Supervised Learning : Characterizing Life Expectancy Drivers Across Countries Using Model-Agnostic Interpretation Methods for Black-Box Models
John Pauline Pineda
July 23, 2023
- 1.
Table of Contents
- 1.1 Introduction
- 1.2 Methodology
- 1.3 Results
- 1.3.1 Data Preparation
- 1.3.2 Data Quality Assessment
- 1.3.3 Data Preprocessing
- 1.3.4 Data Exploration
- 1.3.5 Feature Selection
- 1.3.6 Model Development and Performance Estimation
- 1.3.7 Model Performance Validation
- 1.3.8 Model Selection
- 1.3.9
Model Presentation
- 1.3.9.1 Dataset Level Exploration : Variable Importance
- 1.3.9.2 Dataset Level Exploration : Partial Dependence Plots
- 1.3.9.3 Instance Level Exploration : Breakdown Plots
- 1.3.9.4 Instance Level Exploration : Shapley Additive Explanations
- 1.3.9.5 Instance Level Exploration : Ceteris Paribus Profiles
- 1.3.9.6 Instance Level Exploration : Local Fidelity Plots
- 1.3.9.7 Instance Level Exploration : Local Stability Plots
- 2. Summary
- 3. References
1. Table of Contents
1.1 Introduction
Life expectancy is a statistical measure that represents the average number of years a person is expected to live from birth, assuming current mortality rates remain constant along the entire life course. It provides an estimation of the overall health and well-being of a population and is often reflective of the local conditions encompassing numerous factors including demographic, socio-economic, healthcare access and healthcare quality.
Using an open dataset from Kaggle (with all credits attributed to Kiran Shahi) as primarily sourced from The World Bank, this study hypothesized that various world development and health indicators influence life expectancy across countries. A number of regression models was formulated to explore the relationship between life expectancy and these factors.
Subsequent analysis and modelling steps involving data understanding, data preparation, data exploration, model development, model validation and model presentation were individually detailed below, with all the results consolidated in a Summary provided at the end of the document.
1.1.1 Study Objectives
The main objective of the study is to develop an interpretable regression model which could provide robust and reliable estimates of life expectancy from an optimal set of observations and predictors, while delivering accurate predictions when applied to new unseen data.
Specific objectives are given as follows:
[A] Obtain an optimal subset of observations and predictors by conducting data quality assessment and feature selection, excluding cases or variables noted with irregularities and applying preprocessing operations most suitable for the downstream analysis
[B] Develop multiple regression models with optimized hyperparameters through internal resampling validation
[C] Select the final regression model among candidates based on robust performance estimates
[D] Evaluate the final model performance and generalization ability through external validation in an independent set
[E] Conduct a post-hoc exploration of the model results, even for black-box models, to provide general insights on the importance, contribution and effect of the various predictors to model prediction
1.1.2 Outcome
The analysis endpoint for the study is described below:
[A] LIFEXP (numeric): Life Expectancy; Number of years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life.
1.1.3 Predictors
Detailed descriptions for each individual predictor used in the study are provided as follows:
[A] COUNTRY (character): Country; Political unit with sovereignty (legitimate and total political power) over a territory and inhabitants within its borders.
[B] YEAR (numeric): Year; Year when all relevant data were gathered, fixed at 2019 for this analysis.
[C] GENDER (factor): Gender, Biological categorization group upon which all relevant data are associated with.
[D] CONTIN (factor): Continent; Collective region where the specified country belongs to.
[E] UNEMPR (numeric, in %): Unemployment Rate; Proportion of unemployed individuals in a group among individuals currently in the labor force.
[F] INFMOR (numeric, in number of deaths): Infant Mortality; Number of infants dying before reaching one year of age in agroup per 1,000 live births in a given year
[G] GDP (numeric, in USD): Gross Domestic Product; Sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources..
[H] GNI (numeric, in USD): Gross National Income; Sum of value added by all resident producers plus any product taxes (less subsidies) not included in the valuation of output plus net receipts of primary income (compensation of employees and property income) from abroad.
[I] CLTECH (numeric, in %): Access to Clean Fuels and Technologies for Cooking; Proportion of the population in a group with access to clean fuels and technologies for cooking. Under WHO guidelines, kerosene is excluded from clean cooking fuels.
[J] PERCAP (numeric, in USD): Gross Domestic Product Per Capita; Annual gross domestic product divided by midyear population within a group.
[K] RTIMOR (numeric, in number of deaths): Mortality Caused by Road Traffic Injury; Road traffic fatal injury deaths in a group per 100,000 population.
[L] TUBINC (numeric, in number of cases): Incidence of Tuberculosis; Estimated number of new and relapse tuberculosis cases in a group arising in a given year per 100,000 population.
[M] DPTIMM (numeric, in %): DPT Immunization; Proportion of children ages 12-23 months within a group who received diphtheria, pertussis (or whooping cough) and tetanus (DPT) vaccinations before 12 months or at any time before the survey.
[N] HEPIMM (numeric, in %): Hepatitis B Immunization; Proportion of children ages 12-23 months within a group who received hepatitis B vaccinations before 12 months or at any time before the survey.
[O] MEAIMM (numeric, in %): Measles Immunization; Proportion of children ages 12-23 months within a group who received measles vaccinations before 12 months or at any time before the survey.
[P] HOSBED (numeric, in number of beds): Hospital Beds; Number of hospital beds per 1,000 people in a group which include inpatient beds available in public, private, general, and specialized hospitals and rehabilitation centers.
[Q] SANSER (numeric, in %): People Using At Least Basic Sanitation Services; Proportion of people in a group using at least basic sanitation services, that is, improved sanitation facilities that are not shared with other households including basic sanitation services as well as those using safely managed sanitation services.
[R] TUBTRT (numeric, in %): Tuberculosis Treatment Success Rate; Proportion of all new tuberculosis cases registered under a national tuberculosis control programme within a group in a given year that successfully completed treatment, with or without bacteriological evidence of success (“cured” and “treatment completed” respectively).
[S] URBPOP (numeric, in %): Urban Population; Proportion of people in a group living in urban areas as defined by national statistical offices.
[T] RURPOP (numeric, in %): Rural Population; Proportion of people in a group living in rural areas as defined by national statistical offices:
[U] NCOMOR (numeric, in %): Deaths due to Non-Communicable Diseases; Proportion of deaths for all ages by underlying causes in a group which was attributed to non-communicable diseases including cancer, diabetes mellitus, cardiovascular diseases, digestive diseases, skin diseases, musculoskeletal diseases, and congenital anomalies.
[V] SUIRAT (numeric, in number of deaths): Suicide Mortality Rate; Number of suicide deaths annually in a group per 100,000 population.
1.2 Methodology
1.2.1 Data Assessment
Preliminary data used in the study was evaluated and prepared for analysis and modelling using the following methods:
Data Quality Assessment involves profiling the data to understand its suitability for machine learning tasks. The quality of training data has a huge impact on the efficiency, accuracy and complexity of the predictive modelling methods applied. Data remains susceptible to errors or irregularities that may be introduced during collection, aggregation or annotation stage. Issues such as incorrect labels, synonymous categories in a categorical variable or heterogeneity in columns, among others, which might go undetected by standard pre-processing modules in these frameworks can lead to sub-optimal model performance, inaccurate analysis and unreliable decisions.
Data Preprocessing involves changing the raw feature vectors into a representation that is more suitable for the downstream modelling and estimation processes, including data cleaning, integration, reduction and transformation. Data cleaning aims to identify and correct errors in the dataset that may negatively impact a predictive model such as removing outliers, replacing missing values, smoothing noisy data, and correcting inconsistent data. Data integration addresses potential issues with redundant and inconsistent data obtained from multiple sources through approaches such as detection of tuple duplication and data conflict. The purpose of data reduction is to have a condensed representation of the data set that is smaller in volume, while maintaining the integrity of the original data set. Data transformation converts the data into the most appropriate form for data modeling.
Data Exploration involves analyzing and investigating data sets to summarize their main characteristics, often employing data visualization methods. It helps determine how best to manipulate data sources to discover patterns, spot anomalies, test a hypothesis, or check assumptions. This process is primarily used to see what data can reveal beyond the formal modeling or hypothesis testing task and provides a better understanding of data set variables and the relationships between them.
1.2.2 Feature Selection
Model-independent feature importance metrics were assessed for the numeric predictors in the study to determine the most optimal subset of variables for the subsequent modelling process which included the following:
Locally Weighted Scatterplot Smoothing Pseudo-R-Squared computes the R-squared statistic - a goodness-of-fit measure which represents explained variability, improvement from null to fitted model and square of the correlation on predicted values obtained from a locally weighted scatterplot smoothing process. LOWESS consists of computing a series of local linear regressions, with each local regression restricted to a window of x-values. Smoothness is achieved by using overlapping windows and by gradually down-weighting points in each regression according to their distance from the anchor point of the window (tri-cube weighting).
Pearson’s Correlation Coefficient is a parametric measure of the linear correlation for a pair of features by calculating the ratio between their covariance and the product of their standard deviations. The presence of high absolute correlation values indicate the univariate association between the numeric predictors and the numeric response.
Spearman’s Rank Correlation Coefficient is a non-parametric measure of the linear correlation for a pair of features by applying the Spearman’s rank equation to the sum of the squared differences between their ranks. The presence of high absolute correlation values indicate the univariate association between the numeric predictors and the numeric response.
Maximal Information Coefficient is an information theory-based measure of two-variable dependence through the computation of the mutual information normalized by the minimum joint entropy. It evaluates the strength of linear or non-linear association using binning as a means to apply mutual information between continuous random variables and selecting the maximum over many possible grids. The presence of high coefficient values indicate the univariate association between the numeric predictors and the numeric response.
Relief Values are heuristic measures which estimate the quality of variables according to how well their values compare to instances that are near to each other, but are efficient in detecting contextual information even with strong dependencies between attributes. Random instances and the corresponding K-nearest instances are selected, with the the weights for the different prediction values, different attributes and different prediction consolidated. The rank of the instance in a sequence of instances ordered by the distance is taken into account based on a a user-defined parameter controlling the influence of the distance. The contributions of each K-nearest instances are normalized by dividing the results with the sum of all K contributions. The presence of high relief values indicate the univariate association between the numeric predictors and the numeric response.
1.2.3 Model Formulation
Machine Learning Regression Models are algorithms that learn to predict a continuous numeric value (output or target) based on input features. Regression is a supervised learning task where the models are trained on a labeled dataset, and their goal is to establish a relationship between the input features and the continuous target variable. Once trained, these models can make predictions for new, unseen instances.
In addition to a standard glass-box model, this study implemented predominantly black-box regression modelling procedures with complex structures involving large numbers of model coefficients or mathematical transformations which lacked transparency in terms of the internal processes and weighted factors used in reaching a decision. Models applied in the analysis for predicting the numeric response were the following:
Linear Regression explores the linear relationship between a scalar response and one or more covariates by having the conditional mean of the dependent variable be an affine function of the independent variables. The relationship is modeled through a disturbance term which represents an unobserved random variable that adds noise. The algorithm is typically formulated from the data using the least squares method which seeks to estimate the coefficients by minimizing the squared residual function. The linear equation assigns one scale factor represented by a coefficient to each covariate and an additional coefficient called the intercept or the bias coefficient which gives the line an additional degree of freedom allowing to move up and down a two-dimensional plot.
Stochastic Gradient Boosting is an ensemble learning method which combines multiple weak learners in an additive manner to improve prediction. The process is initialized using a decision tree base learner with the aim of minimizing a specified loss function. The negative gradient of the loss function with respect to the predicted values from the current ensemble is calculated. Residuals are determined as the difference between the actual target values and the learner predictions. A new base learner is subsequently formulated but is trained to predict the residuals. The algorithm involves iteratively improving the ensemble by focusing on the residuals of the previous predictions. Each subsequent base learner is trained to reduce the errors made by the previous ensemble, gradually refining the model’s predictive capabilities.
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 (where a final prediction is formilated by aggregating the individual predictions of all estimators).
Neural Network comprises of node layers - containing an input layer, one or more hidden layers, and an output layer. Each node, or artificial neuron, connects to another and has an associated weight and threshold. If the output of any individual node is above the specified threshold value, that node is activated, sending data to the next layer of the network. The process involves the initialization of weights and biases for each neuron in the network. Forward propagation computates the output of the neural network, as determined by the weighted sum of the inputs and a bias term. An activation function is applied to introduce non-linearity to the hidden layer. Back propagation is used to update the weights and the biases in the network by calculating the gradients of the loss function using the chain rule with the magnitude determined by a learning rate. This step allows the network to learn from the errors and adjust the parameters to minimize the loss.
Partial Least Squares applies dimensionality reduction to address high multicollinearity among predictors in a linear regression. The algorithm calculates summary indices termed as partial least squares components which are linear combinations of the original predictors by considering the variation in both the response and the predictor variables. The method of least squares is then applied to fit a linear regression model using the first principal components as predictors, with the optimal number determined using cross-validation.
Cubist Regression is a rule-based model that is an extension of Quinlan’s M5 model tree. A tree is grown where the terminal leaves contain linear regression models. These models are based on the predictors used in previous splits. Also, there are intermediate linear models at each step of the tree. A prediction is made using the linear regression model at the terminal node of the tree, but is smoothed by taking into account the prediction from the linear model in the previous node of the tree (which also occurs recursively up the tree). The tree is reduced to a set of rules, which initially are paths from the top of the tree to the bottom. Rules are eliminated via pruning and/or combined for simplification. The Cubist model can also use a boosting-like scheme called committees where iterative model trees are created in sequence. Another innovation is about using nearest-neighbors to adjust the predictions from the rule-based model.
1.2.4 Model Hyperparameter Tuning
The optimal combination of hyperparameter values which maximized the performance of the various regression models in the study used the following hyperparameter tuning strategy:
K-Fold Cross-Validation involves dividing the training set after a random shuffle into a user-defined K number of smaller non-overlapping sets called folds. Each unique fold is assigned as the hold-out test data to assess the model trained from the data set collected from all the remaining K-1 folds. The evaluation score is retained but the model is discarded. The process is recursively performed resulting to a total of K fitted models and evaluated on the K hold-out test sets. All K-computed performance measures reported from the process are then averaged to represent the estimated performance of the model. This approach can be computationally expensive and may be highly dependent on how the data was randomly assigned to their respective folds, but does not waste too much data which is a major advantage in problems where the number of samples is very small.
1.2.5 Model Performance Evaluation
Evaluation metrics applied in the analysis to estimate the generalization performance of the regression models on internally sub-sampled or independent datasets were the following:
Root Mean Square Error computes the square root of the average squared difference between the predicted and target values which ranges from zero to infinity. A value of zero indicates perfect prediction of the target values. The metric is weighted according to the square of the error - putting greater influence on large errors than smaller errors which makes it sensitive to outliers but may also encourage conservative prediction.
R-Squared computes the normalized version of the root mean squared error and also referred to as the coefficient of determination. With a value ranging from zero to infinity, a value of one indicates perfect prediction of the target values. The metric can also be interpreted as the fraction of the total variance in the response variable which can be explained by the model.
1.2.6 Model Presentation
Due to the black-box nature of most regression models considered in the study, model presentation was conducted post-hoc and focused on model-agnostic techniques which did not consider any assumptions about the model structures. These methods were grouped into two categories.
Dataset Level Exploration Techniques used model level global explanations which included the following:
Variable Importance pertains to model-agnostic methods which allow the comparison of an explanatory variable’s importance between models with different structures. The process involves measuring how much a model’s performance change if the effect of a selected explanatory variable, or of a group of variables, is removed. To remove the effect, perturbations are applied including resampling from an empirical distribution or permutation of the values of the variable. If a variable is important, the model’s performance is expected to worsen after permuting the values of the variable. A larger change in performance indicates the greater importance of the variable.
Partial Dependence Plots show how the expected values of model prediction behave as a function of a selected explanatory variable using the average of a set of individual ceteris paribus profiles. While a ceteris paribus profile shows the dependence of an instance-level prediction on an explanatory variable, a partial dependence profile is estimated by the mean of the ceteris paribus profiles for all instances in a data set.
Instance Level Exploration Techniques used prediction level local explanations with descriptions given below:
Breakdown Plots present variable attributions by decomposing the model’s prediction into contributions that can be attributed to the different explanatory variables. Given a prediction which is an approximation of the expected value of the dependent variable driven by the values of explanatory variables, the process involves capturing the contribution of an explanatory variable to the model’s prediction by computing the shift in the expected value of the response, while fixing the values of other variables.
Shapley Additive Explanations are based on Shapley values developed in the cooperative game theory. The process involves explaining a prediction by assuming that each explanatory variable for an instance is a player in a game where the prediction is the payout. The game is the prediction task for a single instance of the data set. The gain is the actual prediction for this instance minus the average prediction for all instances. The players are the explanatory variable values of the instance that collaborate to receive the gain (predict a certain value). The determined value is the average marginal contribution of an explanatory variable across all possible coalitions.
Ceteris Paribus Profiles examine the influence of an explanatory variable by assuming that the values of all other variables do not change. The main objective is to understand how changes in the values of the variable affect the model’s predictions. The process involves evaluating the dependence of the conditional expectation of the response on the values of the particular explanatory variable.
Local Fidelity Plots evaluate the local predictive performance of the model around the observation of interest. The process involves summarizing two distributions of residuals including the residuals for the neighbors of the observation of interest and residuals for the entire training dataset except for neighbors. The results help evaluate whether the model-fit for the instance of interest is unbiased (based on small residuals with distributions symmetric around 0).
Local Stability Plots assess the local stability of predictions around the observation of interest. The process involves checking whether small changes in the explanatory variables, as represented by the changes within the set of neighbors, induce much influence on the predictions. The results help evaluate whether the model is locally additive (based on parallel ceteris paribus profiles) and locally stable (based on adjacent ceteris paribus profiles).
1.3 Results
1.3.1 Data Preparation
[A] The initial tabular dataset was comprised of 394 observations and 23 variables (including 2 metadata, 1 response and 20 predictors).
[A.1] 394 rows (observations)
[A.2] 23 columns (variables)
[A.2.1] 1/23 instance labels = COUNTRY (character)
[A.2.2] 1/23 supplementary information = YEAR (numeric)
[A.2.3] 1/23 response = LIFEXP (numeric)
[A.2.4] 20/23 predictors = 18/20 numeric + 2/20 factor
[A.2.4.1] GENDER (factor)
[A.2.4.2] CONTIN (factor)
[A.2.4.3] UNEMPR (numeric)
[A.2.4.4] INFMOR (numeric)
[A.2.4.5] GDP (numeric)
[A.2.4.6] GNI (numeric)
[A.2.4.7] CLTECH (numeric)
[A.2.4.8] PERCAP (numeric)
[A.2.4.9] RTIMOR (numeric)
[A.2.4.10] TUBINC (numeric)
[A.2.4.11] DPTIMM (numeric)
[A.2.4.12] HEPIMM (numeric)
[A.2.4.13] MEAIMM (numeric)
[A.2.4.14] HOSBED (numeric)
[A.2.4.15] SANSER (numeric)
[A.2.4.16] TUBTRT (numeric)
[A.2.4.17] URBPOP (numeric)
[A.2.4.18] RURPOP (numeric)
[A.2.4.19] NCOMOR (numeric)
[A.2.4.20] SUIRAT (numeric)
[B] Preliminary transformation was applied to the GDP, GNI and PERCAP predictors which were noted with high range of values.
[B.1] GDP (numeric)
[B.2] GNI (numeric)
[B.3] PERCAP (numeric)
Code Chunk | Output
##################################
# Loading R libraries
##################################
library(DALEX)
library(caret)
library(randomForest)
library(e1071)
library(gbm)
library(skimr)
library(corrplot)
library(lares)
library(dplyr)
library(minerva)
library(CORElearn)
library(patchwork)
library(lime)
library(DALEXtra)
##################################
# Defining file paths
##################################
DATASETS_ORIGINAL_PATH <- file.path("datasets","original")
##################################
# Loading source and
# formulating the analysis set
##################################
LED <- read.csv(file.path("..", DATASETS_ORIGINAL_PATH, "Life_Expectancy_Data.csv"),
na.strings=c("NA","NaN"," ",""),
stringsAsFactors = FALSE)
LED <- as.data.frame(LED)
##################################
# Performing a general exploration of the data set
##################################
dim(LED)## [1] 394 23str(LED)## 'data.frame': 394 obs. of 23 variables:
## $ COUNTRY: chr "Afghanistan" "Albania" "Algeria" "Angola" ...
## $ YEAR : int 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019 ...
## $ GENDER : chr "Female" "Female" "Female" "Female" ...
## $ CONTIN : chr "Asia" "Europe" "Africa" "Africa" ...
## $ LIFEXP : num 66.4 80.2 78.1 64 78.1 ...
## $ UNEMPR : num 14.06 11.32 18.63 7.84 8.26 ...
## $ INFMOR : num 42.9 7.7 18.6 44.5 5.1 ...
## $ GDP : num 1.88e+10 1.54e+10 1.72e+11 8.94e+10 1.69e+09 ...
## $ GNI : num 1.91e+10 1.52e+10 1.68e+11 8.19e+10 1.58e+09 ...
## $ CLTECH : num 36 80.7 99.3 49.6 100 ...
## $ PERCAP : num 494 5396 3990 2810 17377 ...
## $ RTIMOR : num 15.9 11.7 20.9 26.1 0 ...
## $ TUBINC : num 189 16 61 351 0 29 26 2.2 6.9 6 ...
## $ DPTIMM : num 66 99 91 57 95 ...
## $ HEPIMM : num 66 99 91 53 99 ...
## $ MEAIMM : num 64 95 80 51 93 ...
## $ HOSBED : num 0.432 3.052 1.8 0.8 2.581 ...
## $ SANSER : num 49 99.2 86.1 51.4 85.5 ...
## $ TUBTRT : num 91 88 86 69 72.3 ...
## $ URBPOP : num 25.8 61.2 73.2 66.2 24.5 ...
## $ RURPOP : num 74.2 38.8 26.8 33.8 75.5 ...
## $ NCOMOR : num 36.2 6 12.8 19.4 17.6 ...
## $ SUIRAT : num 3.6 2.7 1.8 2.3 0.8 ...summary(LED)## COUNTRY YEAR GENDER CONTIN
## Length:394 Min. :2019 Length:394 Length:394
## Class :character 1st Qu.:2019 Class :character Class :character
## Mode :character Median :2019 Mode :character Mode :character
## Mean :2019
## 3rd Qu.:2019
## Max. :2019
## LIFEXP UNEMPR INFMOR GDP
## Min. :51.20 Min. : 0.071 Min. : 1.40 Min. :1.880e+08
## 1st Qu.:67.61 1st Qu.: 3.560 1st Qu.: 6.00 1st Qu.:1.130e+10
## Median :74.33 Median : 5.663 Median :15.20 Median :3.865e+10
## Mean :73.07 Mean : 7.769 Mean :21.55 Mean :4.614e+11
## 3rd Qu.:79.30 3rd Qu.: 9.842 3rd Qu.:30.66 3rd Qu.:2.450e+11
## Max. :88.10 Max. :41.153 Max. :88.80 Max. :2.140e+13
## GNI CLTECH PERCAP RTIMOR
## Min. :3.754e+08 Min. : 0.00 Min. : 228.2 Min. : 0.0
## 1st Qu.:1.111e+10 1st Qu.: 33.50 1st Qu.: 2229.9 1st Qu.: 8.2
## Median :4.005e+10 Median : 79.50 Median : 6609.5 Median :16.0
## Mean :4.814e+11 Mean : 65.66 Mean : 16682.2 Mean :17.0
## 3rd Qu.:2.450e+11 3rd Qu.:100.00 3rd Qu.: 19303.5 3rd Qu.:23.9
## Max. :2.170e+13 Max. :100.00 Max. :175813.9 Max. :64.6
## TUBINC DPTIMM HEPIMM MEAIMM
## Min. : 0.0 Min. :35.00 Min. :35.00 Min. :37.00
## 1st Qu.: 12.0 1st Qu.:85.69 1st Qu.:81.31 1st Qu.:84.85
## Median : 46.0 Median :92.00 Median :91.00 Median :92.00
## Mean :103.5 Mean :87.87 Mean :86.64 Mean :87.21
## 3rd Qu.:140.0 3rd Qu.:97.00 3rd Qu.:96.00 3rd Qu.:96.00
## Max. :654.0 Max. :99.00 Max. :99.00 Max. :99.00
## HOSBED SANSER TUBTRT URBPOP
## Min. : 0.200 Min. : 8.632 Min. : 0.00 Min. : 13.25
## 1st Qu.: 1.300 1st Qu.: 63.898 1st Qu.: 73.00 1st Qu.: 41.61
## Median : 2.570 Median : 91.144 Median : 82.00 Median : 58.76
## Mean : 2.987 Mean : 77.495 Mean : 77.68 Mean : 59.09
## 3rd Qu.: 3.746 3rd Qu.: 98.516 3rd Qu.: 88.00 3rd Qu.: 77.94
## Max. :13.710 Max. :100.000 Max. :100.00 Max. :100.00
## RURPOP NCOMOR SUIRAT
## Min. : 0.00 Min. : 4.40 Min. : 0.000
## 1st Qu.:22.06 1st Qu.:13.60 1st Qu.: 3.300
## Median :41.24 Median :19.95 Median : 6.850
## Mean :40.91 Mean :20.02 Mean : 9.572
## 3rd Qu.:58.39 3rd Qu.:24.07 3rd Qu.: 11.175
## Max. :86.75 Max. :58.40 Max. :116.000##################################
# Transforming to appropriate data types
##################################
LED$YEAR <- factor(LED$YEAR,
levels = c("2019"))
LED$GENDER <- factor(LED$GENDER,
levels = c("Male","Female"))
LED$CONTIN <- as.factor(LED$CONTIN)
##################################
# Reducing the range of values
# for certain numeric predictors
##################################
LED$GDP <- LED$GDP/1000000000
LED$GNI <- LED$GNI/1000000000
LED$PERCAP <- LED$PERCAP/1000
##################################
# Formulating a data type assessment summary
##################################
PDA <- LED
(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 COUNTRY character
## 2 2 YEAR factor
## 3 3 GENDER factor
## 4 4 CONTIN factor
## 5 5 LIFEXP numeric
## 6 6 UNEMPR numeric
## 7 7 INFMOR numeric
## 8 8 GDP numeric
## 9 9 GNI numeric
## 10 10 CLTECH numeric
## 11 11 PERCAP numeric
## 12 12 RTIMOR numeric
## 13 13 TUBINC numeric
## 14 14 DPTIMM numeric
## 15 15 HEPIMM numeric
## 16 16 MEAIMM numeric
## 17 17 HOSBED numeric
## 18 18 SANSER numeric
## 19 19 TUBTRT numeric
## 20 20 URBPOP numeric
## 21 21 RURPOP numeric
## 22 22 NCOMOR numeric
## 23 23 SUIRAT numeric1.3.2 Data Quality Assessment
[A] No missing observations noted for any variable.
[B] Low variance observed for 3 numeric predictors with First.Second.Mode.Ratio>5.
[B.1] SANSER = 12.00
[B.2] UNEMPR = 11.00
[B.3] NCOMOR = 6.00
[C] No low variance observed for any predictor with Unique.Count.Ratio<0.01.
[D] High skewness observed for 3 numeric predictors with Skewness>3 or Skewness<(-3).
[D.1] GDP = 8.62
[D.2] GNI = 8.55
[D.3] SUIRAT = 4.08
Code Chunk | Output
##################################
# Loading dataset
##################################
DQA <- LED
##################################
# Formulating an overall data quality assessment summary
##################################
(DQA.Summary <- data.frame(
Column.Index=c(1:length(names(DQA))),
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.Index Column.Name Column.Type Row.Count NA.Count Fill.Rate
## 1 1 COUNTRY character 394 0 1.000
## 2 2 YEAR factor 394 0 1.000
## 3 3 GENDER factor 394 0 1.000
## 4 4 CONTIN factor 394 0 1.000
## 5 5 LIFEXP numeric 394 0 1.000
## 6 6 UNEMPR numeric 394 0 1.000
## 7 7 INFMOR numeric 394 0 1.000
## 8 8 GDP numeric 394 0 1.000
## 9 9 GNI numeric 394 0 1.000
## 10 10 CLTECH numeric 394 0 1.000
## 11 11 PERCAP numeric 394 0 1.000
## 12 12 RTIMOR numeric 394 0 1.000
## 13 13 TUBINC numeric 394 0 1.000
## 14 14 DPTIMM numeric 394 0 1.000
## 15 15 HEPIMM numeric 394 0 1.000
## 16 16 MEAIMM numeric 394 0 1.000
## 17 17 HOSBED numeric 394 0 1.000
## 18 18 SANSER numeric 394 0 1.000
## 19 19 TUBTRT numeric 394 0 1.000
## 20 20 URBPOP numeric 394 0 1.000
## 21 21 RURPOP numeric 394 0 1.000
## 22 22 NCOMOR numeric 394 0 1.000
## 23 23 SUIRAT numeric 394 0 1.000##################################
# Listing all Predictors
##################################
DQA.Predictors <- DQA[,!names(DQA) %in% c("COUNTRY","YEAR","LIFEXP")]
##################################
# Listing all numeric Predictors
##################################
DQA.Predictors.Numeric <- DQA.Predictors[,sapply(DQA.Predictors, is.numeric), drop = FALSE]
if (length(names(DQA.Predictors.Numeric))>0) {
print(paste0("There is (are) ",
(length(names(DQA.Predictors.Numeric))),
" numeric descriptor variable(s)."))
} else {
print("There are no numeric descriptor variables.")
}## [1] "There is (are) 18 numeric descriptor variable(s)."##################################
# Listing all factor Predictors
##################################
DQA.Predictors.Factor <- DQA.Predictors[,sapply(DQA.Predictors, is.factor), drop = FALSE]
if (length(names(DQA.Predictors.Factor))>0) {
print(paste0("There is (are) ",
(length(names(DQA.Predictors.Factor))),
" factor descriptor variable(s)."))
} else {
print("There are no factor descriptor variables.")
}## [1] "There is (are) 2 factor descriptor variable(s)."##################################
# 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)
)
}## Column.Name Column.Type Unique.Count First.Mode.Value Second.Mode.Value
## 1 GENDER factor 2 Female x
## 2 CONTIN factor 6 Africa Asia
## First.Mode.Count Second.Mode.Count Unique.Count.Ratio First.Second.Mode.Ratio
## 1 197 0 0.005 Inf
## 2 106 100 0.015 1.060##################################
# 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 First.Mode.Value
## 1 UNEMPR numeric 370 0.939 8.256
## 2 INFMOR numeric 245 0.622 30.235
## 3 GDP numeric 254 0.645 303.000
## 4 GNI numeric 253 0.642 2040.000
## 5 CLTECH numeric 112 0.284 100.000
## 6 PERCAP numeric 196 0.497 12.669
## 7 RTIMOR numeric 141 0.358 18.229
## 8 TUBINC numeric 146 0.371 136.043
## 9 DPTIMM numeric 45 0.114 99.000
## 10 HEPIMM numeric 45 0.114 81.308
## 11 MEAIMM numeric 48 0.122 99.000
## 12 HOSBED numeric 173 0.439 2.986
## 13 SANSER numeric 186 0.472 100.000
## 14 TUBTRT numeric 59 0.150 84.000
## 15 URBPOP numeric 191 0.485 100.000
## 16 RURPOP numeric 191 0.485 0.000
## 17 NCOMOR numeric 214 0.543 22.100
## 18 SUIRAT numeric 176 0.447 10.619
## Second.Mode.Value First.Mode.Count Second.Mode.Count First.Second.Mode.Ratio
## 1 3.924 22 2 11.000
## 2 2.100 28 7 4.000
## 3 279.000 4 3 1.333
## 4 316.000 8 4 2.000
## 5 60.593 108 34 3.176
## 6 0.494 4 2 2.000
## 7 26.800 28 6 4.667
## 8 35.000 12 10 1.200
## 9 85.685 44 30 1.467
## 10 99.000 40 38 1.053
## 11 84.855 48 30 1.600
## 12 0.400 34 8 4.250
## 13 49.006 24 2 12.000
## 14 83.000 22 20 1.100
## 15 55.985 10 4 2.500
## 16 44.015 10 4 2.500
## 17 6.800 30 5 6.000
## 18 7.600 30 8 3.750
## Minimum Mean Median Maximum Skewness Kurtosis Percentile25th
## 1 0.071 7.769 5.663 41.153 1.751 3.680 3.560
## 2 1.400 21.546 15.200 88.800 1.084 0.567 6.000
## 3 0.188 461.374 38.653 21400.000 8.619 82.715 11.304
## 4 0.375 481.441 40.048 21700.000 8.547 82.139 11.110
## 5 0.000 65.660 79.500 100.000 -0.623 -1.141 33.500
## 6 0.228 16.682 6.610 175.814 2.810 10.880 2.230
## 7 0.000 17.003 16.000 64.600 0.740 1.028 8.200
## 8 0.000 103.489 46.000 654.000 1.864 3.177 12.000
## 9 35.000 87.875 92.000 99.000 -1.856 3.434 85.685
## 10 35.000 86.640 91.000 99.000 -1.595 2.477 81.308
## 11 37.000 87.207 92.000 99.000 -1.688 2.574 84.855
## 12 0.200 2.987 2.570 13.710 1.697 3.859 1.300
## 13 8.632 77.495 91.144 100.000 -1.122 -0.155 63.898
## 14 0.000 77.675 82.000 100.000 -2.194 5.571 73.000
## 15 13.250 59.094 58.760 100.000 -0.132 -0.991 41.612
## 16 0.000 40.906 41.240 86.750 0.132 -0.991 22.058
## 17 4.400 20.021 19.950 58.400 0.864 1.551 13.600
## 18 0.000 9.572 6.850 116.000 4.082 29.352 3.300
## Percentile75th
## 1 9.842
## 2 30.659
## 3 245.000
## 4 245.000
## 5 100.000
## 6 19.304
## 7 23.900
## 8 140.000
## 9 97.000
## 10 96.000
## 11 96.000
## 12 3.746
## 13 98.516
## 14 88.000
## 15 77.942
## 16 58.388
## 17 24.075
## 18 11.175##################################
# 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] "Low variance observed for 1 factor variable(s) with First.Second.Mode.Ratio>5."## Column.Name Column.Type Unique.Count First.Mode.Value Second.Mode.Value
## 1 GENDER factor 2 Female x
## First.Mode.Count Second.Mode.Count Unique.Count.Ratio First.Second.Mode.Ratio
## 1 197 0 0.005 Infif (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$First.Second.Mode.Ratio))>5,]
} else {
print("No low variance numeric Predictors due to high first-second mode ratio noted.")
