (5 fold, repeated 5 times)
## Summary of sample sizes: 729, 729, 730, 730, 730, 730, ...
## Resampling results across tuning parameters:
##
## k ROC Sens Spec
## 1 0.9076428 0.9205797 0.8947059
## 2 0.8730636 0.8202471 0.7252941
## 3 0.8824422 0.8248574 0.6982353
## 4 0.8818437 0.8643417 0.7288235
## 5 0.8815800 0.8887689 0.7488235
## 6 0.8827032 0.8716644 0.7294118
## 7 0.8848091 0.8730648 0.7211765
## 8 0.8832599 0.8825080 0.7247059
## 9 0.8826004 0.8905416 0.7305882
## 10 0.8821762 0.8839146 0.7129412
## 11 0.8817764 0.8867185 0.7164706
## 12 0.8794329 0.8870664 0.7135294
## 13 0.8785997 0.8860046 0.7123529
## 14 0.8818346 0.8821571 0.7117647
## 15 0.8846281 0.8825263 0.7135294
##
## ROC was used to select the optimal model using the largest value.
## The final value used for the model was k = 1.BAL_KNN_Tune$finalModel## 1-nearest neighbor model
## Training set outcome distribution:
##
## B M
## 572 340BAL_KNN_Tune$results## k ROC Sens Spec ROCSD SensSD SpecSD
## 1 1 0.9076428 0.9205797 0.8947059 0.02583023 0.02881542 0.04064787
## 2 2 0.8730636 0.8202471 0.7252941 0.02876280 0.04286672 0.05229039
## 3 3 0.8824422 0.8248574 0.6982353 0.02631797 0.03848173 0.05807117
## 4 4 0.8818437 0.8643417 0.7288235 0.02394691 0.03156119 0.04651958
## 5 5 0.8815800 0.8887689 0.7488235 0.01965491 0.03178715 0.03649904
## 6 6 0.8827032 0.8716644 0.7294118 0.02080533 0.03287310 0.04411765
## 7 7 0.8848091 0.8730648 0.7211765 0.01884352 0.02994811 0.04482280
## 8 8 0.8832599 0.8825080 0.7247059 0.02014577 0.03294398 0.04127265
## 9 9 0.8826004 0.8905416 0.7305882 0.01945929 0.03067128 0.03620157
## 10 10 0.8821762 0.8839146 0.7129412 0.02010782 0.02722610 0.04767519
## 11 11 0.8817764 0.8867185 0.7164706 0.02007380 0.02682363 0.04462939
## 12 12 0.8794329 0.8870664 0.7135294 0.02001825 0.03088131 0.04331791
## 13 13 0.8785997 0.8860046 0.7123529 0.02061021 0.03027290 0.04246920
## 14 14 0.8818346 0.8821571 0.7117647 0.02038839 0.02947233 0.04432142
## 15 15 0.8846281 0.8825263 0.7135294 0.02000838 0.02791806 0.05114040(BAL_KNN_Train_AUROC <- BAL_KNN_Tune$results[BAL_KNN_Tune$results$k==BAL_KNN_Tune$bestTune$k,
c("ROC")])## [1] 0.9076428##################################
# Identifying and plotting the
# best model predictors
##################################
# model does not support variable importance measurement
##################################
# Independently evaluating the model
# on the test set
##################################
BAL_KNN_Test <- data.frame(BAL_KNN_Test_Observed = MA_Test$diagnosis,
BAL_KNN_Test_Predicted = predict(BAL_KNN_Tune,
MA_Test[,!names(MA_Test) %in% c("diagnosis")],
type = "prob"))
BAL_KNN_Test## BAL_KNN_Test_Observed BAL_KNN_Test_Predicted.B BAL_KNN_Test_Predicted.M
## 1 M 0 1
## 2 M 1 0
## 3 M 0 1
## 4 M 0 1
## 5 M 0 1
## 6 M 0 1
## 7 M 0 1
## 8 M 0 1
## 9 M 0 1
## 10 M 0 1
## 11 M 0 1
## 12 M 0 1
## 13 M 0 1
## 14 B 1 0
## 15 B 1 0
## 16 B 1 0
## 17 M 0 1
## 18 M 0 1
## 19 B 1 0
## 20 B 1 0
## 21 M 0 1
## 22 B 1 0
## 23 B 1 0
## 24 M 0 1
## 25 B 0 1
## 26 M 1 0
## 27 B 1 0
## 28 M 0 1
## 29 B 1 0
## 30 B 1 0
## 31 B 1 0
## 32 B 1 0
## 33 M 0 1
## 34 B 1 0
## 35 B 1 0
## 36 M 0 1
## 37 B 1 0
## 38 M 0 1
## 39 M 0 1
## 40 B 1 0
## 41 M 0 1
## 42 M 0 1
## 43 M 0 1
## 44 M 0 1
## 45 M 0 1
## 46 B 1 0
## 47 B 1 0
## 48 M 0 1
## 49 M 0 1
## 50 M 0 1
## 51 B 1 0
## 52 B 0 1
## 53 M 0 1
## 54 M 0 1
## 55 M 0 1
## 56 M 0 1
## 57 B 1 0
## 58 B 1 0
## 59 B 1 0
## 60 B 1 0
## 61 B 1 0
## 62 B 1 0
## 63 B 1 0
## 64 B 1 0
## 65 M 0 1
## 66 B 1 0
## 67 M 0 1
## 68 B 1 0
## 69 B 1 0
## 70 B 1 0
## 71 B 1 0
## 72 M 0 1
## 73 B 1 0
## 74 B 1 0
## 75 B 1 0
## 76 M 0 1
## 77 B 1 0
## 78 B 1 0
## 79 B 1 0
## 80 B 1 0
## 81 B 1 0
## 82 B 1 0
## 83 B 1 0
## 84 B 1 0
## 85 B 1 0
## 86 M 0 1
## 87 B 1 0
## 88 B 0 1
## 89 B 1 0
## 90 B 1 0
## 91 B 1 0
## 92 B 1 0
## 93 B 1 0
## 94 B 1 0
## 95 B 1 0
## 96 B 1 0
## 97 M 0 1
## 98 B 0 1
## 99 B 1 0
## 100 M 0 1
## 101 B 1 0
## 102 B 1 0
## 103 B 1 0
## 104 B 1 0
## 105 B 1 0
## 106 M 1 0
## 107 B 1 0
## 108 M 0 1
## 109 B 1 0
## 110 M 0 1
## 111 B 1 0
## 112 B 1 0
## 113 B 1 0
## 114 B 1 0
## 115 B 1 0
## 116 M 0 1
## 117 M 0 1
## 118 B 1 0
## 119 M 1 0
## 120 M 0 1
## 121 M 0 1
## 122 M 0 1
## 123 M 0 1
## 124 M 0 1
## 125 M 0 1
## 126 M 0 1
## 127 M 0 1
## 128 M 0 1
## 129 B 1 0
## 130 M 0 1
## 131 M 0 1
## 132 M 0 1
## 133 B 1 0
## 134 B 1 0
## 135 B 1 0
## 136 M 0 1
## 137 B 1 0
## 138 B 1 0
## 139 B 0 1
## 140 B 1 0
## 141 M 0 1
## 142 M 1 0
## 143 M 0 1
## 144 B 1 0
## 145 B 1 0
## 146 B 1 0
## 147 B 1 0
## 148 B 1 0
## 149 M 0 1
## 150 B 1 0
## 151 M 0 1
## 152 M 0 1
## 153 M 0 1
## 154 M 0 1
## 155 B 1 0
## 156 B 1 0
## 157 B 1 0
## 158 M 0 1
## 159 B 1 0
## 160 M 0 1
## 161 M 0 1
## 162 B 1 0
## 163 B 1 0
## 164 B 1 0
## 165 B 0 1
## 166 B 1 0
## 167 M 0 1
## 168 B 1 0
## 169 B 1 0
## 170 M 0 1
## 171 B 1 0
## 172 B 1 0
## 173 B 1 0
## 174 B 1 0
## 175 M 0 1
## 176 B 1 0
## 177 B 1 0
## 178 B 1 0
## 179 M 0 1
## 180 B 1 0
## 181 B 1 0
## 182 B 1 0
## 183 B 1 0
## 184 B 1 0
## 185 M 0 1
## 186 B 1 0
## 187 B 1 0
## 188 B 1 0
## 189 B 1 0
## 190 B 1 0
## 191 B 1 0
## 192 B 1 0
## 193 B 1 0
## 194 B 1 0
## 195 B 1 0
## 196 B 1 0
## 197 B 1 0
## 198 B 1 0
## 199 M 0 1
## 200 B 1 0
## 201 B 1 0
## 202 B 1 0
## 203 B 1 0
## 204 M 0 1
## 205 B 0 1
## 206 B 1 0
## 207 B 1 0
## 208 B 1 0
## 209 M 0 1
## 210 M 0 1
## 211 B 0 1
## 212 B 1 0
## 213 B 1 0
## 214 M 1 0
## 215 B 1 0
## 216 B 1 0
## 217 B 1 0
## 218 B 1 0
## 219 B 1 0
## 220 M 0 1
## 221 B 1 0
## 222 B 1 0
## 223 B 1 0
## 224 B 1 0
## 225 B 1 0
## 226 M 0 1##################################
# Reporting the independent evaluation results
# for the test set
##################################
BAL_KNN_Test_ROC <- roc(response = BAL_KNN_Test$BAL_KNN_Test_Observed,
predictor = BAL_KNN_Test$BAL_KNN_Test_Predicted.M,
levels = rev(levels(BAL_KNN_Test$BAL_KNN_Test_Observed)))
(BAL_KNN_Test_AUROC <- auc(BAL_KNN_Test_ROC)[1])## [1] 0.93611671.7.5 Base Learner Model Development using Naive Bayes (BAL_NB)
Naive Bayes Classifier categorizes instances by applying Bayes Theorem in determining posterior probabilities as conditioned by the likelihood of features, and prior probabilities pertaining to both events and features. The algorithm naively assumes independence between features and assigns the same weight (degree of significance) to all given features. The class conditional probabilities and the prior probabilities are calculated to yield the posterior probability, and operates by returning the class, which has the maximum posterior probability out of a group of classes.
[A] The naive bayes model from the klaR package was implemented through the caret package.
[B] The model contains 3 hyperparameters:
[B.1] fL = laplace correction held constant at a value of 2
[B.2] adjust = bandwidth adjustment held constant at a value of FALSE
[B.3] usekernel = distribution type made to vary across a range of levels equal to TRUE and FALSE
[C] The 5-cycle repeated 5-fold cross-validated model performance of the final model is summarized as follows:
[C.1] Final model configuration involves fL=2, adjust=FALSE and usekernel=FALSE
[C.2] AUROC = 0.88735
[D] The model does not allow for ranking of predictors in terms of variable importance.
[E] The independent test model performance of the final model is summarized as follows:
[E.1] AUROC = 0.90383
Code Chunk | Output
##################################
# Setting the conditions
# for hyperparameter tuning
##################################
NB_Grid = data.frame(usekernel = c(TRUE, FALSE),
fL = 2,
adjust = FALSE)
##################################
# Running the naive bayes model
# by setting the caret method to 'nb'
##################################
set.seed(12345678)
BAL_NB_Tune <- train(x = MA_Train[,!names(MA_Train) %in% c("diagnosis")],
y = MA_Train$diagnosis,
method = "nb",
tuneGrid = NB_Grid,
metric = "ROC",
trControl = RKFold_Control)
##################################
# Reporting the cross-validation results
# for the train set
##################################
BAL_NB_Tune## Naive Bayes
##
## 912 samples
## 6 predictor
## 2 classes: 'B', 'M'
##
## No pre-processing
## Resampling: Cross-Validated (5 fold, repeated 5 times)
## Summary of sample sizes: 729, 729, 730, 730, 730, 730, ...
## Resampling results across tuning parameters:
##
## usekernel ROC Sens Spec
## FALSE 0.8873525 0.8552525 0.7605882
## TRUE NaN NaN NaN
##
## Tuning parameter 'fL' was held constant at a value of 2
## Tuning
## parameter 'adjust' was held constant at a value of FALSE
## ROC was used to select the optimal model using the largest value.
## The final values used for the model were fL = 2, usekernel = FALSE and adjust
## = FALSE.BAL_NB_Tune$finalModel## $apriori
## grouping
## B M
## 0.627193 0.372807
##
## $tables
## $tables$texture_mean
## [,1] [,2]
## B 2.866111 0.2131833
## M 3.054338 0.1756238
##
## $tables$smoothness_mean
## [,1] [,2]
## B -2.394224 0.1422449
## M -2.276397 0.1231980
##
## $tables$compactness_se
## [,1] [,2]
## B -4.044941 0.6453164
## M -3.559438 0.5400556
##
## $tables$texture_worst
## [,1] [,2]
## B 4.374373 0.4270218
## M 4.792405 0.3581525
##
## $tables$smoothness_worst
## [,1] [,2]
## B -1.553385 0.08631008
## M -1.469949 0.08502371
##
## $tables$symmetry_worst
## [,1] [,2]
## B -1.882256 0.3136605
## M -1.584759 0.3956037
##
##
## $levels
## [1] "B" "M"
##
## $call
## NaiveBayes.default(x = x, grouping = y, usekernel = FALSE, fL = param$fL)
##
## $x
## texture_mean smoothness_mean compactness_se texture_worst
## X1 2.339881 -2.133687 -3.015119 3.845649
## X2 2.877512 -2.468168 -4.336671 4.393994
## X3 3.056357 -2.210918 -3.217377 4.558289
## X6 2.753661 -2.057289 -3.397703 4.421124
## X7 2.994732 -2.357781 -4.281638 4.712710
## X8 3.036394 -2.129472 -3.496938 4.746189
## X9 3.082827 -2.061209 -3.351836 4.919334
## X10 3.179719 -2.131999 -2.628731 5.491708
## X11 3.145875 -2.500305 -4.681080 5.114832
## X12 2.884242 -2.332014 -3.203741 4.685875
## X13 3.210844 -2.328929 -2.489276 4.867801
## X15 3.118392 -2.179483 -2.824135 5.000625
## X16 3.315639 -2.172434 -3.160607 5.301845
## X17 3.002211 -2.315974 -4.455028 4.928999
## X18 3.029167 -2.145581 -3.688480 4.967287
## X20 2.664447 -2.324933 -4.226734 4.034440
## X21 2.754297 -2.230264 -3.964369 4.146994
## X22 2.520917 -2.278869 -4.246098 3.668189
## X23 2.657458 -2.232127 -2.932194 4.017490
## X25 3.062456 -2.188364 -3.972835 4.972347
## X26 2.797281 -2.131999 -3.270432 4.226835
## X27 3.069447 -2.249993 -3.488391 5.074506
## X28 3.008155 -2.360214 -3.603803 4.684455
## X30 2.711378 -2.318003 -3.495618 4.058702
## X32 2.928524 -2.199126 -3.377286 4.744803
## X35 2.883683 -2.263364 -3.551555 4.684455
## X38 2.913437 -2.409836 -5.318724 4.345339
## X39 3.226844 -2.365844 -4.515329 4.533450
## X40 3.035914 -2.286712 -3.799141 4.594701
## X42 3.061052 -2.098013 -4.037586 5.200544
## X43 3.211247 -2.398986 -2.296603 5.072078
## X45 3.082369 -2.331602 -4.280192 4.864503
## X46 2.867899 -2.208184 -3.220377 4.219926
## X47 2.823757 -2.453408 -4.106822 4.274627
## X48 2.926382 -2.155891 -3.756730 4.732992
## X49 2.683074 -2.272056 -4.249596 4.165667
## X52 2.793616 -2.565900 -4.442201 4.376271
## X53 2.903617 -2.493625 -4.781907 4.220791
## X54 2.928524 -2.164564 -3.519643 4.451081
## X55 3.091951 -2.401743 -4.575611 4.980549
## X56 2.931194 -2.351355 -4.741907 4.317312
## X57 2.921547 -2.250942 -3.769656 4.746189
## X58 3.072230 -2.174192 -3.556098 4.917397
## X59 2.960623 -2.518257 -4.756807 4.298995
## X61 2.700018 -2.176834 -4.510770 3.857866
## X62 3.043570 -2.085057 -3.453965 4.668773
## X64 2.629007 -2.561226 -3.234497 4.031624
## X65 3.171365 -2.187472 -3.631366 5.090232
## X67 3.044999 -2.259526 -4.042701 4.972347
## X68 2.946542 -2.508503 -4.686814 4.428254
## X69 2.852439 -2.238672 -2.452711 4.332192
## X71 3.059176 -2.406946 -4.103184 4.635650
## X73 3.199489 -2.233992 -2.879551 5.111247
## X74 2.759377 -2.295609 -3.880040 4.179793
## X75 2.804572 -2.389015 -4.006883 4.377888
## X78 2.781920 -2.239610 -2.840611 4.001364
## X79 3.176803 -2.051048 -2.683114 4.982438
## X80 2.890372 -2.309207 -4.097750 4.504524
## X81 3.043093 -2.205458 -4.071019 5.009980
## X82 2.763800 -2.227478 -3.331205 4.376271
## X83 3.215269 -2.241490 -2.865933 5.099260
## X84 3.269189 -2.107841 -2.805112 5.044600
## X85 2.750471 -2.330676 -4.010739 4.510643
## X86 2.918851 -2.315265 -4.105001 4.714115
## X87 3.066191 -2.359791 -3.512241 4.821893
## X88 3.202340 -2.404729 -3.994318 4.898589
## X89 3.081910 -2.433605 -3.691683 4.904441
## X90 2.723924 -2.178599 -3.120842 3.936655
## X91 3.178887 -2.410839 -4.007433 4.812472
## X92 3.125005 -2.385967 -3.711534 4.581390
## X93 2.691921 -2.609790 -4.567874 4.307339
## X94 2.906901 -2.280824 -4.205723 4.588794
## X95 2.987196 -2.264326 -3.292792 4.458901
## X96 3.136798 -2.399316 -3.357563 4.974243
## X97 2.881443 -2.258568 -4.440504 4.185067
## X99 2.552565 -2.409836 -4.319991 3.828226
## X100 2.984166 -2.327698 -3.542185 4.927712
## X102 2.597491 -2.145581 -4.524512 4.060557
## X103 3.021400 -2.524105 -5.099794 5.051957
## X104 2.965273 -2.297598 -3.818533 4.653708
## X105 2.959587 -2.303686 -3.808114 4.385955
## X106 2.744704 -1.967542 -3.536330 4.311499
## X108 2.919931 -2.467814 -4.559241 4.700742
## X109 2.979095 -2.020418 -2.445532 4.737167
## X110 3.056827 -2.435088 -4.162409 4.815168
## X111 2.832625 -2.266253 -3.529485 4.232863
## X112 3.033028 -2.309308 -3.208431 4.553792
## X113 2.978077 -2.546314 -2.597493 4.419537
## X114 3.005187 -2.187472 -3.284215 4.340417
## X115 2.761907 -2.162823 -3.810821 4.067964
## X116 3.069447 -2.326058 -3.686083 4.604270
## X117 2.757475 -2.357886 -2.694147 3.818947
## X118 2.813611 -2.152442 -3.663992 4.692258
## X119 3.131573 -2.158485 -3.224894 4.904441
## X120 2.996232 -2.476700 -4.776908 4.724620
## X121 2.381396 -2.367337 -4.180556 3.702239
## X123 3.005683 -1.933093 -2.322176 4.440088
## X125 2.796671 -2.642965 -3.420380 4.340417
## X126 2.845491 -2.432124 -4.691927 4.407598
## X127 3.206398 -2.379682 -4.444753 5.217803
## X128 2.939691 -2.498965 -3.600502 4.573218
## X129 2.796671 -2.162823 -3.173663 3.945456
## X130 3.223664 -2.287696 -3.448604 5.096856
## X131 2.587012 -2.238672 -3.706636 3.894116
## X132 2.969388 -2.214574 -4.210429 4.593226
## X133 3.069912 -2.294617 -3.936316 4.979920
## X134 2.634045 -2.357886 -4.189755 4.033502
## X135 3.086943 -2.361274 -4.253106 4.961581
## X137 2.813611 -2.252843 -4.283087 4.554542
## X138 2.733718 -2.339353 -4.185802 4.279690
## X139 2.866193 -2.148149 -3.358138 4.229421
## X140 2.594508 -2.150723 -3.352979 3.680332
## X141 2.482404 -2.380547 -5.175038 3.488165
## X142 2.893146 -2.330882 -3.943514 4.538741
## X144 2.767576 -2.444494 -4.158563 4.262772
## X146 2.684440 -2.161086 -3.048922 3.760309
## X147 2.808197 -2.215490 -3.315111 4.621105
## X148 2.932260 -2.508626 -3.020640 4.553792
## X149 2.719979 -2.305590 -3.773566 4.089126
## X150 2.885359 -2.532753 -4.140179 4.316482
## X152 3.030134 -2.363929 -2.915813 4.853256
## X154 2.571084 -2.327493 -4.712199 3.737909
## X155 2.730464 -2.366164 -3.903559 4.147887
## X156 2.887033 -2.447149 -4.159203 4.534963
## X158 2.968361 -2.597628 -3.626468 4.741335
## X159 2.544747 -2.373974 -4.626496 3.953251
## X160 2.561868 -2.588269 -5.093908 3.932732
## X161 3.004692 -2.217325 -3.727620 4.608673
## X162 2.768832 -2.442537 -3.488391 3.894116
## X163 2.898671 -2.189256 -3.782311 4.621834
## X164 3.100993 -2.290657 -3.449863 4.783310
## X165 3.092859 -2.472306 -3.671433 4.751724
## X166 2.983660 -2.474442 -4.830441 4.579906
## X168 2.933857 -2.423059 -3.700952 4.615263