}## [1] "Low variance observed for 3 numeric variable(s) with First.Second.Mode.Ratio>5."## Column.Name Column.Type Unique.Count Unique.Count.Ratio First.Mode.Value
## 1 UNEMPR numeric 370 0.939 8.256
## 13 SANSER numeric 186 0.472 100.000
## 17 NCOMOR numeric 214 0.543 22.100
## Second.Mode.Value First.Mode.Count Second.Mode.Count First.Second.Mode.Ratio
## 1 3.924 22 2 11.000
## 13 49.006 24 2 12.000
## 17 6.800 30 5 6.000
## Minimum Mean Median Maximum Skewness Kurtosis Percentile25th
## 1 0.071 7.769 5.663 41.153 1.751 3.680 3.560
## 13 8.632 77.495 91.144 100.000 -1.122 -0.155 63.898
## 17 4.400 20.021 19.950 58.400 0.864 1.551 13.600
## Percentile75th
## 1 9.842
## 13 98.516
## 17 24.075if (length(names(DQA.Predictors.Numeric))==0) {
print("No numeric Predictors noted.")
} else if (nrow(DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$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 3 numeric variable(s) with Skewness>3 or Skewness<(-3)."## Column.Name Column.Type Unique.Count Unique.Count.Ratio First.Mode.Value
## 3 GDP numeric 254 0.645 303.000
## 4 GNI numeric 253 0.642 2040.000
## 18 SUIRAT numeric 176 0.447 10.619
## Second.Mode.Value First.Mode.Count Second.Mode.Count First.Second.Mode.Ratio
## 3 279.000 4 3 1.333
## 4 316.000 8 4 2.000
## 18 7.600 30 8 3.750
## Minimum Mean Median Maximum Skewness Kurtosis Percentile25th
## 3 0.188 461.374 38.653 21400.000 8.619 82.715 11.304
## 4 0.375 481.441 40.048 21700.000 8.547 82.139 11.110
## 18 0.000 9.572 6.850 116.000 4.082 29.352 3.300
## Percentile75th
## 3 245.000
## 4 245.000
## 18 11.1751.3.3 Data Preprocessing
1.3.3.1 Outlier Detection
[A] Outliers noted for 15 out of the 18 numeric 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] UNEMPR = 30
[A.2] INFMOR = 10
[A.3] GDP = 46
[A.4] GNI = 48
[A.5] PERCAP = 46
[A.6] RTIMOR = 2
[A.7] TUBINC = 32
[A.8] DPTIMM = 34
[A.9] HEPIMM = 26
[A.10] MEAIMM = 40
[A.11] HOSBED = 16
[A.12] SANSER = 8
[A.13] TUBTRT = 34
[A.14] NCOMOR = 11
[A.15] SUIRAT = 29
[B] Distributional anomalies observed for 9 predictors showing a high number of observations reporting the exact same set of values.
[B.1] INFMOR=30.20
[B.2] CLTECH=60.60
[B.3] RTIMOR=18.20
[B.4] DPTIMM=85.70
[B.5] HEPIMM=81.30
[B.6] MEAIMM=84.90
[B.7] HOSBED=3.00
[B.8] NCOMOR=22.10
[B.9] SUIRAT=10.60
[C] A total of 30 observations representing 15 countries associated with these unreliable values were removed for the subsequent analysis.
[C.1] COUNTRY=Aruba
[C.2] COUNTRY=Bermuda
[C.3] COUNTRY=Channel Islands
[C.4] COUNTRY=Faroe Islands
[C.5] COUNTRY=French Polynesia
[C.6] COUNTRY=Guam
[C.7] COUNTRY=Hong Kong SAR, China
[C.8] COUNTRY=Kosovo
[C.9] COUNTRY=Liechtenstein
[C.10] COUNTRY=Macao SAR, China
[C.11] COUNTRY=New Caledonia
[C.12] COUNTRY=Puerto Rico
[C.13] COUNTRY=St. Martin (French part)
[C.14] COUNTRY=Virgin Islands (U.S.)
[C.15] COUNTRY=West Bank and Gaza
Code Chunk | Output
##################################
# Loading dataset
##################################
DPA <- LED
##################################
# Gathering descriptive statistics
##################################
(DPA_Skimmed <- skim(DPA))| Name | DPA |
| Number of rows | 394 |
| Number of columns | 23 |
| _______________________ | |
| Column type frequency: | |
| character | 1 |
| factor | 3 |
| numeric | 19 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| COUNTRY | 0 | 1 | 4 | 30 | 0 | 197 | 0 |
Variable type: factor
| skim_variable | n_missing | complete_rate | ordered | n_unique | top_counts |
|---|---|---|---|---|---|
| YEAR | 0 | 1 | FALSE | 1 | 201: 394 |
| GENDER | 0 | 1 | FALSE | 2 | Mal: 197, Fem: 197 |
| CONTIN | 0 | 1 | FALSE | 6 | Afr: 106, Asi: 100, Eur: 86, Nor: 52 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| LIFEXP | 0 | 1 | 73.07 | 7.82 | 51.20 | 67.61 | 74.32 | 79.30 | 88.10 | ▁▃▆▇▃ |
| UNEMPR | 0 | 1 | 7.77 | 6.35 | 0.07 | 3.56 | 5.66 | 9.84 | 41.15 | ▇▂▁▁▁ |
| INFMOR | 0 | 1 | 21.55 | 18.67 | 1.40 | 6.00 | 15.20 | 30.66 | 88.80 | ▇▃▂▁▁ |
| GDP | 0 | 1 | 461.37 | 1920.36 | 0.19 | 11.30 | 38.65 | 245.00 | 21400.00 | ▇▁▁▁▁ |
| GNI | 0 | 1 | 481.44 | 1942.86 | 0.38 | 11.11 | 40.05 | 245.00 | 21700.00 | ▇▁▁▁▁ |
| CLTECH | 0 | 1 | 65.66 | 36.33 | 0.00 | 33.50 | 79.50 | 100.00 | 100.00 | ▃▁▁▂▇ |
| PERCAP | 0 | 1 | 16.68 | 24.32 | 0.23 | 2.23 | 6.61 | 19.30 | 175.81 | ▇▁▁▁▁ |
| RTIMOR | 0 | 1 | 17.00 | 10.34 | 0.00 | 8.20 | 16.00 | 23.90 | 64.60 | ▇▇▅▁▁ |
| TUBINC | 0 | 1 | 103.49 | 133.68 | 0.00 | 12.00 | 46.00 | 140.00 | 654.00 | ▇▂▁▁▁ |
| DPTIMM | 0 | 1 | 87.87 | 12.41 | 35.00 | 85.69 | 92.00 | 97.00 | 99.00 | ▁▁▁▃▇ |
| HEPIMM | 0 | 1 | 86.64 | 12.72 | 35.00 | 81.31 | 91.00 | 96.00 | 99.00 | ▁▁▁▃▇ |
| MEAIMM | 0 | 1 | 87.21 | 13.17 | 37.00 | 84.85 | 92.00 | 96.00 | 99.00 | ▁▁▁▃▇ |
| HOSBED | 0 | 1 | 2.99 | 2.35 | 0.20 | 1.30 | 2.57 | 3.75 | 13.71 | ▇▅▂▁▁ |
| SANSER | 0 | 1 | 77.49 | 27.63 | 8.63 | 63.90 | 91.14 | 98.52 | 100.00 | ▁▁▁▂▇ |
| TUBTRT | 0 | 1 | 77.68 | 16.97 | 0.00 | 73.00 | 82.00 | 88.00 | 100.00 | ▁▁▁▅▇ |
| URBPOP | 0 | 1 | 59.09 | 23.24 | 13.25 | 41.61 | 58.76 | 77.94 | 100.00 | ▅▆▇▇▆ |
| RURPOP | 0 | 1 | 40.91 | 23.24 | 0.00 | 22.06 | 41.24 | 58.39 | 86.75 | ▆▇▇▆▅ |
| NCOMOR | 0 | 1 | 20.02 | 8.40 | 4.40 | 13.60 | 19.95 | 24.08 | 58.40 | ▅▇▂▁▁ |
| SUIRAT | 0 | 1 | 9.57 | 10.49 | 0.00 | 3.30 | 6.85 | 11.17 | 116.00 | ▇▁▁▁▁ |
##################################
# Outlier Treatment
##################################
##################################
# Listing all Predictors
##################################
DPA.Predictors <- DPA[,!names(DPA) %in% c("COUNTRY","YEAR","LIFEXP")]
##################################
# 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])$out
OutlierCount <- length(Outliers)
OutlierCountList <- append(OutlierCountList,OutlierCount)
OutlierIndices <- which(DPA.Predictors.Numeric[,i] %in% c(Outliers))
print(
ggplot(DPA.Predictors.Numeric, aes(x=DPA.Predictors.Numeric[,i])) +
geom_boxplot() +
theme_bw() +
theme(axis.text.y=element_blank(),
axis.ticks.y=element_blank()) +
xlab(names(DPA.Predictors.Numeric)[i]) +
labs(title=names(DPA.Predictors.Numeric)[i],
subtitle=paste0(OutlierCount, " Outlier(s) Detected")))
}
##################################
# Formulating the histogram
# for the numeric predictors
##################################
for (i in 1:ncol(DPA.Predictors.Numeric)) {
Median <- format(round(median(DPA.Predictors.Numeric[,i],na.rm = TRUE),2), nsmall=2)
Mean <- format(round(mean(DPA.Predictors.Numeric[,i],na.rm = TRUE),2), nsmall=2)
Skewness <- format(round(skewness(DPA.Predictors.Numeric[,i],na.rm = TRUE),2), nsmall=2)
print(
ggplot(DPA.Predictors.Numeric, aes(x=DPA.Predictors.Numeric[,i])) +
geom_histogram(binwidth=1,color="black", fill="white") +
geom_vline(aes(xintercept=mean(DPA.Predictors.Numeric[,i])),
color="blue", size=1) +
geom_vline(aes(xintercept=median(DPA.Predictors.Numeric[,i])),
color="red", size=1) +
theme_bw() +
ylab("Count") +
xlab(names(DPA.Predictors.Numeric)[i]) +
labs(title=names(DPA.Predictors.Numeric)[i],
subtitle=paste0("Median = ", Median,
", Mean = ", Mean,
", Skewness = ", Skewness)))
}
##################################
# Investigating distributional anomalies
# observed for several predictors
##################################
(INFMOR_Unique <- DPA %>%
group_by(INFMOR) %>%
summarize(Distinct_INFMOR = n_distinct(COUNTRY)) %>%
arrange(desc(Distinct_INFMOR)) %>%
slice(1:5))## # A tibble: 5 × 2
## INFMOR Distinct_INFMOR
## <dbl> <int>
## 1 30.2 14
## 2 2.1 7
## 3 6.4 6
## 4 1.7 4
## 5 2.5 4(INFMOR_Unique_Country <- DPA[round(DPA$INFMOR,digits=1)==30.2,c("COUNTRY")])## [1] "Aruba" "Bermuda"
## [3] "Channel Islands" "Faroe Islands"
## [5] "French Polynesia" "Guam"
## [7] "Hong Kong SAR, China" "Kosovo"
## [9] "Liechtenstein" "Macao SAR, China"
## [11] "New Caledonia" "Puerto Rico"
## [13] "St. Martin (French part)" "Virgin Islands (U.S.)"
## [15] "Aruba" "Bermuda"
## [17] "Channel Islands" "Faroe Islands"
## [19] "French Polynesia" "Guam"
## [21] "Hong Kong SAR, China" "Kosovo"
## [23] "Liechtenstein" "Macao SAR, China"
## [25] "New Caledonia" "Puerto Rico"
## [27] "St. Martin (French part)" "Virgin Islands (U.S.)"DPA %>%
group_by(CLTECH) %>%
summarize(Distinct_CLTECH = n_distinct(COUNTRY)) %>%
arrange(desc(Distinct_CLTECH)) %>%
slice(1:5)## # A tibble: 5 × 2
## CLTECH Distinct_CLTECH
## <dbl> <int>
## 1 100 54
## 2 60.6 17
## 3 9.30 3
## 4 99.9 3
## 5 0.2 2(CLTECH_Unique_Country <- DPA[round(DPA$CLTECH,digits=1)==60.6,c("COUNTRY")])## [1] "Aruba" "Bermuda"
## [3] "Channel Islands" "Faroe Islands"
## [5] "French Polynesia" "Guam"
## [7] "Hong Kong SAR, China" "Kosovo"
## [9] "Lebanon" "Libya"
## [11] "Liechtenstein" "Macao SAR, China"
## [13] "New Caledonia" "Puerto Rico"
## [15] "St. Martin (French part)" "Virgin Islands (U.S.)"
## [17] "West Bank and Gaza" "Aruba"
## [19] "Bermuda" "Channel Islands"
## [21] "Faroe Islands" "French Polynesia"
## [23] "Guam" "Hong Kong SAR, China"
## [25] "Kosovo" "Lebanon"
## [27] "Libya" "Liechtenstein"
## [29] "Macao SAR, China" "New Caledonia"
## [31] "Puerto Rico" "St. Martin (French part)"
## [33] "Virgin Islands (U.S.)" "West Bank and Gaza"DPA %>%
group_by(RTIMOR) %>%
summarize(Distinct_RTIMOR = n_distinct(COUNTRY)) %>%
arrange(desc(Distinct_RTIMOR)) %>%
slice(1:5)## # A tibble: 5 × 2
## RTIMOR Distinct_RTIMOR
## <dbl> <int>
## 1 18.2 14
## 2 3.9 3
## 3 5.1 3
## 4 5.3 3
## 5 12.7 3(RTIMOR_Unique_Country <- DPA[round(DPA$RTIMOR,digits=1)==18.2,c("COUNTRY")])## [1] "Aruba" "Bermuda"
## [3] "Channel Islands" "Faroe Islands"
## [5] "French Polynesia" "Guam"
## [7] "Hong Kong SAR, China" "Kosovo"
## [9] "Liechtenstein" "Macao SAR, China"
## [11] "New Caledonia" "Puerto Rico"
## [13] "St. Martin (French part)" "Virgin Islands (U.S.)"
## [15] "Aruba" "Bermuda"
## [17] "Channel Islands" "Faroe Islands"
## [19] "French Polynesia" "Guam"
## [21] "Hong Kong SAR, China" "Kosovo"
## [23] "Liechtenstein" "Macao SAR, China"
## [25] "New Caledonia" "Puerto Rico"
## [27] "St. Martin (French part)" "Virgin Islands (U.S.)"DPA %>%
group_by(DPTIMM) %>%
summarize(Distinct_DPTIMM = n_distinct(COUNTRY)) %>%
arrange(desc(Distinct_DPTIMM)) %>%
slice(1:5)## # A tibble: 5 × 2
## DPTIMM Distinct_DPTIMM
## <dbl> <int>
## 1 99 22
## 2 85.7 15
## 3 97 14
## 4 98 14
## 5 95 13(DPTIMM_Unique_Country <- DPA[round(DPA$DPTIMM,digits=1)==85.7,c("COUNTRY")])## [1] "Aruba" "Bermuda"
## [3] "Channel Islands" "Faroe Islands"
## [5] "French Polynesia" "Guam"
## [7] "Hong Kong SAR, China" "Kosovo"
## [9] "Liechtenstein" "Macao SAR, China"
## [11] "New Caledonia" "Puerto Rico"
## [13] "St. Martin (French part)" "Virgin Islands (U.S.)"
## [15] "West Bank and Gaza" "Aruba"
## [17] "Bermuda" "Channel Islands"
## [19] "Faroe Islands" "French Polynesia"
## [21] "Guam" "Hong Kong SAR, China"
## [23] "Kosovo" "Liechtenstein"
## [25] "Macao SAR, China" "New Caledonia"
## [27] "Puerto Rico" "St. Martin (French part)"
## [29] "Virgin Islands (U.S.)" "West Bank and Gaza"DPA %>%
group_by(HEPIMM) %>%
summarize(Distinct_HEPIMM = n_distinct(COUNTRY)) %>%
arrange(desc(Distinct_HEPIMM)) %>%
slice(1:5)## # A tibble: 5 × 2
## HEPIMM Distinct_HEPIMM
## <dbl> <int>
## 1 81.3 20
## 2 99 19
## 3 97 17
## 4 98 11
## 5 92 10(HEPIMM_Unique_Country <- DPA[round(DPA$HEPIMM,digits=1)==81.3,c("COUNTRY")])## [1] "Aruba" "Bermuda"
## [3] "Channel Islands" "Denmark"
## [5] "Faroe Islands" "Finland"
## [7] "French Polynesia" "Guam"
## [9] "Hong Kong SAR, China" "Hungary"
## [11] "Iceland" "Kosovo"
## [13] "Liechtenstein" "Macao SAR, China"
## [15] "New Caledonia" "Puerto Rico"
## [17] "Slovenia" "St. Martin (French part)"
## [19] "Virgin Islands (U.S.)" "West Bank and Gaza"
## [21] "Aruba" "Bermuda"
## [23] "Channel Islands" "Denmark"
## [25] "Faroe Islands" "Finland"
## [27] "French Polynesia" "Guam"
## [29] "Hong Kong SAR, China" "Hungary"
## [31] "Iceland" "Kosovo"
## [33] "Liechtenstein" "Macao SAR, China"
## [35] "New Caledonia" "Puerto Rico"
## [37] "Slovenia" "St. Martin (French part)"
## [39] "Virgin Islands (U.S.)" "West Bank and Gaza"DPA %>%
group_by(MEAIMM) %>%
summarize(Distinct_MEAIMM = n_distinct(COUNTRY)) %>%
arrange(desc(Distinct_MEAIMM)) %>%
slice(1:5)## # A tibble: 5 × 2
## MEAIMM Distinct_MEAIMM
## <dbl> <int>
## 1 99 24
## 2 84.9 15
## 3 95 14
## 4 96 14
## 5 98 13(MEAIMM_Unique_Country <- DPA[round(DPA$MEAIMM,digits=1)==84.9,c("COUNTRY")])## [1] "Aruba" "Bermuda"
## [3] "Channel Islands" "Faroe Islands"
## [5] "French Polynesia" "Guam"
## [7] "Hong Kong SAR, China" "Kosovo"
## [9] "Liechtenstein" "Macao SAR, China"
## [11] "New Caledonia" "Puerto Rico"
## [13] "St. Martin (French part)" "Virgin Islands (U.S.)"
## [15] "West Bank and Gaza" "Aruba"
## [17] "Bermuda" "Channel Islands"
## [19] "Faroe Islands" "French Polynesia"
## [21] "Guam" "Hong Kong SAR, China"
## [23] "Kosovo" "Liechtenstein"
## [25] "Macao SAR, China" "New Caledonia"
## [27] "Puerto Rico" "St. Martin (French part)"
## [29] "Virgin Islands (U.S.)" "West Bank and Gaza"DPA %>%
group_by(HOSBED) %>%
summarize(Distinct_HOSBED = n_distinct(COUNTRY)) %>%
arrange(desc(Distinct_HOSBED)) %>%
slice(1:5)## # A tibble: 5 × 2
## HOSBED Distinct_HOSBED
## <dbl> <int>
## 1 2.99 17
## 2 0.4 4
## 3 0.8 2
## 4 0.85 2
## 5 0.9 2(HOSBED_Unique_Country <- DPA[round(DPA$HOSBED,digits=1)==3.0,c("COUNTRY")])## [1] "Aruba" "Bermuda"
## [3] "Channel Islands" "Faroe Islands"
## [5] "French Polynesia" "Guam"
## [7] "Hong Kong SAR, China" "Kosovo"
## [9] "Liechtenstein" "Macao SAR, China"
## [11] "Namibia" "New Caledonia"
## [13] "Papua New Guinea" "Puerto Rico"
## [15] "South Sudan" "St. Martin (French part)"
## [17] "Virgin Islands (U.S.)" "West Bank and Gaza"
## [19] "Aruba" "Bermuda"
## [21] "Channel Islands" "Faroe Islands"
## [23] "French Polynesia" "Guam"
## [25] "Hong Kong SAR, China" "Kosovo"
## [27] "Liechtenstein" "Macao SAR, China"
## [29] "Namibia" "New Caledonia"
## [31] "Papua New Guinea" "Puerto Rico"
## [33] "South Sudan" "St. Martin (French part)"
## [35] "Virgin Islands (U.S.)" "West Bank and Gaza"DPA %>%
group_by(NCOMOR) %>%
summarize(Distinct_NCOMOR = n_distinct(COUNTRY)) %>%
arrange(desc(Distinct_NCOMOR)) %>%
slice(1:5)## # A tibble: 5 × 2
## NCOMOR Distinct_NCOMOR
## <dbl> <int>
## 1 22.1 15
## 2 6.8 5
## 3 13.6 5
## 4 15.2 5
## 5 17.5 5(NCOMOR_Unique_Country <- DPA[round(DPA$NCOMOR,digits=1)==22.1,c("COUNTRY")])## [1] "Aruba" "Bermuda"
## [3] "Burkina Faso" "Channel Islands"
## [5] "Faroe Islands" "French Polynesia"
## [7] "Guam" "Hong Kong SAR, China"
## [9] "Kosovo" "Liechtenstein"
## [11] "Macao SAR, China" "New Caledonia"
## [13] "Puerto Rico" "St. Martin (French part)"
## [15] "Virgin Islands (U.S.)" "West Bank and Gaza"
## [17] "Aruba" "Bermuda"
## [19] "Channel Islands" "Dominican Republic"
## [21] "Equatorial Guinea" "Estonia"
## [23] "Faroe Islands" "French Polynesia"
## [25] "Guam" "Hong Kong SAR, China"
## [27] "Kosovo" "Liechtenstein"
## [29] "Macao SAR, China" "New Caledonia"
## [31] "Puerto Rico" "Sierra Leone"
## [33] "St. Martin (French part)" "Virgin Islands (U.S.)"
## [35] "West Bank and Gaza"DPA %>%
group_by(SUIRAT) %>%
summarize(Distinct_SUIRAT = n_distinct(COUNTRY)) %>%
arrange(desc(Distinct_SUIRAT)) %>%
slice(1:5)## # A tibble: 5 × 2
## SUIRAT Distinct_SUIRAT
## <dbl> <int>
## 1 10.6 15
## 2 7.6 8
## 3 1.7 7
## 4 2 7
## 5 2.8 7(SUIRAT_Unique_Country <- DPA[round(DPA$SUIRAT,digits=1)==10.6,c("COUNTRY")])## [1] "Aruba" "Bermuda"
## [3] "Channel Islands" "Faroe Islands"
## [5] "French Polynesia" "Guam"
## [7] "Hong Kong SAR, China" "Kosovo"
## [9] "Liechtenstein" "Macao SAR, China"
## [11] "New Caledonia" "Puerto Rico"
## [13] "St. Martin (French part)" "Virgin Islands (U.S.)"
## [15] "West Bank and Gaza" "Aruba"
## [17] "Bermuda" "Channel Islands"
## [19] "Congo, Dem. Rep." "Faroe Islands"
## [21] "French Polynesia" "Guam"
## [23] "Hong Kong SAR, China" "Kosovo"
## [25] "Liechtenstein" "Macao SAR, China"
## [27] "New Caledonia" "Puerto Rico"
## [29] "St. Martin (French part)" "Virgin Islands (U.S.)"
## [31] "West Bank and Gaza"(AnomalousVariables_Unique_Country <- MEAIMM_Unique_Country)## [1] "Aruba" "Bermuda"
## [3] "Channel Islands" "Faroe Islands"
## [5] "French Polynesia" "Guam"
## [7] "Hong Kong SAR, China" "Kosovo"
## [9] "Liechtenstein" "Macao SAR, China"
## [11] "New Caledonia" "Puerto Rico"
## [13] "St. Martin (French part)" "Virgin Islands (U.S.)"
## [15] "West Bank and Gaza" "Aruba"
## [17] "Bermuda" "Channel Islands"
## [19] "Faroe Islands" "French Polynesia"
## [21] "Guam" "Hong Kong SAR, China"
## [23] "Kosovo" "Liechtenstein"
## [25] "Macao SAR, China" "New Caledonia"
## [27] "Puerto Rico" "St. Martin (French part)"
## [29] "Virgin Islands (U.S.)" "West Bank and Gaza"##################################
# Removing rows with anomalous values
##################################
dim(DPA)## [1] 394 23DPA <- DPA[!(DPA$COUNTRY %in% AnomalousVariables_Unique_Country),]
dim(DPA)## [1] 364 23##################################
# Listing all Predictors
# for the updated data
##################################
DPA.Predictors <- DPA[,!names(DPA) %in% c("COUNTRY","YEAR","LIFEXP")]
##################################
# Listing all numeric predictors
# for the updated data
##################################
DPA.Predictors.Numeric <- DPA.Predictors[,sapply(DPA.Predictors, is.numeric)]1.3.3.2 Zero and Near-Zero Variance
[A] No low variance observed for any predictor using a preprocessing summary from the caret package. The nearZeroVar method using both the freqCut and uniqueCut criteria set at 95/5 and 10, respectively, were applied on the dataset.
Code Chunk | Output
##################################
# Zero and Near-Zero Variance
##################################
##################################
# Identifying columns with low variance
###################################
DPA_LowVariance <- nearZeroVar(DPA,
freqCut = 80/20,
uniqueCut = 10,
saveMetrics= TRUE)
(DPA_LowVariance[DPA_LowVariance$nzv,])## freqRatio percentUnique zeroVar nzv
## YEAR 0 0.2747253 TRUE TRUEif ((nrow(DPA_LowVariance[DPA_LowVariance$nzv,]))==0){
print("No low variance predictors noted.")
} else {
print(paste0("Low variance observed for ",
(nrow(DPA_LowVariance[DPA_LowVariance$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] "Low variance observed for 1 numeric variable(s) with First.Second.Mode.Ratio>4 and Unique.Count.Ratio<0.10."
## [1] "Low variance can be resolved by removing 1 numeric variable(s)."
## [1] "Variable 1 for removal: YEAR"| Name | Piped data |
| Number of rows | 364 |
| Number of columns | 23 |
| _______________________ | |
| Column type frequency: | |
| factor | 1 |
| ________________________ | |
| Group variables | None |
Variable type: factor
| skim_variable | n_missing | complete_rate | ordered | n_unique | top_counts |
|---|---|---|---|---|---|
| YEAR | 0 | 1 | FALSE | 1 | 201: 364 |
1.3.3.3 Collinearity
[A] High correlation values were noted for 7 pairs of numeric predictors with Pearson correlation coefficients >80% as confirmed using the preprocessing summaries from the caret package.
[A.1] URBPOP and RURPOP = -100.00%
[A.2] GDP and GNI = +99.99%
[A.3] DPTIMM and HEPIMM = +95.03%
[A.4] DPTIMM and MEAIMM = +88.17%
[A.5] HEPIMM and MEAIMM = +86.42%
[A.6] CLTECH and SANSER = +86.21%
[A.7] INFMOR and SANSER = -82.41%
Code Chunk | Output
##################################
# Collinearity
##################################
##################################
# Visualizing pairwise correlation between predictors
##################################
(DPA_Correlation <- cor(DPA.Predictors.Numeric,
method = "pearson",
use="pairwise.complete.obs"))## UNEMPR INFMOR GDP GNI CLTECH PERCAP
## UNEMPR 1.00000000 0.08921802 -0.09715921 -0.09686799 0.06069602 -0.18518395
## INFMOR 0.08921802 1.00000000 -0.16691246 -0.16605106 -0.77581569 -0.51823250
## GDP -0.09715921 -0.16691246 1.00000000 0.99990577 0.13752124 0.26220470
## GNI -0.09686799 -0.16605106 0.99990577 1.00000000 0.13685071 0.26229594
## CLTECH 0.06069602 -0.77581569 0.13752124 0.13685071 1.00000000 0.52998364
## PERCAP -0.18518395 -0.51823250 0.26220470 0.26229594 0.52998364 1.00000000
## RTIMOR 0.13453252 0.65132565 -0.11475473 -0.11451028 -0.59183716 -0.56088001
## TUBINC 0.16124757 0.58376872 -0.09323916 -0.09283727 -0.55243739 -0.38062151
## DPTIMM -0.12876443 -0.58547646 0.10789977 0.10739430 0.45449772 0.31093085
## HEPIMM -0.09314054 -0.51310504 0.08470102 0.08441932 0.38820759 0.19114586
## MEAIMM -0.15097736 -0.58103197 0.10108399 0.10027512 0.50455989 0.31282969
## HOSBED -0.09030568 -0.52521834 0.13340127 0.13526591 0.43901930 0.32835573
## SANSER 0.01743227 -0.82413990 0.15602727 0.15526943 0.86208147 0.48509427
## TUBTRT -0.05294186 0.30145355 -0.02991535 -0.02993892 -0.32585084 -0.38908786
## URBPOP 0.08774280 -0.53383928 0.16951798 0.16915063 0.64092796 0.57384548
## RURPOP -0.08774280 0.53383928 -0.16951798 -0.16915063 -0.64092796 -0.57384548
## NCOMOR 0.08876876 0.47793168 -0.15463824 -0.15418319 -0.47327879 -0.51434429
## SUIRAT 0.04227853 0.01383619 0.05563045 0.05591448 0.05255453 0.08998525
## RTIMOR TUBINC DPTIMM HEPIMM MEAIMM HOSBED
## UNEMPR 0.13453252 0.16124757 -0.1287644 -0.09314054 -0.15097736 -0.09030568
## INFMOR 0.65132565 0.58376872 -0.5854765 -0.51310504 -0.58103197 -0.52521834
## GDP -0.11475473 -0.09323916 0.1078998 0.08470102 0.10108399 0.13340127
## GNI -0.11451028 -0.09283727 0.1073943 0.08441932 0.10027512 0.13526591
## CLTECH -0.59183716 -0.55243739 0.4544977 0.38820759 0.50455989 0.43901930
## PERCAP -0.56088001 -0.38062151 0.3109309 0.19114586 0.31282969 0.32835573
## RTIMOR 1.00000000 0.41804642 -0.3375163 -0.26020225 -0.30876424 -0.49070005
## TUBINC 0.41804642 1.00000000 -0.3789808 -0.32542031 -0.38118795 -0.19831333
## DPTIMM -0.33751628 -0.37898079 1.0000000 0.95025399 0.88174261 0.32804245
## HEPIMM -0.26020225 -0.32542031 0.9502540 1.00000000 0.86420155 0.27828765
## MEAIMM -0.30876424 -0.38118795 0.8817426 0.86420155 1.00000000 0.33921761
## HOSBED -0.49070005 -0.19831333 0.3280425 0.27828765 0.33921761 1.00000000
## SANSER -0.65108358 -0.55657212 0.4749098 0.41097807 0.52634353 0.48298288
## TUBTRT 0.32106450 0.23187021 -0.1508346 -0.10718796 -0.15128872 -0.20100265
## URBPOP -0.39930232 -0.33604248 0.2477115 0.16791625 0.27392265 0.29499246
## RURPOP 0.39930232 0.33604248 -0.2477115 -0.16791625 -0.27392265 -0.29499246
## NCOMOR 0.27623132 0.44177318 -0.2463052 -0.18345234 -0.25685607 -0.14297975
## SUIRAT -0.08928389 0.13461778 0.1016538 0.07309085 0.07173647 0.23601976
## SANSER TUBTRT URBPOP RURPOP NCOMOR SUIRAT
## UNEMPR 0.01743227 -0.05294186 0.08774280 -0.08774280 0.08876876 0.04227853
## INFMOR -0.82413990 0.30145355 -0.53383928 0.53383928 0.47793168 0.01383619
## GDP 0.15602727 -0.02991535 0.16951798 -0.16951798 -0.15463824 0.05563045
## GNI 0.15526943 -0.02993892 0.16915063 -0.16915063 -0.15418319 0.05591448
## CLTECH 0.86208147 -0.32585084 0.64092796 -0.64092796 -0.47327879 0.05255453
## PERCAP 0.48509427 -0.38908786 0.57384548 -0.57384548 -0.51434429 0.08998525
## RTIMOR -0.65108358 0.32106450 -0.39930232 0.39930232 0.27623132 -0.08928389
## TUBINC -0.55657212 0.23187021 -0.33604248 0.33604248 0.44177318 0.13461778
## DPTIMM 0.47490980 -0.15083460 0.24771148 -0.24771148 -0.24630524 0.10165384
## HEPIMM 0.41097807 -0.10718796 0.16791625 -0.16791625 -0.18345234 0.07309085
## MEAIMM 0.52634353 -0.15128872 0.27392265 -0.27392265 -0.25685607 0.07173647
## HOSBED 0.48298288 -0.20100265 0.29499246 -0.29499246 -0.14297975 0.23601976
## SANSER 1.00000000 -0.29773553 0.57044364 -0.57044364 -0.38904255 0.06984751
## TUBTRT -0.29773553 1.00000000 -0.31075615 0.31075615 0.24910749 -0.03195240
## URBPOP 0.57044364 -0.31075615 1.00000000 -1.00000000 -0.49977042 0.00916115
## RURPOP -0.57044364 0.31075615 -1.00000000 1.00000000 0.49977042 -0.00916115
## NCOMOR -0.38904255 0.24910749 -0.49977042 0.49977042 1.00000000 0.43953077
## SUIRAT 0.06984751 -0.03195240 0.00916115 -0.00916115 0.43953077 1.00000000DPA_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$p,
insig = "blank")
corrplot(cor(DPA.Predictors.Numeric,
method = "pearson",
use="pairwise.complete.obs"),
method = "number",
type = "upper",
order = "original",
tl.col = "black",
tl.cex = 0.75,
tl.srt = 90,
sig.level = 0.05,
p.mat = DPA_CorrelationTest$p,
insig = "blank")
##################################
# Identifying the highly correlated variables
##################################
DPA_Correlation <- cor(DPA.Predictors.Numeric,
method = "pearson",
use="pairwise.complete.obs")
(DPA_HighlyCorrelatedCount <- sum(abs(DPA_Correlation[upper.tri(DPA_Correlation)])>0.80))## [1] 7if (DPA_HighlyCorrelatedCount > 0) {
DPA_HighlyCorrelated <- findCorrelation(DPA_Correlation, cutoff = 0.80)
(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 6 numeric variable(s)."
## [1] "Variable 1 for removal: INFMOR"
## [1] "Variable 2 for removal: CLTECH"
## [1] "Variable 3 for removal: URBPOP"
## [1] "Variable 4 for removal: MEAIMM"
## [1] "Variable 5 for removal: DPTIMM"
## [1] "Variable 6 for removal: GNI"1.3.3.4 Linear Dependencies
[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))
}
}1.3.3.5 Shape Transformation
[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 numeric predictor with Skewness>3.
[B.1] SUIRAT = 3.95
[C] Outliers were minimized for most except for 9 numeric predictors which continued to contain outlying points as noted using the IQR criterion.
[C.1] UNEMPR = 5
[C.2] RTIMOR = 2
[C.3] TUBINC = 28
[C.4] DPTIMM = 24
[C.5] HEPIMM = 8
[C.6] MEAIMM = 18
[C.7] TUBTRT = 30
[C.8] NCOMOR = 2
[C.9] SUIRAT = 27
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 <- cbind(DPA_BoxCoxTransformed,DPA[,c("COUNTRY",
"YEAR",
"GENDER",
"CONTIN",
"LIFEXP")])1.3.3.6 Pre-Processed Dataset
[A] A total of 9 variables (8 predictors and 1 metadata) were removed prior to data exploration and modelling due to issues identified during data preprocessing.
[A.1] YEAR = Metadata containing only a single value
[A.2] GNI = High correlation with GDP
[A.3] DPTIMM = High correlation with HEPIMM
[A.4] MEAIMM = High correlation with HEPIMM
[A.5] URBPOP = High correlation with RURPOP
[A.6] SANSER = High correlation with INFMOR and CLTECH
[A.7] TUBINC = High outlier count even after shape transformation
[A.8] TUBTRT = High outlier count even after shape transformation
[A.9] SUIRAT = High skewness even after shape transformation
[B] A total of 30 observations were removed prior to data exploration and modelling due to issues identified during data preprocessing.
[B.1] 15 countries were identified to be associated with distributional anomalies showing high number of observations reporting the exact same set of values.
[C] The preprocessed tabular dataset was comprised of 364 observations and 14 variables (including 1 metadata, 1 response and 12 predictors).
[C.1] 364 rows (observations)
[C.2] 14 columns (variables)
[C.2.1] 1/14 instance labels = COUNTRY (character)
[C.2.2] 1/14 response = LIFEXP (numeric)
[C.2.3] 12/14 predictors = 10/12 numeric + 2/12 factor
[C.2.3.1] GENDER (factor)
[C.2.3.2] CONTIN (factor)
[C.2.3.3] UNEMPR (numeric)
[C.2.3.4] INFMOR (numeric)
[C.2.3.5] GDP (numeric)
[C.2.3.6] CLTECH (numeric)
[C.2.3.7] PERCAP (numeric)
[C.2.3.8] RTIMOR (numeric)
[C.2.3.9] HEPIMM (numeric)
[C.2.3.10] HOSBED (numeric)
[C.2.3.11] RURPOP (numeric)
[C.2.3.12] NCOMOR (numeric)
Code Chunk | Output
##################################
# Creating the pre-modelling
# train set
##################################
PMA <- DPA_BoxCoxTransformed[,!names(DPA_BoxCoxTransformed) %in% c("YEAR",
"GNI",
"DPTIMM",
"MEAIMM",
"URBPOP",
"SANSER",
"TUBINC",
"TUBTRT",
"SUIRAT")]
##################################
# Gathering descriptive statistics
##################################
(PMA_Skimmed <- skim(PMA))| Name | PMA |
| Number of rows | 364 |
| Number of columns | 14 |
| _______________________ | |
| Column type frequency: | |
| character | 1 |
| factor | 2 |
| numeric | 11 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| COUNTRY | 0 | 1 | 4 | 30 | 0 | 182 | 0 |
Variable type: factor
| skim_variable | n_missing | complete_rate | ordered | n_unique | top_counts |
|---|---|---|---|---|---|
| GENDER | 0 | 1 | FALSE | 2 | Mal: 182, Fem: 182 |
| CONTIN | 0 | 1 | FALSE | 6 | Afr: 106, Asi: 94, Eur: 78, Nor: 42 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| UNEMPR | 0 | 1 | 2.13 | 1.21 | -2.05 | 1.42 | 2.00 | 2.90 | 5.26 | ▁▂▇▅▂ |
| INFMOR | 0 | 1 | 2.56 | 1.06 | 0.34 | 1.70 | 2.63 | 3.49 | 4.49 | ▅▆▇▇▆ |
| GDP | 0 | 1 | 3.85 | 2.21 | -1.67 | 2.55 | 3.76 | 5.53 | 9.97 | ▂▇▇▆▁ |
| CLTECH | 0 | 1 | 66.08 | 37.77 | 0.00 | 30.30 | 83.95 | 100.00 | 100.00 | ▃▁▁▂▇ |
| PERCAP | 0 | 1 | 1.75 | 1.40 | -1.48 | 0.65 | 1.80 | 2.82 | 4.73 | ▂▆▇▆▃ |
| RTIMOR | 0 | 1 | 16.97 | 10.72 | 0.00 | 8.00 | 15.30 | 25.60 | 64.60 | ▇▆▅▁▁ |
| HEPIMM | 0 | 1 | 3877.07 | 1006.71 | 612.00 | 3304.98 | 4231.50 | 4704.00 | 4900.00 | ▁▁▁▃▇ |
| HOSBED | 0 | 1 | 0.76 | 0.86 | -1.61 | 0.14 | 0.83 | 1.40 | 2.62 | ▁▅▇▇▃ |
| RURPOP | 0 | 1 | 41.73 | 22.68 | 0.00 | 22.62 | 41.64 | 59.76 | 86.75 | ▅▇▇▆▅ |
| NCOMOR | 0 | 1 | 4.67 | 1.08 | 1.87 | 3.93 | 4.72 | 5.38 | 7.96 | ▂▅▇▃▁ |
| LIFEXP | 0 | 1 | 72.50 | 7.77 | 51.20 | 66.93 | 73.53 | 78.54 | 87.45 | ▁▃▆▇▅ |
1.3.4 Data Exploration
[A] Numeric predictors which demonstrated a positive linear relationship with the response variable LIFEXP included:
[A.1] PERCAP (numeric)
[A.2] CLTECH (numeric)
[B] Numeric predictors which demonstrated a negative linear relationship with the response variable LIFEXP included:
[B.1] INFMOR (numeric)
[B.2] NCOMOR (numeric)
[C] Both factor predictors demonstrated a differential relationship with the response variable LIFEXP included:
[C.1] GENDER (factor)
[C.2] CONTIN (factor)
Code Chunk | Output
##################################
# Loading dataset
##################################
PME <- PMA
PME.Numeric <- PME[,sapply(PME, is.numeric), drop = FALSE]
##################################
# Listing all Predictors
##################################
PME.Predictors <- PME[,!names(PME) %in% c("COUNTRY","LIFEXP")]
##################################
# Listing all numeric Predictors
##################################
PME.Predictors.Numeric <- PME.Predictors[,sapply(PME.Predictors, is.numeric), drop = FALSE]
ncol(PME.Predictors.Numeric)## [1] 10##################################
# Listing all numeric Predictors
##################################
PME.Predictors.Factor <- PME.Predictors[,sapply(PME.Predictors, is.factor), drop = FALSE]
ncol(PME.Predictors.Factor)## [1] 2##################################
# Formulating the scatter plot
##################################
featurePlot(x = PME.Predictors.Numeric,
y = PME$LIFEXP,
plot = "scatter",
type = c("p", "smooth"),
span = .5,
layout = c(4, 3))
##################################
# Formulating the box plot
##################################
featurePlot(x = PME.Numeric,
y = PME$GENDER,
plot = "box",
scales = list(x = list(relation="free", rot = 90),
y = list(relation="free")),
adjust = 1.5,
layout = c(4, 3))
##################################
# Formulating the box plot
##################################
featurePlot(x = PME.Numeric,
y = PME$CONTIN,
plot = "box",
scales = list(x = list(relation="free", rot = 90),
y = list(relation="free")),
adjust = 1.5,
layout = c(4, 3))
1.3.5 Feature Selection
1.3.5.1 Locally Weighted Scatterplot Smoothing Pseudo-R-Squared
[A] The numeric predictors which demonstrated the best feature importance in terms of the locally weighted scatterplot smoothing pseudo-r-squared statistic as obtained using the caret package included:
[A.1] INFMOR = 0.8254
[A.2] PERCAP = 0.6217
[A.3] NCOMOR = 0.5966
[A.4] CLTECH = 0.5819
Code Chunk | Output
##################################
# Evaluating model-independent
# feature importance metrics
##################################
##################################
# Obtaining the LOWESSPR pseudo-R-Squared
##################################
FE_LOWESSPR <- filterVarImp(x = PME.Predictors.Numeric,
y = PME$LIFEXP,
nonpara = TRUE)
##################################
# Formulating the summary table
##################################
FE_LOWESSPR_Summary <- FE_LOWESSPR
FE_LOWESSPR_Summary$Predictor <- rownames(FE_LOWESSPR)
names(FE_LOWESSPR_Summary)[1] <- "LOWESSPR"
FE_LOWESSPR_Summary$Metric <- rep("LOWESSPR",nrow(FE_LOWESSPR))
FE_LOWESSPR_Summary## LOWESSPR Predictor Metric
## UNEMPR 0.03707208 UNEMPR LOWESSPR
## INFMOR 0.82546121 INFMOR LOWESSPR
## GDP 0.25427692 GDP LOWESSPR
## CLTECH 0.58186548 CLTECH LOWESSPR
## PERCAP 0.62170474 PERCAP LOWESSPR
## RTIMOR 0.51428354 RTIMOR LOWESSPR
## HEPIMM 0.18618812 HEPIMM LOWESSPR
## HOSBED 0.34642501 HOSBED LOWESSPR
## RURPOP 0.35023499 RURPOP LOWESSPR
## NCOMOR 0.59655877 NCOMOR LOWESSPR##################################
# Exploring predictor performance
# using LOWESS
##################################
dotplot(Predictor ~ LOWESSPR | Metric,
FE_LOWESSPR_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), ...)
})
1.3.5.2 Pearson’s Correlation Coefficient
[A] The numeric predictors which demonstrated the best feature importance in terms of the higher Pearson’s correlation coefficients as obtained using the stats package included:
[A.1] INFMOR = 0.8796
[A.2] PERCAP = 0.7852
[A.3] NCOMOR = 0.7524
[A.4] CLTECH = 0.7332
Code Chunk | Output
##################################
# Obtaining the Pearson correlation coefficient
##################################
(FE_PCC <- abs(cor(PME.Numeric, method="pearson")[-11,11]))## UNEMPR INFMOR GDP CLTECH PERCAP RTIMOR HEPIMM
## 0.01518356 0.87964210 0.45844159 0.75234946 0.78523930 0.65975953 0.43149521
## HOSBED RURPOP NCOMOR
## 0.56281916 0.57322144 0.73325984##################################
# Formulating the summary table
##################################
FE_PCC_Summary <- data.frame(Predictor = names(PME.Numeric)[1:(ncol(PME.Numeric)-1)],
PCC = FE_PCC,
Metric = rep("PCC", length(FE_PCC)),
row.names = NULL)
FE_PCC_Summary## Predictor PCC Metric
## 1 UNEMPR 0.01518356 PCC
## 2 INFMOR 0.87964210 PCC
## 3 GDP 0.45844159 PCC
## 4 CLTECH 0.75234946 PCC
## 5 PERCAP 0.78523930 PCC
## 6 RTIMOR 0.65975953 PCC
## 7 HEPIMM 0.43149521 PCC
## 8 HOSBED 0.56281916 PCC
## 9 RURPOP 0.57322144 PCC
## 10 NCOMOR 0.73325984 PCC##################################
# Exploring predictor performance
# using PCC
##################################
dotplot(Predictor ~ PCC | Metric,
FE_PCC_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), ...)
})
1.3.5.3 Spearman’s Rank Correlation Coefficient
[A] The numeric predictors which demonstrated the best feature importance in terms of the higher Spearman’s rank correlation coefficients as obtained using the stats package included:
[A.1] INFMOR = 0.8919
[A.2] PERCAP = 0.7982
[A.3] NCOMOR = 0.7891
[A.4] CLTECH = 0.7837
Code Chunk | Output
##################################
# Obtaining the Spearman's rank correlation coefficient
##################################
(FE_SRCC <- abs(cor(PME.Numeric, method="spearman")[-11,11]))## UNEMPR INFMOR GDP CLTECH PERCAP RTIMOR
## 0.003729075 0.891871299 0.499048543 0.783732221 0.798156088 0.698820899
## HEPIMM HOSBED RURPOP NCOMOR
## 0.382149849 0.555871706 0.600468144 0.789128052##################################
# Formulating the summary table
##################################
FE_SRCC_Summary <- data.frame(Predictor = names(PME.Numeric)[1:(ncol(PME.Numeric)-1)],
SRCC = FE_SRCC,
Metric = rep("SRCC", length(FE_SRCC)),
row.names = NULL)
FE_SRCC_Summary## Predictor SRCC Metric
## 1 UNEMPR 0.003729075 SRCC
## 2 INFMOR 0.891871299 SRCC
## 3 GDP 0.499048543 SRCC
## 4 CLTECH 0.783732221 SRCC
## 5 PERCAP 0.798156088 SRCC
## 6 RTIMOR 0.698820899 SRCC
## 7 HEPIMM 0.382149849 SRCC
## 8 HOSBED 0.555871706 SRCC
## 9 RURPOP 0.600468144 SRCC
## 10 NCOMOR 0.789128052 SRCC##################################
# Exploring predictor performance
# using SRCC
##################################
dotplot(Predictor ~ SRCC | Metric,
FE_SRCC_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), ...)
})
1.3.5.4 Maximal Information Coefficient
[A] The numeric predictors which demonstrated the best feature importance in terms of the higher maximal information coefficients as obtained using the minerva package included:
[A.1] INFMOR = 0.7084
[A.2] NCOMOR = 0.6439
[A.3] PERCAP = 0.5502
[A.4] CLTECH = 0.5099
Code Chunk | Output
##################################
# Obtaining the maximal information coefficient
##################################
FE_MIC <- mine(x = PME.Numeric[,!names(PME.Numeric) %in% c("LIFEXP")],
y = PME$LIFEXP)$MIC
##################################
# Formulating the summary table
##################################
FE_MIC_Summary <- data.frame(Predictor = names(PME.Numeric)[1:(ncol(PME.Numeric)-1)],
MIC = FE_MIC[,1],
Metric = rep("MIC", length(FE_MIC)))
FE_MIC_Summary## Predictor MIC Metric
## 1 UNEMPR 0.1911950 MIC
## 2 INFMOR 0.7083938 MIC
## 3 GDP 0.3234249 MIC
## 4 CLTECH 0.5099735 MIC
## 5 PERCAP 0.5502257 MIC
## 6 RTIMOR 0.4771528 MIC
## 7 HEPIMM 0.2652364 MIC
## 8 HOSBED 0.3711493 MIC
## 9 RURPOP 0.4097827 MIC
## 10 NCOMOR 0.6438989 MIC##################################
# Exploring predictor performance
# using MIC
##################################
dotplot(Predictor ~ MIC | Metric,
FE_MIC_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), ...)
})
1.3.5.5 Relief Values
[A] The numeric predictors which demonstrated the best feature importance in terms of the higher relief values as obtained using the CORElearn package included:
[A.1] NCOMOR = 0.2991
[A.2] INFMOR = 00936
Code Chunk | Output
##################################
# Obtaining the relief values
##################################
FE_RV <- attrEval(LIFEXP ~ .,
data = PME.Numeric,
estimator = "RReliefFequalK")
##################################
# Formulating the summary table
##################################
FE_RV_Summary <- data.frame(Predictor = names(FE_RV),
RV = FE_RV,
Metric = rep("RV", length(FE_RV)))
FE_RV_Summary## Predictor RV Metric
## UNEMPR UNEMPR -0.01810789 RV
## INFMOR INFMOR 0.09365306 RV
## GDP GDP -0.09938056 RV
## CLTECH CLTECH 0.02105440 RV
## PERCAP PERCAP -0.04869230 RV
## RTIMOR RTIMOR 0.01046617 RV
## HEPIMM HEPIMM -0.03530648 RV
## HOSBED HOSBED -0.06597469 RV
## RURPOP RURPOP -0.10732377 RV
## NCOMOR NCOMOR 0.29910591 RV##################################
# Exploring predictor performance
##################################
dotplot(Predictor ~ RV | Metric,
FE_RV_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), ...)
})
1.3.5.6 Pre-Modelling Dataset
[A] The final list of predictors to be applied during the modelling process involved 4 numeric predictors (which consistently demonstrated the best feature importance in terms of the aforementioned metrics) and 2 factor predictors enumerated as follows:
[A.1] INFMOR (numeric)
[A.2] PERCAP (numeric)
[A.3] NCOMOR (numeric)
[A.4] CLTECH (numeric)
[A.5] GENDER (factor)
[A.6] CONTIN (factor)
[B] The dataset was divided into two groups using a fixed random seed.
[B.1] 80% were allocated for the model development set.
[B.1.1] 10 folds were formulated for the model development set using a fixed random seed.
[B.1.2] Fold assignments will be used for internal 10-fold cross-validation and hyperparameter tuning.
[B.2] 20% were allocated for the model test set.
[B.2.1] Model test will be used for external validation.
[C] The model development set was comprised of 292 observations and 8 variables (including 1 metadata, 1 response and 6 predictors).
[C.1] 292 rows (observations)
[C.2] 8 columns (variables)
[C.2.1] 1/8 instance labels = COUNTRY variable (character)
[C.2.2] 1/8 response = LIFEXP variable (numeric)
[C.2.3] 6/8 predictors = 4/6 numeric + 2/6 factor
[C.2.3.1] INFMOR (numeric)
[C.2.3.2] PERCAP (numeric)
[C.2.3.3] NCOMOR (numeric)
[C.2.3.4] CLTECH (numeric)
[C.2.3.5] GENDER (factor)
[C.2.3.6] CONTIN (factor)
[D] The model test set was comprised of 72 observations and 8 variables (including 1 metadata, 1 response and 6 predictors).
[D.1] 72 rows (observations)
[D.2] 8 columns (variables)
[D.2.1] 1/8 instance labels = COUNTRY variable (character)
[D.2.2] 1/8 response = LIFEXP variable (numeric)
[D.2.3] 6/8 predictors = 4/6 numeric + 2/6 factor
[D.2.3.1] INFMOR (numeric)
[D.2.3.2] PERCAP (numeric)
[D.2.3.3] NCOMOR (numeric)
[D.2.3.4] CLTECH (numeric)
[D.2.3.5] GENDER (factor)
[D.2.3.6] CONTIN (factor)
Code Chunk | Output
##################################
# Preparing the dataset for
# model development and test
##################################
set.seed(12345678)
trainIndex <- createDataPartition(PME$LIFEXP,
p = 0.8,
list = FALSE,
times = 1)
##################################
# Formulating the model development data
##################################
MD <- PME[ trainIndex,]
##################################
# Formulating the model test data
##################################
MT <- PME[-trainIndex,]
##################################
# Preparing the dataset for
# model development
##################################
MD <- MD[,c("GENDER","CONTIN","INFMOR","PERCAP","CLTECH","NCOMOR","LIFEXP")]
dim(MD)## [1] 292 7MD.Model.Predictors <- MD[,c("GENDER","CONTIN","INFMOR","PERCAP","CLTECH","NCOMOR")]
##################################
# Preparing the dataset for
# model test
##################################
MT <- MT[,c("GENDER","CONTIN","INFMOR","PERCAP","CLTECH","NCOMOR","LIFEXP")]
dim(MT)## [1] 72 7MT.Model.Predictors <- MT[,c("GENDER","CONTIN","INFMOR","PERCAP","CLTECH","NCOMOR")]
##################################
# Creating consistent fold assignments
# for the 10-Fold Cross Validation process
##################################
set.seed(12345678)
KFold_Indices <- createFolds(MD$LIFEXP,
k = 10,
returnTrain=TRUE)
KFold_Control <- trainControl(method="cv",
index=KFold_Indices)1.3.6 Model Development and Performance Estimation
1.3.6.1 Linear Regression
[A] The linear regression model from the stats package was implemented through the caret package.
[B] The model contains 1 hyperparameter:
[B.1] intercept = intercept held constant at a value of TRUE
[C] The 10-fold cross-validated model performance of the optimal model is summarized as follows:
[C.1] Optimal model used intercept=TRUE
[C.2] Root Mean Square Error = 2.4078
[C.3] R-Squared = 0.9116
[D] The apparent model performance of the optimal model is summarized as follows:
[D.1] Root Mean Square Error = 2.3622
[D.2] R-Squared = 0.9098
[E] The top-performing predictors in the model ranked using the root mean square error loss after permutations are as follows:
[E.1] INFMOR (numeric) = 7.15
[E.2] NCOMOR (numeric) = 3.23
[E.3] CONTIN (factor) = 3.18
[E.5] CLTECH (numeric) = 3.10
[E.4] GENDER (factor) = 3.06
[E.6] PERCAP (numeric) = 2.43
Code Chunk | Output
##################################
# No hyperparameter tuning process conducted
# for the LR model
# hyperparameter=intercept fixed to TRUE
##################################
##################################
# Running the LR model
# by setting the caret method to 'lm'
##################################
set.seed(12345678)
LR_Tune <- train(x = MD.Model.Predictors,
y = MD$LIFEXP,
method = "lm",
trControl = KFold_Control)
##################################
# Reporting the apparent results
# for the LR model
##################################
LR_DALEX <- DALEX::explain(LR_Tune,
data = MD.Model.Predictors,
y = MD$LIFEXP,
verbose = FALSE,
label = "LR")
(LR_DALEX_Performance <- model_performance(LR_DALEX))## Measures for: regression
## mse : 5.58016
## rmse : 2.362236
## r2 : 0.9098275
## mad : 1.395866
##
## Residuals:
## 0% 10% 20% 30% 40% 50%
## -7.54609872 -2.70120396 -1.67144520 -1.06388223 -0.49677829 0.05953136
## 60% 70% 80% 90% 100%
## 0.56643413 1.03111778 1.80727895 2.72612936 7.57156135(LR_DALEX_Diagnostics <- model_diagnostics(LR_DALEX))## GENDER CONTIN INFMOR PERCAP
## Male :139 Africa :89 Min. :0.3365 Min. :-1.4775
## Female:153 Asia :73 1st Qu.:1.7047 1st Qu.: 0.6183
## Europe :62 Median :2.6100 Median : 1.7960
## North America:31 Mean :2.5569 Mean : 1.7571
## Oceania :18 3rd Qu.:3.5025 3rd Qu.: 2.7938
## South America:19 Max. :4.4864 Max. : 4.7293
## CLTECH NCOMOR y y_hat
## Min. : 0.00 Min. :1.866 Min. :52.84 Min. :58.11
## 1st Qu.: 24.35 1st Qu.:3.944 1st Qu.:66.93 1st Qu.:66.54
## Median : 82.80 Median :4.717 Median :73.53 Median :73.13
## Mean : 64.60 Mean :4.652 Mean :72.47 Mean :72.47
## 3rd Qu.:100.00 3rd Qu.:5.347 3rd Qu.:78.54 3rd Qu.:78.29
## Max. :100.00 Max. :7.959 Max. :87.45 Max. :87.96
## residuals abs_residuals label ids
## Min. :-7.54610 Min. :0.01202 Length:292 Min. : 1.00
## 1st Qu.:-1.32434 1st Qu.:0.72522 Class :character 1st Qu.: 73.75
## Median : 0.05953 Median :1.39587 Mode :character Median :146.50
## Mean : 0.00000 Mean :1.80627 Mean :146.50
## 3rd Qu.: 1.52469 3rd Qu.:2.52337 3rd Qu.:219.25
## Max. : 7.57156 Max. :7.57156 Max. :292.00plot(LR_DALEX_Diagnostics,
variable = "y",
yvariable = "y_hat") +
geom_point(size=3) +
scale_x_continuous("Observed LIFEXP") +
scale_y_continuous("Predicted LIFEXP") +
geom_abline(slope = 1) +
ggtitle("LR: Observed and Predicted LIFEXP")
(LR_DALEX_VariableImportance <- model_parts(LR_DALEX,
loss_function = loss_root_mean_square,
B = 200,
N = NULL))## variable mean_dropout_loss label
## 1 _full_model_ 2.362236 LR
## 2 PERCAP 2.427498 LR
## 3 GENDER 3.063092 LR
## 4 CLTECH 3.101405 LR
## 5 CONTIN 3.184817 LR
## 6 NCOMOR 3.231935 LR
## 7 INFMOR 7.153165 LR
## 8 _baseline_ 10.873968 LRplot(LR_DALEX_VariableImportance)
##################################
# Reporting the cross-validation results
# for the LR model
##################################
LR_Tune## Linear Regression
##
## 292 samples
## 6 predictor
##
## No pre-processing
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 264, 263, 263, 262, 263, 264, ...
## Resampling results:
##
## RMSE Rsquared MAE
## 2.407871 0.9116813 1.866752
##
## Tuning parameter 'intercept' was held constant at a value of TRUELR_Tune$finalModel##
## Call:
## lm(formula = .outcome ~ ., data = dat)
##
## Coefficients:
## (Intercept) GENDERFemale CONTINAsia
## 85.69806 2.76133 2.83144
## CONTINEurope CONTINNorth America CONTINOceania
## 0.83630 3.33009 4.69879
## CONTINSouth America INFMOR PERCAP
## 2.83668 -4.48540 -0.28654
## CLTECH NCOMOR
## 0.03709 -1.46345(LR_Tune_RMSE <- LR_Tune$results$RMSE)## [1] 2.407871(LR_Tune_Rsquared <- LR_Tune$results$Rsquared)## [1] 0.9116813(LR_Tune_MAE <- LR_Tune$results$MAE)## [1] 1.8667521.3.6.2 Stochastic Gradient Boosting
[A] The stochastic gradient boosting regression model from the gbm package was implemented through the caret package.