## X169 3.205993 -2.254748 -3.314836 5.020540
## X171 2.516890 -2.274970 -4.439656 3.665973
## X172 2.977059 -2.402626 -4.544075 4.863182
## X175 2.718001 -2.431328 -4.588313 4.028805
## X176 2.670694 -2.392729 -4.827439 3.815845
## X177 2.893700 -2.333147 -2.429510 4.471360
## X178 3.001217 -2.319630 -3.195648 4.767568
## X179 3.100993 -2.772429 -6.095937 4.806397
## X180 2.569554 -2.437374 -5.057098 3.721768
## X182 3.279783 -2.170680 -3.045133 5.090835
## X183 3.011113 -2.343720 -3.788479 5.050733
## X184 2.702703 -2.401411 -3.289298 3.883102
## X187 2.922086 -2.454804 -4.690619 4.619646
## X188 2.844328 -2.325444 -4.743973 4.225973
## X189 2.855895 -2.295609 -4.735735 4.628388
## X190 2.766319 -2.515778 -3.811273 4.065190
## X191 3.140698 -2.230264 -1.999522 5.304618
## X192 3.063858 -2.436231 -4.022955 4.401206
## X193 2.902520 -2.666429 -5.000289 4.177151
## X194 3.290638 -2.269150 -3.258397 5.421659
## X196 2.793004 -2.533131 -4.143325 4.278004
## X197 3.104138 -2.120264 -3.396807 5.122583
## X198 3.083743 -2.607617 -2.938218 4.495315
## X200 3.006672 -2.315468 -4.137043 4.879637
## X201 2.973487 -2.344866 -4.029119 4.761381
## X202 2.961141 -2.411508 -3.661653 4.581390
## X203 3.283539 -2.170680 -2.971820 5.042143
## X204 3.167583 -2.022683 -3.471191 5.551376
## X205 2.923162 -2.306091 -3.957544 4.490698
## X206 2.814210 -2.421819 -3.953366 4.124564
## X207 2.848971 -2.217325 -4.382827 4.378696
## X208 3.008648 -2.433605 -4.197707 4.522074
## X209 3.115292 -2.300587 -3.492984 4.815841
## X210 2.558002 -2.503234 -4.393290 3.696783
## X211 3.097386 -2.397995 -3.321185 4.725319
## X212 2.941276 -2.422383 -4.058784 4.517508
## X214 3.241029 -2.296603 -2.458654 4.741335
## X216 2.829087 -2.276917 -3.370280 4.660893
## X217 2.909630 -2.368404 -3.171992 4.673060
## X218 2.861057 -2.519001 -3.467337 4.477566
## X220 3.480317 -2.474560 -3.632877 5.725074
## X222 2.631889 -2.252843 -3.766193 3.825137
## X223 2.863914 -2.243373 -4.268698 4.347796
## X224 3.008155 -2.277892 -3.740594 4.890764
## X225 2.834389 -2.471596 -4.506230 4.409194
## X226 2.600465 -2.312030 -4.248895 3.801311
## X228 2.741485 -2.480397 -3.439834 4.039126
## X229 3.176803 -2.537928 -3.774873 4.956498
## X230 3.105931 -2.218244 -3.255021 4.881604
## X232 3.298795 -2.676116 -4.106215 5.107058
## X233 3.520757 -2.553614 -4.988923 5.547844
## X234 3.325396 -2.390433 -3.881494 5.315680
## X235 2.766948 -2.469348 -4.565949 4.025039
## X236 3.056357 -2.400198 -4.280915 4.890111
## X239 3.326833 -2.498235 -3.559607 5.484477
## X240 3.670715 -2.321564 -3.580922 5.699444
## X241 2.747271 -2.362017 -4.383628 4.014653
## X242 2.710713 -2.535022 -5.312416 4.136255
## X243 2.900872 -2.344241 -2.827848 4.733688
## X244 3.168424 -2.520368 -3.624216 4.618186
## X245 3.157000 -2.275943 -3.425900 4.906389
## X246 2.988708 -2.234926 -4.278748 4.835955
## X248 2.646884 -2.434974 -2.883833 3.883102
## X249 3.227637 -2.337487 -4.570769 5.191870
## X250 2.703373 -2.289669 -4.399783 4.208655
## X251 3.159550 -2.295609 -3.242144 4.665910
## X253 2.986692 -2.242431 -2.984397 4.562778
## X254 2.837908 -2.294617 -4.197707 4.525113
## X255 2.961658 -2.268184 -4.017384 4.485299
## X256 2.836150 -2.210918 -3.619727 4.283900
## X257 3.359333 -2.379466 -3.043873 5.253674
## X258 2.848971 -2.013654 -3.081726 4.333015
## X259 3.144152 -2.199126 -2.814244 4.977398
## X263 3.096934 -2.408057 -2.816582 4.683033
## X264 2.964242 -2.545931 -4.698932 4.979289
## X266 3.437851 -2.357147 -4.214480 5.806493
## X267 2.941804 -2.334282 -3.329528 4.355967
## X268 3.083743 -2.531244 -3.727205 4.874383
## X269 2.785628 -2.361804 -3.826763 4.412381
## X270 3.015045 -2.223774 -3.039684 4.534207
## X272 2.568022 -2.319324 -4.490057 3.725005
## X273 3.044046 -2.364354 -3.003764 4.748958
## X274 2.751748 -2.403843 -4.540319 4.181552
## X275 3.197856 -2.424188 -4.449022 5.162741
## X276 2.854169 -2.099644 -4.207065 4.008967
## X277 2.650421 -2.366697 -4.710753 4.008967
## X278 2.994732 -2.416538 -4.497213 4.464360
## X279 2.881443 -2.532250 -5.244966 4.600594
## X281 3.280911 -2.282782 -3.709490 5.232668
## X282 2.640485 -2.549381 -4.239139 3.938613
## X283 2.900322 -2.266253 -3.817167 4.781263
## X284 2.932260 -2.238672 -3.357851 4.525113
## X285 2.753661 -2.548741 -3.228674 4.074426
## X286 2.912351 -2.477772 -4.827314 4.367359
## X287 3.033028 -2.452827 -2.965009 4.686585
## X288 2.574138 -2.665709 -4.308776 3.654863
## X289 2.993730 -2.523232 -2.493503 4.305672
## X290 2.938633 -2.440354 -4.272276 4.603535
## X291 2.982140 -2.435317 -2.240550 4.288943
## X292 2.949688 -2.408835 -3.856115 4.607206
## X293 2.773838 -2.297598 -3.910524 4.096440
## X294 2.859913 -2.480277 -4.199705 4.574706
## X295 2.623218 -2.336452 -4.439656 3.860909
## X296 2.585506 -2.386184 -4.809369 3.804433
## X297 2.513656 -2.462989 -4.405500 3.573135
## X298 2.898119 -2.305790 -4.600183 4.392389
## X299 2.899772 -2.721744 -4.285263 4.537986
## X300 3.139400 -2.287696 -4.238446 4.458120
## X301 2.939162 -2.162823 -3.441082 4.610871
## X302 2.990217 -2.470885 -3.390554 4.366547
## X303 3.172203 -2.225624 -3.050822 4.833951
## X304 2.923699 -2.236797 -4.671096 4.482982
## X306 3.198265 -2.593740 -3.756302 4.976136
## X307 2.761275 -2.463811 -4.845841 4.143420
## X308 2.667228 -2.658546 -5.321995 4.109184
## X309 2.542389 -2.606939 -5.587067 3.805473
## X310 2.627563 -2.482669 -5.357855 3.852784
## X312 2.753024 -2.574656 -5.112502 4.256821
## X313 2.593013 -2.431101 -3.587045 3.748604
## X314 2.372111 -2.453757 -4.312501 3.334618
## X316 2.824351 -2.463811 -5.805151 4.076268
## X318 2.937573 -2.328313 -4.098955 4.518270
## X319 2.939162 -2.305790 -2.719617 4.393191
## X320 2.833213 -2.582696 -4.529135 4.121857
## X321 2.783776 -2.243373 -3.154728 4.157683
## X322 2.978586 -2.523232 -4.285989 4.363297
## X323 2.589267 -2.176834 -3.904055 4.199074
## X324 3.068518 -2.145581 -3.716867 4.991235
## X325 2.721953 -2.444955 -4.255923 4.225110
## X326 2.850707 -2.274970 -4.654991 4.200819
## X329 3.030617 -2.146436 -3.871361 4.896635
## X331 2.741485 -2.354826 -3.428055 4.276316
## X332 2.962692 -2.345597 -3.424978 4.273783
## X333 2.988708 -2.249993 -4.506230 4.576936
## X335 2.945491 -2.487350 -4.775721 4.768255
## X336 3.044522 -2.190150 -4.019052 5.070864
## X337 2.655352 -2.357886 -3.770090 3.802352
## X339 2.863914 -2.295609 -4.234297 4.654427
## X340 3.189241 -2.235861 -3.926629 4.919334
## X341 2.805782 -2.327800 -3.489045 4.236301
## X342 2.823757 -2.467342 -3.360727 4.366547
## X343 2.705380 -2.270118 -3.975495 4.093700
## X344 3.076390 -2.323094 -3.390851 5.160983
## X345 2.737609 -2.162823 -4.512591 3.928802
## X346 2.688528 -2.314455 -3.063797 4.054986
## X347 2.939162 -2.478607 -4.513503 4.670202
## X348 2.690565 -2.421932 -4.195713 3.906069
## X349 2.774462 -2.399537 -4.762058 4.173623
## X350 2.705380 -2.155891 -3.722643 3.885109
## X351 2.837323 -2.582167 -4.963132 4.079030
## X352 2.955951 -2.085057 -2.724332 4.454212
## X353 2.859913 -2.163693 -3.159900 4.407598
## X354 3.248046 -2.278869 -3.716867 5.075113
## X355 2.644045 -2.620864 -3.385226 3.685830
## X358 2.785628 -2.436917 -4.835968 4.562030
## X359 2.740195 -2.489758 -3.540804 3.883102
## X360 2.907993 -2.293625 -4.712533 4.519792
## X361 2.894253 -2.598837 -5.361683 4.190330
## X362 3.071303 -2.455503 -3.889772 4.818532
## X363 2.935982 -2.335522 -4.106822 4.592488
## X365 2.830268 -2.533635 -4.382027 4.252561
## X366 3.080992 -2.391416 -3.960163 4.620376
## X367 3.289521 -2.312131 -3.063155 5.110649
## X369 2.847812 -2.366164 -4.499010 4.625478
## X370 3.086487 -2.241490 -3.568079 4.578422
## X371 3.148024 -2.328724 -3.239844 4.938626
## X372 2.580974 -2.530364 -4.209755 3.675924
## X374 2.853593 -2.359579 -3.965951 4.374653
## X375 2.776954 -2.488674 -4.143325 4.121857
## X376 2.776954 -2.314658 -4.010739 4.023154
## X377 3.006672 -2.399867 -2.571380 4.346158
## X378 3.339677 -2.588003 -4.420352 5.217230
## X379 2.718001 -2.492778 -3.511906 4.069812
## X380 2.935451 -2.107018 -3.090263 5.050733
## X381 2.561868 -2.089896 -4.030244 4.150563
## X382 2.703373 -2.527355 -3.961739 4.177151
## X383 3.123246 -2.668589 -3.087848 4.785356
## X384 2.861057 -2.261443 -3.295487 4.371414
## X385 2.618855 -2.481353 -3.876173 3.849729
## X386 3.148024 -2.443918 -4.050136 4.981809
## X387 2.645465 -2.512319 -3.497929 4.037253
## X390 3.144583 -2.292635 -3.194915 4.900541
## X392 2.823757 -2.264326 -3.929169 4.344519
## X394 3.103689 -2.148149 -3.289835 4.787400
## X395 2.874694 -2.273998 -4.115977 4.578422
## X398 2.859913 -2.520244 -3.359000 4.197328
## X399 2.696652 -2.558639 -4.220588 4.134460
## X400 2.848392 -2.398325 -3.930187 4.479114
## X401 3.045474 -2.095571 -3.291984 4.721123
## X402 2.389680 -2.422270 -4.419521 4.115529
## X403 2.906354 -2.610334 -3.293330 4.488386
## X405 2.704711 -2.443918 -4.768748 3.796097
## X406 2.922624 -2.298593 -3.847172 4.562030
## X407 2.698673 -2.354405 -4.385232 4.064264
## X408 3.061988 -2.583490 -3.105547 4.666626
## X409 3.028199 -2.267218 -3.585601 4.549287
## X410 2.885917 -2.443573 -3.767923 4.796917
## X411 2.866193 -2.423849 -4.610484 5.256500
## X412 2.823163 -2.228406 -4.671844 4.625478
## X413 3.076390 -2.529611 -3.621595 4.735776
## X414 3.096030 -2.463341 -3.572698 4.971715
## X415 3.394844 -2.486508 -4.249596 5.289608
## X416 3.052585 -2.325547 -3.489045 4.680900
## X417 3.077312 -2.259526 -4.089954 4.952042
## X418 3.048325 -2.189256 -3.247018 4.712008
## X419 2.498974 -2.432124 -4.371680 3.803393
## X421 2.946542 -2.459707 -3.888795 4.664478
## X423 2.773838 -2.218244 -3.909526 4.072581
## X424 2.951258 -2.401632 -3.572342 4.556042
## X425 2.950735 -2.230264 -3.971242 4.374653
## X426 3.057768 -2.511210 -5.167816 4.800985
## X428 3.090133 -2.430305 -3.840633 5.002499
## X431 3.114848 -2.307899 -2.778526 4.706382
## X432 2.872434 -2.249993 -3.412764 4.353519
## X433 2.972464 -2.177716 -3.606378 4.523594
## X434 3.089678 -2.284745 -3.197114 4.932212
## X436 2.976549 -2.244316 -4.022396 4.923849
## X437 2.972464 -2.392948 -4.296216 4.470584
## X438 2.771338 -2.470057 -4.346659 4.242305
## X439 2.975530 -2.443688 -4.514416 4.737167
## X440 2.751110 -2.529988 -4.684430 4.039126
## X441 2.844909 -2.417435 -3.070887 4.656584
## X443 2.759377 -2.428489 -4.460204 3.862936
## X444 2.907993 -2.508134 -4.319240 4.385955
## X445 2.824351 -2.413852 -4.160484 4.279690
## X446 3.214466 -2.273026 -3.899600 4.895332
## X447 3.333275 -2.302885 -3.904551 5.378924
## X448 2.871302 -2.388252 -4.447312 4.339596
## X449 2.962175 -2.478368 -3.888306 4.763445
## X450 3.021400 -2.334695 -3.875209 5.004371
## X451 3.069912 -2.716133 -2.802965 4.748958
## X452 3.218876 -2.271086 -3.948168 4.934138
## X453 3.340385 -2.472543 -3.653898 5.343130
## X455 2.841998 -2.455387 -4.763111 4.290621
## X456 3.424914 -2.381087 -4.385232 5.539246
## X457 3.377246 -2.369045 -3.722229 5.393426
## X459 3.224062 -2.480636 -4.713424 4.992489
## X460 3.339322 -2.527731 -4.366153 5.290165
## X461 3.301377 -2.312837 -3.808114 5.150996
## X462 3.268428 -2.221927 -2.923598 4.960311
## X464 2.910174 -2.464163 -4.073954 4.442448
## X465 2.902520 -2.594811 -4.204383 4.432205
## X466 3.002211 -2.490844 -2.709501 4.556042
## X467 3.032064 -2.444725 -3.448604 4.553042
## X468 2.895912 -2.487590 -4.376442 4.489157
## X469 3.149740 -2.376339 -2.655695 4.796239
## X471 2.917230 -2.413964 -3.917538 4.565019
## X472 3.337192 -2.435888 -4.163695 5.081777
## X473 2.703373 -2.513553 -4.034191 3.934694
## X475 2.748552 -2.295609 -3.476029 4.042868
## X476 2.755570 -2.403511 -4.019608 4.042868
## X477 3.021887 -2.415642 -3.414891 4.684455
## X480 2.970927 -2.276917 -2.905892 4.364923
## X481 2.892037 -2.398325 -4.094745 4.727414
## X482 2.956991 -2.526854 -4.526359 4.624021
## X483 2.643334 -2.233992 -4.232228 3.944480
## X485 2.423031 -2.260484 -4.006334 3.500171
## X487 2.824351 -2.448652 -4.273710 4.551541
## X488 2.934920 -2.217325 -3.563834 4.897287
## X493 3.023347 -2.301586 -3.611918 4.597650
## X494 2.551786 -2.607481 -4.602175 3.744333
## X495 3.022374 -2.612513 -3.886355 4.768255
## X496 3.006178 -2.344762 -4.289630 4.769627
## X497 2.899772 -2.229335 -3.688080 4.290621
## X498 2.851284 -2.415978 -4.289630 4.467474
## X500 3.055886 -2.221005 -3.578770 4.921270
## X502 3.198265 -2.152442 -3.255540 5.058072
## X504 2.987196 -2.370650 -3.394420 4.430625
## X505 2.554899 -1.811554 -3.091803 3.746469
## X506 2.575661 -2.075450 -3.018387 3.916970
## X508 2.840247 -2.125276 -3.751606 4.169207
## X510 3.175968 -2.134532 -3.007805 5.257064
## X511 2.687167 -2.513430 -3.240099 3.873042
## X512 2.687847 -2.468404 -4.425352 3.871024
## X513 3.021400 -2.201835 -3.787595 4.849274
## X515 2.948116 -2.384338 -4.116590 4.740641
## X516 2.923699 -2.254748 -4.109864 4.363297
## X517 3.024320 -2.236797 -3.975495 4.607940
## X519 2.902520 -2.105375 -3.759731 4.469807
## X520 2.815409 -2.184802 -4.159844 4.255969
## X521 2.631889 -1.987045 -3.673006 3.897110
## X522 3.072693 -2.273026 -3.438276 4.660893
## X524 2.927453 -2.311021 -3.793796 4.565765
## X525 2.752386 -2.354721 -3.891240 4.360042
## X526 2.572612 -2.267218 -4.038721 3.957138
## X527 2.931194 -2.230264 -4.238446 4.530422
## X528 2.507157 -2.407612 -4.691927 4.035378
## X529 2.577942 -2.081043 -3.427439 3.636967
## X530 2.598235 -2.207275 -4.462803 3.680332
## X531 2.865624 -2.232127 -4.009085 4.735080
## X532 2.996732 -2.286712 -4.058784 4.792163
## X533 2.793004 -2.377632 -4.929793 4.120954
## X537 3.115735 -2.265289 -3.656219 5.138013
## X538 3.196221 -2.090705 -3.353837 5.011847
## X539 3.238286 -2.513553 -4.636454 4.931570
## X540 3.236323 -2.445532 -2.740005 4.993116
## X541 2.670002 -2.304186 -3.191261 4.073504
## X543 3.235536 -2.491931 -4.446458 5.018060
## X544 3.334345 -2.445186 -4.288901 5.304063
## X546 3.145445 -2.380979 -3.863709 4.811124
## X547 2.794228 -2.360850 -4.927168 4.258523
## X548 2.808197 -2.421707 -3.478943 4.281375
## X551 3.067122 -2.599510 -4.506230 4.500691
## X552 3.110845 -2.346955 -3.489701 4.754487
## X553 3.382015 -2.491810 -4.395720 5.238363
## X554 3.088311 -2.381628 -3.998671 4.522074
## X555 3.364533 -2.510471 -3.838308 5.223531
## X556 3.318178 -2.404618 -3.598673 5.175599
## X557 2.975019 -2.299590 -3.806762 4.351068
## X558 3.327910 -2.510471 -4.488276 5.136237
## X559 3.121483 -2.468286 -3.070671 4.685165
## X560 3.175133 -2.379358 -3.512576 5.303509
## X562 3.379974 -2.597090 -4.724179 5.365966
## X564 3.222469 -2.208184 -3.144232 4.832614
## X565 3.108614 -2.198225 -3.543568 4.622564
## X567 3.335058 -2.470412 -3.288494 5.129122
## X568 3.378611 -2.138767 -2.787418 5.425895
## X570 2.339881 -2.133687 -3.015119 3.845649
## X571 2.877512 -2.468168 -4.336671 4.393994
## X572 3.056357 -2.210918 -3.217377 4.558289
## X573 3.014554 -1.948413 -2.595883 4.629842
## X575 2.753661 -2.057289 -3.397703 4.421124
## X576 2.994732 -2.357781 -4.281638 4.712710
## X577 3.036394 -2.129472 -3.496938 4.746189
## X578 3.082827 -2.061209 -3.351836 4.919334
## X579 3.179719 -2.131999 -2.628731 5.491708
## X580 3.145875 -2.500305 -4.681080 5.114832
## X583 3.175968 -2.476819 -3.465416 4.712710
## X584 3.118392 -2.179483 -2.824135 5.000625
## X585 3.315639 -2.172434 -3.160607 5.301845
## X586 3.002211 -2.315974 -4.455028 4.928999
## X587 3.029167 -2.145581 -3.688480 4.967287
## X588 3.097837 -2.319630 -3.967007 4.928999
## X589 2.664447 -2.324933 -4.226734 4.034440
## X590 2.754297 -2.230264 -3.964369 4.146994
## X591 2.520917 -2.278869 -4.246098 3.668189
## X592 2.657458 -2.232127 -2.932194 4.017490
## X593 3.137232 -2.361486 -4.374852 5.214935
## X595 2.797281 -2.131999 -3.270432 4.226835
## X601 2.928524 -2.199126 -3.377286 4.744803
## X602 3.177220 -2.122767 -3.479591 5.005619
## X603 3.276012 -2.364354 -3.405808 4.930285
## X604 2.883683 -2.263364 -3.551555 4.684455
## X605 3.072230 -2.342366 -3.689280 4.806397
## X606 3.078233 -2.320444 -3.508226 4.895332
## X607 2.913437 -2.409836 -5.318724 4.345339
## X608 3.226844 -2.365844 -4.515329 4.533450
## X609 3.035914 -2.286712 -3.799141 4.594701
## X610 3.071767 -2.505681 -4.508043 4.888151
## X611 3.061052 -2.098013 -4.037586 5.200544
## X613 3.009635 -2.262403 -3.841099 4.736472
## X614 3.082369 -2.331602 -4.280192 4.864503
## X615 2.867899 -2.208184 -3.220377 4.219926
## X618 2.683074 -2.272056 -4.249596 4.165667
## X619 3.104587 -2.435888 -4.281638 4.988725
## X620 3.072693 -2.449115 -4.629668 4.572474
## X621 2.793616 -2.565900 -4.442201 4.376271
## X622 2.903617 -2.493625 -4.781907 4.220791
## X623 2.928524 -2.164564 -3.519643 4.451081
## X624 3.091951 -2.401743 -4.575611 4.980549
## X625 2.931194 -2.351355 -4.741907 4.317312
## X627 3.072230 -2.174192 -3.556098 4.917397
## X628 2.960623 -2.518257 -4.756807 4.298995
## X629 2.467252 -2.327698 -4.551629 3.639212
## X630 2.700018 -2.176834 -4.510770 3.857866
## X631 3.043570 -2.085057 -3.453965 4.668773
## X632 3.097837 -2.254748 -2.651292 4.839292
## X633 2.629007 -2.561226 -3.234497 4.031624
## X635 3.175551 -2.143873 -3.767923 5.085404
## X636 3.044999 -2.259526 -4.042701 4.972347
## X637 2.946542 -2.508503 -4.686814 4.428254
## X638 2.852439 -2.238672 -2.452711 4.332192
## X640 3.059176 -2.406946 -4.103184 4.635650
## X642 3.199489 -2.233992 -2.879551 5.111247
## X643 2.759377 -2.295609 -3.880040 4.179793
## X644 2.804572 -2.389015 -4.006883 4.377888
## X645 2.978077 -2.389451 -3.815350 4.484527
## X646 2.392426 -2.047168 -3.561718 3.284809
## X647 2.781920 -2.239610 -2.840611 4.001364
## X648 3.176803 -2.051048 -2.683114 4.982438
## X649 2.890372 -2.309207 -4.097750 4.504524
## X651 2.763800 -2.227478 -3.331205 4.376271
## X652 3.215269 -2.241490 -2.865933 5.099260
## X653 3.269189 -2.107841 -2.805112 5.044600
## X654 2.750471 -2.330676 -4.010739 4.510643
## X655 2.918851 -2.315265 -4.105001 4.714115
## X656 3.066191 -2.359791 -3.512241 4.821893
## X657 3.202340 -2.404729 -3.994318 4.898589
## X658 3.081910 -2.433605 -3.691683 4.904441
## X659 2.723924 -2.178599 -3.120842 3.936655
## X660 3.178887 -2.410839 -4.007433 4.812472
## X661 3.125005 -2.385967 -3.711534 4.581390
## X662 2.691921 -2.609790 -4.567874 4.307339
## X663 2.906901 -2.280824 -4.205723 4.588794
## X664 2.987196 -2.264326 -3.292792 4.458901
## X667 2.992728 -2.278869 -4.224681 4.614531
## X668 2.552565 -2.409836 -4.319991 3.828226
## X669 2.984166 -2.327698 -3.542185 4.927712
## X670 3.218076 -2.355142 -4.207737 5.196499
## X671 2.597491 -2.145581 -4.524512 4.060557
## X672 3.021400 -2.524105 -5.099794 5.051957
## X674 2.959587 -2.303686 -3.808114 4.385955
## X675 2.744704 -1.967542 -3.536330 4.311499
## X677 2.919931 -2.467814 -4.559241 4.700742
## X678 2.979095 -2.020418 -2.445532 4.737167
## X679 3.056827 -2.435088 -4.162409 4.815168
## X680 2.832625 -2.266253 -3.529485 4.232863
## X681 3.033028 -2.309308 -3.208431 4.553792
## X682 2.978077 -2.546314 -2.597493 4.419537
## X683 3.005187 -2.187472 -3.284215 4.340417
## X685 3.069447 -2.326058 -3.686083 4.604270
## X686 2.757475 -2.357886 -2.694147 3.818947
## X687 2.813611 -2.152442 -3.663992 4.692258
## X689 2.996232 -2.476700 -4.776908 4.724620
## X690 2.381396 -2.367337 -4.180556 3.702239
## X692 3.005683 -1.933093 -2.322176 4.440088
## X693 2.387845 -2.206366 -4.361440 3.703328
## X694 2.796671 -2.642965 -3.420380 4.340417
## X695 2.845491 -2.432124 -4.691927 4.407598
## X697 2.939691 -2.498965 -3.600502 4.573218
## X698 2.796671 -2.162823 -3.173663 3.945456
## X699 3.223664 -2.287696 -3.448604 5.096856
## X700 2.587012 -2.238672 -3.706636 3.894116
## X701 2.969388 -2.214574 -4.210429 4.593226
## X702 3.069912 -2.294617 -3.936316 4.979920
## X703 2.634045 -2.357886 -4.189755 4.033502
## X704 3.086943 -2.361274 -4.253106 4.961581
## X705 3.112181 -2.401853 -4.421183 5.084195
## X706 2.813611 -2.252843 -4.283087 4.554542
## X707 2.733718 -2.339353 -4.185802 4.279690
## X708 2.866193 -2.148149 -3.358138 4.229421
## X711 2.893146 -2.330882 -3.943514 4.538741
## X712 2.851284 -2.214574 -4.054163 4.648665
## X714 2.706048 -2.551944 -4.027995 4.167437
## X715 2.684440 -2.161086 -3.048922 3.760309
## X716 2.808197 -2.215490 -3.315111 4.621105
## X717 2.932260 -2.508626 -3.020640 4.553792
## X718 2.719979 -2.305590 -3.773566 4.089126
## X719 2.885359 -2.532753 -4.140179 4.316482
## X720 3.033991 -2.175952 -4.472389 4.449513
## X721 3.030134 -2.363929 -2.915813 4.853256
## X722 2.730464 -2.233059 -2.344866 4.055916
## X723 2.571084 -2.327493 -4.712199 3.737909
## X724 2.730464 -2.366164 -3.903559 4.147887
## X725 2.887033 -2.447149 -4.159203 4.534963
## X726 3.032064 -2.193731 -3.004975 4.526631
## X728 2.544747 -2.373974 -4.626496 3.953251
## X729 2.561868 -2.588269 -5.093908 3.932732
## X731 2.768832 -2.442537 -3.488391 3.894116
## X733 3.100993 -2.290657 -3.449863 4.783310
## X734 3.092859 -2.472306 -3.671433 4.751724
## X735 2.983660 -2.474442 -4.830441 4.579906
## X736 2.273156 -2.344032 -4.623742 3.221497
## X737 2.933857 -2.423059 -3.700952 4.615263
## X738 3.205993 -2.254748 -3.314836 5.020540
## X739 2.830268 -2.317191 -4.290359 4.360856
## X742 2.475698 -2.073857 -3.763172 3.815845
## X743 2.688528 -2.296603 -3.853283 3.792962
## X744 2.718001 -2.431328 -4.588313 4.028805
## X745 2.670694 -2.392729 -4.827439 3.815845
## X746 2.893700 -2.333147 -2.429510 4.471360
## X747 3.001217 -2.319630 -3.195648 4.767568
## X748 3.100993 -2.772429 -6.095937 4.806397
## X749 2.569554 -2.437374 -5.057098 3.721768
## X750 3.085116 -2.212744 -3.674188 5.052569
## X751 3.279783 -2.170680 -3.045133 5.090835
## X752 3.011113 -2.343720 -3.788479 5.050733
## X753 2.702703 -2.401411 -3.289298 3.883102
## X754 3.109507 -2.401632 -4.272276 4.738557
## X755 2.715357 -2.378710 -4.422849 4.207786
## X756 2.922086 -2.454804 -4.690619 4.619646
## X757 2.844328 -2.325444 -4.743973 4.225973
## X758 2.855895 -2.295609 -4.735735 4.628388
## X759 2.766319 -2.515778 -3.811273 4.065190
## X760 3.140698 -2.230264 -1.999522 5.304618
## X761 3.063858 -2.436231 -4.022955 4.401206
## X762 2.902520 -2.666429 -5.000289 4.177151
## X764 3.144583 -2.259526 -2.971625 4.721123
## X765 2.793004 -2.533131 -4.143325 4.278004
## X766 3.104138 -2.120264 -3.396807 5.122583
## X767 3.083743 -2.607617 -2.938218 4.495315
## X768 3.113071 -2.462402 -3.297378 5.003747
## X770 2.973487 -2.344866 -4.029119 4.761381
## X771 2.961141 -2.411508 -3.661653 4.581390
## X772 3.283539 -2.170680 -2.971820 5.042143
## X773 3.167583 -2.022683 -3.471191 5.551376
## X774 2.923162 -2.306091 -3.957544 4.490698
## X775 2.814210 -2.421819 -3.953366 4.124564
## X776 2.848971 -2.217325 -4.382827 4.378696
## X777 3.008648 -2.433605 -4.197707 4.522074
## X778 3.115292 -2.300587 -3.492984 4.815841
## X779 2.558002 -2.503234 -4.393290 3.696783
## X780 3.097386 -2.397995 -3.321185 4.725319
## X781 2.941276 -2.422383 -4.058784 4.517508
## X782 2.916148 -2.169804 -3.585601 3.959079
## X783 3.241029 -2.296603 -2.458654 4.741335
## X784 3.170106 -2.357781 -3.294138 5.172099
## X785 2.829087 -2.276917 -3.370280 4.660893
## X786 2.909630 -2.368404 -3.171992 4.673060
## X787 2.861057 -2.519001 -3.467337 4.477566
## X789 3.480317 -2.474560 -3.632877 5.725074
## X794 2.834389 -2.471596 -4.506230 4.409194
## X795 2.600465 -2.312030 -4.248895 3.801311
## X796 2.738256 -2.250942 -4.820718 4.084542
## X797 2.741485 -2.480397 -3.439834 4.039126
## X798 3.176803 -2.537928 -3.774873 4.956498
## X799 3.105931 -2.218244 -3.255021 4.881604
## X800 2.948641 -2.170680 -3.908031 4.509879
## X802 3.520757 -2.553614 -4.988923 5.547844
## X804 2.766948 -2.469348 -4.565949 4.025039
## X805 3.056357 -2.400198 -4.280915 4.890111
## X807 3.066191 -2.482310 -3.463179 4.605738
## X808 3.326833 -2.498235 -3.559607 5.484477
## X809 3.670715 -2.321564 -3.580922 5.699444
## X811 2.710713 -2.535022 -5.312416 4.136255
## X814 3.157000 -2.275943 -3.425900 4.906389
## X815 2.988708 -2.234926 -4.278748 4.835955
## X816 2.858193 -2.629008 -4.154732 4.723921
## X817 2.646884 -2.434974 -2.883833 3.883102
## X818 3.227637 -2.337487 -4.570769 5.191870
## X819 2.703373 -2.289669 -4.399783 4.208655
## X820 3.159550 -2.295609 -3.242144 4.665910
## X822 2.986692 -2.242431 -2.984397 4.562778
## X823 2.837908 -2.294617 -4.197707 4.525113
## X824 2.961658 -2.268184 -4.017384 4.485299
## X825 2.836150 -2.210918 -3.619727 4.283900
## X826 3.359333 -2.379466 -3.043873 5.253674
## X827 2.848971 -2.013654 -3.081726 4.333015
## X828 3.144152 -2.199126 -2.814244 4.977398
## X829 3.513335 -2.241490 -3.666727 5.913428
## X830 3.298057 -2.302585 -4.149012 5.412105
## X831 3.138100 -2.446225 -4.504420 4.966653
## X832 3.096934 -2.408057 -2.816582 4.683033
## X833 2.964242 -2.545931 -4.698932 4.979289
## X834 3.094219 -2.330367 -4.417861 4.827259
## X835 3.437851 -2.357147 -4.214480 5.806493
## X837 3.083743 -2.531244 -3.727205 4.874383
## X838 2.785628 -2.361804 -3.826763 4.412381
## X839 3.015045 -2.223774 -3.039684 4.534207
## X840 2.822569 -2.744351 -5.596723 4.161235
## X841 2.568022 -2.319324 -4.490057 3.725005
## X844 3.197856 -2.424188 -4.449022 5.162741
## X845 2.854169 -2.099644 -4.207065 4.008967
## X846 2.650421 -2.366697 -4.710753 4.008967
## X847 2.994732 -2.416538 -4.497213 4.464360
## X849 2.719979 -2.352196 -4.172739 4.255969
## X850 3.280911 -2.282782 -3.709490 5.232668
## X851 2.640485 -2.549381 -4.239139 3.938613
## X852 2.900322 -2.266253 -3.817167 4.781263
## X854 2.753661 -2.548741 -3.228674 4.074426
## X855 2.912351 -2.477772 -4.827314 4.367359
## X856 3.033028 -2.452827 -2.965009 4.686585
## X857 2.574138 -2.665709 -4.308776 3.654863
## X858 2.993730 -2.523232 -2.493503 4.305672
## X859 2.938633 -2.440354 -4.272276 4.603535
## X860 2.982140 -2.435317 -2.240550 4.288943
## X861 2.949688 -2.408835 -3.856115 4.607206
## X863 2.859913 -2.480277 -4.199705 4.574706
## X864 2.623218 -2.336452 -4.439656 3.860909
## X866 2.513656 -2.462989 -4.405500 3.573135
## X867 2.898119 -2.305790 -4.600183 4.392389
## X868 2.899772 -2.721744 -4.285263 4.537986
## X869 3.139400 -2.287696 -4.238446 4.458120
## X870 2.939162 -2.162823 -3.441082 4.610871
## X871 2.990217 -2.470885 -3.390554 4.366547
## X872 3.172203 -2.225624 -3.050822 4.833951
## X873 2.923699 -2.236797 -4.671096 4.482982
## X874 2.899221 -2.424414 -3.629856 4.244873
## X875 3.198265 -2.593740 -3.756302 4.976136
## X876 2.761275 -2.463811 -4.845841 4.143420
## X877 2.667228 -2.658546 -5.321995 4.109184
## X878 2.542389 -2.606939 -5.587067 3.805473
## X880 2.950212 -2.428829 -4.698383 4.633474
## X881 2.753024 -2.574656 -5.112502 4.256821
## X882 2.593013 -2.431101 -3.587045 3.748604
## X883 2.372111 -2.453757 -4.312501 3.334618
## X884 2.923162 -2.231195 -4.266557 4.314822
## X885 2.824351 -2.463811 -5.805151 4.076268
## X886 2.644755 -2.559544 -5.155603 3.756060
## X887 2.937573 -2.328313 -4.098955 4.518270
## X888 2.939162 -2.305790 -2.719617 4.393191
## X889 2.833213 -2.582696 -4.529135 4.121857
## X892 2.589267 -2.176834 -3.904055 4.199074
## X893 3.068518 -2.145581 -3.716867 4.991235
## X894 2.721953 -2.444955 -4.255923 4.225110
## X895 2.850707 -2.274970 -4.654991 4.200819
## X896 2.555676 -2.374189 -4.481184 3.913012
## X898 3.030617 -2.146436 -3.871361 4.896635
## X899 3.085573 -2.149864 -3.281816 4.534207
## X900 2.741485 -2.354826 -3.428055 4.276316
## X901 2.962692 -2.345597 -3.424978 4.273783
## X903 2.693275 -2.488192 -4.944286 4.283059
## X907 3.064792 -2.395139 -3.244963 5.143922
## X908 2.863914 -2.295609 -4.234297 4.654427
## X909 3.189241 -2.235861 -3.926629 4.919334
## X910 2.805782 -2.327800 -3.489045 4.236301
## X911 2.823757 -2.467342 -3.360727 4.366547
## X912 2.705380 -2.270118 -3.975495 4.093700
## X913 3.076390 -2.323094 -3.390851 5.160983
## X914 2.737609 -2.162823 -4.512591 3.928802
## X915 2.688528 -2.314455 -3.063797 4.054986
## X917 2.690565 -2.421932 -4.195713 3.906069
## X918 2.774462 -2.399537 -4.762058 4.173623
## X921 2.955951 -2.085057 -2.724332 4.454212
## X922 2.859913 -2.163693 -3.159900 4.407598
## X923 3.248046 -2.278869 -3.716867 5.075113
## X924 2.644045 -2.620864 -3.385226 3.685830
## X925 2.948116 -2.434974 -3.161787 4.313992
## X926 2.922624 -2.223774 -3.236022 4.506820
## X927 2.785628 -2.436917 -4.835968 4.562030
## X928 2.740195 -2.489758 -3.540804 3.883102
## X929 2.907993 -2.293625 -4.712533 4.519792
## X931 3.071303 -2.455503 -3.889772 4.818532
## X932 2.935982 -2.335522 -4.106822 4.592488
## X933 2.906354 -2.334489 -4.014610 4.552291
## X934 2.830268 -2.533635 -4.382027 4.252561
## X935 3.080992 -2.391416 -3.960163 4.620376
## X936 3.289521 -2.312131 -3.063155 5.110649
## X937 2.891482 -2.382603 -4.277306 4.444020
## X938 2.847812 -2.366164 -4.499010 4.625478
## X939 3.086487 -2.241490 -3.568079 4.578422
## X941 2.580974 -2.530364 -4.209755 3.675924
## X942 2.714695 -2.301586 -3.621221 4.264469
## X943 2.853593 -2.359579 -3.965951 4.374653
## X944 2.776954 -2.488674 -4.143325 4.121857
## X946 3.006672 -2.399867 -2.571380 4.346158
## X949 2.935451 -2.107018 -3.090263 5.050733
## X950 2.561868 -2.089896 -4.030244 4.150563
## X952 3.123246 -2.668589 -3.087848 4.785356
## X953 2.861057 -2.261443 -3.295487 4.371414
## X954 2.618855 -2.481353 -3.876173 3.849729
## X955 3.148024 -2.443918 -4.050136 4.981809
## X956 2.645465 -2.512319 -3.497929 4.037253
## X957 2.782539 -2.655553 -4.258041 4.100089
## X958 2.740840 -2.481114 -2.707700 4.003267
## X959 3.144583 -2.292635 -3.194915 4.900541
## X960 2.503074 -2.302985 -4.294016 3.667081
## X962 2.994231 -2.154165 -3.530851 4.832614
## X963 3.103689 -2.148149 -3.289835 4.787400
## X964 2.874694 -2.273998 -4.115977 4.578422
## X965 2.843746 -2.520119 -4.363794 4.544020
## X967 2.859913 -2.520244 -3.359000 4.197328
## X968 2.696652 -2.558639 -4.220588 4.134460
## X970 3.045474 -2.095571 -3.291984 4.721123
## X971 2.389680 -2.422270 -4.419521 4.115529
## X972 2.906354 -2.610334 -3.293330 4.488386
## X973 2.783158 -2.314759 -4.354411 4.362484
## X974 2.704711 -2.443918 -4.768748 3.796097
## X975 2.922624 -2.298593 -3.847172 4.562030
## X976 2.698673 -2.354405 -4.385232 4.064264
## X977 3.061988 -2.583490 -3.105547 4.666626
## X978 3.028199 -2.267218 -3.585601 4.549287
## X980 2.866193 -2.423849 -4.610484 5.256500
## X981 2.823163 -2.228406 -4.671844 4.625478
## X982 3.076390 -2.529611 -3.621595 4.735776
## X983 3.096030 -2.463341 -3.572698 4.971715
## X984 3.394844 -2.486508 -4.249596 5.289608
## X986 3.077312 -2.259526 -4.089954 4.952042
## X987 3.048325 -2.189256 -3.247018 4.712008
## X988 2.498974 -2.432124 -4.371680 3.803393
## X989 3.063858 -2.284745 -4.662587 4.799630
## X990 2.946542 -2.459707 -3.888795 4.664478
## X992 2.773838 -2.218244 -3.909526 4.072581
## X993 2.951258 -2.401632 -3.572342 4.556042
## X994 2.950735 -2.230264 -3.971242 4.374653
## X995 3.057768 -2.511210 -5.167816 4.800985
## X997 3.090133 -2.430305 -3.840633 5.002499
## X1000 3.114848 -2.307899 -2.778526 4.706382
## X1001 2.872434 -2.249993 -3.412764 4.353519
## X1002 2.972464 -2.177716 -3.606378 4.523594
## X1003 3.089678 -2.284745 -3.197114 4.932212
## X1004 2.829678 -2.416426 -4.095345 4.151454
## X1008 2.975530 -2.443688 -4.514416 4.737167
## X1010 2.844909 -2.417435 -3.070887 4.656584
## X1011 3.235536 -2.485187 -3.144696 5.207462
## X1012 2.759377 -2.428489 -4.460204 3.862936
## X1013 2.907993 -2.508134 -4.319240 4.385955
## X1014 2.824351 -2.413852 -4.160484 4.279690
## X1016 