[B] The model contains 4 hyperparameters:
[B.1] n.trees = total number of trees to fit which is equivalent to the number of iterations and the number of basis functions in the additive expansion made to vary across a range of values equal to 100, 200 and 300
[B.2] interaction.depth = maximum depth of each tree representing the highest level of variable interactions allowed made to vary across a range of values equal to 1, 2 and 3
[B.3] shrinkage = shrinkage parameter applied to each tree in the expansion representing the learning rate or step-size reduction made to vary across a range of values equal to 0.001, 0.01 and 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, 10 and 15
[C] The 10-fold cross-validated model performance of the optimal model is summarized as follows:
[C.1] Optimal model used n.trees=300, interaction.depth=2, shrinkage=0.1 and n.minobsinnode=5 which demonstrated the lowest root mean square error
[C.2] Root Mean Square Error = 2.0978
[C.3] R-Squared = 0.9288
[D] The apparent model performance of the optimal model is summarized as follows:
[D.1] Root Mean Square Error = 1.7044
[D.2] R-Squared = 0.9530
[E] The top-performing predictors in the model ranked using the root mean square error loss after permutations are as follows:
[E.1] INFMOR (numeric) = 7.07
[E.2] NCOMOR (numeric) = 3.57
[E.3] CONTIN (factor) = 1.93
[E.4] GENDER (factor) = 1.92
[E.5] CLTECH (numeric) = 1.84
[E.6] PERCAP (numeric) = 1.52
Code Chunk | Output
##################################
# Defining the model hyperparameter values
# for the GBM model
##################################
GBM_Grid = expand.grid(n.trees = c(100, 200, 300),
interaction.depth = c(1, 2, 3),
shrinkage = c(0.001, 0.005, 0.01, 0.05, 0.10),
n.minobsinnode = c(5,10,15))
##################################
# Running the GBM model
# by setting the caret method to 'gbm'
##################################
set.seed(12345678)
GBM_Tune <- train(x = MD.Model.Predictors,
y = MD$LIFEXP,
method = "gbm",
tuneGrid = GBM_Grid,
trControl = KFold_Control)## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.7314 nan 0.0010 0.0780
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
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##
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
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## 80 39.1858 nan 0.0050 0.1921
## 100 35.4071 nan 0.0050 0.1676
## 120 32.0656 nan 0.0050 0.1538
## 140 29.1513 nan 0.0050 0.1261
## 160 26.5742 nan 0.0050 0.1087
## 180 24.4029 nan 0.0050 0.0835
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## 220 20.7474 nan 0.0050 0.0589
## 240 19.1727 nan 0.0050 0.0647
## 260 17.7907 nan 0.0050 0.0625
## 280 16.5546 nan 0.0050 0.0533
## 300 15.4377 nan 0.0050 0.0534
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.3183 nan 0.0050 0.4876
## 2 60.8492 nan 0.0050 0.4594
## 3 60.3518 nan 0.0050 0.4423
## 4 59.9113 nan 0.0050 0.4776
## 5 59.4253 nan 0.0050 0.4608
## 6 58.9370 nan 0.0050 0.4401
## 7 58.4892 nan 0.0050 0.4357
## 8 58.0379 nan 0.0050 0.4529
## 9 57.5703 nan 0.0050 0.4634
## 10 57.1655 nan 0.0050 0.4319
## 20 52.9620 nan 0.0050 0.4146
## 40 45.7776 nan 0.0050 0.3745
## 60 39.6726 nan 0.0050 0.2757
## 80 34.3010 nan 0.0050 0.2296
## 100 30.0047 nan 0.0050 0.2047
## 120 26.3439 nan 0.0050 0.1414
## 140 23.2501 nan 0.0050 0.1473
## 160 20.6512 nan 0.0050 0.1015
## 180 18.4366 nan 0.0050 0.1074
## 200 16.5790 nan 0.0050 0.0781
## 220 14.9212 nan 0.0050 0.0643
## 240 13.5187 nan 0.0050 0.0443
## 260 12.2996 nan 0.0050 0.0524
## 280 11.2470 nan 0.0050 0.0463
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.3281 nan 0.0050 0.4959
## 2 60.8400 nan 0.0050 0.4515
## 3 60.3679 nan 0.0050 0.4682
## 4 59.8972 nan 0.0050 0.4882
## 5 59.4713 nan 0.0050 0.4463
## 6 59.0338 nan 0.0050 0.4229
## 7 58.6122 nan 0.0050 0.4238
## 8 58.1729 nan 0.0050 0.4370
## 9 57.7493 nan 0.0050 0.4365
## 10 57.3423 nan 0.0050 0.4293
## 20 53.1743 nan 0.0050 0.3420
## 40 45.9690 nan 0.0050 0.3232
## 60 39.7048 nan 0.0050 0.2887
## 80 34.6085 nan 0.0050 0.2706
## 100 30.2483 nan 0.0050 0.1994
## 120 26.4919 nan 0.0050 0.1735
## 140 23.3849 nan 0.0050 0.1502
## 160 20.7488 nan 0.0050 0.1143
## 180 18.5069 nan 0.0050 0.1038
## 200 16.5961 nan 0.0050 0.0790
## 220 14.9226 nan 0.0050 0.0808
## 240 13.5931 nan 0.0050 0.0598
## 260 12.3717 nan 0.0050 0.0419
## 280 11.3383 nan 0.0050 0.0434
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.3132 nan 0.0050 0.4832
## 2 60.8305 nan 0.0050 0.4159
## 3 60.3857 nan 0.0050 0.4552
## 4 59.9519 nan 0.0050 0.4422
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## 7 58.5887 nan 0.0050 0.4213
## 8 58.1446 nan 0.0050 0.4156
## 9 57.7243 nan 0.0050 0.4845
## 10 57.3011 nan 0.0050 0.4325
## 20 53.2001 nan 0.0050 0.3714
## 40 45.8054 nan 0.0050 0.2775
## 60 39.7707 nan 0.0050 0.2795
## 80 34.5800 nan 0.0050 0.2288
## 100 30.1995 nan 0.0050 0.2101
## 120 26.4604 nan 0.0050 0.1636
## 140 23.4614 nan 0.0050 0.1386
## 160 20.8516 nan 0.0050 0.1203
## 180 18.6040 nan 0.0050 0.0806
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## 220 15.0651 nan 0.0050 0.0573
## 240 13.6762 nan 0.0050 0.0389
## 260 12.5008 nan 0.0050 0.0460
## 280 11.4454 nan 0.0050 0.0418
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.2965 nan 0.0050 0.4630
## 2 60.7673 nan 0.0050 0.5376
## 3 60.2610 nan 0.0050 0.5351
## 4 59.7501 nan 0.0050 0.4393
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## 6 58.7616 nan 0.0050 0.5020
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## 8 57.8056 nan 0.0050 0.4545
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## 10 56.8928 nan 0.0050 0.4419
## 20 52.4219 nan 0.0050 0.4211
## 40 44.5994 nan 0.0050 0.3508
## 60 38.1464 nan 0.0050 0.2810
## 80 32.7632 nan 0.0050 0.2527
## 100 28.2622 nan 0.0050 0.1794
## 120 24.4678 nan 0.0050 0.1673
## 140 21.2787 nan 0.0050 0.1257
## 160 18.5928 nan 0.0050 0.1227
## 180 16.3531 nan 0.0050 0.0776
## 200 14.4769 nan 0.0050 0.0881
## 220 12.9282 nan 0.0050 0.0626
## 240 11.5860 nan 0.0050 0.0550
## 260 10.4411 nan 0.0050 0.0443
## 280 9.4774 nan 0.0050 0.0372
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.3026 nan 0.0050 0.4722
## 2 60.7909 nan 0.0050 0.4977
## 3 60.2783 nan 0.0050 0.5161
## 4 59.7878 nan 0.0050 0.4935
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## 6 58.7793 nan 0.0050 0.5116
## 7 58.2909 nan 0.0050 0.4509
## 8 57.7947 nan 0.0050 0.4880
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## 10 56.8342 nan 0.0050 0.4469
## 20 52.3362 nan 0.0050 0.3965
## 40 44.5028 nan 0.0050 0.3664
## 60 38.0418 nan 0.0050 0.2665
## 80 32.6329 nan 0.0050 0.2474
## 100 28.1768 nan 0.0050 0.1930
## 120 24.4039 nan 0.0050 0.1632
## 140 21.3089 nan 0.0050 0.1419
## 160 18.6493 nan 0.0050 0.1447
## 180 16.4301 nan 0.0050 0.0964
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## 220 12.9799 nan 0.0050 0.0620
## 240 11.6491 nan 0.0050 0.0520
## 260 10.5148 nan 0.0050 0.0357
## 280 9.5391 nan 0.0050 0.0397
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.2691 nan 0.0050 0.4829
## 2 60.7439 nan 0.0050 0.4850
## 3 60.2346 nan 0.0050 0.4987
## 4 59.7348 nan 0.0050 0.5476
## 5 59.2410 nan 0.0050 0.4521
## 6 58.7536 nan 0.0050 0.4424
## 7 58.2607 nan 0.0050 0.4742
## 8 57.7913 nan 0.0050 0.4585
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## 10 56.8442 nan 0.0050 0.4820
## 20 52.3889 nan 0.0050 0.4029
## 40 44.5993 nan 0.0050 0.3464
## 60 38.1328 nan 0.0050 0.3073
## 80 32.8046 nan 0.0050 0.2354
## 100 28.3479 nan 0.0050 0.2270
## 120 24.5326 nan 0.0050 0.1497
## 140 21.4164 nan 0.0050 0.1383
## 160 18.7505 nan 0.0050 0.1135
## 180 16.5811 nan 0.0050 0.0891
## 200 14.7176 nan 0.0050 0.0836
## 220 13.1545 nan 0.0050 0.0643
## 240 11.8073 nan 0.0050 0.0477
## 260 10.6733 nan 0.0050 0.0475
## 280 9.7384 nan 0.0050 0.0447
## 300 8.9193 nan 0.0050 0.0360
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.0259 nan 0.0100 0.7957
## 2 60.2660 nan 0.0100 0.8207
## 3 59.5667 nan 0.0100 0.6617
## 4 58.8095 nan 0.0100 0.7140
## 5 58.0253 nan 0.0100 0.7517
## 6 57.2912 nan 0.0100 0.7289
## 7 56.5374 nan 0.0100 0.6663
## 8 55.8036 nan 0.0100 0.7069
## 9 55.0182 nan 0.0100 0.7044
## 10 54.3776 nan 0.0100 0.6594
## 20 48.4372 nan 0.0100 0.5332
## 40 39.0501 nan 0.0100 0.3402
## 60 31.9590 nan 0.0100 0.2954
## 80 26.6543 nan 0.0100 0.2302
## 100 22.4259 nan 0.0100 0.2001
## 120 19.2032 nan 0.0100 0.1285
## 140 16.5708 nan 0.0100 0.1072
## 160 14.5227 nan 0.0100 0.0859
## 180 12.8266 nan 0.0100 0.0568
## 200 11.4716 nan 0.0100 0.0590
## 220 10.3608 nan 0.0100 0.0326
## 240 9.4100 nan 0.0100 0.0306
## 260 8.6169 nan 0.0100 0.0241
## 280 7.9377 nan 0.0100 0.0282
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.0814 nan 0.0100 0.7907
## 2 60.2514 nan 0.0100 0.7743
## 3 59.4094 nan 0.0100 0.7930
## 4 58.6600 nan 0.0100 0.7966
## 5 57.9474 nan 0.0100 0.7079
## 6 57.1818 nan 0.0100 0.7516
## 7 56.4825 nan 0.0100 0.6847
## 8 55.8397 nan 0.0100 0.6299
## 9 55.1527 nan 0.0100 0.6924
## 10 54.4888 nan 0.0100 0.6545
## 20 48.4554 nan 0.0100 0.5276
## 40 38.9476 nan 0.0100 0.3780
## 60 31.7902 nan 0.0100 0.2759
## 80 26.4888 nan 0.0100 0.2076
## 100 22.2980 nan 0.0100 0.1510
## 120 19.0910 nan 0.0100 0.1341
## 140 16.5701 nan 0.0100 0.1059
## 160 14.4824 nan 0.0100 0.1143
## 180 12.8322 nan 0.0100 0.0625
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## 220 10.3602 nan 0.0100 0.0364
## 240 9.4092 nan 0.0100 0.0390
## 260 8.6119 nan 0.0100 0.0259
## 280 7.9145 nan 0.0100 0.0227
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.0671 nan 0.0100 0.7792
## 2 60.2315 nan 0.0100 0.7638
## 3 59.4733 nan 0.0100 0.7846
## 4 58.7222 nan 0.0100 0.7790
## 5 58.0447 nan 0.0100 0.7248
## 6 57.2937 nan 0.0100 0.7220
## 7 56.6312 nan 0.0100 0.6948
## 8 55.9248 nan 0.0100 0.6805
## 9 55.2476 nan 0.0100 0.6856
## 10 54.5930 nan 0.0100 0.6725
## 20 48.3398 nan 0.0100 0.5264
## 40 39.0411 nan 0.0100 0.3951
## 60 32.0237 nan 0.0100 0.3134
## 80 26.5445 nan 0.0100 0.2452
## 100 22.4129 nan 0.0100 0.1822
## 120 19.1531 nan 0.0100 0.1184
## 140 16.6034 nan 0.0100 0.1088
## 160 14.5498 nan 0.0100 0.0674
## 180 12.8573 nan 0.0100 0.0552
## 200 11.5291 nan 0.0100 0.0491
## 220 10.4064 nan 0.0100 0.0517
## 240 9.4760 nan 0.0100 0.0429
## 260 8.6838 nan 0.0100 0.0167
## 280 7.9801 nan 0.0100 0.0218
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.8992 nan 0.0100 0.9126
## 2 59.9128 nan 0.0100 0.9075
## 3 59.0474 nan 0.0100 0.8692
## 4 58.1807 nan 0.0100 0.8340
## 5 57.2786 nan 0.0100 0.8972
## 6 56.4909 nan 0.0100 0.7975
## 7 55.5981 nan 0.0100 0.8828
## 8 54.7066 nan 0.0100 0.8425
## 9 53.8495 nan 0.0100 0.8164
## 10 53.1001 nan 0.0100 0.7709
## 20 45.8305 nan 0.0100 0.6532
## 40 34.4791 nan 0.0100 0.3490
## 60 26.3919 nan 0.0100 0.3377
## 80 20.5346 nan 0.0100 0.2135
## 100 16.5371 nan 0.0100 0.1639
## 120 13.4933 nan 0.0100 0.1027
## 140 11.3140 nan 0.0100 0.0565
## 160 9.5838 nan 0.0100 0.0655
## 180 8.2633 nan 0.0100 0.0314
## 200 7.2523 nan 0.0100 0.0439
## 220 6.4841 nan 0.0100 0.0301
## 240 5.8374 nan 0.0100 0.0244
## 260 5.3348 nan 0.0100 0.0146
## 280 4.9464 nan 0.0100 0.0070
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.9490 nan 0.0100 0.8696
## 2 60.0335 nan 0.0100 0.8668
## 3 59.1876 nan 0.0100 0.7986
## 4 58.2273 nan 0.0100 0.9547
## 5 57.3208 nan 0.0100 0.9097
## 6 56.5093 nan 0.0100 0.8691
## 7 55.6579 nan 0.0100 0.8855
## 8 54.8031 nan 0.0100 0.8957
## 9 53.9360 nan 0.0100 0.7772
## 10 53.1756 nan 0.0100 0.7192
## 20 45.7907 nan 0.0100 0.7218
## 40 34.5109 nan 0.0100 0.4867
## 60 26.5776 nan 0.0100 0.2989
## 80 20.7947 nan 0.0100 0.1947
## 100 16.7472 nan 0.0100 0.1567
## 120 13.7496 nan 0.0100 0.1137
## 140 11.4284 nan 0.0100 0.0861
## 160 9.7080 nan 0.0100 0.0581
## 180 8.4437 nan 0.0100 0.0494
## 200 7.3566 nan 0.0100 0.0391
## 220 6.5726 nan 0.0100 0.0208
## 240 5.9168 nan 0.0100 0.0230
## 260 5.4290 nan 0.0100 0.0137
## 280 5.0539 nan 0.0100 0.0056
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.9365 nan 0.0100 0.7847
## 2 60.0191 nan 0.0100 0.8861
## 3 59.1064 nan 0.0100 0.9121
## 4 58.1848 nan 0.0100 0.8694
## 5 57.2883 nan 0.0100 0.8085
## 6 56.4124 nan 0.0100 0.8571
## 7 55.5699 nan 0.0100 0.8311
## 8 54.7537 nan 0.0100 0.7562
## 9 53.9323 nan 0.0100 0.7686
## 10 53.0774 nan 0.0100 0.8794
## 20 45.6775 nan 0.0100 0.6815
## 40 34.3273 nan 0.0100 0.4085
## 60 26.1986 nan 0.0100 0.2648
## 80 20.6354 nan 0.0100 0.2196
## 100 16.5685 nan 0.0100 0.1601
## 120 13.6057 nan 0.0100 0.1250
## 140 11.4132 nan 0.0100 0.0773
## 160 9.7243 nan 0.0100 0.0796
## 180 8.4128 nan 0.0100 0.0563
## 200 7.4161 nan 0.0100 0.0389
## 220 6.6031 nan 0.0100 0.0283
## 240 6.0109 nan 0.0100 0.0124
## 260 5.5255 nan 0.0100 0.0184
## 280 5.1231 nan 0.0100 0.0058
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.8320 nan 0.0100 0.9874
## 2 59.8158 nan 0.0100 0.9658
## 3 58.8192 nan 0.0100 0.9144
## 4 57.8154 nan 0.0100 0.9664
## 5 56.9260 nan 0.0100 1.0306
## 6 56.0139 nan 0.0100 0.9234
## 7 55.0791 nan 0.0100 0.8274
## 8 54.2060 nan 0.0100 0.8090
## 9 53.2759 nan 0.0100 0.9091
## 10 52.3863 nan 0.0100 0.9540
## 20 44.5841 nan 0.0100 0.7099
## 40 32.7282 nan 0.0100 0.4816
## 60 24.5785 nan 0.0100 0.3229
## 80 18.7484 nan 0.0100 0.2126
## 100 14.6501 nan 0.0100 0.1688
## 120 11.7573 nan 0.0100 0.1336
## 140 9.6034 nan 0.0100 0.0899
## 160 7.9960 nan 0.0100 0.0619
## 180 6.8335 nan 0.0100 0.0428
## 200 5.9452 nan 0.0100 0.0387
## 220 5.2929 nan 0.0100 0.0233
## 240 4.7893 nan 0.0100 0.0146
## 260 4.3890 nan 0.0100 0.0115
## 280 4.0727 nan 0.0100 0.0086
## 300 3.8032 nan 0.0100 0.0042
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.7904 nan 0.0100 0.9557
## 2 59.7730 nan 0.0100 1.0454
## 3 58.7917 nan 0.0100 1.0417
## 4 57.8315 nan 0.0100 0.9792
## 5 56.9066 nan 0.0100 0.8869
## 6 55.9281 nan 0.0100 0.9816
## 7 55.0769 nan 0.0100 0.8482
## 8 54.1720 nan 0.0100 0.8625
## 9 53.3030 nan 0.0100 0.8170
## 10 52.3861 nan 0.0100 0.9409
## 20 44.6284 nan 0.0100 0.7512
## 40 32.7364 nan 0.0100 0.5014
## 60 24.5003 nan 0.0100 0.2941
## 80 18.7508 nan 0.0100 0.2436
## 100 14.6621 nan 0.0100 0.1474
## 120 11.7826 nan 0.0100 0.1067
## 140 9.6512 nan 0.0100 0.0743
## 160 8.0976 nan 0.0100 0.0475
## 180 6.9165 nan 0.0100 0.0305
## 200 6.0396 nan 0.0100 0.0288
## 220 5.3353 nan 0.0100 0.0250
## 240 4.8169 nan 0.0100 0.0081
## 260 4.4316 nan 0.0100 0.0081
## 280 4.1213 nan 0.0100 0.0112
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.8081 nan 0.0100 0.9900
## 2 59.7973 nan 0.0100 1.0073
## 3 58.7935 nan 0.0100 1.0323
## 4 57.8178 nan 0.0100 1.0066
## 5 56.7987 nan 0.0100 1.0428
## 6 55.8656 nan 0.0100 0.8628
## 7 54.9656 nan 0.0100 0.8988
## 8 54.0472 nan 0.0100 0.9129
## 9 53.1807 nan 0.0100 0.8804
## 10 52.2988 nan 0.0100 0.8608
## 20 44.5616 nan 0.0100 0.6756
## 40 32.7940 nan 0.0100 0.4907
## 60 24.5151 nan 0.0100 0.3260
## 80 18.7760 nan 0.0100 0.2158
## 100 14.7725 nan 0.0100 0.1636
## 120 11.8296 nan 0.0100 0.0990
## 140 9.7265 nan 0.0100 0.0777
## 160 8.2204 nan 0.0100 0.0601
## 180 7.0602 nan 0.0100 0.0458
## 200 6.1641 nan 0.0100 0.0306
## 220 5.4894 nan 0.0100 0.0202
## 240 4.9914 nan 0.0100 0.0153
## 260 4.6193 nan 0.0100 0.0117
## 280 4.3293 nan 0.0100 0.0086
## 300 4.1106 nan 0.0100 -0.0039
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.6367 nan 0.0500 3.7829
## 2 54.1927 nan 0.0500 2.9824
## 3 51.1455 nan 0.0500 2.9591
## 4 48.1393 nan 0.0500 2.8497
## 5 45.6493 nan 0.0500 2.5293
## 6 43.2058 nan 0.0500 2.2645
## 7 40.7404 nan 0.0500 2.3167
## 8 38.5356 nan 0.0500 2.2107
## 9 36.6174 nan 0.0500 2.0769
## 10 34.8477 nan 0.0500 1.5948
## 20 22.0342 nan 0.0500 0.8360
## 40 11.3991 nan 0.0500 0.3324
## 60 7.2507 nan 0.0500 0.0992
## 80 5.4196 nan 0.0500 0.0357
## 100 4.6045 nan 0.0500 0.0130
## 120 4.1839 nan 0.0500 -0.0155
## 140 3.9602 nan 0.0500 -0.0099
## 160 3.8138 nan 0.0500 -0.0058
## 180 3.7117 nan 0.0500 -0.0033
## 200 3.6061 nan 0.0500 -0.0165
## 220 3.5128 nan 0.0500 -0.0034
## 240 3.4413 nan 0.0500 -0.0108
## 260 3.3928 nan 0.0500 -0.0027
## 280 3.3415 nan 0.0500 -0.0067
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.9054 nan 0.0500 3.8857
## 2 54.3030 nan 0.0500 3.4940
## 3 51.2208 nan 0.0500 3.0389
## 4 48.2572 nan 0.0500 3.1144
## 5 45.3278 nan 0.0500 2.4434
## 6 42.6968 nan 0.0500 2.4270
## 7 40.5577 nan 0.0500 2.1199
## 8 38.3126 nan 0.0500 2.1356
## 9 36.4973 nan 0.0500 1.7005
## 10 34.6338 nan 0.0500 1.7883
## 20 21.5674 nan 0.0500 0.7282
## 40 10.9142 nan 0.0500 0.2002
## 60 7.1152 nan 0.0500 0.0735
## 80 5.4089 nan 0.0500 0.0449
## 100 4.5409 nan 0.0500 0.0085
## 120 4.1214 nan 0.0500 -0.0001
## 140 3.9205 nan 0.0500 -0.0085
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## 180 3.7049 nan 0.0500 -0.0182
## 200 3.6339 nan 0.0500 -0.0035
## 220 3.5570 nan 0.0500 -0.0055
## 240 3.5027 nan 0.0500 -0.0032
## 260 3.4655 nan 0.0500 -0.0096
## 280 3.4122 nan 0.0500 -0.0129
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.2305 nan 0.0500 3.9085
## 2 54.7853 nan 0.0500 3.3344
## 3 51.5226 nan 0.0500 3.3525
## 4 48.5667 nan 0.0500 2.8294
## 5 45.7910 nan 0.0500 2.4082
## 6 43.3759 nan 0.0500 2.1217
## 7 41.0817 nan 0.0500 2.2774
## 8 38.7663 nan 0.0500 2.1823
## 9 36.5169 nan 0.0500 1.8743
## 10 34.7807 nan 0.0500 1.7050
## 20 22.2531 nan 0.0500 0.7362
## 40 11.2829 nan 0.0500 0.2258
## 60 7.3259 nan 0.0500 0.1276
## 80 5.5680 nan 0.0500 0.0351
## 100 4.7147 nan 0.0500 0.0055
## 120 4.3297 nan 0.0500 0.0054
## 140 4.1335 nan 0.0500 -0.0074
## 160 4.0020 nan 0.0500 -0.0092
## 180 3.9211 nan 0.0500 -0.0069
## 200 3.8576 nan 0.0500 0.0008
## 220 3.7740 nan 0.0500 -0.0108
## 240 3.7133 nan 0.0500 -0.0080
## 260 3.6554 nan 0.0500 0.0009
## 280 3.6029 nan 0.0500 -0.0106
## 300 3.5605 nan 0.0500 -0.0041
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.4503 nan 0.0500 4.4528
## 2 52.9398 nan 0.0500 4.1806
## 3 49.0146 nan 0.0500 3.7527
## 4 45.3843 nan 0.0500 3.7903
## 5 42.4031 nan 0.0500 2.7306
## 6 39.8386 nan 0.0500 2.4538
## 7 37.0293 nan 0.0500 2.7359
## 8 34.4023 nan 0.0500 2.3397
## 9 31.8148 nan 0.0500 2.1953
## 10 29.7094 nan 0.0500 1.9236
## 20 16.2992 nan 0.0500 0.8451
## 40 7.1653 nan 0.0500 0.1844
## 60 4.6180 nan 0.0500 0.0473
## 80 3.7778 nan 0.0500 0.0064
## 100 3.3986 nan 0.0500 -0.0065
## 120 3.1764 nan 0.0500 -0.0015
## 140 3.0100 nan 0.0500 -0.0101
## 160 2.8751 nan 0.0500 -0.0070
## 180 2.7546 nan 0.0500 -0.0055
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## 220 2.5630 nan 0.0500 -0.0061
## 240 2.4538 nan 0.0500 -0.0067
## 260 2.3795 nan 0.0500 -0.0085
## 280 2.2839 nan 0.0500 -0.0142
## 300 2.2192 nan 0.0500 -0.0145
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.0723 nan 0.0500 4.3820
## 2 52.6643 nan 0.0500 3.8235
## 3 48.5900 nan 0.0500 4.1792
## 4 44.8559 nan 0.0500 3.5430
## 5 41.4246 nan 0.0500 3.4289
## 6 38.7557 nan 0.0500 2.6508
## 7 35.9352 nan 0.0500 2.6063
## 8 33.3089 nan 0.0500 2.1359
## 9 30.8391 nan 0.0500 2.3369
## 10 28.9206 nan 0.0500 2.1003
## 20 16.2028 nan 0.0500 0.8256
## 40 7.0065 nan 0.0500 0.1624
## 60 4.6302 nan 0.0500 0.0392
## 80 3.8074 nan 0.0500 0.0035
## 100 3.4752 nan 0.0500 -0.0192
## 120 3.2392 nan 0.0500 -0.0062
## 140 3.0759 nan 0.0500 -0.0094
## 160 2.9289 nan 0.0500 -0.0164
## 180 2.8173 nan 0.0500 -0.0188
## 200 2.7131 nan 0.0500 -0.0099
## 220 2.6112 nan 0.0500 -0.0175
## 240 2.5456 nan 0.0500 -0.0102
## 260 2.4779 nan 0.0500 -0.0024
## 280 2.4042 nan 0.0500 -0.0113
## 300 2.3505 nan 0.0500 -0.0099
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.7684 nan 0.0500 3.8894
## 2 53.1666 nan 0.0500 4.2435
## 3 49.3629 nan 0.0500 3.8022
## 4 45.5040 nan 0.0500 3.8475
## 5 42.3453 nan 0.0500 3.0308
## 6 39.1540 nan 0.0500 3.3306
## 7 36.6976 nan 0.0500 2.6829
## 8 34.3849 nan 0.0500 2.5129
## 9 32.0814 nan 0.0500 2.4229
## 10 29.8997 nan 0.0500 1.8279
## 20 16.1290 nan 0.0500 0.7897
## 40 7.3229 nan 0.0500 0.1302
## 60 4.8530 nan 0.0500 0.0345
## 80 4.0995 nan 0.0500 0.0129
## 100 3.7455 nan 0.0500 -0.0139
## 120 3.5456 nan 0.0500 -0.0292
## 140 3.3553 nan 0.0500 -0.0058
## 160 3.2244 nan 0.0500 -0.0032
## 180 3.1015 nan 0.0500 -0.0163
## 200 3.0004 nan 0.0500 -0.0115
## 220 2.9326 nan 0.0500 -0.0190
## 240 2.8497 nan 0.0500 -0.0091
## 260 2.7820 nan 0.0500 -0.0065
## 280 2.7062 nan 0.0500 -0.0066
## 300 2.6457 nan 0.0500 -0.0346
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.8320 nan 0.0500 4.7680
## 2 52.3853 nan 0.0500 4.8503
## 3 48.4297 nan 0.0500 3.4382
## 4 44.7212 nan 0.0500 3.6736
## 5 41.3924 nan 0.0500 3.4279
## 6 38.1576 nan 0.0500 3.0952
## 7 35.4374 nan 0.0500 2.6561
## 8 32.8001 nan 0.0500 2.5846
## 9 30.4555 nan 0.0500 2.4770
## 10 28.2191 nan 0.0500 2.3488
## 20 14.2285 nan 0.0500 0.8097
## 40 5.7770 nan 0.0500 0.1912
## 60 3.7045 nan 0.0500 0.0345
## 80 3.0621 nan 0.0500 -0.0097
## 100 2.7720 nan 0.0500 -0.0083
## 120 2.5784 nan 0.0500 -0.0237
## 140 2.3980 nan 0.0500 -0.0034
## 160 2.2078 nan 0.0500 -0.0156
## 180 2.0766 nan 0.0500 -0.0104
## 200 1.9768 nan 0.0500 -0.0023
## 220 1.8854 nan 0.0500 -0.0088
## 240 1.8070 nan 0.0500 -0.0093
## 260 1.7241 nan 0.0500 -0.0184
## 280 1.6545 nan 0.0500 -0.0052
## 300 1.5879 nan 0.0500 -0.0112
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.8497 nan 0.0500 4.8386
## 2 52.4863 nan 0.0500 4.4785
## 3 48.1555 nan 0.0500 3.9833
## 4 44.3572 nan 0.0500 3.2813
## 5 40.8570 nan 0.0500 3.4341
## 6 37.7221 nan 0.0500 2.7754
## 7 34.9781 nan 0.0500 2.7129
## 8 32.3076 nan 0.0500 2.6170
## 9 30.0510 nan 0.0500 2.4223
## 10 27.7476 nan 0.0500 1.8976
## 20 14.1932 nan 0.0500 0.7534
## 40 5.9201 nan 0.0500 0.1045
## 60 4.0067 nan 0.0500 0.0221
## 80 3.3741 nan 0.0500 0.0030
## 100 3.0452 nan 0.0500 -0.0267
## 120 2.8261 nan 0.0500 -0.0118
## 140 2.6483 nan 0.0500 -0.0130
## 160 2.4960 nan 0.0500 -0.0213
## 180 2.3863 nan 0.0500 -0.0080
## 200 2.2920 nan 0.0500 -0.0002
## 220 2.1874 nan 0.0500 -0.0018
## 240 2.1048 nan 0.0500 -0.0093
## 260 2.0098 nan 0.0500 -0.0165
## 280 1.9314 nan 0.0500 -0.0168
## 300 1.8766 nan 0.0500 -0.0147
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.9244 nan 0.0500 5.1171
## 2 52.4571 nan 0.0500 4.6444
## 3 48.1993 nan 0.0500 3.1985
## 4 44.3256 nan 0.0500 3.9093
## 5 40.9387 nan 0.0500 3.3371
## 6 37.8626 nan 0.0500 2.5901
## 7 34.9852 nan 0.0500 2.5577
## 8 32.3301 nan 0.0500 2.6774
## 9 29.9433 nan 0.0500 2.1083
## 10 27.7714 nan 0.0500 2.2560
## 20 14.3742 nan 0.0500 0.8475
## 40 6.0227 nan 0.0500 0.1771
## 60 4.0113 nan 0.0500 0.0170
## 80 3.4283 nan 0.0500 -0.0105
## 100 3.1594 nan 0.0500 -0.0122
## 120 2.9644 nan 0.0500 -0.0196
## 140 2.8001 nan 0.0500 -0.0173
## 160 2.6724 nan 0.0500 -0.0115
## 180 2.5816 nan 0.0500 -0.0161
## 200 2.4819 nan 0.0500 -0.0052
## 220 2.4003 nan 0.0500 -0.0140
## 240 2.3195 nan 0.0500 -0.0153
## 260 2.2276 nan 0.0500 -0.0047
## 280 2.1602 nan 0.0500 -0.0104
## 300 2.0971 nan 0.0500 -0.0051
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 54.3433 nan 0.1000 7.2148