3.333275 -2.302885 -3.904551 5.378924
## X1017 2.871302 -2.388252 -4.447312 4.339596
## X1018 2.962175 -2.478368 -3.888306 4.763445
## X1019 3.021400 -2.334695 -3.875209 5.004371
## X1020 3.069912 -2.716133 -2.802965 4.748958
## X1022 3.340385 -2.472543 -3.653898 5.343130
## X1023 2.637628 -2.208184 -4.381227 3.804433
## X1024 2.841998 -2.455387 -4.763111 4.290621
## X1025 3.424914 -2.381087 -4.385232 5.539246
## X1026 3.377246 -2.369045 -3.722229 5.393426
## X1028 3.224062 -2.480636 -4.713424 4.992489
## X1029 3.339322 -2.527731 -4.366153 5.290165
## X1030 3.301377 -2.312837 -3.808114 5.150996
## X1031 3.268428 -2.221927 -2.923598 4.960311
## X1032 3.295466 -2.659975 -4.086972 4.998750
## X1034 2.902520 -2.594811 -4.204383 4.432205
## X1035 3.002211 -2.490844 -2.709501 4.556042
## X1036 3.032064 -2.444725 -3.448604 4.553042
## X1037 2.895912 -2.487590 -4.376442 4.489157
## X1038 3.149740 -2.376339 -2.655695 4.796239
## X1039 2.900322 -2.141317 -3.440146 4.548535
## X1040 2.917230 -2.413964 -3.917538 4.565019
## X1041 3.337192 -2.435888 -4.163695 5.081777
## X1043 3.400197 -2.564080 -4.798391 5.352397
## X1045 2.755570 -2.403511 -4.019608 4.042868
## X1046 3.021887 -2.415642 -3.414891 4.684455
## X1047 2.810607 -2.684138 -4.331334 4.261073
## X1048 2.680336 -2.257612 -3.856588 4.269554
## X1050 2.892037 -2.398325 -4.094745 4.727414
## X1051 2.956991 -2.526854 -4.526359 4.624021
## X1052 2.643334 -2.233992 -4.232228 3.944480
## X1053 2.870169 -2.307598 -4.805330 4.403605
## X1054 2.423031 -2.260484 -4.006334 3.500171
## X1055 2.797891 -2.352406 -2.594141 4.194706
## X1056 2.824351 -2.448652 -4.273710 4.551541
## X1057 2.934920 -2.217325 -3.563834 4.897287
## X1058 2.783158 -2.182139 -4.348979 4.243161
## X1061 2.582487 -2.546186 -4.794637 3.954223
## X1062 3.023347 -2.301586 -3.611918 4.597650
## X1064 3.022374 -2.612513 -3.886355 4.768255
## X1065 3.006178 -2.344762 -4.289630 4.769627
## X1066 2.899772 -2.229335 -3.688080 4.290621
## X1067 2.851284 -2.415978 -4.289630 4.467474
## X1068 2.863343 -2.290657 -3.437654 4.351068
## X1069 3.055886 -2.221005 -3.578770 4.921270
## X1070 2.817801 -2.314354 -4.006883 4.141631
## X1071 3.198265 -2.152442 -3.255540 5.058072
## X1072 2.792391 -2.155891 -4.156007 4.226835
## X1073 2.987196 -2.370650 -3.394420 4.430625
## X1074 2.554899 -1.811554 -3.091803 3.746469
## X1076 2.997730 -2.210918 -3.817622 4.454212
## X1077 2.840247 -2.125276 -3.751606 4.169207
## X1078 2.753661 -2.361592 -4.420352 3.889116
## X1079 3.175968 -2.134532 -3.007805 5.257064
## X1080 2.687167 -2.513430 -3.240099 3.873042
## X1081 2.687847 -2.468404 -4.425352 3.871024
## X1082 3.021400 -2.201835 -3.787595 4.849274
## X1083 2.614472 -2.319528 -4.010739 3.836443
## X1084 2.948116 -2.384338 -4.116590 4.740641
## X1085 2.923699 -2.254748 -4.109864 4.363297
## X1086 3.024320 -2.236797 -3.975495 4.607940
## X1088 2.902520 -2.105375 -3.759731 4.469807
## X1090 2.631889 -1.987045 -3.673006 3.897110
## X1091 3.072693 -2.273026 -3.438276 4.660893
## X1092 2.987196 -2.463811 -5.132803 4.624750
## X1093 2.927453 -2.311021 -3.793796 4.565765
## X1094 2.752386 -2.354721 -3.891240 4.360042
## X1096 2.931194 -2.230264 -4.238446 4.530422
## X1097 2.507157 -2.407612 -4.691927 4.035378
## X1100 2.865624 -2.232127 -4.009085 4.735080
## X1101 2.996732 -2.286712 -4.058784 4.792163
## X1103 3.028683 -2.390761 -3.477323 4.676626
## X1104 2.869035 -2.334385 -3.387886 4.630569
## X1105 3.037833 -2.257612 -3.643524 4.554542
## X1107 3.196221 -2.090705 -3.353837 5.011847
## X1110 2.670002 -2.304186 -3.191261 4.073504
## X1111 3.218476 -2.426223 -3.067658 4.983068
## X1112 3.235536 -2.491931 -4.446458 5.018060
## X1115 3.145445 -2.380979 -3.863709 4.811124
## X1116 2.794228 -2.360850 -4.927168 4.258523
## X1117 2.808197 -2.421707 -3.478943 4.281375
## X1118 2.962175 -2.466163 -4.489167 4.562778
## X1120 3.067122 -2.599510 -4.506230 4.500691
## X1121 3.110845 -2.346955 -3.489701 4.754487
## X1122 3.382015 -2.491810 -4.395720 5.238363
## X1123 3.088311 -2.381628 -3.998671 4.522074
## X1124 3.364533 -2.510471 -3.838308 5.223531
## X1125 3.318178 -2.404618 -3.598673 5.175599
## X1126 2.975019 -2.299590 -3.806762 4.351068
## X1127 3.327910 -2.510471 -4.488276 5.136237
## X1128 3.121483 -2.468286 -3.070671 4.685165
## X1129 3.175133 -2.379358 -3.512576 5.303509
## X1130 3.301377 -2.309710 -3.620100 5.072078
## X1131 3.379974 -2.597090 -4.724179 5.365966
## X1132 3.421653 -2.255702 -3.027429 5.598355
## X1134 3.108614 -2.198225 -3.543568 4.622564
## X1135 3.341093 -2.324831 -3.720164 5.363258
## X1136 3.335058 -2.470412 -3.288494 5.129122
## X1137 3.378611 -2.138767 -2.787418 5.425895
## X1138 3.200304 -2.944469 -5.368740 4.895984
## smoothness_worst symmetry_worst
## X1 -1.401837 -0.9485186
## X2 -1.552206 -1.8138504
## X3 -1.468032 -1.3273311
## X6 -1.343543 -1.1682237
## X7 -1.468808 -1.6137366
## X8 -1.390483 -1.5377457
## X9 -1.373392 -1.0226796
## X10 -1.323124 -1.0268307
## X11 -1.577215 -1.6835473
## X12 -1.486854 -1.2478490
## X13 -1.644401 -1.5488672
## X15 -1.391541 -1.3351867
## X16 -1.382068 -1.0794752
## X17 -1.460319 -1.6339618
## X18 -1.344209 -1.2853171
## X20 -1.469584 -1.6655621
## X21 -1.520913 -1.5444060
## X22 -1.515956 -2.0406102
## X23 -1.489239 -0.9275957
## X25 -1.338889 -1.3273311
## X26 -1.429814 -1.1365073
## X27 -1.437240 -1.0628195
## X28 -1.510212 -2.1336080
## X30 -1.544904 -1.8096966
## X32 -1.396495 -0.8985507
## X35 -1.467258 -1.0606668
## X38 -1.677854 -2.4867416
## X39 -1.694115 -3.0556014
## X40 -1.406135 -1.7749290
## X42 -1.305088 -1.6735918
## X43 -1.548331 -0.9266552
## X45 -1.445488 -1.2910944
## X46 -1.381719 -1.2448554
## X47 -1.527155 -1.5892127
## X48 -1.345211 -1.2025631
## X49 -1.448886 -1.8159323
## X52 -1.619427 -2.1292007
## X53 -1.593905 -1.7898096
## X54 -1.534290 -1.6387702
## X55 -1.489637 -1.8669460
## X56 -1.547473 -1.4783924
## X57 -1.401122 -1.3628885
## X58 -1.498044 -1.2888687
## X59 -1.652261 -2.0497116
## X61 -1.536401 -1.3534209
## X62 -1.395786 -1.6686442
## X64 -1.670976 -1.4910873
## X65 -1.323775 -1.4385789
## X67 -1.428706 -1.7280747
## X68 -1.530085 -2.0824829
## X69 -1.453440 -1.0758313
## X71 -1.571881 -1.9598138
## X73 -1.415161 -1.4747157
## X74 -1.480924 -1.9306463
## X75 -1.579449 -1.9088155
## X78 -1.454964 -1.2655509
## X79 -1.395786 -0.7116307
## X80 -1.530504 -1.7938986
## X81 -1.425391 -1.8055564
## X82 -1.433147 -1.3676525
## X83 -1.419530 -2.1213029
## X84 -1.488443 -2.1603515
## X85 -1.494430 -1.4406136
## X86 -1.486061 -1.2902036
## X87 -1.523404 -1.6393726
## X88 -1.547473 -1.1798150
## X89 -1.524236 -1.6686442
## X90 -1.535556 -1.5629177
## X91 -1.607252 -1.9825153
## X92 -1.544049 -1.9559389
## X93 -1.659708 -2.4422513
## X94 -1.509803 -1.8647794
## X95 -1.427599 -1.7569038
## X96 -1.573211 -1.2928782
## X97 -1.595732 -2.2380872
## X99 -1.473086 -1.7986859
## X100 -1.473086 -1.8362356
## X102 -1.415525 -1.6935843
## X103 -1.603546 -1.8532856
## X104 -1.424656 -1.9058328
## X105 -1.560451 -1.7622177
## X106 -1.320199 -1.5651813
## X108 -1.575878 -1.6618738
## X109 -1.374083 -1.1407587
## X110 -1.374774 -1.7602223
## X111 -1.459169 -1.9738585
## X112 -1.531344 -2.2390390
## X113 -1.717446 -2.0841850
## X114 -1.525902 -2.0970190
## X115 -1.366172 -1.6973693
## X116 -1.446619 -2.0505420
## X117 -1.578108 -2.9206783
## X118 -1.315025 -1.3402996
## X119 -1.322473 -1.4953499
## X120 -1.627498 -0.8624052
## X121 -1.428706 -1.6417852
## X123 -1.375812 -1.5234428
## X125 -1.650288 -2.4194174
## X126 -1.587999 -2.1134503
## X127 -1.457637 -1.3951992
## X128 -1.652261 -1.7522726
## X129 -1.490832 -1.9298875
## X130 -1.536401 -1.4789186
## X131 -1.474648 -1.3956885
## X132 -1.429444 -1.7549169
## X133 -1.487251 -1.3903175
## X134 -1.558708 -1.8327119
## X135 -1.459935 -1.5869030
## X137 -1.538094 -2.8336824
## X138 -1.573211 -1.8662234
## X139 -1.480924 -1.4229317
## X140 -1.498446 -2.3622810
## X141 -1.553935 -1.5892127
## X142 -1.520085 -1.7850558
## X144 -1.520913 -1.3571983
## X146 -1.475821 -1.8298999
## X147 -1.491231 -0.6320347
## X148 -1.664214 -1.7450294
## X149 -1.519257 -1.8554329
## X150 -1.677342 -2.1256850
## X152 -1.398983 -1.4700056
## X154 -1.508987 -1.7404419
## X155 -1.454964 -1.2237105
## X156 -1.561324 -1.5845978
## X158 -1.726991 -1.9785734
## X159 -1.536401 -1.9888468
## X160 -1.581689 -1.8221988
## X161 -1.502079 -1.5533453
## X162 -1.603084 -2.0463949
## X163 -1.470361 -1.3136025
## X164 -1.464938 -2.1996053
## X165 -1.556535 -1.3384376
## X166 -1.640502 -1.8880790
## X168 -1.583037 -1.7729134
## X169 -1.494831 -2.3033148
## X171 -1.491231 -1.7615522
## X172 -1.484873 -1.7241946
## X175 -1.625591 -1.8418938
## X176 -1.585739 -1.9283710
## X177 -1.525485 -1.9118051
## X178 -1.479351 -1.6190575
## X179 -1.763600 -2.1748286
## X180 -1.585739 -2.7364649
## X182 -1.450022 -1.1242373
## X183 -1.479744 -1.4114596
## X184 -1.604471 -2.6997069
## X187 -1.553935 -1.5322240
## X188 -1.516368 -1.9436150
## X189 -1.502888 -1.5355339
## X190 -1.616129 -2.0144782
## X191 -1.434262 -0.7826129
## X192 -1.694063 -2.2845122
## X193 -1.824755 -2.5774861
## X194 -1.345545 -1.5272765
## X196 -1.615659 -1.6369648
## X197 -1.361734 -1.6037499
## X198 -1.724360 -2.1091071
## X200 -1.427231 -0.9009890
## X201 -1.473867 -1.8720155
## X202 -1.492829 -1.6961064
## X203 -1.433147 -1.5366393
## X204 -1.209422 -1.0042088
## X205 -1.475039 -1.6429933
## X206 -1.450022 -1.4224305
## X207 -1.479351 -1.6581966
## X208 -1.609112 -1.4948162
## X209 -1.505728 -1.1128640
## X210 -1.559143 -2.1522740
## X211 -1.578555 -1.7081572
## X212 -1.534290 -1.9722906
## X214 -1.550051 -2.9953191
## X216 -1.461856 -1.3195307
## X217 -1.483291 -1.4314859
## X218 -1.686819 -1.7345684
## X220 -1.482107 -1.8397690
## X222 -1.494831 -1.6125574
## X223 -1.482896 -1.6184651
## X224 -1.427231 -1.1650483
## X225 -1.535978 -1.9952086
## X226 -1.527155 -1.6143267
## X228 -1.597563 -1.6798045
## X229 -1.556969 -1.7622177
## X230 -1.348222 -1.4264463
## X232 -1.703821 -1.7470007
## X233 -1.663010 -1.7068831
## X234 -1.558708 -2.0513730
## X235 -1.445111 -1.8090056
## X236 -1.531344 -2.2390390
## X239 -1.623214 -2.6004371
## X240 -1.499252 -1.7443730
## X241 -1.535133 -1.9762139
## X242 -1.644401 -1.7132666
## X243 -1.506542 -1.4773407
## X244 -1.695111 -1.8756488
## X245 -1.460703 -1.7011661
## X246 -1.440979 -1.7248404
## X248 -1.545331 -1.8932321
## X249 -1.446997 -1.4254410
## X250 -1.489637 -1.8749213
## X251 -1.563950 -1.5771365
## X253 -1.370978 -1.8145440
## X254 -1.478958 -1.5702903
## X255 -1.447752 -1.4406136
## X256 -1.442104 -1.6107907
## X257 -1.533868 -1.7675541
## X258 -1.352253 -1.5039222
## X259 -1.445111 -1.4937496
## X263 -1.598480 -1.6113793
## X264 -1.621791 -1.8611765
## X266 -1.484873 -1.7345684
## X267 -1.563074 -1.6885556
## X268 -1.660208 -2.0439127
## X269 -1.544476 -1.3314830
## X270 -1.511439 -1.9185567
## X272 -1.502079 -1.8256936
## X273 -1.537670 -1.7575668
## X274 -1.459169 -1.7522726
## X275 -1.519671 -1.9968038
## X276 -1.501675 -2.2447636
## X277 -1.543196 -1.8083150
## X278 -1.550051 -1.9474538
## X279 -1.638076 -2.1389154
## X281 -1.346547 -1.5039222
## X282 -1.644889 -1.5915268
## X283 -1.439855 -1.3379726
## X284 -1.499252 -1.8000570
## X285 -1.666428 -2.4732544
## X286 -1.643912 -1.9960060
## X287 -1.606788 -2.0282975
## X288 -1.682219 -2.1621529
## X289 -1.648811 -1.6791818
## X290 -1.605860 -1.4990925
## X291 -1.663562 -2.1959068
## X292 -1.520499 -1.6748318
## X293 -1.453060 -1.4401046
## X294 -1.497641 -1.5915268
## X295 -1.526737 -2.1091071
## X296 -1.582138 -1.7642162
## X297 -1.698055 -2.3203498
## X298 -1.597105 -2.4969375
## X299 -1.691083 -1.8954469
## X300 -1.594361 -2.2380872
## X301 -1.448508 -1.6711155
## X302 -1.638076 -1.8597382
## X303 -1.506542 -1.4847225
## X304 -1.480137 -2.2514717
## X306 -1.685886 -1.5114749
## X307 -1.601239 -1.8844106
## X308 -1.669710 -1.6569733
## X309 -1.713449 -2.2005314
## X310 -1.654735 -2.3571020
## X312 -1.657217 -1.9762139
## X313 -1.571438 -1.9497625
## X314 -1.618012 -1.4350269
## X316 -1.612379 -2.5679247
## X318 -1.471917 -1.7715714
## X319 -1.559579 -1.5719981
## X320 -1.747337 -2.5871077
## X321 -1.484477 -1.9163022
## X322 -1.636142 -1.6184651
## X323 -1.429075 -2.0978789
## X324 -1.412624 -0.6826914
## X325 -1.544476 -1.8771050
## X326 -1.491630 -1.8575837
## X329 -1.413348 -1.5903692
## X331 -1.471528 -1.6399753
## X332 -1.530924 -1.3351867
## X333 -1.475821 -1.4857809
## X335 -1.559143 -1.9574875
## X336 -1.466097 -1.9050882
## X337 -1.594818 -2.0555355
## X339 -1.484477 -1.7177547
## X340 -1.440979 -1.9276134
## X341 -1.528409 -1.6196501
## X342 -1.554367 -1.6624877
## X343 -1.478172 -1.4810257
## X344 -1.560888 -1.1446385
## X345 -1.461856 -1.8034913
## X346 -1.501270 -2.0538690
## X347 -1.530504 -1.7267800
## X348 -1.560015 -1.5869030
## X349 -1.435005 -1.7456862
## X350 -1.528827 -1.5782813
## X351 -1.664817 -1.8270941
## X352 -1.427968 -1.0696662
## X353 -1.435377 -1.2924320
## X354 -1.388371 -1.8822146
## X355 -1.722066 -1.9405520
## X358 -1.589811 -2.1151915
## X359 -1.655231 -2.0538690
## X360 -1.512259 -2.0373158
## X361 -1.699057 -2.2323896
## X362 -1.595732 -1.8947083
## X363 -1.526737 -1.8180177
## X365 -1.563074 -1.8201066
## X366 -1.514721 -1.9155516
## X367 -1.535133 -1.4969524
## X369 -1.500059 -1.9920239
## X370 -1.528409 -1.8201066
## X371 -1.479351 -0.8795614
## X372 -1.602161 -2.0104407
## X374 -1.423555 -1.8568664
## X375 -1.611911 -1.4694835
## X376 -1.553502 -1.5617875
## X377 -1.594361 -1.9245875
## X378 -1.610510 -1.8532856
## X379 -1.536401 -1.4365479
## X380 -1.221525 -1.1031070
## X381 -1.406135 -1.4590896
## X382 -1.616599 -1.5344296
## X383 -1.725620 -2.2727630
## X384 -1.474648 -1.7668858
## X385 -1.583937 -1.8235956
## X386 -1.520913 -2.0185279
## X387 -1.649795 -1.8655012
## X390 -1.546616 -1.9405520
## X392 -1.461856 -2.0447396
## X394 -1.445865 -1.2325409
## X395 -1.472696 -1.6220237
## X398 -1.686456 -2.4856130
## X399 -1.604934 -1.9298875
## X400 -1.549621 -1.7938986
## X401 -1.316639 -1.5109338
## X402 -1.495633 -2.0323892
## X403 -1.697160 -1.5316732
## X405 -1.639046 -2.1721026
## X406 -1.500059 -2.2154336
## X407 -1.566145 -1.7945814
## X408 -1.693330 -2.0096347
## X409 -1.453440 -1.6155076
## X410 -1.551343 -1.4025614
## X411 -1.464552 -1.6680271
## X412 -1.497641 -1.6527015
## X413 -1.620845 -2.1030497
## X414 -1.625115 -1.5561527
## X415 -1.592083 -1.5174436
## X416 -1.475039 -1.6066785
## X417 -1.436867 -1.7319669
## X418 -1.440230 -1.6496593
## X419 -1.520085 -1.7966320
## X421 -1.547473 -1.6303680
## X423 -1.479351 -1.7884496
## X424 -1.592538 -1.8180177
## X425 -1.486061 -1.5377457
## X426 -1.602161 -2.1265631
## X428 -1.524652 -1.6729722
## X431 -1.477780 -1.7358713
## X432 -1.465711 -1.9559389
## X433 -1.386615 -1.6321636
## X434 -1.489239 -1.6472311
## X436 -1.405058 -1.5471923
## X437 -1.562636 -1.4747157
## X438 -1.570996 -1.8313051
## X439 -1.605860 -1.9896404
## X440 -1.645868 -2.3274232
## X441 -1.488841 -1.9683790
## X443 -1.627498 -2.6386361
## X444 -1.673109 -1.8497148
## X445 -1.541491 -1.7516124
## X446 -1.521328 -1.9230772
## X447 -1.484873 -1.6686442
## X448 -1.557403 -1.3333333
## X449 -1.619427 -2.0234038
## X450 -1.498044 -2.1996053
## X451 -1.690457 -2.1657627
## X452 -1.436122 -2.1766489
## X453 -1.565266 -2.0430863
## X455 -1.557838 -1.4974871
## X456 -1.581241 -2.2678960
## X457 -1.482896 -1.7241946
## X459 -1.560888 -2.1648594
## X460 -1.609578 -2.1513794
## X461 -1.409733 -1.6454131
## X462 -1.502484 -1.8917577
## X464 -1.508579 -1.5300225
## X465 -1.533447 -2.2304954
## X466 -1.568346 -1.7496338
## X467 -1.504916 -1.9505330