## 2 48.2822 nan 0.1000 6.1631
## 3 42.9661 nan 0.1000 4.6968
## 4 38.6355 nan 0.1000 4.1501
## 5 34.5418 nan 0.1000 3.6537
## 6 31.2170 nan 0.1000 3.1128
## 7 28.0413 nan 0.1000 3.5156
## 8 25.3631 nan 0.1000 2.9212
## 9 23.3109 nan 0.1000 1.9813
## 10 21.3265 nan 0.1000 1.7683
## 20 11.1729 nan 0.1000 0.3999
## 40 5.5902 nan 0.1000 0.0192
## 60 4.2209 nan 0.1000 0.0152
## 80 3.8460 nan 0.1000 0.0043
## 100 3.6546 nan 0.1000 -0.0200
## 120 3.4935 nan 0.1000 -0.0112
## 140 3.3707 nan 0.1000 -0.0154
## 160 3.2773 nan 0.1000 -0.0278
## 180 3.1866 nan 0.1000 -0.0016
## 200 3.0995 nan 0.1000 -0.0203
## 220 3.0396 nan 0.1000 -0.0089
## 240 2.9781 nan 0.1000 -0.0002
## 260 2.9254 nan 0.1000 -0.0097
## 280 2.8698 nan 0.1000 -0.0137
## 300 2.8346 nan 0.1000 -0.0302
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.2265 nan 0.1000 7.1163
## 2 48.6576 nan 0.1000 6.7157
## 3 43.3939 nan 0.1000 5.4547
## 4 39.1375 nan 0.1000 3.8792
## 5 35.1892 nan 0.1000 3.6797
## 6 31.7823 nan 0.1000 3.2926
## 7 29.0199 nan 0.1000 2.6872
## 8 26.2560 nan 0.1000 2.5243
## 9 24.0254 nan 0.1000 2.1068
## 10 22.0876 nan 0.1000 1.8066
## 20 11.2781 nan 0.1000 0.4406
## 40 5.4104 nan 0.1000 0.1236
## 60 4.2618 nan 0.1000 -0.0026
## 80 3.9181 nan 0.1000 -0.0045
## 100 3.7332 nan 0.1000 -0.0036
## 120 3.5872 nan 0.1000 -0.0068
## 140 3.4643 nan 0.1000 -0.0017
## 160 3.3637 nan 0.1000 -0.0343
## 180 3.2859 nan 0.1000 -0.0340
## 200 3.2221 nan 0.1000 -0.0231
## 220 3.1691 nan 0.1000 -0.0073
## 240 3.0955 nan 0.1000 -0.0079
## 260 3.0492 nan 0.1000 -0.0262
## 280 2.9865 nan 0.1000 -0.0069
## 300 2.9380 nan 0.1000 -0.0176
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 53.5896 nan 0.1000 7.2854
## 2 47.2658 nan 0.1000 6.3954
## 3 42.6525 nan 0.1000 4.3513
## 4 37.5398 nan 0.1000 4.4499
## 5 33.4385 nan 0.1000 3.6988
## 6 30.4646 nan 0.1000 3.0329
## 7 27.3210 nan 0.1000 2.5673
## 8 25.1630 nan 0.1000 1.9908
## 9 22.9821 nan 0.1000 1.6630
## 10 21.1884 nan 0.1000 1.6891
## 20 11.6206 nan 0.1000 0.3988
## 40 5.4796 nan 0.1000 0.0868
## 60 4.3355 nan 0.1000 -0.0441
## 80 4.0089 nan 0.1000 -0.0224
## 100 3.8022 nan 0.1000 -0.0101
## 120 3.6473 nan 0.1000 -0.0070
## 140 3.5454 nan 0.1000 -0.0202
## 160 3.4319 nan 0.1000 -0.0074
## 180 3.3575 nan 0.1000 -0.0116
## 200 3.3045 nan 0.1000 -0.0150
## 220 3.2355 nan 0.1000 -0.0074
## 240 3.1843 nan 0.1000 -0.0134
## 260 3.1475 nan 0.1000 -0.0263
## 280 3.0981 nan 0.1000 -0.0145
## 300 3.0611 nan 0.1000 -0.0057
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.3253 nan 0.1000 9.0578
## 2 44.9801 nan 0.1000 7.4187
## 3 38.6441 nan 0.1000 6.7459
## 4 33.5339 nan 0.1000 5.3417
## 5 29.0168 nan 0.1000 4.2637
## 6 25.0130 nan 0.1000 3.5301
## 7 22.0532 nan 0.1000 2.2281
## 8 19.7221 nan 0.1000 2.3725
## 9 17.6946 nan 0.1000 2.1436
## 10 15.9447 nan 0.1000 1.8126
## 20 7.2957 nan 0.1000 0.4078
## 40 3.8740 nan 0.1000 0.0048
## 60 3.2939 nan 0.1000 -0.0107
## 80 2.9777 nan 0.1000 -0.0428
## 100 2.7342 nan 0.1000 -0.0276
## 120 2.5645 nan 0.1000 -0.0194
## 140 2.3557 nan 0.1000 -0.0009
## 160 2.2340 nan 0.1000 -0.0187
## 180 2.1065 nan 0.1000 -0.0149
## 200 1.9824 nan 0.1000 -0.0176
## 220 1.8480 nan 0.1000 -0.0096
## 240 1.7487 nan 0.1000 -0.0057
## 260 1.6816 nan 0.1000 -0.0298
## 280 1.6078 nan 0.1000 -0.0216
## 300 1.5328 nan 0.1000 -0.0178
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 53.1920 nan 0.1000 8.4287
## 2 45.6560 nan 0.1000 7.6100
## 3 39.0527 nan 0.1000 6.2033
## 4 33.8656 nan 0.1000 5.0410
## 5 29.2611 nan 0.1000 4.5767
## 6 25.9289 nan 0.1000 3.9663
## 7 22.6981 nan 0.1000 3.2224
## 8 19.9674 nan 0.1000 2.5695
## 9 18.0166 nan 0.1000 1.8034
## 10 16.1158 nan 0.1000 1.8290
## 20 7.3542 nan 0.1000 0.3304
## 40 4.0083 nan 0.1000 0.0100
## 60 3.4565 nan 0.1000 -0.0362
## 80 3.1531 nan 0.1000 -0.0207
## 100 2.9160 nan 0.1000 -0.0298
## 120 2.7438 nan 0.1000 -0.0135
## 140 2.5858 nan 0.1000 -0.0233
## 160 2.4474 nan 0.1000 -0.0231
## 180 2.3484 nan 0.1000 -0.0482
## 200 2.2436 nan 0.1000 -0.0224
## 220 2.1506 nan 0.1000 -0.0364
## 240 2.0510 nan 0.1000 -0.0081
## 260 1.9908 nan 0.1000 -0.0107
## 280 1.9431 nan 0.1000 -0.0290
## 300 1.8669 nan 0.1000 -0.0130
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.3275 nan 0.1000 9.4974
## 2 44.2549 nan 0.1000 7.6157
## 3 38.6369 nan 0.1000 5.9531
## 4 33.4839 nan 0.1000 5.0089
## 5 29.2254 nan 0.1000 4.3562
## 6 25.6599 nan 0.1000 3.8464
## 7 22.6107 nan 0.1000 3.0290
## 8 20.2821 nan 0.1000 2.5958
## 9 18.2525 nan 0.1000 1.4180
## 10 16.4281 nan 0.1000 1.7547
## 20 7.0672 nan 0.1000 0.3417
## 40 4.0312 nan 0.1000 -0.0068
## 60 3.4853 nan 0.1000 -0.0167
## 80 3.2674 nan 0.1000 -0.0048
## 100 3.0778 nan 0.1000 -0.0106
## 120 2.9331 nan 0.1000 -0.0378
## 140 2.7580 nan 0.1000 -0.0313
## 160 2.6134 nan 0.1000 -0.0342
## 180 2.4682 nan 0.1000 -0.0143
## 200 2.3750 nan 0.1000 -0.0194
## 220 2.2613 nan 0.1000 -0.0112
## 240 2.1905 nan 0.1000 -0.0090
## 260 2.1189 nan 0.1000 -0.0141
## 280 2.0420 nan 0.1000 -0.0335
## 300 1.9699 nan 0.1000 -0.0025
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.9265 nan 0.1000 8.7612
## 2 43.8835 nan 0.1000 8.1881
## 3 37.3005 nan 0.1000 6.0563
## 4 31.8111 nan 0.1000 5.3362
## 5 27.4137 nan 0.1000 4.6490
## 6 23.4880 nan 0.1000 3.4302
## 7 20.1364 nan 0.1000 2.5398
## 8 17.6012 nan 0.1000 2.3238
## 9 15.5160 nan 0.1000 2.0101
## 10 13.6728 nan 0.1000 1.6297
## 20 5.6776 nan 0.1000 0.2772
## 40 3.1189 nan 0.1000 -0.0087
## 60 2.6115 nan 0.1000 -0.0255
## 80 2.2601 nan 0.1000 -0.0272
## 100 2.0398 nan 0.1000 -0.0170
## 120 1.8655 nan 0.1000 -0.0194
## 140 1.7149 nan 0.1000 -0.0254
## 160 1.5710 nan 0.1000 -0.0068
## 180 1.4608 nan 0.1000 -0.0085
## 200 1.3697 nan 0.1000 -0.0194
## 220 1.2765 nan 0.1000 -0.0187
## 240 1.1875 nan 0.1000 -0.0129
## 260 1.1115 nan 0.1000 -0.0232
## 280 1.0552 nan 0.1000 -0.0025
## 300 1.0118 nan 0.1000 -0.0227
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.5487 nan 0.1000 9.2113
## 2 43.7437 nan 0.1000 6.9570
## 3 37.1399 nan 0.1000 6.4899
## 4 32.0482 nan 0.1000 5.3504
## 5 27.3076 nan 0.1000 4.1418
## 6 23.6405 nan 0.1000 3.6704
## 7 20.2073 nan 0.1000 3.1131
## 8 17.8362 nan 0.1000 2.4140
## 9 15.7391 nan 0.1000 2.0090
## 10 13.8770 nan 0.1000 1.9535
## 20 5.7722 nan 0.1000 0.3817
## 40 3.2480 nan 0.1000 -0.0183
## 60 2.7303 nan 0.1000 0.0037
## 80 2.4467 nan 0.1000 -0.0066
## 100 2.2034 nan 0.1000 -0.0174
## 120 2.0286 nan 0.1000 -0.0261
## 140 1.8625 nan 0.1000 -0.0134
## 160 1.7286 nan 0.1000 -0.0166
## 180 1.6334 nan 0.1000 -0.0250
## 200 1.5486 nan 0.1000 -0.0197
## 220 1.4927 nan 0.1000 -0.0132
## 240 1.4073 nan 0.1000 -0.0208
## 260 1.3326 nan 0.1000 -0.0211
## 280 1.2626 nan 0.1000 -0.0113
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.9039 nan 0.1000 9.4957
## 2 44.0682 nan 0.1000 8.3789
## 3 37.2209 nan 0.1000 6.2207
## 4 31.8807 nan 0.1000 5.4912
## 5 27.7441 nan 0.1000 4.4991
## 6 23.7615 nan 0.1000 3.9009
## 7 20.3627 nan 0.1000 2.9989
## 8 17.8239 nan 0.1000 2.0788
## 9 15.5639 nan 0.1000 1.7318
## 10 13.7400 nan 0.1000 1.5380
## 20 5.8499 nan 0.1000 0.3245
## 40 3.5452 nan 0.1000 -0.0382
## 60 2.9582 nan 0.1000 -0.0419
## 80 2.6949 nan 0.1000 -0.0237
## 100 2.4741 nan 0.1000 0.0003
## 120 2.3281 nan 0.1000 -0.0204
## 140 2.1966 nan 0.1000 -0.0129
## 160 2.0623 nan 0.1000 -0.0341
## 180 1.9241 nan 0.1000 -0.0240
## 200 1.8304 nan 0.1000 -0.0366
## 220 1.7530 nan 0.1000 -0.0252
## 240 1.6707 nan 0.1000 -0.0285
## 260 1.6037 nan 0.1000 -0.0147
## 280 1.5368 nan 0.1000 -0.0180
## 300 1.4784 nan 0.1000 -0.0205
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.9047 nan 0.0010 0.0788
## 2 61.8279 nan 0.0010 0.0761
## 3 61.7481 nan 0.0010 0.0787
## 4 61.6664 nan 0.0010 0.0775
## 5 61.5810 nan 0.0010 0.0783
## 6 61.5040 nan 0.0010 0.0759
## 7 61.4260 nan 0.0010 0.0763
## 8 61.3528 nan 0.0010 0.0783
## 9 61.2725 nan 0.0010 0.0740
## 10 61.1975 nan 0.0010 0.0697
## 20 60.4555 nan 0.0010 0.0777
## 40 58.9680 nan 0.0010 0.0722
## 60 57.5325 nan 0.0010 0.0731
## 80 56.1118 nan 0.0010 0.0667
## 100 54.7654 nan 0.0010 0.0635
## 120 53.4871 nan 0.0010 0.0629
## 140 52.2557 nan 0.0010 0.0615
## 160 51.0256 nan 0.0010 0.0595
## 180 49.8596 nan 0.0010 0.0572
## 200 48.7126 nan 0.0010 0.0503
## 220 47.6063 nan 0.0010 0.0528
## 240 46.5243 nan 0.0010 0.0528
## 260 45.4936 nan 0.0010 0.0494
## 280 44.4961 nan 0.0010 0.0415
## 300 43.5359 nan 0.0010 0.0495
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.9063 nan 0.0010 0.0749
## 2 61.8334 nan 0.0010 0.0766
## 3 61.7574 nan 0.0010 0.0749
## 4 61.6811 nan 0.0010 0.0717
## 5 61.6026 nan 0.0010 0.0755
## 6 61.5221 nan 0.0010 0.0851
## 7 61.4374 nan 0.0010 0.0815
## 8 61.3619 nan 0.0010 0.0749
## 9 61.2967 nan 0.0010 0.0593
## 10 61.2210 nan 0.0010 0.0737
## 20 60.4294 nan 0.0010 0.0753
## 40 58.9290 nan 0.0010 0.0626
## 60 57.4533 nan 0.0010 0.0715
## 80 56.0471 nan 0.0010 0.0654
## 100 54.6910 nan 0.0010 0.0663
## 120 53.3973 nan 0.0010 0.0631
## 140 52.1601 nan 0.0010 0.0588
## 160 50.9380 nan 0.0010 0.0601
## 180 49.7538 nan 0.0010 0.0618
## 200 48.6307 nan 0.0010 0.0565
## 220 47.5321 nan 0.0010 0.0491
## 240 46.4709 nan 0.0010 0.0524
## 260 45.4613 nan 0.0010 0.0454
## 280 44.4670 nan 0.0010 0.0481
## 300 43.4806 nan 0.0010 0.0495
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.9090 nan 0.0010 0.0793
## 2 61.8353 nan 0.0010 0.0767
## 3 61.7586 nan 0.0010 0.0774
## 4 61.6753 nan 0.0010 0.0801
## 5 61.5921 nan 0.0010 0.0790
## 6 61.5092 nan 0.0010 0.0786
## 7 61.4324 nan 0.0010 0.0758
## 8 61.3539 nan 0.0010 0.0771
## 9 61.2769 nan 0.0010 0.0804
## 10 61.1994 nan 0.0010 0.0724
## 20 60.4473 nan 0.0010 0.0789
## 40 58.9810 nan 0.0010 0.0737
## 60 57.5474 nan 0.0010 0.0735
## 80 56.1389 nan 0.0010 0.0679
## 100 54.7820 nan 0.0010 0.0673
## 120 53.5155 nan 0.0010 0.0614
## 140 52.2711 nan 0.0010 0.0592
## 160 51.0785 nan 0.0010 0.0583
## 180 49.8919 nan 0.0010 0.0547
## 200 48.7708 nan 0.0010 0.0539
## 220 47.6624 nan 0.0010 0.0454
## 240 46.6294 nan 0.0010 0.0518
## 260 45.6064 nan 0.0010 0.0503
## 280 44.5913 nan 0.0010 0.0457
## 300 43.6070 nan 0.0010 0.0462
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.8925 nan 0.0010 0.0980
## 2 61.8005 nan 0.0010 0.0960
## 3 61.7061 nan 0.0010 0.0874
## 4 61.6107 nan 0.0010 0.1019
## 5 61.5165 nan 0.0010 0.0872
## 6 61.4140 nan 0.0010 0.0973
## 7 61.3136 nan 0.0010 0.0979
## 8 61.2262 nan 0.0010 0.0893
## 9 61.1303 nan 0.0010 0.0982
## 10 61.0349 nan 0.0010 0.0904
## 20 60.1110 nan 0.0010 0.0891
## 40 58.2950 nan 0.0010 0.0842
## 60 56.5682 nan 0.0010 0.0872
## 80 54.8649 nan 0.0010 0.0798
## 100 53.2125 nan 0.0010 0.0763
## 120 51.6491 nan 0.0010 0.0754
## 140 50.0973 nan 0.0010 0.0699
## 160 48.6284 nan 0.0010 0.0765
## 180 47.2307 nan 0.0010 0.0675
## 200 45.8426 nan 0.0010 0.0624
## 220 44.5136 nan 0.0010 0.0473
## 240 43.2237 nan 0.0010 0.0482
## 260 41.9802 nan 0.0010 0.0655
## 280 40.7712 nan 0.0010 0.0556
## 300 39.6414 nan 0.0010 0.0651
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.8950 nan 0.0010 0.0944
## 2 61.8004 nan 0.0010 0.0890
## 3 61.7074 nan 0.0010 0.0884
## 4 61.6198 nan 0.0010 0.0957
## 5 61.5233 nan 0.0010 0.0870
## 6 61.4285 nan 0.0010 0.0964
## 7 61.3329 nan 0.0010 0.0996
## 8 61.2386 nan 0.0010 0.0978
## 9 61.1415 nan 0.0010 0.0932
## 10 61.0518 nan 0.0010 0.0949
## 20 60.0968 nan 0.0010 0.0945
## 40 58.2794 nan 0.0010 0.0834
## 60 56.5040 nan 0.0010 0.0831
## 80 54.8189 nan 0.0010 0.0797
## 100 53.1943 nan 0.0010 0.0817
## 120 51.6208 nan 0.0010 0.0748
## 140 50.0731 nan 0.0010 0.0694
## 160 48.5785 nan 0.0010 0.0766
## 180 47.1601 nan 0.0010 0.0732
## 200 45.7723 nan 0.0010 0.0721
## 220 44.4162 nan 0.0010 0.0602
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## 260 41.8912 nan 0.0010 0.0574
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.8939 nan 0.0010 0.0970
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.8827 nan 0.0010 0.1070
## 2 61.7783 nan 0.0010 0.0971
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.8778 nan 0.0010 0.1140
## 2 61.7649 nan 0.0010 0.1242
## 3 61.6661 nan 0.0010 0.1039
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.8810 nan 0.0010 0.1037
## 2 61.7810 nan 0.0010 0.0998
## 3 61.6741 nan 0.0010 0.0967
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.5838 nan 0.0050 0.3776
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## 3 60.7948 nan 0.0050 0.3947
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.6057 nan 0.0050 0.4000
## 2 61.2221 nan 0.0050 0.3945
## 3 60.8513 nan 0.0050 0.4147
## 4 60.4704 nan 0.0050 0.3650
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## 80 39.1019 nan 0.0050 0.1995
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.5996 nan 0.0050 0.4038
## 2 61.1802 nan 0.0050 0.3783
## 3 60.7942 nan 0.0050 0.3802
## 4 60.4187 nan 0.0050 0.3387
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## 80 38.9514 nan 0.0050 0.1957
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.5136 nan 0.0050 0.4677
## 2 61.0485 nan 0.0050 0.4603
## 3 60.5376 nan 0.0050 0.4764
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## 20 53.1846 nan 0.0050 0.3887
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.5145 nan 0.0050 0.4536
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## 3 60.5275 nan 0.0050 0.4441
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.5067 nan 0.0050 0.4745
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## 3 60.5558 nan 0.0050 0.4782
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.4904 nan 0.0050 0.5337
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## 80 32.5680 nan 0.0050 0.2518
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.4712 nan 0.0050 0.5428
## 2 60.9357 nan 0.0050 0.5044
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## 40 44.4832 nan 0.0050 0.3639
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.4575 nan 0.0050 0.4843
## 2 60.9044 nan 0.0050 0.4772
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## 40 44.5719 nan 0.0050 0.3438
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## 80 32.5481 nan 0.0050 0.2326
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.1647 nan 0.0100 0.7958
## 2 60.4184 nan 0.0100 0.7308
## 3 59.6617 nan 0.0100 0.7604
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## 20 48.7185 nan 0.0100 0.5932
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## 60 31.6075 nan 0.0100 0.3131
## 80 26.0135 nan 0.0100 0.2300
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## 180 12.5449 nan 0.0100 0.0652
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.0612 nan 0.0100 0.7739
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## 3 59.5110 nan 0.0100 0.7994
## 4 58.7806 nan 0.0100 0.7220
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## 20 48.5313 nan 0.0100 0.5561
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.1350 nan 0.0100 0.8268
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## 20 48.6373 nan 0.0100 0.5669
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## 60 31.9783 nan 0.0100 0.2920
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.0813 nan 0.0100 0.9560
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## 20 45.5582 nan 0.0100 0.6563
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.8838 nan 0.0100 0.9030
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## 140 11.0178 nan 0.0100 0.0958
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.0333 nan 0.0100 0.9402
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.9872 nan 0.0100 1.0890
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## 10 52.4392 nan 0.0100 0.8392
## 20 44.5560 nan 0.0100 0.7174
## 40 32.6754 nan 0.0100 0.5510
## 60 24.3277 nan 0.0100 0.2933
## 80 18.4884 nan 0.0100 0.2457
## 100 14.4181 nan 0.0100 0.1763
## 120 11.4948 nan 0.0100 0.1148
## 140 9.4056 nan 0.0100 0.0977
## 160 7.8124 nan 0.0100 0.0508
## 180 6.6020 nan 0.0100 0.0401
## 200 5.6959 nan 0.0100 0.0358
## 220 5.0296 nan 0.0100 0.0233
## 240 4.5105 nan 0.0100 0.0118
## 260 4.0991 nan 0.0100 0.0143
## 280 3.7803 nan 0.0100 0.0058
## 300 3.5483 nan 0.0100 0.0088
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.9609 nan 0.0100 1.0500
## 2 59.9385 nan 0.0100 1.0232
## 3 58.9577 nan 0.0100 0.9980
## 4 57.9773 nan 0.0100 0.8821
## 5 57.0379 nan 0.0100 0.9165
## 6 56.1190 nan 0.0100 1.0049
## 7 55.1745 nan 0.0100 0.9355
## 8 54.2590 nan 0.0100 0.8135
## 9 53.3314 nan 0.0100 0.9974
## 10 52.3967 nan 0.0100 0.9167
## 20 44.6137 nan 0.0100 0.7029
## 40 32.5796 nan 0.0100 0.4813
## 60 24.2693 nan 0.0100 0.3399
## 80 18.5295 nan 0.0100 0.2506
## 100 14.3730 nan 0.0100 0.1522
## 120 11.4961 nan 0.0100 0.1000
## 140 9.3377 nan 0.0100 0.0570
## 160 7.7016 nan 0.0100 0.0504
## 180 6.5687 nan 0.0100 0.0352
## 200 5.6922 nan 0.0100 0.0329
## 220 5.0193 nan 0.0100 0.0225
## 240 4.5271 nan 0.0100 0.0041
## 260 4.1554 nan 0.0100 0.0153
## 280 3.8604 nan 0.0100 0.0068
## 300 3.6335 nan 0.0100 0.0071
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.9859 nan 0.0100 1.0270
## 2 59.9441 nan 0.0100 0.9545
## 3 59.0000 nan 0.0100 0.9373
## 4 57.9834 nan 0.0100 0.9870
## 5 57.0868 nan 0.0100 0.9415
## 6 56.1159 nan 0.0100 1.0084
## 7 55.2131 nan 0.0100 0.9263
## 8 54.3198 nan 0.0100 0.8925
## 9 53.4255 nan 0.0100 0.8983
## 10 52.5792 nan 0.0100 0.9115
## 20 44.6467 nan 0.0100 0.6834
## 40 32.5454 nan 0.0100 0.4411
## 60 24.3081 nan 0.0100 0.3089
## 80 18.6229 nan 0.0100 0.2473
## 100 14.5670 nan 0.0100 0.1281
## 120 11.7029 nan 0.0100 0.0859
## 140 9.6454 nan 0.0100 0.0775
## 160 8.0488 nan 0.0100 0.0633
## 180 6.8790 nan 0.0100 0.0347
## 200 6.0007 nan 0.0100 0.0329
## 220 5.3424 nan 0.0100 0.0224
## 240 4.8462 nan 0.0100 0.0181
## 260 4.4433 nan 0.0100 0.0147
## 280 4.1454 nan 0.0100 0.0086
## 300 3.9159 nan 0.0100 0.0030
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.3406 nan 0.0500 3.6100
## 2 54.8376 nan 0.0500 3.5943
## 3 51.6376 nan 0.0500 2.9496
## 4 48.7875 nan 0.0500 3.0223
## 5 45.9671 nan 0.0500 2.5645
## 6 43.3504 nan 0.0500 2.2556
## 7 40.9930 nan 0.0500 2.2145
## 8 38.6440 nan 0.0500 1.9215
## 9 36.5424 nan 0.0500 1.9080
## 10 34.7890 nan 0.0500 1.7267
## 20 21.6082 nan 0.0500 0.8681
## 40 10.9810 nan 0.0500 0.3097
## 60 6.8696 nan 0.0500 0.1304
## 80 5.0664 nan 0.0500 0.0423
## 100 4.1643 nan 0.0500 0.0276
## 120 3.6756 nan 0.0500 0.0014
## 140 3.4485 nan 0.0500 -0.0090
## 160 3.3275 nan 0.0500 -0.0028
## 180 3.2376 nan 0.0500 -0.0134
## 200 3.1617 nan 0.0500 -0.0079
## 220 3.0980 nan 0.0500 -0.0167
## 240 3.0510 nan 0.0500 -0.0077
## 260 2.9982 nan 0.0500 -0.0135
## 280 2.9322 nan 0.0500 -0.0064
## 300 2.9115 nan 0.0500 -0.0082
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.1631 nan 0.0500 3.6160
## 2 54.4058 nan 0.0500 3.5224
## 3 51.2331 nan 0.0500 3.0876
## 4 48.2793 nan 0.0500 3.0026
## 5 45.7939 nan 0.0500 2.4321
## 6 43.0283 nan 0.0500 2.5027
## 7 40.6083 nan 0.0500 2.1154
## 8 38.6153 nan 0.0500 2.0846
## 9 36.4825 nan 0.0500 1.9096
## 10 34.3867 nan 0.0500 1.7707
## 20 21.9291 nan 0.0500 0.8479
## 40 11.2009 nan 0.0500 0.2573
## 60 7.0674 nan 0.0500 0.0848
## 80 5.1370 nan 0.0500 0.0587
## 100 4.2747 nan 0.0500 0.0264
## 120 3.8531 nan 0.0500 -0.0019
## 140 3.6265 nan 0.0500 -0.0006
## 160 3.4898 nan 0.0500 -0.0083
## 180 3.3791 nan 0.0500 -0.0159
## 200 3.3041 nan 0.0500 -0.0231
## 220 3.2286 nan 0.0500 -0.0042
## 240 3.1636 nan 0.0500 -0.0025
## 260 3.1096 nan 0.0500 -0.0083
## 280 3.0635 nan 0.0500 -0.0055
## 300 3.0338 nan 0.0500 -0.0021
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.0815 nan 0.0500 3.9049
## 2 54.4389 nan 0.0500 3.0603
## 3 51.4887 nan 0.0500 2.5421
## 4 48.3038 nan 0.0500 2.9980
## 5 45.6288 nan 0.0500 2.7591
## 6 42.9723 nan 0.0500 2.2922
## 7 40.6824 nan 0.0500 2.2699
## 8 38.5272 nan 0.0500 2.0421
## 9 36.6396 nan 0.0500 1.7796
## 10 34.9800 nan 0.0500 1.5705
## 20 21.7975 nan 0.0500 0.9844
## 40 11.0331 nan 0.0500 0.2067
## 60 7.1457 nan 0.0500 0.0926
## 80 5.3386 nan 0.0500 0.0384
## 100 4.5243 nan 0.0500 0.0173
## 120 4.1071 nan 0.0500 0.0127
## 140 3.9024 nan 0.0500 -0.0184
## 160 3.7637 nan 0.0500 0.0023
## 180 3.6729 nan 0.0500 -0.0044
## 200 3.6010 nan 0.0500 -0.0075
## 220 3.5278 nan 0.0500 -0.0016
## 240 3.4552 nan 0.0500 -0.0107
## 260 3.3825 nan 0.0500 -0.0249
## 280 3.3146 nan 0.0500 -0.0063
## 300 3.2644 nan 0.0500 -0.0033
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.0822 nan 0.0500 4.5350
## 2 53.1920 nan 0.0500 4.3482
## 3 49.0676 nan 0.0500 4.5919
## 4 45.4201 nan 0.0500 3.6448
## 5 42.4360 nan 0.0500 2.8953
## 6 39.2181 nan 0.0500 2.9422
## 7 36.5558 nan 0.0500 2.4219
## 8 33.8519 nan 0.0500 2.5676
## 9 31.7862 nan 0.0500 1.9308
## 10 29.6750 nan 0.0500 2.2506
## 20 16.2357 nan 0.0500 0.8649
## 40 7.0019 nan 0.0500 0.1715
## 60 4.4630 nan 0.0500 0.0369
## 80 3.5934 nan 0.0500 -0.0159
## 100 3.2175 nan 0.0500 -0.0032
## 120 2.9647 nan 0.0500 -0.0063
## 140 2.8101 nan 0.0500 0.0063
## 160 2.6601 nan 0.0500 -0.0154
## 180 2.5126 nan 0.0500 -0.0084
## 200 2.3973 nan 0.0500 -0.0108
## 220 2.2945 nan 0.0500 -0.0152
## 240 2.2071 nan 0.0500 -0.0114
## 260 2.1369 nan 0.0500 -0.0147
## 280 2.0419 nan 0.0500 -0.0131
## 300 1.9695 nan 0.0500 -0.0071
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.1889 nan 0.0500 4.2978
## 2 52.8319 nan 0.0500 4.2209
## 3 48.5002 nan 0.0500 4.6319
## 4 44.9872 nan 0.0500 3.5194
## 5 41.5698 nan 0.0500 3.3478
## 6 38.9664 nan 0.0500 2.6736
## 7 36.1042 nan 0.0500 2.7678
## 8 33.6160 nan 0.0500 2.4856
## 9 31.4214 nan 0.0500 2.1666
## 10 29.2768 nan 0.0500 1.9314
## 20 16.0683 nan 0.0500 0.8117
## 40 6.9752 nan 0.0500 0.1579
## 60 4.4902 nan 0.0500 0.0450
## 80 3.6336 nan 0.0500 -0.0101
## 100 3.3173 nan 0.0500 -0.0007
## 120 3.0948 nan 0.0500 -0.0185
## 140 2.9128 nan 0.0500 -0.0009
## 160 2.7752 nan 0.0500 -0.0090
## 180 2.6654 nan 0.0500 -0.0142
## 200 2.5714 nan 0.0500 -0.0227
## 220 2.4831 nan 0.0500 -0.0079
## 240 2.4103 nan 0.0500 -0.0073
## 260 2.3361 nan 0.0500 -0.0078
## 280 2.2774 nan 0.0500 -0.0042
## 300 2.2133 nan 0.0500 -0.0052
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.0342 nan 0.0500 4.4961
## 2 52.8230 nan 0.0500 4.0789
## 3 49.0417 nan 0.0500 3.9235
## 4 45.4260 nan 0.0500 3.3557
## 5 41.8591 nan 0.0500 3.8067
## 6 38.5487 nan 0.0500 3.1670
## 7 36.1062 nan 0.0500 2.4307
## 8 33.4894 nan 0.0500 2.5642
## 9 31.1210 nan 0.0500 2.4336
## 10 28.9905 nan 0.0500 2.0548
## 20 15.7379 nan 0.0500 0.8003
## 40 7.0411 nan 0.0500 0.1531
## 60 4.4777 nan 0.0500 0.0520
## 80 3.7185 nan 0.0500 -0.0131
## 100 3.4273 nan 0.0500 -0.0253
## 120 3.2396 nan 0.0500 -0.0117
## 140 3.0712 nan 0.0500 -0.0039
## 160 2.9422 nan 0.0500 -0.0109
## 180 2.8399 nan 0.0500 -0.0092
## 200 2.7412 nan 0.0500 -0.0053
## 220 2.6648 nan 0.0500 -0.0021
## 240 2.5749 nan 0.0500 -0.0023
## 260 2.5142 nan 0.0500 -0.0113
## 280 2.4506 nan 0.0500 -0.0101
## 300 2.3913 nan 0.0500 -0.0110
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.8439 nan 0.0500 5.1196