## X468 -1.490035 -1.6172812
## X469 -1.565705 -2.1693820
## X471 -1.553935 -1.5499851
## X472 -1.642449 -2.0790851
## X473 -1.630847 -1.8575837
## X475 -1.512668 -1.9367333
## X476 -1.515133 -1.6478377
## X477 -1.589811 -1.9730743
## X480 -1.494430 -1.4720966
## X481 -1.565266 -2.0773893
## X482 -1.631327 -2.1204282
## X483 -1.488046 -1.5207121
## X485 -1.431294 -1.9551652
## X487 -1.594818 -2.0364934
## X488 -1.441353 -1.4996282
## X493 -1.522157 -1.5076925
## X494 -1.691396 -2.1885391
## X495 -1.605860 -1.8583015
## X496 -1.561761 -2.1091071
## X497 -1.434262 -1.5190767
## X498 -1.535978 -1.6303680
## X500 -1.460319 -2.0160966
## X502 -1.342543 -1.3099702
## X504 -1.569228 -1.7087947
## X505 -1.307322 -1.6285751
## X506 -1.274705 -1.7476584
## X508 -1.387668 -1.7932162
## X510 -1.363438 -1.6435978
## X511 -1.627020 -1.9193090
## X512 -1.596189 -2.1398020
## X513 -1.419165 -1.3402996
## X515 -1.548761 -2.1867032
## X516 -1.453060 -1.6155076
## X517 -1.449644 -1.6072651
## X519 -1.463396 -1.9359709
## X520 -1.456108 -1.6090266
## X521 -1.324101 -1.2964545
## X522 -1.508579 -1.5595304
## X524 -1.475430 -1.7470007
## X525 -1.477780 -1.9984010
## X526 -1.395077 -1.6618738
## X527 -1.401122 -1.3719574
## X528 -1.529246 -1.5863263
## X529 -1.487648 -2.3033148
## X530 -1.438733 -1.7925342
## X531 -1.505728 -2.0177170
## X532 -1.427968 -1.5322240
## X533 -1.541066 -1.7756016
## X537 -1.493229 -1.8504281
## X538 -1.351243 -1.7776215
## X539 -1.544476 -1.6166897
## X540 -1.411177 -1.7864122
## X541 -1.507356 -2.1442434
## X543 -1.633249 -1.8334159
## X544 -1.627498 -2.0217765
## X546 -1.561761 -1.8910211
## X547 -1.532184 -1.8626165
## X548 -1.461471 -1.8554329
## X551 -1.662208 -2.0340294
## X552 -1.620372 -1.5527846
## X553 -1.553935 -2.0765423
## X554 -1.612846 -2.0530365
## X555 -1.556969 -2.1065078
## X556 -1.491630 -2.2390390
## X557 -1.540640 -2.2051713
## X558 -1.627020 -2.0201513
## X559 -1.649795 -2.2088944
## X560 -1.526737 -2.3519414
## X562 -1.700430 -3.0539870
## X564 -1.482501 -1.6954754
## X565 -1.481318 -2.4065265
## X567 -1.596189 -2.2466769
## X568 -1.391894 -1.1284389
## X570 -1.401837 -0.9485186
## X571 -1.552206 -1.8138504
## X572 -1.468032 -1.3273311
## X573 -1.246824 -0.4547732
## X575 -1.343543 -1.1682237
## X576 -1.468808 -1.6137366
## X577 -1.390483 -1.5377457
## X578 -1.373392 -1.0226796
## X579 -1.323124 -1.0268307
## X580 -1.577215 -1.6835473
## X583 -1.599859 -1.7735849
## X584 -1.391541 -1.3351867
## X585 -1.382068 -1.0794752
## X586 -1.460319 -1.6339618
## X587 -1.344209 -1.2853171
## X588 -1.442104 -1.8014296
## X589 -1.469584 -1.6655621
## X590 -1.520913 -1.5444060
## X591 -1.515956 -2.0406102
## X592 -1.489239 -0.9275957
## X593 -1.484873 -1.7648831
## X595 -1.429814 -1.1365073
## X601 -1.396495 -0.8985507
## X602 -1.397561 -1.3662211
## X603 -1.443230 -1.3004918
## X604 -1.467258 -1.0606668
## X605 -1.423188 -0.8679915
## X606 -1.467258 -1.3375078
## X607 -1.677854 -2.4867416
## X608 -1.694115 -3.0556014
## X609 -1.406135 -1.7749290
## X610 -1.617070 -1.6551407
## X611 -1.305088 -1.6735918
## X613 -1.435377 -1.2707870
## X614 -1.445488 -1.2910944
## X615 -1.381719 -1.2448554
## X618 -1.448886 -1.8159323
## X619 -1.585739 -1.7326167
## X620 -1.621318 -2.0547020
## X621 -1.619427 -2.1292007
## X622 -1.593905 -1.7898096
## X623 -1.534290 -1.6387702
## X624 -1.489637 -1.8669460
## X625 -1.547473 -1.4783924
## X627 -1.498044 -1.2888687
## X628 -1.652261 -2.0497116
## X629 -1.363097 -1.5245369
## X630 -1.536401 -1.3534209
## X631 -1.395786 -1.6686442
## X632 -1.395431 -1.7502930
## X633 -1.670976 -1.4910873
## X635 -1.392600 -1.4705280
## X636 -1.428706 -1.7280747
## X637 -1.530085 -2.0824829
## X638 -1.453440 -1.0758313
## X640 -1.571881 -1.9598138
## X642 -1.415161 -1.4747157
## X643 -1.480924 -1.9306463
## X644 -1.579449 -1.9088155
## X645 -1.446619 -1.8851434
## X646 -1.465324 -1.8418938
## X647 -1.454964 -1.2655509
## X648 -1.395786 -0.7116307
## X649 -1.530504 -1.7938986
## X651 -1.433147 -1.3676525
## X652 -1.419530 -2.1213029
## X653 -1.488443 -2.1603515
## X654 -1.494430 -1.4406136
## X655 -1.486061 -1.2902036
## X656 -1.523404 -1.6393726
## X657 -1.547473 -1.1798150
## X658 -1.524236 -1.6686442
## X659 -1.535556 -1.5629177
## X660 -1.607252 -1.9825153
## X661 -1.544049 -1.9559389
## X662 -1.659708 -2.4422513
## X663 -1.509803 -1.8647794
## X664 -1.427599 -1.7569038
## X667 -1.519257 -2.5478042
## X668 -1.473086 -1.7986859
## X669 -1.473086 -1.8362356
## X670 -1.540640 -1.8844106
## X671 -1.415525 -1.6935843
## X672 -1.603546 -1.8532856
## X674 -1.560451 -1.7622177
## X675 -1.320199 -1.5651813
## X677 -1.575878 -1.6618738
## X678 -1.374083 -1.1407587
## X679 -1.374774 -1.7602223
## X680 -1.459169 -1.9738585
## X681 -1.531344 -2.2390390
## X682 -1.717446 -2.0841850
## X683 -1.525902 -2.0970190
## X685 -1.446619 -2.0505420
## X686 -1.578108 -2.9206783
## X687 -1.315025 -1.3402996
## X689 -1.627498 -0.8624052
## X690 -1.428706 -1.6417852
## X692 -1.375812 -1.5234428
## X693 -1.520499 -1.7209705
## X694 -1.650288 -2.4194174
## X695 -1.587999 -2.1134503
## X697 -1.652261 -1.7522726
## X698 -1.490832 -1.9298875
## X699 -1.536401 -1.4789186
## X700 -1.474648 -1.3956885
## X701 -1.429444 -1.7549169
## X702 -1.487251 -1.3903175
## X703 -1.558708 -1.8327119
## X704 -1.459935 -1.5869030
## X705 -1.477780 -1.7602223
## X706 -1.538094 -2.8336824
## X707 -1.573211 -1.8662234
## X708 -1.480924 -1.4229317
## X711 -1.520085 -1.7850558
## X712 -1.480137 -1.9344474
## X714 -1.625591 -2.1702883
## X715 -1.475821 -1.8298999
## X716 -1.491231 -0.6320347
## X717 -1.664214 -1.7450294
## X718 -1.519257 -1.8554329
## X719 -1.677342 -2.1256850
## X720 -1.527155 -1.5377457
## X721 -1.398983 -1.4700056
## X722 -1.529246 -1.5874800
## X723 -1.508987 -1.7404419
## X724 -1.454964 -1.2237105
## X725 -1.561324 -1.5845978
## X726 -1.478172 -2.0299327
## X728 -1.536401 -1.9888468
## X729 -1.581689 -1.8221988
## X731 -1.603084 -2.0463949
## X733 -1.464938 -2.1996053
## X734 -1.556535 -1.3384376
## X735 -1.640502 -1.8880790
## X736 -1.471139 -2.3747864
## X737 -1.583037 -1.7729134
## X738 -1.494831 -2.3033148
## X739 -1.561761 -2.0790851
## X742 -1.435005 -1.5267281
## X743 -1.561761 -2.5859017
## X744 -1.625591 -1.8418938
## X745 -1.585739 -1.9283710
## X746 -1.525485 -1.9118051
## X747 -1.479351 -1.6190575
## X748 -1.763600 -2.1748286
## X749 -1.585739 -2.7364649
## X750 -1.457254 -1.7424059
## X751 -1.450022 -1.1242373
## X752 -1.479744 -1.4114596
## X753 -1.604471 -2.6997069
## X754 -1.525485 -1.5494260
## X755 -1.438733 -1.6929546
## X756 -1.553935 -1.5322240
## X757 -1.516368 -1.9436150
## X758 -1.502888 -1.5355339
## X759 -1.616129 -2.0144782
## X760 -1.434262 -0.7826129
## X761 -1.694063 -2.2845122
## X762 -1.824755 -2.5774861
## X764 -1.519257 -1.6514837
## X765 -1.615659 -1.6369648
## X766 -1.361734 -1.6037499
## X767 -1.724360 -2.1091071
## X768 -1.516780 -1.5394073
## X770 -1.473867 -1.8720155
## X771 -1.492829 -1.6961064
## X772 -1.433147 -1.5366393
## X773 -1.209422 -1.0042088
## X774 -1.475039 -1.6429933
## X775 -1.450022 -1.4224305
## X776 -1.479351 -1.6581966
## X777 -1.609112 -1.4948162
## X778 -1.505728 -1.1128640
## X779 -1.559143 -2.1522740
## X780 -1.578555 -1.7081572
## X781 -1.534290 -1.9722906
## X782 -1.594818 -2.9266464
## X783 -1.550051 -2.9953191
## X784 -1.424656 -0.9098798
## X785 -1.461856 -1.3195307
## X786 -1.483291 -1.4314859
## X787 -1.686819 -1.7345684
## X789 -1.482107 -1.8397690
## X794 -1.535978 -1.9952086
## X795 -1.527155 -1.6143267
## X796 -1.508987 -1.9110570
## X797 -1.597563 -1.6798045
## X798 -1.556969 -1.7622177
## X799 -1.348222 -1.4264463
## X800 -1.373392 -1.5869030
## X802 -1.663010 -1.7068831
## X804 -1.445111 -1.8090056
## X805 -1.531344 -2.2390390
## X807 -1.556535 -2.2276590
## X808 -1.623214 -2.6004371
## X809 -1.499252 -1.7443730
## X811 -1.644401 -1.7132666
## X814 -1.460703 -1.7011661
## X815 -1.440979 -1.7248404
## X816 -1.613783 -1.8021165
## X817 -1.545331 -1.8932321
## X818 -1.446997 -1.4254410
## X819 -1.489637 -1.8749213
## X820 -1.563950 -1.5771365
## X822 -1.370978 -1.8145440
## X823 -1.478958 -1.5702903
## X824 -1.447752 -1.4406136
## X825 -1.442104 -1.6107907
## X826 -1.533868 -1.7675541
## X827 -1.352253 -1.5039222
## X828 -1.445111 -1.4937496
## X829 -1.313415 -1.3748365
## X830 -1.438360 -1.5629177
## X831 -1.551343 -2.0389620
## X832 -1.598480 -1.6113793
## X833 -1.621791 -1.8611765
## X834 -1.425023 -1.5267281
## X835 -1.484873 -1.7345684
## X837 -1.660208 -2.0439127
## X838 -1.544476 -1.3314830
## X839 -1.511439 -1.9185567
## X840 -1.738456 -2.0340294
## X841 -1.502079 -1.8256936
## X844 -1.519671 -1.9968038
## X845 -1.501675 -2.2447636
## X846 -1.543196 -1.8083150
## X847 -1.550051 -1.9474538
## X849 -1.575433 -1.6791818
## X850 -1.346547 -1.5039222
## X851 -1.644889 -1.5915268
## X852 -1.439855 -1.3379726
## X854 -1.666428 -2.4732544
## X855 -1.643912 -1.9960060
## X856 -1.606788 -2.0282975
## X857 -1.682219 -2.1621529
## X858 -1.648811 -1.6791818
## X859 -1.605860 -1.4990925
## X860 -1.663562 -2.1959068
## X861 -1.520499 -1.6748318
## X863 -1.497641 -1.5915268
## X864 -1.526737 -2.1091071
## X866 -1.698055 -2.3203498
## X867 -1.597105 -2.4969375
## X868 -1.691083 -1.8954469
## X869 -1.594361 -2.2380872
## X870 -1.448508 -1.6711155
## X871 -1.638076 -1.8597382
## X872 -1.506542 -1.4847225
## X873 -1.480137 -2.2514717
## X874 -1.593905 -2.2562827
## X875 -1.685886 -1.5114749
## X876 -1.601239 -1.8844106
## X877 -1.669710 -1.6569733
## X878 -1.713449 -2.2005314
## X880 -1.558708 -1.3869129
## X881 -1.657217 -1.9762139
## X882 -1.571438 -1.9497625
## X883 -1.618012 -1.4350269
## X884 -1.506542 -1.5680169
## X885 -1.612379 -2.5679247
## X886 -1.662208 -2.1766489
## X887 -1.471917 -1.7715714
## X888 -1.559579 -1.5719981
## X889 -1.747337 -2.5871077
## X892 -1.429075 -2.0978789
## X893 -1.412624 -0.6826914
## X894 -1.544476 -1.8771050
## X895 -1.491630 -1.8575837
## X896 -1.533868 -2.3643578
## X898 -1.413348 -1.5903692
## X899 -1.475039 -1.8235956
## X900 -1.471528 -1.6399753
## X901 -1.530924 -1.3351867
## X903 -1.583937 -1.7695612
## X907 -1.447374 -1.2973504
## X908 -1.484477 -1.7177547
## X909 -1.440979 -1.9276134
## X910 -1.528409 -1.6196501
## X911 -1.554367 -1.6624877
## X912 -1.478172 -1.4810257
## X913 -1.560888 -1.1446385
## X914 -1.461856 -1.8034913
## X915 -1.501270 -2.0538690
## X917 -1.560015 -1.5869030
## X918 -1.435005 -1.7456862
## X921 -1.427968 -1.0696662
## X922 -1.435377 -1.2924320
## X923 -1.388371 -1.8822146
## X924 -1.722066 -1.9405520
## X925 -1.616129 -2.3426983
## X926 -1.508171 -1.5845978
## X927 -1.589811 -2.1151915
## X928 -1.655231 -2.0538690
## X929 -1.512259 -2.0373158
## X931 -1.595732 -1.8947083
## X932 -1.526737 -1.8180177
## X933 -1.510212 -2.0875956
## X934 -1.563074 -1.8201066
## X935 -1.514721 -1.9155516
## X936 -1.535133 -1.4969524
## X937 -1.498044 -1.5256320
## X938 -1.500059 -1.9920239
## X939 -1.528409 -1.8201066
## X941 -1.602161 -2.0104407
## X942 -1.572324 -1.8277950
## X943 -1.423555 -1.8568664
## X944 -1.611911 -1.4694835
## X946 -1.594361 -1.9245875
## X949 -1.221525 -1.1031070
## X950 -1.406135 -1.4590896
## X952 -1.725620 -2.2727630
## X953 -1.474648 -1.7668858
## X954 -1.583937 -1.8235956
## X955 -1.520913 -2.0185279
## X956 -1.649795 -1.8655012
## X957 -1.743107 -1.9668175
## X958 -1.613314 -2.3063064
## X959 -1.546616 -1.9405520
## X960 -1.508171 -1.6904389
## X962 -1.381022 -1.5427374
## X963 -1.445865 -1.2325409
## X964 -1.472696 -1.6220237
## X965 -1.630368 -1.9817260
## X967 -1.686456 -2.4856130
## X968 -1.604934 -1.9298875
## X970 -1.316639 -1.5109338
## X971 -1.495633 -2.0323892
## X972 -1.697160 -1.5316732
## X973 -1.581241 -1.4831367
## X974 -1.639046 -2.1721026
## X975 -1.500059 -2.2154336
## X976 -1.566145 -1.7945814
## X977 -1.693330 -2.0096347
## X978 -1.453440 -1.6155076
## X980 -1.464552 -1.6680271
## X981 -1.497641 -1.6527015
## X982 -1.620845 -2.1030497
## X983 -1.625115 -1.5561527
## X984 -1.592083 -1.5174436
## X986 -1.436867 -1.7319669
## X987 -1.440230 -1.6496593
## X988 -1.520085 -1.7966320
## X989 -1.533447 -1.6661779
## X990 -1.547473 -1.6303680
## X992 -1.479351 -1.7884496
## X993 -1.592538 -1.8180177
## X994 -1.486061 -1.5377457
## X995 -1.602161 -2.1265631
## X997 -1.524652 -1.6729722
## X1000 -1.477780 -1.7358713
## X1001 -1.465711 -1.9559389
## X1002 -1.386615 -1.6321636
## X1003 -1.489239 -1.6472311
## X1004 -1.560888 -1.9801488
## X1008 -1.605860 -1.9896404
## X1010 -1.488841 -1.9683790
## X1011 -1.471139 -2.0000000
## X1012 -1.627498 -2.6386361
## X1013 -1.673109 -1.8497148
## X1014 -1.541491 -1.7516124
## X1016 -1.484873 -1.6686442
## X1017 -1.557403 -1.3333333
## X1018 -1.619427 -2.0234038
## X1019 -1.498044 -2.1996053
## X1020 -1.690457 -2.1657627
## X1022 -1.565266 -2.0430863
## X1023 -1.506542 -1.9178048
## X1024 -1.557838 -1.4974871
## X1025 -1.581241 -2.2678960
## X1026 -1.482896 -1.7241946
## X1028 -1.560888 -2.1648594
## X1029 -1.609578 -2.1513794
## X1030 -1.409733 -1.6454131
## X1031 -1.502484 -1.8917577
## X1032 -1.654239 -2.1300810
## X1034 -1.533447 -2.2304954
## X1035 -1.568346 -1.7496338
## X1036 -1.504916 -1.9505330
## X1037 -1.490035 -1.6172812
## X1038 -1.565705 -2.1693820
## X1039 -1.347217 -1.8778337
## X1040 -1.553935 -1.5499851
## X1041 -1.642449 -2.0790851
## X1043 -1.692284 -2.0748497
## X1045 -1.515133 -1.6478377
## X1046 -1.589811 -1.9730743
## X1047 -1.694272 -1.8640580
## X1048 -1.504510 -1.6879284
## X1050 -1.565266 -2.0773893
## X1051 -1.631327 -2.1204282
## X1052 -1.488046 -1.5207121
## X1053 -1.569228 -1.9856773
## X1054 -1.431294 -1.9551652
## X1055 -1.579897 -1.5185321
## X1056 -1.594818 -2.0364934
## X1057 -1.441353 -1.4996282
## X1058 -1.436867 -1.7769479
## X1061 -1.669659 -2.7364649
## X1062 -1.522157 -1.5076925
## X1064 -1.605860 -1.8583015
## X1065 -1.561761 -2.1091071
## X1066 -1.434262 -1.5190767
## X1067 -1.535978 -1.6303680
## X1068 -1.480531 -1.9920239
## X1069 -1.460319 -2.0160966
## X1070 -1.598022 -2.2864798
## X1071 -1.342543 -1.3099702
## X1072 -1.416979 -1.5606584
## X1073 -1.569228 -1.7087947
## X1074 -1.307322 -1.6285751
## X1076 -1.484477 -1.8426028
## X1077 -1.387668 -1.7932162
## X1078 -1.503699 -2.1702883
## X1079 -1.363438 -1.6435978
## X1080 -1.627020 -1.9193090
## X1081 -1.596189 -2.1398020
## X1082 -1.419165 -1.3402996
## X1083 -1.558708 -1.9028570
## X1084 -1.548761 -2.1867032
## X1085 -1.453060 -1.6155076
## X1086 -1.449644 -1.6072651
## X1088 -1.463396 -1.9359709
## X1090 -1.324101 -1.2964545
## X1091 -1.508579 -1.5595304
## X1092 -1.610510 -1.9551652
## X1093 -1.475430 -1.7470007
## X1094 -1.477780 -1.9984010
## X1096 -1.401122 -1.3719574
## X1097 -1.529246 -1.5863263
## X1100 -1.505728 -2.0177170
## X1101 -1.427968 -1.5322240
## X1103 -1.615659 -1.5245369
## X1104 -1.474257 -2.1802966
## X1105 -1.539366 -1.6055062
## X1107 -1.351243 -1.7776215
## X1110 -1.507356 -2.1442434
## X1111 -1.509395 -1.5427374
## X1112 -1.633249 -1.8334159
## X1115 -1.561761 -1.8910211
## X1116 -1.532184 -1.8626165
## X1117 -1.461471 -1.8554329
## X1118 -1.569228 -1.9590379
## X1120 -1.662208 -2.0340294
## X1121 -1.620372 -1.5527846
## X1122 -1.553935 -2.0765423
## X1123 -1.612846 -2.0530365
## X1124 -1.556969 -2.1065078
## X1125 -1.491630 -2.2390390
## X1126 -1.540640 -2.2051713
## X1127 -1.627020 -2.0201513
## X1128 -1.649795 -2.2088944
## X1129 -1.526737 -2.3519414
## X1130 -1.550912 -2.2163702
## X1131 -1.700430 -3.0539870
## X1132 -1.478565 -1.1276737
## X1134 -1.481318 -2.4065265
## X1135 -1.583937 -1.9436150
## X1136 -1.596189 -2.2466769
## X1137 -1.391894 -1.1284389
## X1138 -1.714905 -1.7326167
##
## $usekernel
## [1] FALSE
##
## $varnames
## [1] "texture_mean" "smoothness_mean" "compactness_se" "texture_worst"
## [5] "smoothness_worst" "symmetry_worst"
##
## $xNames
## [1] "texture_mean" "smoothness_mean" "compactness_se" "texture_worst"
## [5] "smoothness_worst" "symmetry_worst"
##
## $problemType
## [1] "Classification"
##
## $tuneValue
## fL usekernel adjust
## 1 2 FALSE FALSE
##
## $obsLevels
## [1] "B" "M"
## attr(,"ordered")
## [1] FALSE
##
## $param
## list()
##
## attr(,"class")
## [1] "NaiveBayes"BAL_NB_Tune$results## usekernel fL adjust ROC Sens Spec ROCSD SensSD
## 1 FALSE 2 FALSE 0.8873525 0.8552525 0.7605882 0.02125535 0.03336686
## 2 TRUE 2 FALSE NaN NaN NaN NA NA
## SpecSD
## 1 0.04679768
## 2 NA(BAL_NB_Train_AUROC <- BAL_NB_Tune$results[BAL_NB_Tune$results$usekernel==BAL_NB_Tune$bestTune$usekernel &
BAL_NB_Tune$results$adjust==BAL_NB_Tune$bestTune$adjust &
BAL_NB_Tune$results$fL==BAL_NB_Tune$bestTune$fL,
c("ROC")])## [1] 0.8873525##################################
# Identifying and plotting the
# best model predictors
##################################
# model does not support variable importance measurement
##################################
# Independently evaluating the model
# on the test set
##################################
BAL_NB_Test <- data.frame(BAL_NB_Test_Observed = MA_Test$diagnosis,
BAL_NB_Test_Predicted = predict(BAL_NB_Tune,
MA_Test[,!names(MA_Test) %in% c("diagnosis")],
type = "prob"))
BAL_NB_Test## BAL_NB_Test_Observed BAL_NB_Test_Predicted.B BAL_NB_Test_Predicted.M
## 4 M 8.418867e-06 9.999916e-01
## 5 M 9.947802e-01 5.219820e-03
## 14 M 6.863230e-01 3.136770e-01
## 19 M 1.487708e-01 8.512292e-01
## 24 M 2.813452e-01 7.186548e-01
## 29 M 1.320029e-03 9.986800e-01
## 31 M 8.611625e-03 9.913884e-01
## 33 M 3.780788e-03 9.962192e-01
## 34 M 1.443492e-02 9.855651e-01
## 36 M 3.884397e-03 9.961156e-01
## 37 M 3.340474e-02 9.665953e-01
## 41 M 9.299440e-01 7.005599e-02
## 44 M 3.661805e-02 9.633819e-01
## 50 B 7.332968e-01 2.667032e-01
## 51 B 9.811725e-01 1.882750e-02
## 60 B 9.987229e-01 1.277059e-03
## 63 M 2.245885e-02 9.775412e-01
## 66 M 7.032281e-03 9.929677e-01
## 70 B 9.984081e-01 1.591913e-03
## 72 B 9.969872e-01 3.012839e-03
## 76 M 5.911188e-01 4.088812e-01
## 77 B 9.997844e-01 2.155780e-04
## 98 B 7.482378e-01 2.517622e-01
## 101 M 3.492114e-01 6.507886e-01
## 107 B 7.730772e-02 9.226923e-01
## 122 M 4.615057e-01 5.384943e-01
## 124 B 9.997801e-01 2.199472e-04
## 136 M 4.127956e-01 5.872044e-01
## 143 B 5.884881e-01 4.115119e-01
## 145 B 9.993874e-01 6.125853e-04
## 151 B 5.333960e-01 4.666040e-01
## 153 B 8.349582e-01 1.650418e-01
## 157 M 1.931211e-01 8.068789e-01
## 167 B 9.999992e-01 8.482450e-07
## 170 B 9.702524e-01 2.974760e-02
## 173 M 9.813259e-01 1.867412e-02
## 174 B 9.974796e-01 2.520361e-03
## 181 M 5.137427e-02 9.486257e-01
## 185 M 5.043588e-01 4.956412e-01
## 186 B 9.677181e-01 3.228189e-02
## 195 M 8.459365e-02 9.154064e-01
## 199 M 1.767134e-01 8.232866e-01
## 213 M 9.316257e-01 6.837434e-02
## 215 M 1.321134e-03 9.986789e-01
## 219 M 1.859137e-01 8.140863e-01
## 221 B 9.992881e-01 7.119289e-04
## 227 B 9.935420e-01 6.458020e-03
## 231 M 9.611758e-02 9.038824e-01
## 237 M 3.190678e-02 9.680932e-01
## 238 M 8.379804e-01 1.620196e-01
## 247 B 9.931943e-01 6.805651e-03
## 252 B 9.770018e-01 2.299823e-02
## 260 M 1.145796e-03 9.988542e-01
## 261 M 3.113683e-02 9.688632e-01
## 262 M 8.395667e-01 1.604333e-01
## 265 M 1.729267e-01 8.270733e-01
## 271 B 9.999991e-01 8.758747e-07
## 280 B 9.825885e-01 1.741150e-02
## 305 B 9.765564e-01 2.344359e-02
## 311 B 8.728224e-01 1.271776e-01
## 315 B 6.945252e-01 3.054748e-01
## 317 B 9.999968e-01 3.184820e-06
## 327 B 9.996640e-01 3.360282e-04
## 328 B 9.997532e-01 2.468207e-04
## 330 M 1.151531e-01 8.848469e-01
## 334 B 9.993082e-01 6.917658e-04
## 338 M 2.069080e-02 9.793092e-01
## 356 B 9.581310e-01 4.186896e-02
## 357 B 2.390588e-01 7.609412e-01
## 364 B 8.143429e-01 1.856571e-01
## 368 B 7.906987e-01 2.093013e-01
## 373 M 9.604987e-01 3.950130e-02
## 388 B 9.999464e-01 5.360802e-05
## 389 B 9.967851e-01 3.214911e-03
## 391 B 9.994831e-01 5.168794e-04
## 393 M 1.943944e-02 9.805606e-01
## 396 B 9.950741e-01 4.925881e-03
## 397 B 3.901841e-01 6.098159e-01
## 404 B 9.410957e-01 5.890427e-02
## 420 B 6.205150e-01 3.794850e-01
## 422 B 9.717101e-01 2.828993e-02
## 427 B 8.754185e-01 1.245815e-01
## 429 B 9.998516e-01 1.484434e-04
## 430 B 9.990260e-01 9.740379e-04
## 435 B 9.885298e-01 1.147020e-02
## 442 M 1.714923e-01 8.285077e-01
## 454 B 9.970224e-01 2.977611e-03
## 458 B 8.144607e-01 1.855393e-01
## 463 B 9.808887e-01 1.911127e-02
## 470 B 8.588458e-02 9.141154e-01
## 474 B 9.838966e-01 1.610339e-02
## 478 B 9.998429e-01 1.570680e-04
## 479 B 9.162635e-01 8.373653e-02
## 484 B 9.840122e-01 1.598781e-02
## 486 B 8.471689e-01 1.528311e-01
## 489 B 8.396732e-01 1.603268e-01
## 490 M 7.804773e-01 2.195227e-01
## 491 B 7.257897e-01 2.742103e-01
## 492 B 9.999859e-01 1.405010e-05
## 499 M 6.982981e-01 3.017019e-01
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## 503 B 5.931235e-01 4.068765e-01
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## 509 B 9.970054e-01 2.994587e-03
## 514 B 9.983239e-01 1.676132e-03
## 518 M 4.200733e-01 5.799267e-01
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## 534 M 5.478869e-01 4.521131e-01
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## 536 M 3.012400e-01 6.987600e-01
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## 545 B 7.804988e-01 2.195012e-01
## 549 B 9.727728e-01 2.722715e-02
## 550 B 5.809883e-01 4.190117e-01
## 561 B 2.193662e-01 7.806338e-01
## 563 M 1.423350e-03 9.985767e-01
## 566 M 1.961924e-01 8.038076e-01
## 569 B 9.999903e-01 9.708791e-06
## 574 M 9.947802e-01 5.219820e-03
## 581 M 9.135214e-02 9.086479e-01
## 582 M 1.856151e-01 8.143849e-01
## 594 M 6.759268e-03 9.932407e-01
## 596 M 3.445315e-03 9.965547e-01
## 597 M 5.599734e-01 4.400266e-01
## 598 M 1.320029e-03 9.986800e-01
## 599 M 9.731682e-01 2.683176e-02
## 600 M 8.611625e-03 9.913884e-01
## 612 M 5.645765e-03 9.943542e-01
## 616 B 9.570635e-01 4.293653e-02
## 617 M 8.665746e-03 9.913343e-01
## 626 M 4.669960e-02 9.533004e-01
## 634 M 3.012508e-03 9.969875e-01
## 639 B 9.984081e-01 1.591913e-03
## 641 B 9.969872e-01 3.012839e-03
## 650 B 8.920921e-02 9.107908e-01
## 665 M 9.192660e-02 9.080734e-01
## 666 B 9.860749e-01 1.392512e-02
## 673 B 2.933858e-01 7.066142e-01
## 676 B 7.730772e-02 9.226923e-01
## 684 B 6.234754e-01 3.765246e-01
## 688 M 3.351730e-03 9.966483e-01
## 691 M 4.615057e-01 5.384943e-01
## 696 M 9.143037e-02 9.085696e-01
## 709 B 9.951737e-01 4.826330e-03
## 710 B 9.999904e-01 9.624905e-06
## 713 B 9.354000e-01 6.460004e-02
## 727 B 9.923178e-01 7.682178e-03
## 730 B 1.784058e-01 8.215942e-01
## 732 M 9.242316e-02 9.075768e-01
## 740 B 9.994710e-01 5.289834e-04
## 741 M 7.095309e-01 2.904691e-01
## 763 M 2.659527e-03 9.973405e-01
## 769 M 9.119714e-03 9.908803e-01
## 788 M 1.859137e-01 8.140863e-01
## 790 B 9.992881e-01 7.119289e-04
## 791 B 9.837746e-01 1.622540e-02
## 792 B 7.322536e-01 2.677464e-01
## 793 M 1.451720e-02 9.854828e-01
## 801 B 9.807689e-01 1.923105e-02
## 803 M 3.144980e-01 6.855020e-01
## 806 M 3.190678e-02 9.680932e-01
## 810 B 9.954171e-01 4.582943e-03
## 812 B 1.753027e-01 8.246973e-01
## 813 B 9.524541e-01 4.754592e-02
## 821 B 9.770018e-01 2.299823e-02
## 836 B 7.036007e-01 2.963993e-01
## 842 M 2.889183e-01 7.110817e-01
## 843 B 9.827448e-01 1.725516e-02
## 848 B 9.994349e-01 5.651353e-04
## 853 M 3.506119e-01 6.493881e-01
## 862 B 7.849815e-01 2.150185e-01
## 865 B 9.998603e-01 1.396787e-04
## 879 B 9.999949e-01 5.115656e-06
## 890 B 8.090039e-01 1.909961e-01
## 891 M 9.859185e-01 1.408150e-02
## 897 B 9.997532e-01 2.468207e-04
## 902 B 4.109252e-01 5.890748e-01
## 904 B 9.792663e-01 2.073368e-02
## 905 M 1.257893e-01 8.742107e-01
## 906 B 9.988321e-01 1.167869e-03
## 916 B 9.339058e-01 6.609417e-02
## 919 B 9.531747e-01 4.682528e-02
## 920 B 9.998864e-01 1.136243e-04
## 930 B 9.999744e-01 2.560407e-05
## 940 M 2.528686e-03 9.974713e-01
## 945 B 9.669483e-01 3.305167e-02
## 947 B 9.167996e-01 8.320035e-02
## 948 B 9.765901e-01 2.340987e-02
## 951 B 9.964543e-01 3.545711e-03
## 961 B 8.111540e-01 1.888460e-01
## 966 B 3.901841e-01 6.098159e-01
## 969 B 9.113059e-01 8.869412e-02
## 979 B 5.619843e-01 4.380157e-01
## 985 B 1.493080e-01 8.506920e-01
## 991 B 9.717101e-01 2.828993e-02
## 996 B 8.754185e-01 1.245815e-01
## 998 B 9.998516e-01 1.484434e-04
## 999 B 9.990260e-01 9.740379e-04
## 1005 M 6.556220e-02 9.344378e-01
## 1006 B 8.166196e-01 1.833804e-01
## 1007 B 9.949829e-01 5.017136e-03
## 1009 B 9.998712e-01 1.287548e-04
## 1015 B 2.149525e-01 7.850475e-01
## 1021 M 1.238714e-01 8.761286e-01
## 1027 B 8.144607e-01 1.855393e-01
## 1033 B 8.411580e-01 1.588420e-01
## 1042 B 9.994990e-01 5.009985e-04
## 1044 B 9.564314e-01 4.356862e-02
## 1049 M 1.943295e-01 8.056705e-01
## 1059 M 7.804773e-01 2.195227e-01
## 1060 B 7.257897e-01 2.742103e-01
## 1063 B 9.999978e-01 2.202499e-06
## 1075 B 6.721752e-01 3.278248e-01
## 1087 M 4.200733e-01 5.799267e-01
## 1089 B 6.945497e-01 3.054503e-01
## 1095 B 9.728210e-01 2.717904e-02
## 1098 B 9.955472e-01 4.452756e-03
## 1099 B 9.972506e-01 2.749390e-03
## 1102 B 9.966100e-01 3.389963e-03
## 1106 M 9.370223e-02 9.062978e-01
## 1108 B 8.002142e-01 1.997858e-01
## 1109 B 5.369302e-02 9.463070e-01
## 1113 B 7.557723e-01 2.442277e-01
## 1114 B 7.804988e-01 2.195012e-01
## 1119 B 5.809883e-01 4.190117e-01
## 1133 M 3.531121e-02 9.646888e-01##################################
# Reporting the independent evaluation results
# for the test set
##################################
BAL_NB_Test_ROC <- roc(response = BAL_NB_Test$BAL_NB_Test_Observed,
predictor = BAL_NB_Test$BAL_NB_Test_Predicted.M,
levels = rev(levels(BAL_NB_Test$BAL_NB_Test_Observed)))
(BAL_NB_Test_AUROC <- auc(BAL_NB_Test_ROC)[1])## [1] 0.89696511.7.6 Base Learner Ensemble
[A] An ensemble of optimal individual base learners based on previous hyperparameter tuning evaluation was formulated as follows:
[A.1] Linear Discriminant Analysis
[A.2] Classification and Regression Trees
[A.2.1] cp = 0.001
[A.3] Support Vector Machine - Radial Basis Function Kernel
[A.3.1] sigma = 0.17905
[A.3.2] C = 2048
[A.4] K-Nearest Neighbors
[A.4.1] k = 1
[A.5] Naive Bayes
[A.5.1] fL = 2
[A.5.2] adjust = FALSE
[A.5.3] usekernel = FALSE
[B] Among pairs of base learners, no high correlation was observed.
Code Chunk | Output
##################################
# Consolidating the base learners
# with optimal hyperparameters
##################################
set.seed(12345678)
BAL_LIST <- caretList(x = MA_Train[,!names(MA_Train) %in% c("diagnosis")],
y = MA_Train$diagnosis,
trControl=RKFold_Control,
metric="ROC",
tuneList=list(
BAL_LDA=caretModelSpec(method="lda",
preProcess=c("center","scale")),
BAL_CART=caretModelSpec(method="rpart",
tuneGrid=data.frame(cp=0.001)),
BAL_SVM_R=caretModelSpec(method="svmRadial",
preProcess=c("center","scale"),
tuneGrid=data.frame(C = 2048, sigma = 0.1790538)),
BAL_KNN=caretModelSpec(method="knn",
preProcess=c("center","scale"),
tuneGrid=data.frame(k = 1)),
BAL_NB=caretModelSpec(method="nb",
tuneGrid=data.frame(usekernel=FALSE,fL = 2,adjust = FALSE)))
)
BAL_LIST## $BAL_LDA
## Linear Discriminant Analysis
##
## Pre-processing: centered (6), scaled (6)
## Resampling results:
##
## ROC Sens Spec
## 0.8762815 0.8720214 0.7105882
##
##
## $BAL_CART
## CART
##
## No pre-processing
## Resampling results:
##
## ROC Sens Spec
## 0.8522563 0.8531716 0.7235294
##
## Tuning parameter 'cp' was held constant at a value of 0.001
##
## $BAL_SVM_R
## Support Vector Machines with Radial Basis Function Kernel
##
## Pre-processing: centered (6), scaled (6)
## Resampling results:
##
## ROC Sens Spec
## 0.9106172 0.9437193 0.7976471
##
## Tuning parameter 'sigma' was held constant at a value of 0.1790538
##
## Tuning parameter 'C' was held constant at a value of 2048
##
## $BAL_KNN
## k-Nearest Neighbors
##
## Pre-processing: centered (6), scaled (6)
## Resampling results:
##
## ROC Sens Spec
## 0.902676 0.9171167 0.8882353
##
## Tuning parameter 'k' was held constant at a value of 1
##
## $BAL_NB
## Naive Bayes
##
## No pre-processing
## Resampling results:
##
## ROC Sens Spec
## 0.8873915 0.852418 0.7629412
##
## Tuning parameter 'fL' was held constant at a value of 2
## Tuning
## parameter 'usekernel' was held constant at a value of FALSE
## Tuning
## parameter 'adjust' was held constant at a value of FALSE
##
## attr(,"class")
## [1] "caretList" "list"##################################
# Comparing the base learners
# with optimal hyperparameters
##################################
BAL_LIST_RESAMPLES <- resamples(BAL_LIST)
summary(BAL_LIST_RESAMPLES)##
## Call:
## summary.resamples(object = BAL_LIST_RESAMPLES)
##
## Models: BAL_LDA, BAL_CART, BAL_SVM_R, BAL_KNN, BAL_NB
## Number of resamples: 25
##
## ROC
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## BAL_LDA 0.8102302 0.8641641 0.8828689 0.8762815 0.8913829 0.9296675 0
## BAL_CART 0.7877967 0.8317136 0.8553708 0.8522563 0.8726343 0.9186061 0
## BAL_SVM_R 0.8675181 0.8980818 0.9066047 0.9106172 0.9307276 0.9522704 0
## BAL_KNN 0.8533282 0.8855779 0.9037084 0.9026760 0.9237616 0.9608696 0
## BAL_NB 0.8363003 0.8818369 0.8868286 0.8873915 0.8980908 0.9285166 0
##
## Sens
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## BAL_LDA 0.8260870 0.8508772 0.8684211 0.8720214 0.8947368 0.9304348 0
## BAL_CART 0.7368421 0.8333333 0.8596491 0.8531716 0.8956522 0.9122807 0
## BAL_SVM_R 0.8947368 0.9217391 0.9478261 0.9437193 0.9565217 0.9824561 0
## BAL_KNN 0.8684211 0.9035088 0.9210526 0.9171167 0.9385965 0.9652174 0
## BAL_NB 0.7807018 0.8333333 0.8508772 0.8524180 0.8771930 0.9391304 0
##
## Spec
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## BAL_LDA 0.6323529 0.6764706 0.7058824 0.7105882 0.7352941 0.8235294 0
## BAL_CART 0.6029412 0.6764706 0.7205882 0.7235294 0.7647059 0.8382353 0
## BAL_SVM_R 0.6911765 0.7647059 0.8088235 0.7976471 0.8382353 0.9117647 0
## BAL_KNN 0.7941176 0.8529412 0.8823529 0.8882353 0.9264706 1.0000000 0
## BAL_NB 0.6323529 0.7352941 0.7647059 0.7629412 0.7941176 0.8823529 0dotplot(BAL_LIST_RESAMPLES)
splom(BAL_LIST_RESAMPLES)
##################################
# Measuring the correlation among
# base learners
##################################
(BAL_LIST_COR <- modelCor(resamples(BAL_LIST)))## BAL_LDA BAL_CART BAL_SVM_R BAL_KNN BAL_NB
## BAL_LDA 1.00000000 -0.06342888 0.122350102 0.08479269 -0.131117037
## BAL_CART -0.06342888 1.00000000 0.078299147 -0.24343738 -0.081248867
## BAL_SVM_R 0.12235010 0.07829915 1.000000000 0.21687863 -0.005995804
## BAL_KNN 0.08479269 -0.24343738 0.216878631 1.00000000 -0.189499723
## BAL_NB -0.13111704 -0.08124887 -0.005995804 -0.18949972 1.0000000001.7.7 Meta-Learner Model Development using Logistic Regression (MEL_LR)
Logistic Regression models the relationship between the probability of an event (among two outcome levels) by having the log-odds of the event be a linear combination of a set of predictors weighted by their respective parameter estimates. The parameters are estimated via maximum likelihood estimation by testing different values through multiple iterations to optimize for the best fit of log odds. All of these iterations produce the log likelihood function, and logistic regression seeks to maximize this function to find the best parameter estimates. Given the optimal parameters, the conditional probabilities for each observation can be calculated, logged, and summed together to yield a predicted probability.