## 2 52.1836 nan 0.0500 4.0785
## 3 48.0675 nan 0.0500 4.1071
## 4 44.3136 nan 0.0500 3.9526
## 5 40.8429 nan 0.0500 3.1805
## 6 37.6835 nan 0.0500 3.0092
## 7 34.7897 nan 0.0500 2.9148
## 8 32.0832 nan 0.0500 2.1908
## 9 29.8040 nan 0.0500 2.0844
## 10 27.7402 nan 0.0500 2.1527
## 20 14.1468 nan 0.0500 0.8592
## 40 5.5997 nan 0.0500 0.1799
## 60 3.5173 nan 0.0500 0.0261
## 80 2.8544 nan 0.0500 -0.0133
## 100 2.5544 nan 0.0500 -0.0203
## 120 2.3411 nan 0.0500 -0.0013
## 140 2.1714 nan 0.0500 -0.0115
## 160 2.0142 nan 0.0500 -0.0087
## 180 1.9045 nan 0.0500 -0.0157
## 200 1.7973 nan 0.0500 -0.0045
## 220 1.7014 nan 0.0500 -0.0092
## 240 1.6043 nan 0.0500 -0.0030
## 260 1.5483 nan 0.0500 -0.0077
## 280 1.4847 nan 0.0500 -0.0044
## 300 1.4220 nan 0.0500 -0.0067
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.0483 nan 0.0500 5.2854
## 2 52.2865 nan 0.0500 4.6184
## 3 47.9988 nan 0.0500 3.5869
## 4 44.3251 nan 0.0500 3.8253
## 5 41.1108 nan 0.0500 3.5265
## 6 37.9991 nan 0.0500 3.0439
## 7 35.0440 nan 0.0500 2.4667
## 8 32.5812 nan 0.0500 2.4444
## 9 30.1370 nan 0.0500 2.2520
## 10 28.1206 nan 0.0500 1.7663
## 20 14.1672 nan 0.0500 0.7067
## 40 5.7655 nan 0.0500 0.1638
## 60 3.7812 nan 0.0500 0.0188
## 80 3.1687 nan 0.0500 0.0034
## 100 2.8823 nan 0.0500 -0.0020
## 120 2.6905 nan 0.0500 -0.0234
## 140 2.5091 nan 0.0500 -0.0127
## 160 2.3883 nan 0.0500 -0.0150
## 180 2.2657 nan 0.0500 -0.0235
## 200 2.1526 nan 0.0500 -0.0130
## 220 2.0644 nan 0.0500 -0.0060
## 240 1.9774 nan 0.0500 -0.0165
## 260 1.8967 nan 0.0500 -0.0077
## 280 1.8302 nan 0.0500 -0.0108
## 300 1.7662 nan 0.0500 -0.0081
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.8050 nan 0.0500 5.0140
## 2 52.0361 nan 0.0500 4.2447
## 3 47.8615 nan 0.0500 4.0182
## 4 44.3698 nan 0.0500 3.8011
## 5 40.9006 nan 0.0500 3.3083
## 6 37.7024 nan 0.0500 3.0409
## 7 34.8810 nan 0.0500 2.7991
## 8 32.2393 nan 0.0500 2.5359
## 9 29.8549 nan 0.0500 2.2440
## 10 27.6664 nan 0.0500 2.0211
## 20 14.4270 nan 0.0500 1.0210
## 40 5.9805 nan 0.0500 0.1906
## 60 3.9463 nan 0.0500 0.0352
## 80 3.3943 nan 0.0500 -0.0078
## 100 3.0919 nan 0.0500 -0.0174
## 120 2.8465 nan 0.0500 -0.0074
## 140 2.6928 nan 0.0500 -0.0014
## 160 2.5514 nan 0.0500 -0.0041
## 180 2.4254 nan 0.0500 -0.0043
## 200 2.3324 nan 0.0500 -0.0145
## 220 2.2486 nan 0.0500 -0.0094
## 240 2.1501 nan 0.0500 -0.0096
## 260 2.0865 nan 0.0500 -0.0047
## 280 2.0129 nan 0.0500 -0.0068
## 300 1.9556 nan 0.0500 -0.0178
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 54.9121 nan 0.1000 7.4315
## 2 48.5845 nan 0.1000 6.0045
## 3 43.8312 nan 0.1000 4.8603
## 4 38.4251 nan 0.1000 4.6192
## 5 34.4334 nan 0.1000 3.7980
## 6 31.1216 nan 0.1000 3.3404
## 7 28.3596 nan 0.1000 2.8261
## 8 25.8323 nan 0.1000 2.5244
## 9 23.2232 nan 0.1000 2.6930
## 10 21.2334 nan 0.1000 2.0096
## 20 11.0616 nan 0.1000 0.5831
## 40 4.9473 nan 0.1000 0.0667
## 60 3.7326 nan 0.1000 0.0219
## 80 3.4144 nan 0.1000 -0.0083
## 100 3.1935 nan 0.1000 -0.0007
## 120 3.0636 nan 0.1000 -0.0105
## 140 2.9600 nan 0.1000 -0.0217
## 160 2.8791 nan 0.1000 -0.0031
## 180 2.8091 nan 0.1000 -0.0102
## 200 2.7477 nan 0.1000 -0.0025
## 220 2.6876 nan 0.1000 -0.0061
## 240 2.6407 nan 0.1000 -0.0103
## 260 2.6001 nan 0.1000 -0.0152
## 280 2.5749 nan 0.1000 -0.0225
## 300 2.5307 nan 0.1000 -0.0254
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 54.2148 nan 0.1000 7.3864
## 2 48.2950 nan 0.1000 6.0476
## 3 43.0202 nan 0.1000 5.0165
## 4 38.2326 nan 0.1000 4.3455
## 5 34.2887 nan 0.1000 3.5050
## 6 30.7665 nan 0.1000 3.5456
## 7 27.7987 nan 0.1000 2.8402
## 8 25.4344 nan 0.1000 2.3078
## 9 22.9854 nan 0.1000 2.0686
## 10 21.3471 nan 0.1000 1.6071
## 20 10.8164 nan 0.1000 0.5374
## 40 5.0398 nan 0.1000 0.0803
## 60 3.8078 nan 0.1000 0.0003
## 80 3.4920 nan 0.1000 0.0042
## 100 3.3100 nan 0.1000 -0.0126
## 120 3.1760 nan 0.1000 -0.0217
## 140 3.0664 nan 0.1000 -0.0042
## 160 2.9876 nan 0.1000 -0.0100
## 180 2.9107 nan 0.1000 -0.0327
## 200 2.8476 nan 0.1000 -0.0227
## 220 2.7819 nan 0.1000 -0.0088
## 240 2.7289 nan 0.1000 -0.0138
## 260 2.6829 nan 0.1000 -0.0140
## 280 2.6178 nan 0.1000 -0.0132
## 300 2.5711 nan 0.1000 -0.0223
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 54.2812 nan 0.1000 8.5123
## 2 47.6133 nan 0.1000 5.6541
## 3 42.1697 nan 0.1000 5.4322
## 4 37.8215 nan 0.1000 3.8891
## 5 33.7367 nan 0.1000 4.2834
## 6 30.5024 nan 0.1000 3.3270
## 7 27.8132 nan 0.1000 2.7797
## 8 25.3126 nan 0.1000 2.6102
## 9 22.9212 nan 0.1000 2.0698
## 10 21.0742 nan 0.1000 1.8068
## 20 10.7568 nan 0.1000 0.4479
## 40 5.0763 nan 0.1000 0.0703
## 60 3.9888 nan 0.1000 0.0072
## 80 3.6971 nan 0.1000 -0.0257
## 100 3.5362 nan 0.1000 0.0073
## 120 3.3926 nan 0.1000 -0.0090
## 140 3.2708 nan 0.1000 -0.0005
## 160 3.1838 nan 0.1000 -0.0010
## 180 3.0927 nan 0.1000 -0.0454
## 200 3.0170 nan 0.1000 -0.0022
## 220 2.9626 nan 0.1000 -0.0251
## 240 2.8947 nan 0.1000 -0.0054
## 260 2.8306 nan 0.1000 -0.0141
## 280 2.7785 nan 0.1000 -0.0111
## 300 2.7469 nan 0.1000 -0.0146
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.6408 nan 0.1000 8.6365
## 2 45.7096 nan 0.1000 7.0176
## 3 38.9234 nan 0.1000 6.9578
## 4 33.4433 nan 0.1000 5.7451
## 5 28.6404 nan 0.1000 4.1381
## 6 24.9271 nan 0.1000 3.2045
## 7 21.9680 nan 0.1000 2.7443
## 8 19.3427 nan 0.1000 2.4943
## 9 16.9798 nan 0.1000 2.0831
## 10 15.2603 nan 0.1000 1.7460
## 20 6.5731 nan 0.1000 0.3682
## 40 3.4559 nan 0.1000 -0.0031
## 60 2.9010 nan 0.1000 0.0062
## 80 2.6370 nan 0.1000 -0.0339
## 100 2.4506 nan 0.1000 -0.0184
## 120 2.2681 nan 0.1000 -0.0315
## 140 2.1066 nan 0.1000 -0.0081
## 160 1.9878 nan 0.1000 -0.0193
## 180 1.8726 nan 0.1000 -0.0202
## 200 1.7560 nan 0.1000 -0.0134
## 220 1.6735 nan 0.1000 -0.0153
## 240 1.5781 nan 0.1000 -0.0173
## 260 1.4883 nan 0.1000 -0.0087
## 280 1.4370 nan 0.1000 -0.0085
## 300 1.3575 nan 0.1000 -0.0145
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 53.5001 nan 0.1000 9.0345
## 2 46.2983 nan 0.1000 7.1671
## 3 39.3178 nan 0.1000 6.6901
## 4 34.5022 nan 0.1000 5.3388
## 5 29.8965 nan 0.1000 4.2990
## 6 26.2545 nan 0.1000 3.5709
## 7 23.2597 nan 0.1000 2.8078
## 8 20.1441 nan 0.1000 2.9103
## 9 18.1489 nan 0.1000 1.9750
## 10 16.3128 nan 0.1000 1.9434
## 20 6.9948 nan 0.1000 0.3623
## 40 3.7730 nan 0.1000 0.0136
## 60 3.1966 nan 0.1000 -0.0060
## 80 2.8918 nan 0.1000 -0.0107
## 100 2.7135 nan 0.1000 -0.0428
## 120 2.5042 nan 0.1000 -0.0211
## 140 2.3312 nan 0.1000 -0.0049
## 160 2.1887 nan 0.1000 -0.0270
## 180 2.0816 nan 0.1000 -0.0171
## 200 2.0033 nan 0.1000 -0.0276
## 220 1.9192 nan 0.1000 -0.0279
## 240 1.8431 nan 0.1000 -0.0225
## 260 1.7768 nan 0.1000 -0.0081
## 280 1.6974 nan 0.1000 -0.0207
## 300 1.6430 nan 0.1000 -0.0101
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.4953 nan 0.1000 10.2753
## 2 45.1567 nan 0.1000 7.0394
## 3 38.6851 nan 0.1000 6.2842
## 4 33.4988 nan 0.1000 4.6885
## 5 29.3604 nan 0.1000 3.8766
## 6 25.6678 nan 0.1000 4.0648
## 7 22.3451 nan 0.1000 2.9846
## 8 19.6402 nan 0.1000 2.0801
## 9 17.4188 nan 0.1000 2.2269
## 10 15.4389 nan 0.1000 1.7248
## 20 6.9057 nan 0.1000 0.3909
## 40 3.8441 nan 0.1000 0.0087
## 60 3.3870 nan 0.1000 -0.0245
## 80 3.0457 nan 0.1000 -0.0291
## 100 2.8391 nan 0.1000 -0.0415
## 120 2.6431 nan 0.1000 -0.0278
## 140 2.5185 nan 0.1000 -0.0178
## 160 2.3983 nan 0.1000 -0.0391
## 180 2.2918 nan 0.1000 -0.0163
## 200 2.2119 nan 0.1000 -0.0238
## 220 2.1361 nan 0.1000 -0.0181
## 240 2.0684 nan 0.1000 -0.0131
## 260 1.9967 nan 0.1000 -0.0129
## 280 1.9491 nan 0.1000 -0.0152
## 300 1.8864 nan 0.1000 -0.0148
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.7282 nan 0.1000 9.6703
## 2 43.4594 nan 0.1000 8.7364
## 3 36.9107 nan 0.1000 5.4281
## 4 31.1231 nan 0.1000 4.2147
## 5 27.4326 nan 0.1000 3.6018
## 6 23.4869 nan 0.1000 3.3213
## 7 20.1302 nan 0.1000 2.7392
## 8 17.6290 nan 0.1000 2.4668
## 9 15.4407 nan 0.1000 2.2859
## 10 13.5710 nan 0.1000 1.9470
## 20 5.3500 nan 0.1000 0.2728
## 40 2.8285 nan 0.1000 -0.0139
## 60 2.3325 nan 0.1000 -0.0165
## 80 2.0824 nan 0.1000 -0.0278
## 100 1.8687 nan 0.1000 -0.0199
## 120 1.7068 nan 0.1000 -0.0363
## 140 1.5669 nan 0.1000 -0.0180
## 160 1.4492 nan 0.1000 -0.0253
## 180 1.3330 nan 0.1000 -0.0021
## 200 1.2323 nan 0.1000 -0.0066
## 220 1.1409 nan 0.1000 -0.0200
## 240 1.0611 nan 0.1000 -0.0184
## 260 0.9884 nan 0.1000 -0.0146
## 280 0.9277 nan 0.1000 -0.0089
## 300 0.8569 nan 0.1000 -0.0102
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.0862 nan 0.1000 9.6627
## 2 44.4768 nan 0.1000 8.2155
## 3 37.5703 nan 0.1000 7.5586
## 4 31.7847 nan 0.1000 5.0706
## 5 27.2804 nan 0.1000 4.2598
## 6 23.9999 nan 0.1000 2.1671
## 7 20.4015 nan 0.1000 3.0058
## 8 17.6562 nan 0.1000 2.7762
## 9 15.4641 nan 0.1000 1.8034
## 10 13.6100 nan 0.1000 1.5997
## 20 5.6733 nan 0.1000 0.3334
## 40 3.1525 nan 0.1000 0.0137
## 60 2.5912 nan 0.1000 -0.0161
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## 280 1.2304 nan 0.1000 -0.0152
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.0757 nan 0.1000 9.2442
## 2 44.5695 nan 0.1000 8.4028
## 3 37.7659 nan 0.1000 6.5388
## 4 32.0082 nan 0.1000 5.5434
## 5 27.6184 nan 0.1000 3.9919
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## 7 20.6457 nan 0.1000 3.1952
## 8 18.1607 nan 0.1000 2.6427
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## 20 5.8150 nan 0.1000 0.2879
## 40 3.3884 nan 0.1000 -0.0358
## 60 2.8704 nan 0.1000 -0.0093
## 80 2.5522 nan 0.1000 -0.0317
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## 140 1.9988 nan 0.1000 -0.0113
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## 180 1.7333 nan 0.1000 -0.0210
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## 260 1.4142 nan 0.1000 -0.0179
## 280 1.3592 nan 0.1000 -0.0154
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.1795 nan 0.0010 0.0778
## 2 60.1037 nan 0.0010 0.0772
## 3 60.0279 nan 0.0010 0.0714
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## 40 57.2916 nan 0.0010 0.0666
## 60 55.8836 nan 0.0010 0.0675
## 80 54.5519 nan 0.0010 0.0638
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.1723 nan 0.0010 0.0737
## 2 60.0953 nan 0.0010 0.0725
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.1695 nan 0.0010 0.0722
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.1451 nan 0.0010 0.0930
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## 3 59.9524 nan 0.0010 0.0959
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.1505 nan 0.0010 0.1005
## 2 60.0561 nan 0.0010 0.0804
## 3 59.9636 nan 0.0010 0.0915
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## 140 48.8445 nan 0.0010 0.0700
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## 280 39.8527 nan 0.0010 0.0535
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.1607 nan 0.0010 0.0843
## 2 60.0752 nan 0.0010 0.0860
## 3 59.9899 nan 0.0010 0.0875
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## 140 48.8012 nan 0.0010 0.0726
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.1474 nan 0.0010 0.0974
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## 3 59.9533 nan 0.0010 0.1027
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## 40 56.3925 nan 0.0010 0.0996
## 60 54.5630 nan 0.0010 0.0900
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.1460 nan 0.0010 0.1009
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## 3 59.9490 nan 0.0010 0.0974
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## 20 58.2859 nan 0.0010 0.1004
## 40 56.3956 nan 0.0010 0.0894
## 60 54.5705 nan 0.0010 0.0860
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.1459 nan 0.0010 0.1016
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## 40 56.4116 nan 0.0010 0.0868
## 60 54.5824 nan 0.0010 0.0891
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.8603 nan 0.0050 0.4031
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## 3 59.1198 nan 0.0050 0.3488
## 4 58.8240 nan 0.0050 0.2815
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## 40 47.4974 nan 0.0050 0.2886
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## 80 38.2588 nan 0.0050 0.1864
## 100 34.5607 nan 0.0050 0.1524
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## 140 28.5353 nan 0.0050 0.1255
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## 180 23.9345 nan 0.0050 0.1001
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## 240 18.9093 nan 0.0050 0.0627
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.8479 nan 0.0050 0.3577
## 2 59.4820 nan 0.0050 0.3800
## 3 59.1054 nan 0.0050 0.3741
## 4 58.7191 nan 0.0050 0.3659
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## 40 47.6534 nan 0.0050 0.2615
## 60 42.7898 nan 0.0050 0.2351
## 80 38.5239 nan 0.0050 0.1837
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## 120 31.6929 nan 0.0050 0.1474
## 140 28.8287 nan 0.0050 0.1243
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## 180 24.1505 nan 0.0050 0.0781
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.8666 nan 0.0050 0.3734
## 2 59.4857 nan 0.0050 0.3645
## 3 59.0859 nan 0.0050 0.3770
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## 40 47.3626 nan 0.0050 0.2384
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## 80 38.3375 nan 0.0050 0.1924
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## 140 28.6494 nan 0.0050 0.1386
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.7844 nan 0.0050 0.4356
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## 20 51.7912 nan 0.0050 0.3392
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.7996 nan 0.0050 0.4785
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## 3 58.9062 nan 0.0050 0.4804
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## 180 18.2485 nan 0.0050 0.0998
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.7475 nan 0.0050 0.4592
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.7437 nan 0.0050 0.5467
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.7618 nan 0.0050 0.4742
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.7261 nan 0.0050 0.5060
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## 160 18.5764 nan 0.0050 0.1054
## 180 16.4427 nan 0.0050 0.0986
## 200 14.6420 nan 0.0050 0.0853
## 220 13.0988 nan 0.0050 0.0666
## 240 11.8210 nan 0.0050 0.0403
## 260 10.7052 nan 0.0050 0.0488
## 280 9.7551 nan 0.0050 0.0301
## 300 8.9399 nan 0.0050 0.0390
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.5301 nan 0.0100 0.7121
## 2 58.7557 nan 0.0100 0.7238
## 3 58.0288 nan 0.0100 0.7509
## 4 57.3049 nan 0.0100 0.7483
## 5 56.6425 nan 0.0100 0.6792
## 6 55.9880 nan 0.0100 0.7523
## 7 55.3099 nan 0.0100 0.6834
## 8 54.5972 nan 0.0100 0.6280
## 9 53.9108 nan 0.0100 0.6188
## 10 53.2549 nan 0.0100 0.6996
## 20 47.3159 nan 0.0100 0.5227
## 40 38.0748 nan 0.0100 0.3057
## 60 31.2711 nan 0.0100 0.2870
## 80 26.1392 nan 0.0100 0.2147
## 100 22.1332 nan 0.0100 0.1594
## 120 18.9530 nan 0.0100 0.1246
## 140 16.4653 nan 0.0100 0.0946
## 160 14.4619 nan 0.0100 0.0833
## 180 12.8350 nan 0.0100 0.0573
## 200 11.4477 nan 0.0100 0.0377
## 220 10.3397 nan 0.0100 0.0512
## 240 9.4086 nan 0.0100 0.0321
## 260 8.6175 nan 0.0100 0.0331
## 280 7.9549 nan 0.0100 0.0289
## 300 7.3609 nan 0.0100 0.0233
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.5025 nan 0.0100 0.7670
## 2 58.7792 nan 0.0100 0.7396
## 3 58.0228 nan 0.0100 0.6962
## 4 57.3227 nan 0.0100 0.6367
## 5 56.6319 nan 0.0100 0.5897
## 6 55.9651 nan 0.0100 0.6187
## 7 55.3250 nan 0.0100 0.6897
## 8 54.6190 nan 0.0100 0.6896
## 9 54.0017 nan 0.0100 0.5919
## 10 53.3662 nan 0.0100 0.6411
## 20 47.5606 nan 0.0100 0.5612
## 40 38.4455 nan 0.0100 0.3616
## 60 31.4149 nan 0.0100 0.2690
## 80 26.1982 nan 0.0100 0.2095
## 100 22.1650 nan 0.0100 0.1689
## 120 19.0083 nan 0.0100 0.1105
## 140 16.4846 nan 0.0100 0.0967
## 160 14.4699 nan 0.0100 0.0943
## 180 12.7560 nan 0.0100 0.0482
## 200 11.4073 nan 0.0100 0.0440
## 220 10.3047 nan 0.0100 0.0315
## 240 9.3806 nan 0.0100 0.0350
## 260 8.5782 nan 0.0100 0.0269
## 280 7.8870 nan 0.0100 0.0191
## 300 7.2898 nan 0.0100 0.0224
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.4379 nan 0.0100 0.7410
## 2 58.6478 nan 0.0100 0.7647
## 3 57.9257 nan 0.0100 0.7299
## 4 57.2356 nan 0.0100 0.7545
## 5 56.5742 nan 0.0100 0.6628
## 6 55.8594 nan 0.0100 0.6682
## 7 55.1789 nan 0.0100 0.6331
## 8 54.5105 nan 0.0100 0.6045
## 9 53.8799 nan 0.0100 0.5812
## 10 53.2213 nan 0.0100 0.6400
## 20 47.2757 nan 0.0100 0.5458
## 40 38.0557 nan 0.0100 0.3920
## 60 31.2341 nan 0.0100 0.3090
## 80 25.8533 nan 0.0100 0.2244
## 100 21.7828 nan 0.0100 0.1777
## 120 18.7267 nan 0.0100 0.1334
## 140 16.3387 nan 0.0100 0.0867
## 160 14.4160 nan 0.0100 0.0813
## 180 12.7616 nan 0.0100 0.0636
## 200 11.4273 nan 0.0100 0.0489
## 220 10.3186 nan 0.0100 0.0387
## 240 9.4409 nan 0.0100 0.0324
## 260 8.6969 nan 0.0100 0.0266
## 280 8.0304 nan 0.0100 0.0242
## 300 7.4719 nan 0.0100 0.0129
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.3279 nan 0.0100 0.9335
## 2 58.3807 nan 0.0100 0.9140
## 3 57.4267 nan 0.0100 0.8765
## 4 56.5539 nan 0.0100 0.8451
## 5 55.7523 nan 0.0100 0.6643
## 6 54.9846 nan 0.0100 0.7482
## 7 54.1062 nan 0.0100 0.8696
## 8 53.3126 nan 0.0100 0.7473
## 9 52.4951 nan 0.0100 0.8363
## 10 51.6730 nan 0.0100 0.7740
## 20 44.5239 nan 0.0100 0.6623
## 40 33.5092 nan 0.0100 0.4765
## 60 25.7755 nan 0.0100 0.3151
## 80 20.2420 nan 0.0100 0.2195
## 100 16.3169 nan 0.0100 0.1444
## 120 13.3336 nan 0.0100 0.1099
## 140 11.1305 nan 0.0100 0.0714
## 160 9.4807 nan 0.0100 0.0625
## 180 8.2087 nan 0.0100 0.0401
## 200 7.1880 nan 0.0100 0.0309
## 220 6.4012 nan 0.0100 0.0296
## 240 5.7566 nan 0.0100 0.0332
## 260 5.2300 nan 0.0100 0.0203
## 280 4.8163 nan 0.0100 0.0053
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.2937 nan 0.0100 0.8528
## 2 58.3829 nan 0.0100 0.8335
## 3 57.4928 nan 0.0100 0.8818
## 4 56.5719 nan 0.0100 0.8709
## 5 55.7264 nan 0.0100 0.8367
## 6 54.8647 nan 0.0100 0.8706
## 7 54.0510 nan 0.0100 0.7981
## 8 53.2680 nan 0.0100 0.8063
## 9 52.4768 nan 0.0100 0.8153
## 10 51.7084 nan 0.0100 0.8145
## 20 44.5023 nan 0.0100 0.6304
## 40 33.6787 nan 0.0100 0.4127
## 60 25.8800 nan 0.0100 0.3685
## 80 20.3155 nan 0.0100 0.2628
## 100 16.3839 nan 0.0100 0.1613
## 120 13.3850 nan 0.0100 0.1262
## 140 11.2099 nan 0.0100 0.0691
## 160 9.5074 nan 0.0100 0.0633
## 180 8.2201 nan 0.0100 0.0529
## 200 7.2295 nan 0.0100 0.0418
## 220 6.4306 nan 0.0100 0.0365
## 240 5.8156 nan 0.0100 0.0200
## 260 5.3021 nan 0.0100 0.0220
## 280 4.8863 nan 0.0100 0.0094
## 300 4.5524 nan 0.0100 0.0020
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.3458 nan 0.0100 0.9447
## 2 58.4801 nan 0.0100 0.8597
## 3 57.5790 nan 0.0100 0.8684
## 4 56.7152 nan 0.0100 0.8660
## 5 55.8615 nan 0.0100 0.8794
## 6 54.9927 nan 0.0100 0.8379
## 7 54.1352 nan 0.0100 0.8406
## 8 53.2912 nan 0.0100 0.7745
## 9 52.4999 nan 0.0100 0.8266
## 10 51.7232 nan 0.0100 0.7589
## 20 44.4926 nan 0.0100 0.7387
## 40 33.5892 nan 0.0100 0.3962
## 60 25.8645 nan 0.0100 0.3257
## 80 20.2375 nan 0.0100 0.1955
## 100 16.3022 nan 0.0100 0.1587
## 120 13.5019 nan 0.0100 0.1066
## 140 11.3529 nan 0.0100 0.0646
## 160 9.6748 nan 0.0100 0.0596
## 180 8.4075 nan 0.0100 0.0520
## 200 7.4900 nan 0.0100 0.0356
## 220 6.7147 nan 0.0100 0.0307
## 240 6.0887 nan 0.0100 0.0297
## 260 5.5958 nan 0.0100 0.0153
## 280 5.2038 nan 0.0100 0.0120
## 300 4.8790 nan 0.0100 0.0096
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.2867 nan 0.0100 0.8900
## 2 58.2498 nan 0.0100 1.0645
## 3 57.3169 nan 0.0100 0.9356
## 4 56.3990 nan 0.0100 0.9202
## 5 55.4437 nan 0.0100 0.9580
## 6 54.5730 nan 0.0100 0.9240
## 7 53.7357 nan 0.0100 0.9093
## 8 52.8665 nan 0.0100 0.9115
## 9 52.0433 nan 0.0100 0.8780
## 10 51.2267 nan 0.0100 0.7975
## 20 43.5791 nan 0.0100 0.6996
## 40 32.3413 nan 0.0100 0.5134
## 60 24.2072 nan 0.0100 0.3154
## 80 18.5195 nan 0.0100 0.2265
## 100 14.4210 nan 0.0100 0.1525
## 120 11.4767 nan 0.0100 0.1010
## 140 9.3660 nan 0.0100 0.0794
## 160 7.8236 nan 0.0100 0.0429
## 180 6.6703 nan 0.0100 0.0394
## 200 5.8099 nan 0.0100 0.0300
## 220 5.1330 nan 0.0100 0.0216
## 240 4.5887 nan 0.0100 0.0222
## 260 4.1674 nan 0.0100 0.0120
## 280 3.8615 nan 0.0100 0.0085
## 300 3.6184 nan 0.0100 0.0061
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.3172 nan 0.0100 0.9388
## 2 58.3219 nan 0.0100 0.8678
## 3 57.3643 nan 0.0100 0.9162
## 4 56.4423 nan 0.0100 0.9423
## 5 55.5326 nan 0.0100 0.8155
## 6 54.6090 nan 0.0100 0.9197
## 7 53.7418 nan 0.0100 0.8800
## 8 52.8922 nan 0.0100 0.9002
## 9 51.9833 nan 0.0100 0.8495
## 10 51.1142 nan 0.0100 0.8545
## 20 43.6640 nan 0.0100 0.7057
## 40 32.2619 nan 0.0100 0.4472
## 60 24.2101 nan 0.0100 0.3324
## 80 18.6397 nan 0.0100 0.2035
## 100 14.5378 nan 0.0100 0.1596
## 120 11.6281 nan 0.0100 0.0865
## 140 9.5128 nan 0.0100 0.0664
## 160 7.9413 nan 0.0100 0.0597
## 180 6.7344 nan 0.0100 0.0463
## 200 5.8501 nan 0.0100 0.0288
## 220 5.1961 nan 0.0100 0.0236
## 240 4.6940 nan 0.0100 0.0122
## 260 4.3067 nan 0.0100 0.0124
## 280 3.9938 nan 0.0100 0.0073
## 300 3.7397 nan 0.0100 0.0069
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.2635 nan 0.0100 0.9178
## 2 58.2982 nan 0.0100 0.9042
## 3 57.3010 nan 0.0100 0.9459
## 4 56.3472 nan 0.0100 0.9760
## 5 55.4844 nan 0.0100 0.9373
## 6 54.5326 nan 0.0100 0.8566
## 7 53.6754 nan 0.0100 0.7365
## 8 52.7681 nan 0.0100 0.8704
## 9 51.9101 nan 0.0100 0.8760
## 10 51.0392 nan 0.0100 0.8254
## 20 43.4013 nan 0.0100 0.6912
## 40 32.1634 nan 0.0100 0.4103
## 60 24.2525 nan 0.0100 0.2974
## 80 18.6018 nan 0.0100 0.1645
## 100 14.6755 nan 0.0100 0.1842
## 120 11.8420 nan 0.0100 0.1077
## 140 9.7650 nan 0.0100 0.0767
## 160 8.2853 nan 0.0100 0.0443
## 180 7.1396 nan 0.0100 0.0443
## 200 6.2290 nan 0.0100 0.0338
## 220 5.5752 nan 0.0100 0.0253
## 240 5.0699 nan 0.0100 0.0118
## 260 4.6659 nan 0.0100 0.0110
## 280 4.3719 nan 0.0100 0.0029
## 300 4.1274 nan 0.0100 0.0087
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.5478 nan 0.0500 3.6882
## 2 52.9560 nan 0.0500 3.1827
## 3 49.8560 nan 0.0500 2.9290
## 4 46.7298 nan 0.0500 3.1035
## 5 44.1532 nan 0.0500 2.4554
## 6 41.7505 nan 0.0500 1.9597
## 7 39.5955 nan 0.0500 1.9121
## 8 37.4853 nan 0.0500 1.9040
## 9 35.6351 nan 0.0500 1.6778
## 10 33.7467 nan 0.0500 1.6390
## 20 21.7512 nan 0.0500 0.9606
## 40 11.1563 nan 0.0500 0.2683
## 60 7.1306 nan 0.0500 0.1043
## 80 5.2208 nan 0.0500 0.0213
## 100 4.3192 nan 0.0500 0.0206
## 120 3.8543 nan 0.0500 0.0024
## 140 3.5937 nan 0.0500 -0.0035
## 160 3.4771 nan 0.0500 -0.0003
## 180 3.3891 nan 0.0500 -0.0081
## 200 3.3179 nan 0.0500 -0.0026
## 220 3.2396 nan 0.0500 -0.0076
## 240 3.1552 nan 0.0500 -0.0117
## 260 3.1001 nan 0.0500 -0.0062
## 280 3.0501 nan 0.0500 -0.0195
## 300 3.0045 nan 0.0500 -0.0032
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.4586 nan 0.0500 3.8686
## 2 53.1814 nan 0.0500 3.3968
## 3 50.1428 nan 0.0500 2.9569
## 4 47.3283 nan 0.0500 2.7298
## 5 44.6403 nan 0.0500 2.3233
## 6 42.0990 nan 0.0500 2.1398
## 7 39.8159 nan 0.0500 2.3457
## 8 37.6828 nan 0.0500 2.0014
## 9 35.7014 nan 0.0500 1.9536
## 10 34.0483 nan 0.0500 1.6874
## 20 21.5385 nan 0.0500 0.8393
## 40 11.3067 nan 0.0500 0.2403
## 60 7.2000 nan 0.0500 0.1139
## 80 5.3380 nan 0.0500 0.0114
## 100 4.4461 nan 0.0500 0.0327
## 120 3.9339 nan 0.0500 -0.0098
## 140 3.6891 nan 0.0500 -0.0060
## 160 3.5228 nan 0.0500 0.0036
## 180 3.4127 nan 0.0500 -0.0126
## 200 3.3179 nan 0.0500 -0.0067
## 220 3.2445 nan 0.0500 -0.0061
## 240 3.1786 nan 0.0500 -0.0043
## 260 3.1267 nan 0.0500 -0.0062
## 280 3.0669 nan 0.0500 -0.0109
## 300 3.0258 nan 0.0500 -0.0037
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.5169 nan 0.0500 3.4327
## 2 53.1301 nan 0.0500 3.3331
## 3 50.1149 nan 0.0500 2.9409
## 4 47.3597 nan 0.0500 2.7068
## 5 45.1506 nan 0.0500 2.1826
## 6 42.6249 nan 0.0500 2.5142
## 7 40.2458 nan 0.0500 2.5013
## 8 37.9532 nan 0.0500 1.9599
## 9 35.9453 nan 0.0500 1.8534
## 10 34.3348 nan 0.0500 1.6849
## 20 21.8063 nan 0.0500 0.8525
## 40 11.4615 nan 0.0500 0.2026
## 60 7.4609 nan 0.0500 0.1156
## 80 5.6260 nan 0.0500 0.0564
## 100 4.7297 nan 0.0500 0.0161
## 120 4.2963 nan 0.0500 0.0033
## 140 4.0892 nan 0.0500 -0.0020
## 160 3.9575 nan 0.0500 -0.0036
## 180 3.8549 nan 0.0500 -0.0060
## 200 3.7678 nan 0.0500 -0.0176
## 220 3.6726 nan 0.0500 -0.0091
## 240 3.6145 nan 0.0500 -0.0026
## 260 3.5361 nan 0.0500 -0.0050
## 280 3.4791 nan 0.0500 -0.0221
## 300 3.4137 nan 0.0500 -0.0038
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.7740 nan 0.0500 4.0245
## 2 51.4753 nan 0.0500 3.5262
## 3 47.8405 nan 0.0500 3.4442
## 4 44.2474 nan 0.0500 3.6493
## 5 41.0586 nan 0.0500 2.9592
## 6 38.2337 nan 0.0500 2.6567
## 7 35.5399 nan 0.0500 2.4215
## 8 33.4934 nan 0.0500 1.8823
## 9 31.1984 nan 0.0500 2.3343
## 10 29.0450 nan 0.0500 1.9876
## 20 16.0399 nan 0.0500 0.7374
## 40 7.0124 nan 0.0500 0.1564
## 60 4.4400 nan 0.0500 0.0499
## 80 3.5027 nan 0.0500 0.0149
## 100 3.0900 nan 0.0500 0.0015
## 120 2.8543 nan 0.0500 -0.0005
## 140 2.6859 nan 0.0500 0.0038
## 160 2.5553 nan 0.0500 -0.0116
## 180 2.4359 nan 0.0500 -0.0060
## 200 2.3327 nan 0.0500 -0.0114
## 220 2.2432 nan 0.0500 -0.0179
## 240 2.1780 nan 0.0500 -0.0178
## 260 2.1092 nan 0.0500 -0.0047
## 280 2.0451 nan 0.0500 -0.0144
## 300 1.9866 nan 0.0500 -0.0122
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.2700 nan 0.0500 4.5715
## 2 50.9228 nan 0.0500 3.7653
## 3 47.2806 nan 0.0500 3.5590
## 4 43.9280 nan 0.0500 3.2875
## 5 41.0981 nan 0.0500 3.0518
## 6 38.2152 nan 0.0500 2.8586
## 7 35.5367 nan 0.0500 2.4637
## 8 32.9167 nan 0.0500 2.4383
## 9 30.5307 nan 0.0500 2.0516
## 10 28.6581 nan 0.0500 1.8414
## 20 15.8320 nan 0.0500 0.8739
## 40 6.9770 nan 0.0500 0.1664
## 60 4.5397 nan 0.0500 0.0276
## 80 3.6381 nan 0.0500 -0.0045
## 100 3.3304 nan 0.0500 -0.0103
## 120 3.1267 nan 0.0500 -0.0039
## 140 2.9503 nan 0.0500 -0.0092
## 160 2.8024 nan 0.0500 -0.0013
## 180 2.6996 nan 0.0500 -0.0027
## 200 2.6192 nan 0.0500 -0.0103
## 220 2.5369 nan 0.0500 -0.0118
## 240 2.4602 nan 0.0500 -0.0088
## 260 2.3960 nan 0.0500 -0.0150
## 280 2.3308 nan 0.0500 -0.0142
## 300 2.2648 nan 0.0500 -0.0103
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.7213 nan 0.0500 4.6688
## 2 51.7321 nan 0.0500 4.0763
## 3 47.8623 nan 0.0500 3.4833
## 4 44.4081 nan 0.0500 3.2962
## 5 41.3645 nan 0.0500 2.9971
## 6 38.6021 nan 0.0500 2.4006
## 7 35.7745 nan 0.0500 2.6068
## 8 33.1662 nan 0.0500 2.3257
## 9 30.9573 nan 0.0500 1.9084
## 10 29.0350 nan 0.0500 2.0797
## 20 16.5571 nan 0.0500 0.6464
## 40 7.5841 nan 0.0500 0.0948
## 60 4.9780 nan 0.0500 0.0361
## 80 4.1854 nan 0.0500 0.0116
## 100 3.7796 nan 0.0500 -0.0022
## 120 3.5582 nan 0.0500 -0.0124
## 140 3.3944 nan 0.0500 -0.0118
## 160 3.2428 nan 0.0500 -0.0019
## 180 3.1342 nan 0.0500 -0.0052
## 200 3.0410 nan 0.0500 -0.0112
## 220 2.9416 nan 0.0500 -0.0141
## 240 2.8588 nan 0.0500 -0.0120
## 260 2.7756 nan 0.0500 -0.0161
## 280 2.7219 nan 0.0500 -0.0109
## 300 2.6644 nan 0.0500 -0.0062
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.2701 nan 0.0500 5.1863
## 2 51.0344 nan 0.0500 4.7472
## 3 46.8458 nan 0.0500 4.6638
## 4 43.1859 nan 0.0500 3.1811
## 5 39.7185 nan 0.0500 3.4908
## 6 36.5141 nan 0.0500 3.2510
## 7 33.8681 nan 0.0500 2.7073
## 8 31.4515 nan 0.0500 2.6161
## 9 29.3647 nan 0.0500 2.2113
## 10 27.3934 nan 0.0500 2.0472
## 20 14.1183 nan 0.0500 0.8636
## 40 5.7964 nan 0.0500 0.1960
## 60 3.5883 nan 0.0500 0.0155
## 80 3.0078 nan 0.0500 -0.0027
## 100 2.6754 nan 0.0500 -0.0099
## 120 2.4486 nan 0.0500 -0.0057
## 140 2.3034 nan 0.0500 -0.0029
## 160 2.1708 nan 0.0500 -0.0175
## 180 2.0383 nan 0.0500 -0.0120
## 200 1.9251 nan 0.0500 -0.0089
## 220 1.8301 nan 0.0500 -0.0118
## 240 1.7517 nan 0.0500 -0.0087
## 260 1.6867 nan 0.0500 -0.0124
## 280 1.6084 nan 0.0500 -0.0064
## 300 1.5271 nan 0.0500 -0.0027
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.2600 nan 0.0500 4.4398
## 2 50.7526 nan 0.0500 4.4028
## 3 46.8272 nan 0.0500 3.7879
## 4 43.2540 nan 0.0500 3.4182
## 5 39.9372 nan 0.0500 3.4231
## 6 36.7336 nan 0.0500 2.8722
## 7 34.1287 nan 0.0500 2.5602
## 8 31.6628 nan 0.0500 2.0893
## 9 29.2717 nan 0.0500 2.0020
## 10 27.1558 nan 0.0500 1.8754
## 20 14.4907 nan 0.0500 0.8304
## 40 5.8307 nan 0.0500 0.1650
## 60 3.7309 nan 0.0500 0.0146
## 80 3.0997 nan 0.0500 -0.0203
## 100 2.8136 nan 0.0500 -0.0082
## 120 2.5819 nan 0.0500 -0.0113
## 140 2.4285 nan 0.0500 -0.0209
## 160 2.3090 nan 0.0500 -0.0085
## 180 2.1922 nan 0.0500 -0.0154
## 200 2.0896 nan 0.0500 -0.0154
## 220 2.0186 nan 0.0500 -0.0285
## 240 1.9496 nan 0.0500 -0.0127
## 260 1.8690 nan 0.0500 -0.0046
## 280 1.8010 nan 0.0500 -0.0143
## 300 1.7421 nan 0.0500 -0.0123
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.1588 nan 0.0500 5.0826
## 2 50.8569 nan 0.0500 4.5910
## 3 47.0256 nan 0.0500 3.8204
## 4 43.4280 nan 0.0500 3.7800
## 5 40.0490 nan 0.0500 3.1138
## 6 37.1788 nan 0.0500 2.8038
## 7 34.2593 nan 0.0500 2.4781
## 8 31.5909 nan 0.0500 2.5895
## 9 29.3312 nan 0.0500 2.1735
## 10 27.2546 nan 0.0500 2.0750
## 20 14.1653 nan 0.0500 0.7210
## 40 6.0663 nan 0.0500 0.1603
## 60 4.0416 nan 0.0500 0.0342
## 80 3.4186 nan 0.0500 -0.0028
## 100 3.1879 nan 0.0500 -0.0005
## 120 2.9848 nan 0.0500 -0.0221
## 140 2.8146 nan 0.0500 -0.0051
## 160 2.7192 nan 0.0500 -0.0056
## 180 2.6202 nan 0.0500 -0.0154
## 200 2.5166 nan 0.0500 -0.0154
## 220 2.4207 nan 0.0500 -0.0026
## 240 2.3362 nan 0.0500 -0.0114
## 260 2.2724 nan 0.0500 -0.0120
## 280 2.2100 nan 0.0500 -0.0103
## 300 2.1386 nan 0.0500 -0.0192
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.7506 nan 0.1000 7.1763
## 2 47.0920 nan 0.1000 6.0234
## 3 42.3567 nan 0.1000 4.5566
## 4 37.9987 nan 0.1000 4.2984
## 5 34.4241 nan 0.1000 3.8144
## 6 31.0418 nan 0.1000 3.2506
## 7 28.7072 nan 0.1000 1.9377
## 8 26.0735 nan 0.1000 2.5086
## 9 23.9450 nan 0.1000 2.2169
## 10 22.0010 nan 0.1000 1.8381
## 20 11.0767 nan 0.1000 0.4340
## 40 5.3297 nan 0.1000 0.0626
## 60 3.9995 nan 0.1000 0.0336
## 80 3.5982 nan 0.1000 -0.0121
## 100 3.3890 nan 0.1000 0.0000
## 120 3.2635 nan 0.1000 -0.0011
## 140 3.1550 nan 0.1000 -0.0033
## 160 3.0579 nan 0.1000 -0.0257
## 180 2.9935 nan 0.1000 -0.0121
## 200 2.9279 nan 0.1000 -0.0212
## 220 2.8696 nan 0.1000 -0.0138
## 240 2.7978 nan 0.1000 -0.0160
## 260 2.7562 nan 0.1000 -0.0135
## 280 2.7077 nan 0.1000 -0.0150
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 53.4895 nan 0.1000 6.7067
## 2 47.6072 nan 0.1000 5.4591
## 3 42.7737 nan 0.1000 4.9773
## 4 38.1240 nan 0.1000 4.1375
## 5 34.3879 nan 0.1000 3.5969
## 6 31.1107 nan 0.1000 2.9325
## 7 28.1514 nan 0.1000 2.6520
## 8 25.6167 nan 0.1000 2.1981
## 9 23.6287 nan 0.1000 1.9418
## 10 21.6287 nan 0.1000 1.5102
## 20 11.2716 nan 0.1000 0.2185
## 40 5.3970 nan 0.1000 0.1180
## 60 4.0855 nan 0.1000 0.0166
## 80 3.6601 nan 0.1000 -0.0054
## 100 3.4478 nan 0.1000 -0.0093
## 120 3.3129 nan 0.1000 0.0041
## 140 3.1946 nan 0.1000 -0.0135
## 160 3.1082 nan 0.1000 -0.0176
## 180 3.0312 nan 0.1000 0.0025
## 200 2.9617 nan 0.1000 -0.0157
## 220 2.9237 nan 0.1000 -0.0228
## 240 2.8573 nan 0.1000 -0.0102
## 260 2.8250 nan 0.1000 -0.0216
## 280 2.7831 nan 0.1000 -0.0142
## 300 2.7375 nan 0.1000 -0.0037
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 53.4135 nan 0.1000 6.9011
## 2 47.1608 nan 0.1000 6.0421
## 3 42.0795 nan 0.1000 4.6584
## 4 37.4784 nan 0.1000 4.6792
## 5 33.7501 nan 0.1000 3.1221
## 6 30.3582 nan 0.1000 3.5393
## 7 27.5512 nan 0.1000 2.8099
## 8 25.0929 nan 0.1000 2.1795
## 9 23.3808 nan 0.1000 1.5307
## 10 21.1300 nan 0.1000 2.0990
## 20 11.0073 nan 0.1000 0.5348
## 40 5.8692 nan 0.1000 0.0955
## 60 4.6087 nan 0.1000 -0.0144
## 80 4.1453 nan 0.1000 -0.0028
## 100 3.8734 nan 0.1000 -0.0175
## 120 3.7216 nan 0.1000 -0.0134
## 140 3.5858 nan 0.1000 -0.0018
## 160 3.4894 nan 0.1000 -0.0102
## 180 3.3700 nan 0.1000 -0.0157
## 200 3.3196 nan 0.1000 -0.0167
## 220 3.2501 nan 0.1000 -0.0110
## 240 3.2076 nan 0.1000 -0.0283
## 260 3.1164 nan 0.1000 -0.0455
## 280 3.0558 nan 0.1000 -0.0298
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.0281 nan 0.1000 8.7641
## 2 43.5306 nan 0.1000 7.6911
## 3 37.7776 nan 0.1000 5.4204
## 4 33.1069 nan 0.1000 4.8355
## 5 28.7307 nan 0.1000 4.0763
## 6 25.1086 nan 0.1000 3.7259
## 7 21.9585 nan 0.1000 3.0868
## 8 19.6549 nan 0.1000 2.3523
## 9 17.2967 nan 0.1000 2.1958
## 10 15.3565 nan 0.1000 1.9368
## 20 6.9850 nan 0.1000 0.3441
## 40 3.7130 nan 0.1000 -0.0266
## 60 3.0886 nan 0.1000 -0.0235
## 80 2.7270 nan 0.1000 -0.0253
## 100 2.4717 nan 0.1000 -0.0152
## 120 2.3085 nan 0.1000 -0.0102
## 140 2.1622 nan 0.1000 -0.0140
## 160 2.0465 nan 0.1000 -0.0107
## 180 1.9217 nan 0.1000 -0.0234
## 200 1.8410 nan 0.1000 -0.0215
## 220 1.7437 nan 0.1000 -0.0179
## 240 1.6534 nan 0.1000 -0.0188
## 260 1.5732 nan 0.1000 -0.0257
## 280 1.5114 nan 0.1000 -0.0117
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.4574 nan 0.1000 8.5511
## 2 43.6292 nan 0.1000 7.4864
## 3 38.3152 nan 0.1000 4.7751
## 4 33.0019 nan 0.1000 4.5809
## 5 28.3030 nan 0.1000 3.8723
## 6 24.6753 nan 0.1000 3.5918
## 7 22.1672 nan 0.1000 2.6385
## 8 19.7101 nan 0.1000 2.5135
## 9 17.5791 nan 0.1000 2.2540
## 10 15.6014 nan 0.1000 1.7681
## 20 7.3561 nan 0.1000 0.3211
## 40 4.0734 nan 0.1000 0.0111
## 60 3.2907 nan 0.1000 -0.0115
## 80 3.0237 nan 0.1000 -0.0054
## 100 2.7609 nan 0.1000 -0.0041
## 120 2.6178 nan 0.1000 -0.0357
## 140 2.4793 nan 0.1000 -0.0356
## 160 2.3521 nan 0.1000 -0.0151
## 180 2.2526 nan 0.1000 -0.0126
## 200 2.1392 nan 0.1000 -0.0085
## 220 2.0344 nan 0.1000 -0.0252
## 240 1.9688 nan 0.1000 -0.0214
## 260 1.9119 nan 0.1000 -0.0079
## 280 1.8396 nan 0.1000 -0.0153
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.9822 nan 0.1000 9.0965
## 2 44.4752 nan 0.1000 7.7664
## 3 37.7836 nan 0.1000 6.3289
## 4 32.8874 nan 0.1000 4.8919
## 5 28.9346 nan 0.1000 3.5495
## 6 25.2904 nan 0.1000 3.7228
## 7 22.1820 nan 0.1000 2.7445
## 8 19.5773 nan 0.1000 2.0650
## 9 17.3918 nan 0.1000 1.9651
## 10 15.4182 nan 0.1000 1.9007
## 20 7.4054 nan 0.1000 0.4873
## 40 3.9401 nan 0.1000 0.0317
## 60 3.4042 nan 0.1000 0.0036
## 80 3.1202 nan 0.1000 -0.0145
## 100 2.9249 nan 0.1000 -0.0159
## 120 2.8269 nan 0.1000 -0.0433
## 140 2.6982 nan 0.1000 -0.0334
## 160 2.5672 nan 0.1000 -0.0291
## 180 2.4792 nan 0.1000 -0.0078
## 200 2.3850 nan 0.1000 -0.0322
## 220 2.3127 nan 0.1000 -0.0097
## 240 2.2386 nan 0.1000 -0.0205
## 260 2.1881 nan 0.1000 -0.0046
## 280 2.1275 nan 0.1000 -0.0241
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.0240 nan 0.1000 9.7028
## 2 42.9325 nan 0.1000 6.7644
## 3 36.2273 nan 0.1000 5.6676
## 4 31.2167 nan 0.1000 5.5483
## 5 26.7668 nan 0.1000 5.0129
## 6 23.3127 nan 0.1000 3.3081
## 7 20.2583 nan 0.1000 2.9563
## 8 17.8008 nan 0.1000 2.5952
## 9 15.5352 nan 0.1000 2.0502
## 10 14.1459 nan 0.1000 1.1390
## 20 5.8211 nan 0.1000 0.3927
## 40 3.0564 nan 0.1000 0.0117
## 60 2.5176 nan 0.1000 -0.0211
## 80 2.2175 nan 0.1000 -0.0280
## 100 1.9533 nan 0.1000 -0.0098
## 120 1.7455 nan 0.1000 -0.0031
## 140 1.5479 nan 0.1000 -0.0128
## 160 1.4244 nan 0.1000 -0.0490
## 180 1.2917 nan 0.1000 -0.0039
## 200 1.1962 nan 0.1000 -0.0120
## 220 1.1048 nan 0.1000 -0.0234
## 240 1.0309 nan 0.1000 -0.0089
## 260 0.9776 nan 0.1000 -0.0045
## 280 0.9093 nan 0.1000 -0.0064
## 300 0.8666 nan 0.1000 -0.0092
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 50.9763 nan 0.1000 8.6060
## 2 43.6799 nan 0.1000 7.1181
## 3 36.9160 nan 0.1000 6.2173
## 4 31.6626 nan 0.1000 5.6476
## 5 27.2356 nan 0.1000 4.4415
## 6 23.3880 nan 0.1000 3.6540
## 7 20.2227 nan 0.1000 2.8091
## 8 17.6812 nan 0.1000 2.4228
## 9 15.5394 nan 0.1000 2.1536
## 10 13.5147 nan 0.1000 1.9567
## 20 5.4812 nan 0.1000 0.2823
## 40 3.0979 nan 0.1000 0.0123
## 60 2.6104 nan 0.1000 -0.0203
## 80 2.3769 nan 0.1000 -0.0026
## 100 2.1959 nan 0.1000 -0.0202
## 120 1.9909 nan 0.1000 -0.0311
## 140 1.8432 nan 0.1000 -0.0349
## 160 1.7461 nan 0.1000 -0.0151
## 180 1.6309 nan 0.1000 -0.0199
## 200 1.5354 nan 0.1000 -0.0150
## 220 1.4513 nan 0.1000 -0.0194
## 240 1.3796 nan 0.1000 -0.0290
## 260 1.2975 nan 0.1000 -0.0096
## 280 1.2348 nan 0.1000 -0.0036
## 300 1.1840 nan 0.1000 -0.0167
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 50.7784 nan 0.1000 8.5421
## 2 42.8726 nan 0.1000 8.2604
## 3 36.1711 nan 0.1000 5.7266
## 4 31.2270 nan 0.1000 4.8707
## 5 26.7869 nan 0.1000 4.1884
## 6 23.3459 nan 0.1000 3.6392
## 7 20.4667 nan 0.1000 2.9572
## 8 18.0280 nan 0.1000 2.2395
## 9 15.9711 nan 0.1000 1.9120
## 10 14.1127 nan 0.1000 1.7284
## 20 5.8302 nan 0.1000 0.3171
## 40 3.4830 nan 0.1000 -0.0135
## 60 2.9413 nan 0.1000 -0.0300
## 80 2.6941 nan 0.1000 0.0056
## 100 2.5068 nan 0.1000 -0.0259
## 120 2.3335 nan 0.1000 -0.0226
## 140 2.2173 nan 0.1000 -0.0089
## 160 2.1126 nan 0.1000 -0.0125
## 180 2.0073 nan 0.1000 -0.0206
## 200 1.9139 nan 0.1000 -0.0151
## 220 1.8036 nan 0.1000 -0.0129
## 240 1.7371 nan 0.1000 -0.0438
## 260 1.6486 nan 0.1000 -0.0286
## 280 1.5572 nan 0.1000 -0.0155
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.8825 nan 0.0010 0.0755
## 2 62.7997 nan 0.0010 0.0820
## 3 62.7120 nan 0.0010 0.0796
## 4 62.6326 nan 0.0010 0.0841
## 5 62.5551 nan 0.0010 0.0738
## 6 62.4732 nan 0.0010 0.0726
## 7 62.3862 nan 0.0010 0.0820
## 8 62.3102 nan 0.0010 0.0814
## 9 62.2316 nan 0.0010 0.0783
## 10 62.1526 nan 0.0010 0.0818
## 20 61.3510 nan 0.0010 0.0770
## 40 59.8002 nan 0.0010 0.0694
## 60 58.3362 nan 0.0010 0.0768
## 80 56.9166 nan 0.0010 0.0689
## 100 55.5448 nan 0.0010 0.0647
## 120 54.2010 nan 0.0010 0.0642
## 140 52.9172 nan 0.0010 0.0648
## 160 51.6832 nan 0.0010 0.0575
## 180 50.4842 nan 0.0010 0.0533
## 200 49.3385 nan 0.0010 0.0500
## 220 48.2520 nan 0.0010 0.0476
## 240 47.1634 nan 0.0010 0.0495
## 260 46.1139 nan 0.0010 0.0488
## 280 45.1019 nan 0.0010 0.0446
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.8828 nan 0.0010 0.0751
## 2 62.7995 nan 0.0010 0.0753
## 3 62.7190 nan 0.0010 0.0784
## 4 62.6363 nan 0.0010 0.0811
## 5 62.5592 nan 0.0010 0.0777
## 6 62.4789 nan 0.0010 0.0788
## 7 62.3993 nan 0.0010 0.0792
## 8 62.3167 nan 0.0010 0.0809
## 9 62.2358 nan 0.0010 0.0825
## 10 62.1565 nan 0.0010 0.0753
## 20 61.3640 nan 0.0010 0.0788
## 40 59.8455 nan 0.0010 0.0707
## 60 58.3551 nan 0.0010 0.0687
## 80 56.9296 nan 0.0010 0.0692
## 100 55.5428 nan 0.0010 0.0552
## 120 54.2217 nan 0.0010 0.0657
## 140 52.9408 nan 0.0010 0.0657
## 160 51.6988 nan 0.0010 0.0570
## 180 50.5058 nan 0.0010 0.0579
## 200 49.3670 nan 0.0010 0.0550
## 220 48.2158 nan 0.0010 0.0528
## 240 47.1228 nan 0.0010 0.0517
## 260 46.0582 nan 0.0010 0.0500
## 280 45.0281 nan 0.0010 0.0468
## 300 44.0362 nan 0.0010 0.0452
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.8894 nan 0.0010 0.0834
## 2 62.8133 nan 0.0010 0.0824
## 3 62.7347 nan 0.0010 0.0792
## 4 62.6511 nan 0.0010 0.0779
## 5 62.5693 nan 0.0010 0.0762
## 6 62.4917 nan 0.0010 0.0856
## 7 62.4120 nan 0.0010 0.0830
## 8 62.3332 nan 0.0010 0.0747
## 9 62.2501 nan 0.0010 0.0834
## 10 62.1663 nan 0.0010 0.0815
## 20 61.3436 nan 0.0010 0.0844
## 40 59.8015 nan 0.0010 0.0829
## 60 58.3296 nan 0.0010 0.0727
## 80 56.8960 nan 0.0010 0.0727
## 100 55.4990 nan 0.0010 0.0656
## 120 54.1787 nan 0.0010 0.0626
## 140 52.8811 nan 0.0010 0.0620
## 160 51.6447 nan 0.0010 0.0635
## 180 50.4761 nan 0.0010 0.0585
## 200 49.3177 nan 0.0010 0.0582
## 220 48.1919 nan 0.0010 0.0590
## 240 47.0805 nan 0.0010 0.0536
## 260 46.0283 nan 0.0010 0.0453
## 280 44.9761 nan 0.0010 0.0454
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.8705 nan 0.0010 0.0940
## 2 62.7711 nan 0.0010 0.0987
## 3 62.6753 nan 0.0010 0.1006
## 4 62.5810 nan 0.0010 0.0927
## 5 62.4932 nan 0.0010 0.0833
## 6 62.3977 nan 0.0010 0.0958
## 7 62.3018 nan 0.0010 0.0961
## 8 62.2118 nan 0.0010 0.0940
## 9 62.1195 nan 0.0010 0.0897
## 10 62.0211 nan 0.0010 0.0884
## 20 61.0489 nan 0.0010 0.0916
## 40 59.1646 nan 0.0010 0.0865
## 60 57.3869 nan 0.0010 0.0937
## 80 55.6710 nan 0.0010 0.0769
## 100 54.0054 nan 0.0010 0.0779
## 120 52.3845 nan 0.0010 0.0796
## 140 50.8262 nan 0.0010 0.0748
## 160 49.3052 nan 0.0010 0.0755
## 180 47.8376 nan 0.0010 0.0756
## 200 46.4600 nan 0.0010 0.0652
## 220 45.1119 nan 0.0010 0.0592
## 240 43.7933 nan 0.0010 0.0608
## 260 42.5485 nan 0.0010 0.0559
## 280 41.3122 nan 0.0010 0.0547
## 300 40.1469 nan 0.0010 0.0580
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.8680 nan 0.0010 0.0844
## 2 62.7739 nan 0.0010 0.0947
## 3 62.6754 nan 0.0010 0.0945
## 4 62.5748 nan 0.0010 0.0949
## 5 62.4805 nan 0.0010 0.0982
## 6 62.3797 nan 0.0010 0.0966
## 7 62.2897 nan 0.0010 0.0969
## 8 62.1902 nan 0.0010 0.0962
## 9 62.0904 nan 0.0010 0.0881
## 10 61.9928 nan 0.0010 0.0969
## 20 61.0198 nan 0.0010 0.0923
## 40 59.1671 nan 0.0010 0.0861
## 60 57.4029 nan 0.0010 0.0832
## 80 55.7065 nan 0.0010 0.0812
## 100 54.0255 nan 0.0010 0.0818
## 120 52.4131 nan 0.0010 0.0740
## 140 50.8422 nan 0.0010 0.0732
## 160 49.3551 nan 0.0010 0.0751
## 180 47.8968 nan 0.0010 0.0816
## 200 46.5059 nan 0.0010 0.0666
## 220 45.1002 nan 0.0010 0.0657
## 240 43.8084 nan 0.0010 0.0679
## 260 42.5300 nan 0.0010 0.0564
## 280 41.3304 nan 0.0010 0.0595
## 300 40.1824 nan 0.0010 0.0553
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.8673 nan 0.0010 0.1020
## 2 62.7661 nan 0.0010 0.0997
## 3 62.6578 nan 0.0010 0.1055
## 4 62.5510 nan 0.0010 0.0944
## 5 62.4572 nan 0.0010 0.0942
## 6 62.3576 nan 0.0010 0.1012
## 7 62.2729 nan 0.0010 0.0880
## 8 62.1791 nan 0.0010 0.0852
## 9 62.0808 nan 0.0010 0.0947
## 10 61.9835 nan 0.0010 0.0890
## 20 61.0357 nan 0.0010 0.0837
## 40 59.2085 nan 0.0010 0.0855
## 60 57.4224 nan 0.0010 0.0791
## 80 55.6938 nan 0.0010 0.0783
## 100 54.0295 nan 0.0010 0.0810
## 120 52.4082 nan 0.0010 0.0681
## 140 50.8936 nan 0.0010 0.0789
## 160 49.3878 nan 0.0010 0.0733
## 180 47.9130 nan 0.0010 0.0684
## 200 46.5216 nan 0.0010 0.0582
## 220 45.1630 nan 0.0010 0.0646
## 240 43.8802 nan 0.0010 0.0653
## 260 42.5901 nan 0.0010 0.0728
## 280 41.3988 nan 0.0010 0.0537
## 300 40.2071 nan 0.0010 0.0602
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.8576 nan 0.0010 0.1126
## 2 62.7556 nan 0.0010 0.0988
## 3 62.6522 nan 0.0010 0.1084
## 4 62.5466 nan 0.0010 0.1049
## 5 62.4423 nan 0.0010 0.1117
## 6 62.3333 nan 0.0010 0.1007
## 7 62.2282 nan 0.0010 0.1070
## 8 62.1231 nan 0.0010 0.0948
## 9 62.0199 nan 0.0010 0.0970
## 10 61.9177 nan 0.0010 0.0973
## 20 60.8747 nan 0.0010 0.1074
## 40 58.8721 nan 0.0010 0.1054
## 60 56.9608 nan 0.0010 0.0946
## 80 55.1109 nan 0.0010 0.0970
## 100 53.3344 nan 0.0010 0.0834
## 120 51.6122 nan 0.0010 0.0824
## 140 49.9604 nan 0.0010 0.0768
## 160 48.3568 nan 0.0010 0.0776
## 180 46.8296 nan 0.0010 0.0732
## 200 45.3755 nan 0.0010 0.0658
## 220 43.9424 nan 0.0010 0.0630
## 240 42.5839 nan 0.0010 0.0692
## 260 41.2639 nan 0.0010 0.0581
## 280 39.9943 nan 0.0010 0.0656
## 300 38.7702 nan 0.0010 0.0564
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.8565 nan 0.0010 0.1032
## 2 62.7514 nan 0.0010 0.1020
## 3 62.6455 nan 0.0010 0.1107
## 4 62.5387 nan 0.0010 0.1094
## 5 62.4388 nan 0.0010 0.1019
## 6 62.3373 nan 0.0010 0.0906
## 7 62.2367 nan 0.0010 0.1054
## 8 62.1247 nan 0.0010 0.1081
## 9 62.0194 nan 0.0010 0.0936
## 10 61.9096 nan 0.0010 0.1033
## 20 60.8853 nan 0.0010 0.1056
## 40 58.8871 nan 0.0010 0.1048
## 60 56.9849 nan 0.0010 0.0974
## 80 55.1396 nan 0.0010 0.0847
## 100 53.3622 nan 0.0010 0.0845
## 120 51.6691 nan 0.0010 0.0826
## 140 50.0052 nan 0.0010 0.0831
## 160 48.4000 nan 0.0010 0.0728
## 180 46.8521 nan 0.0010 0.0773
## 200 45.3608 nan 0.0010 0.0681
## 220 43.9282 nan 0.0010 0.0719
## 240 42.5511 nan 0.0010 0.0588
## 260 41.2406 nan 0.0010 0.0664
## 280 39.9618 nan 0.0010 0.0665
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.8594 nan 0.0010 0.1052
## 2 62.7608 nan 0.0010 0.0983
## 3 62.6549 nan 0.0010 0.1057
## 4 62.5509 nan 0.0010 0.0922
## 5 62.4491 nan 0.0010 0.0958
## 6 62.3442 nan 0.0010 0.0981
## 7 62.2430 nan 0.0010 0.0977
## 8 62.1394 nan 0.0010 0.0973
## 9 62.0406 nan 0.0010 0.1024
## 10 61.9395 nan 0.0010 0.0962
## 20 60.9170 nan 0.0010 0.1024
## 40 58.9414 nan 0.0010 0.0995
## 60 57.0164 nan 0.0010 0.0958
## 80 55.1626 nan 0.0010 0.1011
## 100 53.3940 nan 0.0010 0.0801
## 120 51.6518 nan 0.0010 0.0868
## 140 50.0027 nan 0.0010 0.0793
## 160 48.4367 nan 0.0010 0.0702
## 180 46.9079 nan 0.0010 0.0752
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## 220 44.0047 nan 0.0010 0.0658
## 240 42.6343 nan 0.0010 0.0657
## 260 41.3117 nan 0.0010 0.0626
## 280 40.0529 nan 0.0010 0.0600
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.5443 nan 0.0050 0.4123
## 2 62.1294 nan 0.0050 0.4006
## 3 61.7335 nan 0.0050 0.3669
## 4 61.3477 nan 0.0050 0.3546
## 5 60.9614 nan 0.0050 0.3642
## 6 60.5639 nan 0.0050 0.3925
## 7 60.1730 nan 0.0050 0.3654
## 8 59.7960 nan 0.0050 0.3739
## 9 59.4221 nan 0.0050 0.3514
## 10 59.0802 nan 0.0050 0.3865
## 20 55.5907 nan 0.0050 0.3005
## 40 49.3976 nan 0.0050 0.2884
## 60 44.0938 nan 0.0050 0.2247
## 80 39.5131 nan 0.0050 0.1870
## 100 35.6475 nan 0.0050 0.1748
## 120 32.3292 nan 0.0050 0.1458
## 140 29.4415 nan 0.0050 0.1443
## 160 26.8428 nan 0.0050 0.1161
## 180 24.5699 nan 0.0050 0.0963
## 200 22.6197 nan 0.0050 0.0849
## 220 20.8872 nan 0.0050 0.0673
## 240 19.3118 nan 0.0050 0.0530
## 260 17.9601 nan 0.0050 0.0403
## 280 16.7009 nan 0.0050 0.0531
## 300 15.5695 nan 0.0050 0.0449
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.5641 nan 0.0050 0.3788