[A] The logistic regression model from the stats package was implemented on the optimal base learner ensemble through the caret package.
[B] The model does not contain any hyperparameter.
[C] The 5-cycle repeated 5-fold cross-validated model performance of the final model is summarized as follows:
[C.1] Final model configuration is fixed due to the absence of a hyperparameter
[C.2] ROC Curve AUC = 0.96993
[D] The independent test model performance of the final model is summarized as follows:
[D.1] ROC Curve AUC = 0.96613
Code Chunk | Output
##################################
# Formulating a stacked model
# using the base learners
# and a linear regression meta-model
##################################
set.seed(12345678)
MEL_LR <- caretStack(BAL_LIST,
metric="ROC",
trControl=RKFold_Control,
method="glm")
print(MEL_LR)## The following models were ensembled: BAL_LDA, BAL_CART, BAL_SVM_R, BAL_KNN, BAL_NB
##
## caret::train model:
## Generalized Linear Model
##
## 912 samples
## 5 predictor
## 2 classes: 'B', 'M'
##
## No pre-processing
## Resampling: Cross-Validated (5 fold, repeated 5 times)
## Summary of sample sizes: 729, 729, 730, 730, 730, 730, ...
## Resampling results:
##
## ROC Sens Spec
## 0.9699332 0.9520763 0.9147059
##
##
## Final model:
##
## Call: NULL
##
## Coefficients:
## (Intercept) BAL_LDA BAL_CART BAL_SVM_R BAL_KNN BAL_NB
## -5.0427 -0.1463 -0.4083 4.4379 4.9060 1.4449
##
## Degrees of Freedom: 911 Total (i.e. Null); 906 Residual
## Null Deviance: 1205
## Residual Deviance: 351.6 AIC: 363.6(MEL_LR_Train_AUROC <- MEL_LR$ens_model$results$ROC)## [1] 0.9699332##################################
# Independently evaluating the model
# on the test set
##################################
MEL_LR_Test <- data.frame(MEL_LR_Test_Observed = MA_Test$diagnosis,
MEL_LR_Test_Predicted = predict(MEL_LR,
MA_Test[,!names(MA_Test) %in% c("diagnosis")]))
MEL_LR_Test## MEL_LR_Test_Observed MEL_LR_Test_Predicted.B MEL_LR_Test_Predicted.M
## 1 M 0.039723064 0.960276936
## 2 M 0.992813330 0.007186670
## 3 M 0.093030883 0.906969117
## 4 M 0.055240146 0.944759854
## 5 M 0.011613039 0.988386961
## 6 M 0.005824800 0.994175200
## 7 M 0.021610536 0.978389464
## 8 M 0.025207859 0.974792141
## 9 M 0.036324604 0.963675396
## 10 M 0.005506428 0.994493572
## 11 M 0.033738105 0.966261895
## 12 M 0.087769722 0.912230278
## 13 M 0.015512894 0.984487106
## 14 B 0.960717222 0.039282778
## 15 B 0.987974397 0.012025603
## 16 B 0.993627901 0.006372099
## 17 M 0.012285296 0.987714704
## 18 M 0.039694581 0.960305419
## 19 B 0.993312443 0.006687557
## 20 B 0.990153313 0.009846687
## 21 M 0.068869028 0.931130972
## 22 B 0.990441703 0.009558297
## 23 B 0.977328360 0.022671640
## 24 M 0.032664172 0.967335828
## 25 B 0.023097789 0.976902211
## 26 M 0.890173319 0.109826681
## 27 B 0.993612505 0.006387495
## 28 M 0.017430390 0.982569610
## 29 B 0.953826350 0.046173650
## 30 B 0.993579684 0.006420316
## 31 B 0.987581569 0.012418431
## 32 B 0.991942308 0.008057692
## 33 M 0.048984333 0.951015667
## 34 B 0.993586401 0.006413599
## 35 B 0.983657463 0.016342537
## 36 M 0.115240160 0.884759840
## 37 B 0.993436612 0.006563388
## 38 M 0.019644163 0.980355837
## 39 M 0.029504261 0.970495739
## 40 B 0.984934307 0.015065693
## 41 M 0.013315236 0.986684764
## 42 M 0.048339823 0.951660177
## 43 M 0.085933965 0.914066035
## 44 M 0.005187671 0.994812329
## 45 M 0.042009276 0.957990724
## 46 B 0.993564944 0.006435056
## 47 B 0.993681727 0.006318273
## 48 M 0.035496426 0.964503574
## 49 M 0.136258886 0.863741114
## 50 M 0.077280505 0.922719495
## 51 B 0.992173894 0.007826106
## 52 B 0.276818129 0.723181871
## 53 M 0.016843944 0.983156056
## 54 M 0.038812249 0.961187751
## 55 M 0.111282956 0.888717044
## 56 M 0.017119890 0.982880110
## 57 B 0.993585994 0.006414006
## 58 B 0.989476557 0.010523443
## 59 B 0.986887769 0.013112231
## 60 B 0.975359027 0.024640973
## 61 B 0.989153126 0.010846874
## 62 B 0.993588575 0.006411425
## 63 B 0.993582913 0.006417087
## 64 B 0.993596387 0.006403613
## 65 M 0.014414467 0.985585533
## 66 B 0.975880185 0.024119815
## 67 M 0.006160309 0.993839691
## 68 B 0.984121889 0.015878111
## 69 B 0.922598056 0.077401944
## 70 B 0.962926075 0.037073925
## 71 B 0.961265926 0.038734074
## 72 M 0.115531624 0.884468376
## 73 B 0.993555676 0.006444324
## 74 B 0.991393186 0.008606814
## 75 B 0.993615606 0.006384394
## 76 M 0.022182563 0.977817437
## 77 B 0.990570316 0.009429684
## 78 B 0.949004486 0.050995514
## 79 B 0.992911402 0.007088598
## 80 B 0.984687898 0.015312102
## 81 B 0.929374115 0.070625885
## 82 B 0.957013816 0.042986184
## 83 B 0.993595135 0.006404865
## 84 B 0.993581652 0.006418348
## 85 B 0.970466553 0.029533447
## 86 M 0.043074904 0.956925096
## 87 B 0.993616743 0.006383257
## 88 B 0.045437260 0.954562740
## 89 B 0.992822679 0.007177321
## 90 B 0.906184184 0.093815816
## 91 B 0.991562837 0.008437163
## 92 B 0.992418479 0.007581521
## 93 B 0.967041347 0.032958653
## 94 B 0.992072925 0.007927075
## 95 B 0.991702005 0.008297995
## 96 B 0.984330688 0.015669312
## 97 M 0.413153339 0.586846661
## 98 B 0.252157172 0.747842828
## 99 B 0.993579045 0.006420955
## 100 M 0.220300330 0.779699670
## 101 B 0.988352487 0.011647513
## 102 B 0.981963831 0.018036169
## 103 B 0.937622039 0.062377961
## 104 B 0.993529244 0.006470756
## 105 B 0.993407340 0.006592660
## 106 M 0.936920039 0.063079961
## 107 B 0.993227309 0.006772691
## 108 M 0.056762017 0.943237983
## 109 B 0.949467953 0.050532047
## 110 M 0.056549992 0.943450008
## 111 B 0.928109326 0.071890674
## 112 B 0.933426348 0.066573652
## 113 B 0.969844773 0.030155227
## 114 B 0.977505336 0.022494664
## 115 B 0.947481014 0.052518986
## 116 M 0.006970534 0.993029466
## 117 M 0.020414638 0.979585362
## 118 B 0.991566610 0.008433390
## 119 M 0.992813330 0.007186670
## 120 M 0.030643981 0.969356019
## 121 M 0.033350529 0.966649471
## 122 M 0.009924604 0.990075396
## 123 M 0.005767727 0.994232273
## 124 M 0.066841205 0.933158795
## 125 M 0.005824800 0.994175200
## 126 M 0.117332343 0.882667657
## 127 M 0.021610536 0.978389464
## 128 M 0.036127391 0.963872609
## 129 B 0.968712476 0.031287524
## 130 M 0.006267137 0.993732863
## 131 M 0.023341681 0.976658319
## 132 M 0.007272537 0.992727463
## 133 B 0.993312443 0.006687557
## 134 B 0.990153313 0.009846687
## 135 B 0.932029832 0.067970168
## 136 M 0.030779260 0.969220740
## 137 B 0.993168100 0.006831900
## 138 B 0.942764322 0.057235678
## 139 B 0.023097789 0.976902211
## 140 B 0.952843940 0.047156060
## 141 M 0.009582801 0.990417199
## 142 M 0.890173319 0.109826681
## 143 M 0.033125162 0.966874838
## 144 B 0.993571447 0.006428553
## 145 B 0.993597135 0.006402865
## 146 B 0.994173240 0.005826760
## 147 B 0.982658594 0.017341406
## 148 B 0.886682831 0.113317169
## 149 M 0.012155682 0.987844318
## 150 B 0.993615936 0.006384064
## 151 M 0.064487566 0.935512434
## 152 M 0.005844224 0.994155776
## 153 M 0.005396298 0.994603702
## 154 M 0.042009276 0.957990724
## 155 B 0.993564944 0.006435056
## 156 B 0.993281946 0.006718054
## 157 B 0.985898056 0.014101944
## 158 M 0.007205215 0.992794785
## 159 B 0.989245052 0.010754948
## 160 M 0.055782402 0.944217598
## 161 M 0.136258886 0.863741114
## 162 B 0.991698882 0.008301118
## 163 B 0.914571647 0.085428353
## 164 B 0.969423444 0.030576556
## 165 B 0.276818129 0.723181871
## 166 B 0.957635359 0.042364641
## 167 M 0.037296645 0.962703355
## 168 B 0.974710495 0.025289505
## 169 B 0.993575528 0.006424472
## 170 M 0.078366328 0.921633672
## 171 B 0.991766240 0.008233760
## 172 B 0.993603018 0.006396982
## 173 B 0.993587245 0.006412755
## 174 B 0.974630559 0.025369441
## 175 M 0.026427134 0.973572866
## 176 B 0.993596387 0.006403613
## 177 B 0.939939729 0.060060271
## 178 B 0.984487845 0.015512155
## 179 M 0.043000906 0.956999094
## 180 B 0.983070682 0.016929318
## 181 B 0.968436712 0.031563288
## 182 B 0.993141110 0.006858890
## 183 B 0.993531242 0.006468758
## 184 B 0.993593580 0.006406420
## 185 M 0.035858081 0.964141919
## 186 B 0.993300177 0.006699823
## 187 B 0.987167610 0.012832390
## 188 B 0.990979341 0.009020659
## 189 B 0.992825697 0.007174303
## 190 B 0.962704544 0.037295456
## 191 B 0.949004486 0.050995514
## 192 B 0.975083251 0.024916749
## 193 B 0.978926877 0.021073123
## 194 B 0.859333533 0.140666467
## 195 B 0.929374115 0.070625885
## 196 B 0.957013816 0.042986184
## 197 B 0.993595135 0.006404865
## 198 B 0.993581652 0.006418348
## 199 M 0.049761316 0.950238684
## 200 B 0.973575429 0.026424571
## 201 B 0.969969214 0.030030786
## 202 B 0.993591460 0.006408540
## 203 B 0.919248489 0.080751511
## 204 M 0.042805339 0.957194661
## 205 B 0.045437260 0.954562740
## 206 B 0.965093751 0.034906249
## 207 B 0.990018236 0.009981764
## 208 B 0.975544476 0.024455524
## 209 M 0.034162318 0.965837682
## 210 M 0.413153339 0.586846661
## 211 B 0.252157172 0.747842828
## 212 B 0.993586898 0.006413102
## 213 B 0.955422702 0.044577298
## 214 M 0.936920039 0.063079961
## 215 B 0.986216621 0.013783379
## 216 B 0.984627120 0.015372880
## 217 B 0.993590377 0.006409623
## 218 B 0.993629375 0.006370625
## 219 B 0.992194182 0.007805818
## 220 M 0.008514817 0.991485183
## 221 B 0.964848237 0.035151763
## 222 B 0.970131269 0.029868731
## 223 B 0.961235491 0.038764509
## 224 B 0.933426348 0.066573652
## 225 B 0.977505336 0.022494664
## 226 M 0.011297304 0.988702696#################################
# Reporting the independent evaluation results
# for the test set
#################################
MEL_LR_Test_ROC <- roc(response = MEL_LR_Test$MEL_LR_Test_Observed,
predictor = MEL_LR_Test$MEL_LR_Test_Predicted.M,
levels = rev(levels(MEL_LR_Test$MEL_LR_Test_Observed)))
(MEL_LR_Test_AUROC <- auc(MEL_LR_Test_ROC)[1])## [1] 0.96613011.7.8 Meta-Learner Model Development using Random Forest (MEL_RF)
Random Forest is an ensemble learning method made up of a large set of small decision trees called estimators, with each producing its own prediction. The random forest model aggregates the predictions of the estimators to produce a more accurate prediction. The algorithm involves bootstrap aggregating (where smaller subsets of the training data are repeatedly subsampled with replacement), random subspacing (where a subset of features are sampled and used to train each individual estimator), estimator training (where unpruned decision trees are formulated for each estimator) and inference by aggregating the predictions of all estimators.
[A] The random forest model from the randomForest package was implemented optimal base learner ensemble through the caret package.
[B] The model contains 1 hyperparameter:
[B.1] mtry = number of randomly selected predictors made to vary across a range of values equal to 2 to 5
[C] The 5-cycle repeated 5-fold cross-validated model performance of the final model is summarized as follows:
[C.1] Final model configuration involves mtry=3
[C.2] AUROC = 0.97611
[D] The independent test model performance of the final model is summarized as follows:
[D.1] AUROC = 0.96898
Code Chunk | Output
##################################
# Formulating a stacked model
# using the base learners
# and a random forest meta-model
##################################
set.seed(12345678)
MEL_RF <- caretStack(BAL_LIST,
metric="ROC",
trControl=RKFold_Control,
method="rf",
tuneGrid = RF_Grid)
print(MEL_RF)## The following models were ensembled: BAL_LDA, BAL_CART, BAL_SVM_R, BAL_KNN, BAL_NB
##
## caret::train model:
## Random Forest
##
## 912 samples
## 5 predictor
## 2 classes: 'B', 'M'
##
## No pre-processing
## Resampling: Cross-Validated (5 fold, repeated 5 times)
## Summary of sample sizes: 729, 729, 730, 730, 730, 730, ...
## Resampling results across tuning parameters:
##
## mtry ROC Sens Spec
## 2 0.9761136 0.9559359 0.9317647
## 3 0.9738558 0.9573364 0.9352941
## 4 0.9733648 0.9597834 0.9382353
## 5 0.9730741 0.9594294 0.9382353
##
## ROC was used to select the optimal model using the largest value.
## The final value used for the model was mtry = 2.
##
## Final model:
##
## Call:
## randomForest(x = x, y = y, mtry = param$mtry)
## Type of random forest: classification
## Number of trees: 500
## No. of variables tried at each split: 2
##
## OOB estimate of error rate: 4.5%
## Confusion matrix:
## B M class.error
## B 551 21 0.03671329
## M 20 320 0.05882353(MEL_RF_Train_AUROC <- max(MEL_RF$ens_model$results$ROC))## [1] 0.9761136##################################
# Independently evaluating the model
# on the test set
##################################
MEL_RF_Test <- data.frame(MEL_RF_Test_Observed = MA_Test$diagnosis,
MEL_RF_Test_Predicted = predict(MEL_RF,
MA_Test[,!names(MA_Test) %in% c("diagnosis")]))
MEL_RF_Test## MEL_RF_Test_Observed MEL_RF_Test_Predicted.B MEL_RF_Test_Predicted.M
## 1 M 0.006 0.994
## 2 M 0.998 0.002
## 3 M 0.104 0.896
## 4 M 0.064 0.936
## 5 M 0.096 0.904
## 6 M 0.000 1.000
## 7 M 0.000 1.000
## 8 M 0.000 1.000
## 9 M 0.026 0.974
## 10 M 0.002 0.998
## 11 M 0.022 0.978
## 12 M 0.088 0.912
## 13 M 0.062 0.938
## 14 B 1.000 0.000
## 15 B 0.994 0.006
## 16 B 1.000 0.000
## 17 M 0.000 1.000
## 18 M 0.006 0.994
## 19 B 1.000 0.000
## 20 B 1.000 0.000
## 21 M 0.012 0.988
## 22 B 0.944 0.056
## 23 B 0.992 0.008
## 24 M 0.004 0.996
## 25 B 0.292 0.708
## 26 M 0.904 0.096
## 27 B 1.000 0.000
## 28 M 0.002 0.998
## 29 B 0.998 0.002
## 30 B 1.000 0.000
## 31 B 0.982 0.018
## 32 B 0.982 0.018
## 33 M 0.148 0.852
## 34 B 1.000 0.000
## 35 B 0.998 0.002
## 36 M 0.146 0.854
## 37 B 1.000 0.000
## 38 M 0.000 1.000
## 39 M 0.112 0.888
## 40 B 0.934 0.066
## 41 M 0.016 0.984
## 42 M 0.126 0.874
## 43 M 0.100 0.900
## 44 M 0.002 0.998
## 45 M 0.032 0.968
## 46 B 1.000 0.000
## 47 B 1.000 0.000
## 48 M 0.012 0.988
## 49 M 0.050 0.950
## 50 M 0.146 0.854
## 51 B 1.000 0.000
## 52 B 0.312 0.688
## 53 M 0.000 1.000
## 54 M 0.014 0.986
## 55 M 0.206 0.794
## 56 M 0.114 0.886
## 57 B 1.000 0.000
## 58 B 0.984 0.016
## 59 B 0.994 0.006
## 60 B 0.902 0.098
## 61 B 0.978 0.022
## 62 B 1.000 0.000
## 63 B 1.000 0.000
## 64 B 1.000 0.000
## 65 M 0.002 0.998
## 66 B 1.000 0.000
## 67 M 0.006 0.994
## 68 B 0.996 0.004
## 69 B 0.910 0.090
## 70 B 0.998 0.002
## 71 B 0.994 0.006
## 72 M 0.054 0.946
## 73 B 1.000 0.000
## 74 B 1.000 0.000
## 75 B 1.000 0.000
## 76 M 0.002 0.998
## 77 B 0.992 0.008
## 78 B 0.948 0.052
## 79 B 0.968 0.032
## 80 B 1.000 0.000
## 81 B 0.858 0.142
## 82 B 0.940 0.060
## 83 B 1.000 0.000
## 84 B 1.000 0.000
## 85 B 1.000 0.000
## 86 M 0.052 0.948
## 87 B 1.000 0.000
## 88 B 0.094 0.906
## 89 B 0.968 0.032
## 90 B 0.706 0.294
## 91 B 0.876 0.124
## 92 B 1.000 0.000
## 93 B 0.950 0.050
## 94 B 0.926 0.074
## 95 B 0.966 0.034
## 96 B 0.942 0.058
## 97 M 0.616 0.384
## 98 B 0.486 0.514
## 99 B 1.000 0.000
## 100 M 0.290 0.710
## 101 B 0.994 0.006
## 102 B 1.000 0.000
## 103 B 0.942 0.058
## 104 B 1.000 0.000
## 105 B 1.000 0.000
## 106 M 0.972 0.028
## 107 B 1.000 0.000
## 108 M 0.042 0.958
## 109 B 0.906 0.094
## 110 M 0.082 0.918
## 111 B 0.924 0.076
## 112 B 0.978 0.022
## 113 B 0.996 0.004
## 114 B 0.982 0.018
## 115 B 0.648 0.352
## 116 M 0.000 1.000
## 117 M 0.004 0.996
## 118 B 1.000 0.000
## 119 M 0.998 0.002
## 120 M 0.278 0.722
## 121 M 0.164 0.836
## 122 M 0.000 1.000
## 123 M 0.000 1.000
## 124 M 0.016 0.984
## 125 M 0.000 1.000
## 126 M 0.134 0.866
## 127 M 0.000 1.000
## 128 M 0.026 0.974
## 129 B 0.998 0.002
## 130 M 0.000 1.000
## 131 M 0.006 0.994
## 132 M 0.002 0.998
## 133 B 1.000 0.000
## 134 B 1.000 0.000
## 135 B 0.862 0.138
## 136 M 0.086 0.914
## 137 B 0.968 0.032
## 138 B 0.840 0.160
## 139 B 0.292 0.708
## 140 B 0.998 0.002
## 141 M 0.000 1.000
## 142 M 0.904 0.096
## 143 M 0.094 0.906
## 144 B 1.000 0.000
## 145 B 1.000 0.000
## 146 B 0.920 0.080
## 147 B 1.000 0.000
## 148 B 0.780 0.220
## 149 M 0.000 1.000
## 150 B 1.000 0.000
## 151 M 0.058 0.942
## 152 M 0.000 1.000
## 153 M 0.008 0.992
## 154 M 0.032 0.968
## 155 B 1.000 0.000
## 156 B 0.892 0.108
## 157 B 1.000 0.000
## 158 M 0.006 0.994
## 159 B 0.988 0.012
## 160 M 0.086 0.914
## 161 M 0.050 0.950
## 162 B 1.000 0.000
## 163 B 0.806 0.194
## 164 B 0.994 0.006
## 165 B 0.312 0.688
## 166 B 0.984 0.016
## 167 M 0.106 0.894
## 168 B 0.952 0.048
## 169 B 1.000 0.000
## 170 M 0.442 0.558
## 171 B 0.950 0.050
## 172 B 1.000 0.000
## 173 B 1.000 0.000
## 174 B 0.992 0.008
## 175 M 0.268 0.732
## 176 B 1.000 0.000
## 177 B 0.956 0.044
## 178 B 0.990 0.010
## 179 M 0.010 0.990
## 180 B 1.000 0.000
## 181 B 0.996 0.004
## 182 B 0.968 0.032
## 183 B 1.000 0.000
## 184 B 1.000 0.000
## 185 M 0.026 0.974
## 186 B 0.998 0.002
## 187 B 1.000 0.000
## 188 B 0.980 0.020
## 189 B 0.998 0.002
## 190 B 0.996 0.004
## 191 B 0.948 0.052
## 192 B 0.886 0.114
## 193 B 0.874 0.126
## 194 B 0.618 0.382
## 195 B 0.858 0.142
## 196 B 0.940 0.060
## 197 B 1.000 0.000
## 198 B 1.000 0.000
## 199 M 0.010 0.990
## 200 B 0.904 0.096
## 201 B 1.000 0.000
## 202 B 1.000 0.000
## 203 B 0.824 0.176
## 204 M 0.030 0.970
## 205 B 0.094 0.906
## 206 B 0.982 0.018
## 207 B 1.000 0.000
## 208 B 0.930 0.070
## 209 M 0.316 0.684
## 210 M 0.616 0.384
## 211 B 0.486 0.514
## 212 B 1.000 0.000
## 213 B 0.992 0.008
## 214 M 0.972 0.028
## 215 B 0.978 0.022
## 216 B 0.998 0.002
## 217 B 1.000 0.000
## 218 B 1.000 0.000
## 219 B 1.000 0.000
## 220 M 0.014 0.986
## 221 B 0.996 0.004
## 222 B 0.700 0.300
## 223 B 0.956 0.044
## 224 B 0.978 0.022
## 225 B 0.982 0.018
## 226 M 0.000 1.000#################################
# Reporting the independent evaluation results
# for the test set
#################################
MEL_RF_Test_ROC <- roc(response = MEL_RF_Test$MEL_RF_Test_Observed,
predictor = MEL_RF_Test$MEL_RF_Test_Predicted.M,
levels = rev(levels(MEL_RF_Test$MEL_RF_Test_Observed)))
(MEL_RF_Test_AUROC <- auc(MEL_RF_Test_ROC)[1])## [1] 0.96898051.8 Consolidated Findings
[A] Models which are based on boosting and bagging algorithms demonstrated excellent cross-validated and test AUROC metrics:
[A.1] MBS_AB: Adaptive Boosting (adabag package)
[A.1.1] Cross-Validation AUROC = 0.97109
[A.1.2] Test ROC Curve AUROC = 0.99564
[A.2] MBS_GBM: Stochastic Gradient Boosting (gbm package)
[A.2.1] Cross-Validation AUROC = 0.96473
[A.2.2] Test ROC Curve AUROC = 0.98306
[A.3] MBS_XGB: Extreme Gradient Boosting (xgboost package)
[A.3.1] Cross-Validation AUROC = 0.96403
[A.3.2] Test ROC Curve AUROC = 0.98977
[A.4] MBG_RF: Random Forest (randomForest package)
[A.4.1] Cross-Validation AUROC = 0.97000
[A.4.2] Test ROC Curve AUROC = 0.99195
[A.5] MBG_BCART: Bagged Classification and Regression Trees (ipred, plyr and e1071 packages)
[A.5.1] Cross-Validation AUROC = 0.96444
[A.5.2] Test ROC Curve AUROC = 0.998583
[B] Models implemented as base learners which are not based on boosting and bagging algorithms individually demonstrated inferior cross-validated and test AUROC metrics:
[B.1] BAL_LDA: Linear Discriminant Analysis (MASS package)
[B.1.1] Cross-Validation AUROC = 0.87628
[B.1.2] Test ROC Curve AUROC = 0.88833
[B.2] BAL_CART: Classification and Regression Trees (rpart package)
[B.2.1] Cross-Validation AUROC = 0.86145
[B.2.2] Test ROC Curve AUROC = 0.92106
[B.3] BAL_SVM_R: Support Vector Machine - Radial Basis Function Kernel (kernlab package)
[B.3.1] Cross-Validation AUROC = 0.90977
[B.3.2] Test ROC Curve AUROC = 0.93762
[B.4] BAL_KNN: K-Nearest Neighbors (caret package)
[B.4.1] Cross-Validation AUROC = 0.90764
[B.4.2] Test ROC Curve AUROC = 0.93611
[B.5] BAL_NB: Naive Bayes (klaR package)
[B.5.1] Cross-Validation AUROC = 0.88735
[B.5.2] Test ROC Curve AUROC = 0.89696
[C] The model stacking process demonstrated improved cross-validated and test AUROC metrics as compared to individual base learners:
[C.1] MEL_LR: Logistic Regression (stats package)
[C.1.1] Cross-Validation AUROC = 0.96993
[C.1.2] Test ROC Curve AUROC = 0.96613
[C.2] MEL_RF: Classification and Regression Trees (randomForest package)
[C.2.1] Cross-Validation AUROC = 0.97611
[C.2.2] Test ROC Curve AUROC = 0.96898
Code Chunk | Output
##################################
# Consolidating the resampling results
# for the formulated individual models
##################################
(Consolidated_Resampling <- resamples(list(MBS_AB = MBS_AB_Tune,
MBS_GBM = MBS_GBM_Tune,
MBS_XGB = MBS_XGB_Tune,
MBG_RF = MBG_RF_Tune,
MBG_BCART = MBG_BCART_Tune,
BAL_LDA = BAL_LDA_Tune,
BAL_CART = BAL_CART_Tune,
BAL_KNN = BAL_KNN_Tune,
BAL_NB = BAL_NB_Tune)))##
## Call:
## resamples.default(x = list(MBS_AB = MBS_AB_Tune, MBS_GBM =
## = MBG_BCART_Tune, BAL_LDA = BAL_LDA_Tune, BAL_CART = BAL_CART_Tune, BAL_KNN
## = BAL_KNN_Tune, BAL_NB = BAL_NB_Tune))
##
## Models: MBS_AB, MBS_GBM, MBS_XGB, MBG_RF, MBG_BCART, BAL_LDA, BAL_CART, BAL_KNN, BAL_NB
## Number of resamples: 25
## Performance metrics: ROC, Sens, Spec
## Time estimates for: everything, final model fitsummary(Consolidated_Resampling)##
## Call:
## summary.resamples(object = Consolidated_Resampling)
##
## Models: MBS_AB, MBS_GBM, MBS_XGB, MBG_RF, MBG_BCART, BAL_LDA, BAL_CART, BAL_KNN, BAL_NB
## Number of resamples: 25
##
## ROC
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## MBS_AB 0.9336945 0.9595908 0.9748452 0.9710985 0.9860614 0.9941176 0
## MBS_GBM 0.9262126 0.9476982 0.9656863 0.9647306 0.9821981 0.9877238 0
## MBS_XGB 0.9220846 0.9460358 0.9700722 0.9640349 0.9820691 0.9900256 0
## MBG_RF 0.9324690 0.9563939 0.9729747 0.9700081 0.9831841 0.9930946 0
## MBG_BCART 0.9224071 0.9521100 0.9696852 0.9644432 0.9808437 0.9901535 0
## BAL_LDA 0.8102302 0.8641641 0.8828689 0.8762815 0.8913829 0.9296675 0
## BAL_CART 0.7863777 0.8392673 0.8597187 0.8614523 0.8853844 0.9405371 0
## BAL_KNN 0.8342363 0.8946931 0.9120227 0.9076428 0.9237616 0.9562020 0
## BAL_NB 0.8354220 0.8777090 0.8886189 0.8873525 0.8989938 0.9317136 0
##
## Sens
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## MBS_AB 0.9043478 0.9298246 0.9565217 0.9527750 0.9736842 0.9913043 0
## MBS_GBM 0.8956522 0.9298246 0.9478261 0.9492784 0.9652174 1.0000000 0
## MBS_XGB 0.9122807 0.9385965 0.9565217 0.9562777 0.9739130 0.9913043 0
## MBG_RF 0.9130435 0.9473684 0.9565217 0.9580351 0.9652174 0.9913043 0
## MBG_BCART 0.8956522 0.9473684 0.9561404 0.9541998 0.9736842 0.9913043 0
## BAL_LDA 0.8260870 0.8508772 0.8684211 0.8720214 0.8947368 0.9304348 0
## BAL_CART 0.7631579 0.8086957 0.8596491 0.8503158 0.8859649 0.9043478 0
## BAL_KNN 0.8596491 0.9035088 0.9210526 0.9205797 0.9391304 0.9736842 0
## BAL_NB 0.7913043 0.8333333 0.8596491 0.8552525 0.8771930 0.9130435 0
##
## Spec
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## MBS_AB 0.7941176 0.8823529 0.8970588 0.8964706 0.9264706 0.9558824 0
## MBS_GBM 0.8235294 0.8676471 0.8970588 0.8964706 0.9264706 0.9705882 0
## MBS_XGB 0.8235294 0.8823529 0.8970588 0.9000000 0.9264706 0.9558824 0
## MBG_RF 0.8088235 0.8676471 0.8823529 0.8970588 0.9411765 0.9852941 0
## MBG_BCART 0.8235294 0.8676471 0.9117647 0.9000000 0.9264706 0.9852941 0
## BAL_LDA 0.6323529 0.6764706 0.7058824 0.7105882 0.7352941 0.8235294 0
## BAL_CART 0.5588235 0.7058824 0.7500000 0.7482353 0.7794118 0.8823529 0
## BAL_KNN 0.8088235 0.8676471 0.8970588 0.8947059 0.9264706 0.9558824 0
## BAL_NB 0.6911765 0.7205882 0.7500000 0.7605882 0.7794118 0.8529412 0##################################
# Exploring the resampling results
# for the formulated individual models
##################################
bwplot(Consolidated_Resampling,
main = "Model Resampling Performance Comparison (Range)",
ylab = "Model",
pch=16,
cex=2,
layout=c(3,1))
##################################
# Consolidating the train and test AUROC
# for the formulated individual models
# together with the ensemble and stacked models
##################################
Model <- c('MBS_AB','MBS_GBM','MBS_XGB',
'MBG_RF','MBG_BCART',
'BAL_LDA','BAL_CART','BAL_SVM_R','BAL_KNN','BAL_NB',
'MEL_LR','MEL_RF',
'MBS_AB','MBS_GBM','MBS_XGB',
'MBG_RF','MBG_BCART',
'BAL_LDA','BAL_CART','BAL_SVM_R','BAL_KNN','BAL_NB',
'MEL_LR','MEL_RF')
Set <- c(rep('Cross-Validation',12),rep('Test',12))
AUROC <- c(MBS_AB_Train_AUROC,MBS_GBM_Train_AUROC,MBS_XGB_Train_AUROC,
MBG_RF_Train_AUROC,MBG_BCART_Train_AUROC,
BAL_LDA_Train_AUROC,BAL_CART_Train_AUROC,BAL_SVM_R_Train_AUROC,BAL_KNN_Train_AUROC,BAL_NB_Train_AUROC,
MEL_LR_Train_AUROC,MEL_RF_Train_AUROC,
MBS_AB_Test_AUROC,MBS_GBM_Test_AUROC,MBS_XGB_Test_AUROC,
MBG_RF_Test_AUROC,MBG_BCART_Test_AUROC,
BAL_LDA_Test_AUROC,BAL_CART_Test_AUROC,BAL_SVM_R_Test_AUROC,BAL_KNN_Test_AUROC,BAL_NB_Test_AUROC,
MEL_LR_Test_AUROC,MEL_RF_Test_AUROC)
AUROC_Summary <- as.data.frame(cbind(Model,Set,AUROC))
AUROC_Summary$AUROC <- as.numeric(as.character(AUROC_Summary$AUROC))
AUROC_Summary$Set <- factor(AUROC_Summary$Set,
levels = c("Cross-Validation",
"Test"))
AUROC_Summary$Model <- factor(AUROC_Summary$Model,
levels = c('MBS_AB',
'MBS_GBM',
'MBS_XGB',
'MBG_RF',
'MBG_BCART',
'BAL_LDA',
'BAL_CART',
'BAL_SVM_R',
'BAL_KNN',
'BAL_NB',
'MEL_LR',
'MEL_RF'))
print(AUROC_Summary, row.names=FALSE)## Model Set AUROC
## MBS_AB Cross-Validation 0.9710985
## MBS_GBM Cross-Validation 0.9647306
## MBS_XGB Cross-Validation 0.9640349
## MBG_RF Cross-Validation 0.9700081
## MBG_BCART Cross-Validation 0.9644432
## BAL_LDA Cross-Validation 0.8762815
## BAL_CART Cross-Validation 0.8614523
## BAL_SVM_R Cross-Validation 0.9097712
## BAL_KNN Cross-Validation 0.9076428
## BAL_NB Cross-Validation 0.8873525
## MEL_LR Cross-Validation 0.9699332
## MEL_RF Cross-Validation 0.9761136
## MBS_AB Test 0.9956405
## MBS_GBM Test 0.9830651
## MBS_XGB Test 0.9897720
## MBG_RF Test 0.9919517
## MBG_BCART Test 0.9858317
## BAL_LDA Test 0.8883300
## BAL_CART Test 0.9210681
## BAL_SVM_R Test 0.9376258
## BAL_KNN Test 0.9361167
## BAL_NB Test 0.8969651
## MEL_LR Test 0.9661301
## MEL_RF Test 0.9689805(AUROC_Plot <- dotplot(Model ~ AUROC,
data = AUROC_Summary,
groups = Set,
main = "Classification Model Performance Comparison",
ylab = "Model",
xlab = "AUROC",
auto.key = list(adj=1, space="top", columns=2),
type=c("p", "h"),
origin = 0,
alpha = 0.45,
pch = 16,
cex = 2))
3. References
[Book] Ensemble Methods for Machine Learning by Gautam Kunapuli
[Book] Statistics and Machine Learning in Python by Edouard Duchesnay, Tommy Lofstedt and Feki Younes
[Book] Applied Predictive Modeling by Max Kuhn and Kjell Johnson
[Book] An Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie and Rob Tibshirani
[Book] Multivariate Data Visualization with R by Deepayan Sarkar
[Book] Machine Learning by Samuel Jackson
[Book] Data Modeling Methods by Jacob Larget
[Book] Introduction to R and Statistics by University of Western Australia
[Book] Feature Engineering and Selection: A Practical Approach for Predictive Models by Max Kuhn and Kjell Johnson
[Book] Introduction to Research Methods by Eric van Holm
[R Package] AppliedPredictiveModeling by Max Kuhn
[R Package] caret by Max Kuhn
[R Package] rpart by Terry Therneau and Beth Atkinson
[R Package] lattice by Deepayan Sarkar
[R Package] dplyr by Hadley Wickham
[R Package] tidyr by Hadley Wickham
[R Package] moments by Lukasz Komsta and Frederick
[R Package] skimr by Elin Waring
[R Package] RANN by Sunil Arya, David Mount, Samuel Kemp and Gregory Jefferis
[R Package] corrplot by Taiyun Wei
[R Package] tidyverse by Hadley Wickham
[R Package] lares by Bernardo Lares
[R Package] DMwR2 by Luis Torgo
[R Package] gridExtra by Baptiste Auguie and Anton Antonov
[R Package] rattle by Graham Williams
[R Package] RColorBrewer by Erich Neuwirth
[R Package] stats by R Core Team
[R Package] caretEnsemble by Zachary Deane-Mayer
[R Package] pROC by Xavier Robin
[R Package] adabag by Esteban Alfaro, Matias Gamez and Noelia Garcia
[R Package] gbm by Brandon Greenwell, Bradley Boehmke and Jay Cunningham
[R Package] xgboost by Jiaming Yuan
[R Package] randomForest by Andy Liaw
[R Package] kernlab by Alexandros Karatzoglou
[R Package] klaR by Christian Roever, Nils Raabe, Karsten Luebke, Uwe Ligges, Gero Szepannek, Marc Zentgraf and David Meyer
[R Package] rpart by Terry Therneau and Beth Atkinson
[R Package] rpart.plot by Stephen Milborrow
[Article] A Brief Introduction to caretEnsemble by Zachary Deane-Mayer
[Article] A Gentle Introduction to Ensemble Learning Algorithms by Jason Brownlee
[Article] Ensemble Methods: Elegant Techniques to Produce Improved Machine Learning Results by Necati Demir
[Article] The Complete Guide to Ensemble Learning by Rohit Kundu
[Article] Develop an Intuition for How Ensemble Learning Works by Jason Brownlee
[Article] How to Build an Ensemble Of Machine Learning Algorithms in R by Jason Brownlee
[Article] Ensemble Learning: Bagging, Boosting, and Stacking by Towards AI Team
[Article] Bagging, Boosting, and Stacking in Machine Learning by Emmanuella Budu
[Article] Stacking Ensemble Machine Learning With Python by Jason Brownlee
[Article] Essence of Boosting Ensembles for Machine Learning by Jason Brownlee
[Article] Ensemble Modeling with R by Deepika Singh
[Article] Creating Ensemble Models in R by Dustin Rogers
[Article] Stacking Ensemble Machine Learning With Python by Jason Brownlee
[Article] Stacking Machine Learning: Everything You Need to Know by Ada Parker
[Article] Ensemble Learning: Bagging, Boosting and Stacking by Edouard Duchesnay, Tommy Lofstedt and Feki Younes
[Article] Stack Machine Learning Models: Get Better Results by IBM Team
[Article] Gradient Boosting vs AdaBoost vs XGBoost vs CatBoost vs LightGBM by Geeks for Geeks Team
[Article] A Gentle Introduction to the Gradient Boosting Algorithm for Machine Learning by Jason Brownlee
[Article] The Ultimate Guide to AdaBoost Algorithm | What is AdaBoost Algorithm? by Ashish Kumar
[Publication] Experiments with a New Boosting Algorithm by Yoav Freund and Robert Schapire (Proceedings of the Thirteenth International Conference on Machine Learning)
[Publication] Stochastic Gradient Boosting by Jerome Friedman (Computational Statistics and Data Analysis)
[Publication] XGBoost: A Scalable Tree Boosting System by Tianqi Chen and Carlos Guestrin (22nd SIGKDD Conference on Knowledge Discovery and Data Mining)
[Publication] Ensemble Selection from Libraries of Models by Rich Caruana, Alexandru Niculescu-Mizil, Geoff Crew and Alex Ksikes (Proceedings of the 21 st International Conference on Machine Learning)
[Publication] Random Forest by Leo Breiman (Machine Learning)
[Publication] Bagging Predictors by Leo Breiman (Machine Learning)
[Publication] The Use of Multiple Measurements in Taxonomic Problems by Ronald Fisher (Annals of Human Genetics)
[Publication] Classification and Regression Trees by Leo Breiman, Jerome Friedman, Richard Olshen, and Charles Stone (Computer Science)
[Publication] A Training Algorithm for Optimal Margin Classifiers by Bernhard Boser, Isabelle Guyon and Vladimir Vapnik (Proceedings of the Fifth Annual Workshop on Computational Learning Theory)
[Publication] Nearest Neighbor Pattern Classification Thomas Cover and Peter Hart (IEEE Transactions on Information Theory)
[Publication] Who Discovered Bayes’s Theorem? by Stephen Stigler (The American Statistician)
[Publication] The Origins of Logistic Regression by JS Cramer (Econometrics eJournal)
[Course] Applied Data Mining and Statistical Learning by Penn State Eberly College of Science