## 2 62.1480 nan 0.0050 0.3904
## 3 61.7432 nan 0.0050 0.3944
## 4 61.3425 nan 0.0050 0.3867
## 5 60.9461 nan 0.0050 0.4089
## 6 60.5935 nan 0.0050 0.3892
## 7 60.2017 nan 0.0050 0.3748
## 8 59.8305 nan 0.0050 0.3686
## 9 59.4536 nan 0.0050 0.3759
## 10 59.0793 nan 0.0050 0.3706
## 20 55.5030 nan 0.0050 0.3150
## 40 49.2377 nan 0.0050 0.2771
## 60 44.0767 nan 0.0050 0.2376
## 80 39.5487 nan 0.0050 0.1882
## 100 35.6495 nan 0.0050 0.1720
## 120 32.2667 nan 0.0050 0.1438
## 140 29.3976 nan 0.0050 0.1269
## 160 26.8571 nan 0.0050 0.1208
## 180 24.6034 nan 0.0050 0.0973
## 200 22.6600 nan 0.0050 0.0844
## 220 20.9145 nan 0.0050 0.0678
## 240 19.3771 nan 0.0050 0.0689
## 260 17.9575 nan 0.0050 0.0581
## 280 16.7070 nan 0.0050 0.0562
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.5366 nan 0.0050 0.4190
## 2 62.1282 nan 0.0050 0.4008
## 3 61.7460 nan 0.0050 0.4005
## 4 61.3495 nan 0.0050 0.4097
## 5 60.9497 nan 0.0050 0.3877
## 6 60.5882 nan 0.0050 0.3742
## 7 60.2097 nan 0.0050 0.3915
## 8 59.8656 nan 0.0050 0.3565
## 9 59.4731 nan 0.0050 0.3559
## 10 59.1312 nan 0.0050 0.3704
## 20 55.4832 nan 0.0050 0.3082
## 40 49.3011 nan 0.0050 0.2728
## 60 44.0469 nan 0.0050 0.1943
## 80 39.5898 nan 0.0050 0.1732
## 100 35.7208 nan 0.0050 0.1650
## 120 32.4376 nan 0.0050 0.1636
## 140 29.4313 nan 0.0050 0.1364
## 160 26.9361 nan 0.0050 0.1189
## 180 24.6651 nan 0.0050 0.0973
## 200 22.6274 nan 0.0050 0.0939
## 220 20.8496 nan 0.0050 0.0667
## 240 19.3040 nan 0.0050 0.0612
## 260 17.9533 nan 0.0050 0.0686
## 280 16.7214 nan 0.0050 0.0399
## 300 15.5876 nan 0.0050 0.0463
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.4947 nan 0.0050 0.4614
## 2 61.9637 nan 0.0050 0.5007
## 3 61.4507 nan 0.0050 0.5398
## 4 60.9477 nan 0.0050 0.4846
## 5 60.4779 nan 0.0050 0.4546
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## 7 59.5934 nan 0.0050 0.3916
## 8 59.1511 nan 0.0050 0.4381
## 9 58.6977 nan 0.0050 0.4291
## 10 58.2525 nan 0.0050 0.4170
## 20 53.8940 nan 0.0050 0.3731
## 40 46.3653 nan 0.0050 0.3225
## 60 40.0471 nan 0.0050 0.2577
## 80 34.7208 nan 0.0050 0.2374
## 100 30.3358 nan 0.0050 0.2124
## 120 26.5822 nan 0.0050 0.1632
## 140 23.3909 nan 0.0050 0.1274
## 160 20.7285 nan 0.0050 0.1225
## 180 18.5170 nan 0.0050 0.0729
## 200 16.6332 nan 0.0050 0.0893
## 220 14.9693 nan 0.0050 0.0789
## 240 13.5296 nan 0.0050 0.0460
## 260 12.2969 nan 0.0050 0.0581
## 280 11.2336 nan 0.0050 0.0470
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.5093 nan 0.0050 0.4526
## 2 62.0194 nan 0.0050 0.4762
## 3 61.5546 nan 0.0050 0.4784
## 4 61.0610 nan 0.0050 0.4403
## 5 60.5772 nan 0.0050 0.4973
## 6 60.1530 nan 0.0050 0.4457
## 7 59.6603 nan 0.0050 0.4744
## 8 59.1793 nan 0.0050 0.4457
## 9 58.7270 nan 0.0050 0.4476
## 10 58.2987 nan 0.0050 0.4349
## 20 53.9819 nan 0.0050 0.3783
## 40 46.6053 nan 0.0050 0.3651
## 60 40.4000 nan 0.0050 0.2748
## 80 35.0193 nan 0.0050 0.2172
## 100 30.5072 nan 0.0050 0.1893
## 120 26.7728 nan 0.0050 0.1865
## 140 23.5792 nan 0.0050 0.1606
## 160 20.9317 nan 0.0050 0.1017
## 180 18.6735 nan 0.0050 0.0994
## 200 16.7612 nan 0.0050 0.0628
## 220 15.0966 nan 0.0050 0.0751
## 240 13.6953 nan 0.0050 0.0478
## 260 12.4609 nan 0.0050 0.0433
## 280 11.4007 nan 0.0050 0.0411
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.4850 nan 0.0050 0.5007
## 2 61.9966 nan 0.0050 0.5226
## 3 61.5164 nan 0.0050 0.4424
## 4 61.0297 nan 0.0050 0.4936
## 5 60.5098 nan 0.0050 0.4665
## 6 60.0319 nan 0.0050 0.4727
## 7 59.5881 nan 0.0050 0.4474
## 8 59.0855 nan 0.0050 0.4730
## 9 58.6597 nan 0.0050 0.4520
## 10 58.2233 nan 0.0050 0.4178
## 20 53.9253 nan 0.0050 0.3854
## 40 46.3772 nan 0.0050 0.3394
## 60 40.0139 nan 0.0050 0.2881
## 80 34.7016 nan 0.0050 0.2193
## 100 30.4297 nan 0.0050 0.2051
## 120 26.6371 nan 0.0050 0.1938
## 140 23.5429 nan 0.0050 0.1365
## 160 20.8405 nan 0.0050 0.1210
## 180 18.5786 nan 0.0050 0.1054
## 200 16.6523 nan 0.0050 0.0855
## 220 15.0039 nan 0.0050 0.0672
## 240 13.6114 nan 0.0050 0.0577
## 260 12.4537 nan 0.0050 0.0512
## 280 11.4039 nan 0.0050 0.0396
## 300 10.4946 nan 0.0050 0.0418
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.4128 nan 0.0050 0.4943
## 2 61.8977 nan 0.0050 0.5442
## 3 61.3554 nan 0.0050 0.5066
## 4 60.8273 nan 0.0050 0.5228
## 5 60.3267 nan 0.0050 0.5455
## 6 59.8153 nan 0.0050 0.4762
## 7 59.3352 nan 0.0050 0.3969
## 8 58.8379 nan 0.0050 0.5082
## 9 58.3447 nan 0.0050 0.4775
## 10 57.8298 nan 0.0050 0.4571
## 20 53.3008 nan 0.0050 0.4280
## 40 45.3115 nan 0.0050 0.3569
## 60 38.6625 nan 0.0050 0.2861
## 80 33.2519 nan 0.0050 0.2217
## 100 28.6592 nan 0.0050 0.1889
## 120 24.8250 nan 0.0050 0.1562
## 140 21.5765 nan 0.0050 0.1501
## 160 18.8668 nan 0.0050 0.0999
## 180 16.6056 nan 0.0050 0.0961
## 200 14.7381 nan 0.0050 0.0872
## 220 13.1185 nan 0.0050 0.0589
## 240 11.7304 nan 0.0050 0.0537
## 260 10.5744 nan 0.0050 0.0512
## 280 9.5780 nan 0.0050 0.0439
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.4183 nan 0.0050 0.5485
## 2 61.8812 nan 0.0050 0.5069
## 3 61.3987 nan 0.0050 0.4995
## 4 60.9045 nan 0.0050 0.5417
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## 6 59.8698 nan 0.0050 0.5244
## 7 59.3998 nan 0.0050 0.4873
## 8 58.9069 nan 0.0050 0.5269
## 9 58.4164 nan 0.0050 0.4375
## 10 57.9417 nan 0.0050 0.5022
## 20 53.3145 nan 0.0050 0.4109
## 40 45.2842 nan 0.0050 0.3383
## 60 38.6862 nan 0.0050 0.3079
## 80 33.1948 nan 0.0050 0.2087
## 100 28.6031 nan 0.0050 0.2194
## 120 24.7347 nan 0.0050 0.1646
## 140 21.5871 nan 0.0050 0.1410
## 160 18.8401 nan 0.0050 0.1174
## 180 16.5791 nan 0.0050 0.1034
## 200 14.6648 nan 0.0050 0.0802
## 220 13.0324 nan 0.0050 0.0749
## 240 11.6701 nan 0.0050 0.0583
## 260 10.5020 nan 0.0050 0.0516
## 280 9.4895 nan 0.0050 0.0391
## 300 8.6464 nan 0.0050 0.0373
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.4536 nan 0.0050 0.4462
## 2 61.9527 nan 0.0050 0.5013
## 3 61.4442 nan 0.0050 0.4758
## 4 60.9338 nan 0.0050 0.5248
## 5 60.4440 nan 0.0050 0.4786
## 6 59.9355 nan 0.0050 0.4982
## 7 59.4205 nan 0.0050 0.4997
## 8 58.9278 nan 0.0050 0.5293
## 9 58.4334 nan 0.0050 0.4685
## 10 57.9502 nan 0.0050 0.5018
## 20 53.2793 nan 0.0050 0.4341
## 40 45.3563 nan 0.0050 0.3742
## 60 38.8085 nan 0.0050 0.3011
## 80 33.3215 nan 0.0050 0.2101
## 100 28.7079 nan 0.0050 0.2057
## 120 24.9046 nan 0.0050 0.1734
## 140 21.7300 nan 0.0050 0.1271
## 160 19.0929 nan 0.0050 0.1252
## 180 16.8283 nan 0.0050 0.1007
## 200 14.9044 nan 0.0050 0.0923
## 220 13.2995 nan 0.0050 0.0673
## 240 11.9476 nan 0.0050 0.0540
## 260 10.8039 nan 0.0050 0.0525
## 280 9.8041 nan 0.0050 0.0326
## 300 8.9619 nan 0.0050 0.0352
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.2832 nan 0.0100 0.7593
## 2 61.5187 nan 0.0100 0.8200
## 3 60.7769 nan 0.0100 0.8081
## 4 60.0223 nan 0.0100 0.7141
## 5 59.2521 nan 0.0100 0.7612
## 6 58.5466 nan 0.0100 0.7627
## 7 57.8354 nan 0.0100 0.7742
## 8 57.1303 nan 0.0100 0.7151
## 9 56.4053 nan 0.0100 0.7120
## 10 55.7060 nan 0.0100 0.6593
## 20 49.4151 nan 0.0100 0.5558
## 40 39.8415 nan 0.0100 0.3337
## 60 32.4128 nan 0.0100 0.2766
## 80 26.9617 nan 0.0100 0.2131
## 100 22.7569 nan 0.0100 0.1708
## 120 19.3905 nan 0.0100 0.1535
## 140 16.7968 nan 0.0100 0.1120
## 160 14.6390 nan 0.0100 0.1150
## 180 12.9285 nan 0.0100 0.0594
## 200 11.4895 nan 0.0100 0.0564
## 220 10.3511 nan 0.0100 0.0387
## 240 9.3876 nan 0.0100 0.0369
## 260 8.6016 nan 0.0100 0.0342
## 280 7.8811 nan 0.0100 0.0249
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.1303 nan 0.0100 0.7596
## 2 61.3487 nan 0.0100 0.8112
## 3 60.5803 nan 0.0100 0.7970
## 4 59.7105 nan 0.0100 0.7761
## 5 58.9840 nan 0.0100 0.7453
## 6 58.2432 nan 0.0100 0.6903
## 7 57.5329 nan 0.0100 0.7628
## 8 56.8050 nan 0.0100 0.6369
## 9 56.0654 nan 0.0100 0.6482
## 10 55.4168 nan 0.0100 0.6654
## 20 49.2600 nan 0.0100 0.5343
## 40 39.5204 nan 0.0100 0.3867
## 60 32.2767 nan 0.0100 0.2953
## 80 26.7313 nan 0.0100 0.2242
## 100 22.5613 nan 0.0100 0.1556
## 120 19.3024 nan 0.0100 0.1316
## 140 16.7283 nan 0.0100 0.0938
## 160 14.6220 nan 0.0100 0.0701
## 180 12.9381 nan 0.0100 0.0733
## 200 11.5245 nan 0.0100 0.0696
## 220 10.3718 nan 0.0100 0.0484
## 240 9.4058 nan 0.0100 0.0410
## 260 8.6053 nan 0.0100 0.0328
## 280 7.9061 nan 0.0100 0.0247
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.1296 nan 0.0100 0.7923
## 2 61.2609 nan 0.0100 0.7581
## 3 60.4651 nan 0.0100 0.7682
## 4 59.7437 nan 0.0100 0.6775
## 5 58.9556 nan 0.0100 0.6944
## 6 58.1828 nan 0.0100 0.6887
## 7 57.5069 nan 0.0100 0.6583
## 8 56.8514 nan 0.0100 0.7161
## 9 56.1973 nan 0.0100 0.6659
## 10 55.4878 nan 0.0100 0.6687
## 20 49.3368 nan 0.0100 0.5697
## 40 39.5327 nan 0.0100 0.3834
## 60 32.2825 nan 0.0100 0.2970
## 80 26.7848 nan 0.0100 0.2432
## 100 22.5853 nan 0.0100 0.1668
## 120 19.2204 nan 0.0100 0.1296
## 140 16.5807 nan 0.0100 0.1156
## 160 14.4859 nan 0.0100 0.0765
## 180 12.8220 nan 0.0100 0.0615
## 200 11.4859 nan 0.0100 0.0503
## 220 10.3590 nan 0.0100 0.0489
## 240 9.4267 nan 0.0100 0.0373
## 260 8.6299 nan 0.0100 0.0322
## 280 7.9595 nan 0.0100 0.0202
## 300 7.3913 nan 0.0100 0.0259
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.0265 nan 0.0100 0.9466
## 2 61.0405 nan 0.0100 0.9829
## 3 60.1110 nan 0.0100 0.9778
## 4 59.2260 nan 0.0100 0.7881
## 5 58.2664 nan 0.0100 0.8659
## 6 57.3761 nan 0.0100 0.9128
## 7 56.4714 nan 0.0100 0.8299
## 8 55.6599 nan 0.0100 0.7751
## 9 54.8167 nan 0.0100 0.8207
## 10 53.9728 nan 0.0100 0.8368
## 20 46.4957 nan 0.0100 0.6579
## 40 34.6954 nan 0.0100 0.4551
## 60 26.6026 nan 0.0100 0.3450
## 80 20.7573 nan 0.0100 0.2486
## 100 16.6759 nan 0.0100 0.1193
## 120 13.5909 nan 0.0100 0.1388
## 140 11.3341 nan 0.0100 0.0988
## 160 9.6004 nan 0.0100 0.0635
## 180 8.2673 nan 0.0100 0.0438
## 200 7.2993 nan 0.0100 0.0291
## 220 6.4684 nan 0.0100 0.0206
## 240 5.8224 nan 0.0100 0.0177
## 260 5.3056 nan 0.0100 0.0191
## 280 4.9022 nan 0.0100 0.0121
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.9812 nan 0.0100 0.8935
## 2 61.0941 nan 0.0100 0.9517
## 3 60.2356 nan 0.0100 0.9473
## 4 59.3107 nan 0.0100 0.8518
## 5 58.4150 nan 0.0100 0.9311
## 6 57.4737 nan 0.0100 0.9340
## 7 56.6270 nan 0.0100 0.8186
## 8 55.7824 nan 0.0100 0.8570
## 9 54.9617 nan 0.0100 0.8788
## 10 54.1662 nan 0.0100 0.7202
## 20 46.5075 nan 0.0100 0.7249
## 40 35.0839 nan 0.0100 0.4038
## 60 26.7777 nan 0.0100 0.3235
## 80 20.9176 nan 0.0100 0.2466
## 100 16.6498 nan 0.0100 0.1577
## 120 13.5756 nan 0.0100 0.1300
## 140 11.2711 nan 0.0100 0.0954
## 160 9.5958 nan 0.0100 0.0701
## 180 8.2889 nan 0.0100 0.0504
## 200 7.2850 nan 0.0100 0.0353
## 220 6.4869 nan 0.0100 0.0303
## 240 5.8473 nan 0.0100 0.0171
## 260 5.3383 nan 0.0100 0.0208
## 280 4.9322 nan 0.0100 0.0087
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.9835 nan 0.0100 0.9180
## 2 61.0386 nan 0.0100 0.9597
## 3 60.0669 nan 0.0100 0.8599
## 4 59.1528 nan 0.0100 0.9600
## 5 58.2846 nan 0.0100 0.8888
## 6 57.4729 nan 0.0100 0.8625
## 7 56.6703 nan 0.0100 0.7663
## 8 55.8268 nan 0.0100 0.7843
## 9 54.9574 nan 0.0100 0.8056
## 10 54.1580 nan 0.0100 0.7095
## 20 46.7363 nan 0.0100 0.6395
## 40 34.9272 nan 0.0100 0.4673
## 60 26.6414 nan 0.0100 0.3474
## 80 20.7724 nan 0.0100 0.2316
## 100 16.5789 nan 0.0100 0.1811
## 120 13.5580 nan 0.0100 0.1075
## 140 11.3287 nan 0.0100 0.0922
## 160 9.6847 nan 0.0100 0.0528
## 180 8.4003 nan 0.0100 0.0532
## 200 7.4142 nan 0.0100 0.0289
## 220 6.6162 nan 0.0100 0.0337
## 240 5.9880 nan 0.0100 0.0250
## 260 5.4691 nan 0.0100 0.0134
## 280 5.0700 nan 0.0100 0.0121
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.9718 nan 0.0100 1.0062
## 2 60.9290 nan 0.0100 1.0526
## 3 59.9158 nan 0.0100 0.9299
## 4 58.9358 nan 0.0100 1.0271
## 5 57.9589 nan 0.0100 0.9050
## 6 57.0092 nan 0.0100 0.9522
## 7 56.0500 nan 0.0100 0.9347
## 8 55.1699 nan 0.0100 0.9476
## 9 54.2829 nan 0.0100 0.9042
## 10 53.4473 nan 0.0100 0.7897
## 20 45.2667 nan 0.0100 0.7336
## 40 33.0906 nan 0.0100 0.4758
## 60 24.6686 nan 0.0100 0.3522
## 80 18.7450 nan 0.0100 0.2074
## 100 14.6281 nan 0.0100 0.1703
## 120 11.6418 nan 0.0100 0.1292
## 140 9.4984 nan 0.0100 0.0793
## 160 7.8969 nan 0.0100 0.0625
## 180 6.7307 nan 0.0100 0.0417
## 200 5.8369 nan 0.0100 0.0221
## 220 5.1777 nan 0.0100 0.0165
## 240 4.6481 nan 0.0100 0.0190
## 260 4.2370 nan 0.0100 0.0091
## 280 3.9336 nan 0.0100 0.0123
## 300 3.7106 nan 0.0100 0.0009
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.9523 nan 0.0100 1.0302
## 2 60.9029 nan 0.0100 1.0564
## 3 59.9044 nan 0.0100 0.9834
## 4 58.9090 nan 0.0100 0.9552
## 5 57.8715 nan 0.0100 1.0070
## 6 56.9527 nan 0.0100 0.9590
## 7 55.9839 nan 0.0100 1.0339
## 8 55.0795 nan 0.0100 0.8628
## 9 54.1876 nan 0.0100 0.8848
## 10 53.3092 nan 0.0100 0.8672
## 20 45.3455 nan 0.0100 0.7517
## 40 33.0585 nan 0.0100 0.4707
## 60 24.7653 nan 0.0100 0.3255
## 80 18.8183 nan 0.0100 0.2304
## 100 14.5638 nan 0.0100 0.1705
## 120 11.6369 nan 0.0100 0.1051
## 140 9.5104 nan 0.0100 0.0742
## 160 7.9323 nan 0.0100 0.0526
## 180 6.7641 nan 0.0100 0.0386
## 200 5.8887 nan 0.0100 0.0262
## 220 5.2114 nan 0.0100 0.0234
## 240 4.7267 nan 0.0100 0.0123
## 260 4.3584 nan 0.0100 0.0080
## 280 4.0592 nan 0.0100 0.0083
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.8541 nan 0.0100 1.0920
## 2 60.8349 nan 0.0100 1.0603
## 3 59.8210 nan 0.0100 1.0862
## 4 58.8215 nan 0.0100 0.9297
## 5 57.8793 nan 0.0100 0.9705
## 6 56.9583 nan 0.0100 0.9763
## 7 56.0094 nan 0.0100 0.8294
## 8 55.0675 nan 0.0100 0.8296
## 9 54.1923 nan 0.0100 0.9959
## 10 53.2587 nan 0.0100 0.9622
## 20 45.3744 nan 0.0100 0.6940
## 40 33.1086 nan 0.0100 0.5425
## 60 24.7941 nan 0.0100 0.3704
## 80 18.9226 nan 0.0100 0.2100
## 100 14.7729 nan 0.0100 0.1807
## 120 11.8164 nan 0.0100 0.1178
## 140 9.6831 nan 0.0100 0.0905
## 160 8.1212 nan 0.0100 0.0601
## 180 6.9524 nan 0.0100 0.0441
## 200 6.0679 nan 0.0100 0.0260
## 220 5.4396 nan 0.0100 0.0194
## 240 4.9613 nan 0.0100 0.0150
## 260 4.5627 nan 0.0100 0.0107
## 280 4.2839 nan 0.0100 0.0088
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.9560 nan 0.0500 3.9126
## 2 55.4464 nan 0.0500 3.6260
## 3 52.3374 nan 0.0500 3.0506
## 4 49.1235 nan 0.0500 2.8797
## 5 46.4583 nan 0.0500 2.7381
## 6 43.8846 nan 0.0500 2.4026
## 7 41.7459 nan 0.0500 2.0138
## 8 39.3495 nan 0.0500 1.6954
## 9 37.1760 nan 0.0500 2.0041
## 10 35.3079 nan 0.0500 1.8952
## 20 22.1902 nan 0.0500 0.7900
## 40 11.1745 nan 0.0500 0.1686
## 60 7.1261 nan 0.0500 0.1005
## 80 5.4424 nan 0.0500 0.0385
## 100 4.5119 nan 0.0500 0.0078
## 120 4.0047 nan 0.0500 0.0059
## 140 3.7283 nan 0.0500 -0.0052
## 160 3.5990 nan 0.0500 -0.0099
## 180 3.4927 nan 0.0500 0.0002
## 200 3.3995 nan 0.0500 -0.0073
## 220 3.3365 nan 0.0500 -0.0179
## 240 3.2969 nan 0.0500 -0.0105
## 260 3.2312 nan 0.0500 -0.0082
## 280 3.1758 nan 0.0500 -0.0047
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.2009 nan 0.0500 3.8766
## 2 55.4665 nan 0.0500 3.8148
## 3 52.1513 nan 0.0500 3.2395
## 4 48.8655 nan 0.0500 3.0102
## 5 46.2337 nan 0.0500 2.8353
## 6 43.5528 nan 0.0500 2.2328
## 7 40.9806 nan 0.0500 2.1283
## 8 38.9168 nan 0.0500 1.7609
## 9 36.8546 nan 0.0500 1.9170
## 10 34.8653 nan 0.0500 1.7486
## 20 22.0435 nan 0.0500 1.0976
## 40 11.0833 nan 0.0500 0.2799
## 60 7.1348 nan 0.0500 0.0754
## 80 5.3257 nan 0.0500 0.0205
## 100 4.4745 nan 0.0500 0.0176
## 120 3.9883 nan 0.0500 -0.0004
## 140 3.7704 nan 0.0500 -0.0411
## 160 3.6182 nan 0.0500 0.0006
## 180 3.5220 nan 0.0500 -0.0112
## 200 3.4582 nan 0.0500 -0.0087
## 220 3.3865 nan 0.0500 -0.0046
## 240 3.3348 nan 0.0500 -0.0080
## 260 3.2857 nan 0.0500 -0.0062
## 280 3.2513 nan 0.0500 -0.0066
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.8908 nan 0.0500 3.7807
## 2 55.3866 nan 0.0500 3.5097
## 3 52.2867 nan 0.0500 3.0657
## 4 49.0731 nan 0.0500 2.8417
## 5 46.3454 nan 0.0500 2.8423
## 6 43.7798 nan 0.0500 2.5147
## 7 41.5543 nan 0.0500 2.2554
## 8 39.4487 nan 0.0500 1.9483
## 9 37.3667 nan 0.0500 1.9094
## 10 35.7581 nan 0.0500 1.5139
## 20 22.4795 nan 0.0500 0.9439
## 40 11.5463 nan 0.0500 0.2498
## 60 7.3063 nan 0.0500 0.0996
## 80 5.5200 nan 0.0500 0.0676
## 100 4.6024 nan 0.0500 0.0100
## 120 4.1813 nan 0.0500 -0.0063
## 140 3.9967 nan 0.0500 -0.0198
## 160 3.8590 nan 0.0500 0.0011
## 180 3.7715 nan 0.0500 -0.0087
## 200 3.6948 nan 0.0500 -0.0062
## 220 3.6163 nan 0.0500 -0.0040
## 240 3.5500 nan 0.0500 -0.0053
## 260 3.4945 nan 0.0500 -0.0131
## 280 3.4488 nan 0.0500 -0.0020
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.4253 nan 0.0500 4.6966
## 2 53.9634 nan 0.0500 4.2847
## 3 49.6699 nan 0.0500 4.2125
## 4 45.8868 nan 0.0500 3.4409
## 5 42.5473 nan 0.0500 2.8862
## 6 39.4897 nan 0.0500 3.1276
## 7 36.8568 nan 0.0500 2.6366
## 8 34.3117 nan 0.0500 2.5762
## 9 31.9347 nan 0.0500 2.2020
## 10 29.8940 nan 0.0500 2.0525
## 20 16.2273 nan 0.0500 0.8444
## 40 7.1071 nan 0.0500 0.1970
## 60 4.5823 nan 0.0500 0.0107
## 80 3.7161 nan 0.0500 -0.0327
## 100 3.3541 nan 0.0500 -0.0088
## 120 3.1400 nan 0.0500 -0.0062
## 140 2.9777 nan 0.0500 -0.0189
## 160 2.8196 nan 0.0500 -0.0056
## 180 2.7033 nan 0.0500 -0.0013
## 200 2.5965 nan 0.0500 0.0001
## 220 2.4976 nan 0.0500 -0.0091
## 240 2.3981 nan 0.0500 -0.0184
## 260 2.3302 nan 0.0500 -0.0103
## 280 2.2701 nan 0.0500 -0.0029
## 300 2.1938 nan 0.0500 -0.0107
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.3477 nan 0.0500 4.9084
## 2 54.2934 nan 0.0500 4.1647
## 3 50.5579 nan 0.0500 3.7938
## 4 46.6384 nan 0.0500 3.8896
## 5 43.2614 nan 0.0500 3.7164
## 6 40.4865 nan 0.0500 2.7945
## 7 37.6723 nan 0.0500 2.6833
## 8 35.0591 nan 0.0500 2.7825
## 9 32.6547 nan 0.0500 2.2913
## 10 30.5994 nan 0.0500 1.8844
## 20 16.5453 nan 0.0500 0.8423
## 40 7.0239 nan 0.0500 0.1771
## 60 4.5198 nan 0.0500 0.0671
## 80 3.7063 nan 0.0500 -0.0013
## 100 3.4010 nan 0.0500 -0.0085
## 120 3.2212 nan 0.0500 -0.0199
## 140 3.0743 nan 0.0500 -0.0077
## 160 2.9758 nan 0.0500 -0.0244
## 180 2.8691 nan 0.0500 -0.0093
## 200 2.7814 nan 0.0500 -0.0059
## 220 2.6954 nan 0.0500 -0.0181
## 240 2.6443 nan 0.0500 -0.0172
## 260 2.5788 nan 0.0500 -0.0189
## 280 2.5264 nan 0.0500 -0.0149
## 300 2.4591 nan 0.0500 -0.0142
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.3862 nan 0.0500 4.5864
## 2 53.9667 nan 0.0500 4.4729
## 3 50.0131 nan 0.0500 3.6939
## 4 46.5729 nan 0.0500 3.4156
## 5 43.0388 nan 0.0500 3.3756
## 6 39.7824 nan 0.0500 3.1427
## 7 36.7714 nan 0.0500 2.7450
## 8 34.3757 nan 0.0500 2.5446
## 9 32.0169 nan 0.0500 2.0726
## 10 29.7659 nan 0.0500 2.0759
## 20 16.3807 nan 0.0500 0.7579
## 40 7.0608 nan 0.0500 0.1909
## 60 4.6775 nan 0.0500 0.0091
## 80 3.9415 nan 0.0500 0.0070
## 100 3.6294 nan 0.0500 -0.0017
## 120 3.4239 nan 0.0500 -0.0064
## 140 3.2621 nan 0.0500 -0.0209
## 160 3.1462 nan 0.0500 0.0000
## 180 3.0543 nan 0.0500 -0.0089
## 200 2.9797 nan 0.0500 -0.0066
## 220 2.9046 nan 0.0500 -0.0213
## 240 2.8206 nan 0.0500 -0.0272
## 260 2.7621 nan 0.0500 -0.0151
## 280 2.6814 nan 0.0500 -0.0102
## 300 2.6183 nan 0.0500 -0.0059
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.9247 nan 0.0500 5.4546
## 2 53.0553 nan 0.0500 4.3184
## 3 48.9382 nan 0.0500 4.3181
## 4 45.1291 nan 0.0500 3.4513
## 5 41.4989 nan 0.0500 3.2036
## 6 38.1482 nan 0.0500 3.1123
## 7 35.0341 nan 0.0500 2.4355
## 8 32.3123 nan 0.0500 2.7390
## 9 30.0885 nan 0.0500 2.4174
## 10 27.9827 nan 0.0500 2.1111
## 20 14.4365 nan 0.0500 0.8682
## 40 5.9348 nan 0.0500 0.1653
## 60 3.7934 nan 0.0500 0.0469
## 80 3.1225 nan 0.0500 -0.0145
## 100 2.8022 nan 0.0500 -0.0271
## 120 2.5658 nan 0.0500 -0.0200
## 140 2.4117 nan 0.0500 -0.0190
## 160 2.2681 nan 0.0500 -0.0055
## 180 2.1425 nan 0.0500 -0.0135
## 200 2.0489 nan 0.0500 -0.0078
## 220 1.9526 nan 0.0500 -0.0094
## 240 1.8625 nan 0.0500 -0.0199
## 260 1.7897 nan 0.0500 -0.0090
## 280 1.6920 nan 0.0500 -0.0026
## 300 1.6074 nan 0.0500 -0.0130
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.9036 nan 0.0500 4.9821
## 2 53.2876 nan 0.0500 4.6603
## 3 48.9071 nan 0.0500 3.3489
## 4 44.9750 nan 0.0500 4.0076
## 5 41.3711 nan 0.0500 3.4419
## 6 38.1743 nan 0.0500 3.3251
## 7 35.2947 nan 0.0500 2.9852
## 8 32.7600 nan 0.0500 2.5656
## 9 30.4111 nan 0.0500 2.3776
## 10 28.1840 nan 0.0500 2.2222
## 20 14.1924 nan 0.0500 0.7508
## 40 5.7796 nan 0.0500 0.1948
## 60 3.8388 nan 0.0500 0.0197
## 80 3.2693 nan 0.0500 -0.0056
## 100 3.0105 nan 0.0500 -0.0142
## 120 2.8090 nan 0.0500 -0.0040
## 140 2.6581 nan 0.0500 -0.0110
## 160 2.5307 nan 0.0500 -0.0066
## 180 2.4038 nan 0.0500 -0.0138
## 200 2.2980 nan 0.0500 -0.0203
## 220 2.2061 nan 0.0500 -0.0058
## 240 2.1266 nan 0.0500 -0.0030
## 260 2.0378 nan 0.0500 -0.0117
## 280 1.9636 nan 0.0500 -0.0215
## 300 1.8878 nan 0.0500 -0.0054
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.5635 nan 0.0500 5.5358
## 2 52.7612 nan 0.0500 4.1533
## 3 48.4721 nan 0.0500 4.3334
## 4 44.6635 nan 0.0500 3.9356
## 5 41.1353 nan 0.0500 3.4570
## 6 38.0138 nan 0.0500 3.2370
## 7 35.1633 nan 0.0500 2.4116
## 8 32.4939 nan 0.0500 2.3436
## 9 30.0283 nan 0.0500 2.1785
## 10 28.0504 nan 0.0500 1.8232
## 20 14.6118 nan 0.0500 0.6705
## 40 6.0048 nan 0.0500 0.1580
## 60 4.0608 nan 0.0500 0.0167
## 80 3.4826 nan 0.0500 -0.0108
## 100 3.2351 nan 0.0500 -0.0187
## 120 3.0370 nan 0.0500 -0.0069
## 140 2.9091 nan 0.0500 -0.0109
## 160 2.8012 nan 0.0500 -0.0030
## 180 2.6941 nan 0.0500 -0.0102
## 200 2.5856 nan 0.0500 -0.0099
## 220 2.4865 nan 0.0500 -0.0144
## 240 2.3915 nan 0.0500 -0.0164
## 260 2.2967 nan 0.0500 -0.0084
## 280 2.2343 nan 0.0500 -0.0148
## 300 2.1527 nan 0.0500 -0.0176
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.1894 nan 0.1000 8.1645
## 2 48.7712 nan 0.1000 6.0979
## 3 43.1874 nan 0.1000 5.6211
## 4 39.2351 nan 0.1000 3.3671
## 5 35.3567 nan 0.1000 3.8516
## 6 31.4599 nan 0.1000 3.4232
## 7 28.3864 nan 0.1000 2.7887
## 8 25.7380 nan 0.1000 2.4851
## 9 23.4115 nan 0.1000 1.9785
## 10 21.5754 nan 0.1000 1.8081
## 20 11.1188 nan 0.1000 0.5856
## 40 5.3030 nan 0.1000 0.0966
## 60 4.0478 nan 0.1000 0.0050
## 80 3.6946 nan 0.1000 -0.0089
## 100 3.4906 nan 0.1000 0.0024
## 120 3.3449 nan 0.1000 -0.0092
## 140 3.2623 nan 0.1000 -0.0240
## 160 3.1720 nan 0.1000 -0.0145
## 180 3.1180 nan 0.1000 -0.0177
## 200 3.0466 nan 0.1000 -0.0119
## 220 2.9882 nan 0.1000 -0.0202
## 240 2.9425 nan 0.1000 -0.0180
## 260 2.8880 nan 0.1000 -0.0119
## 280 2.8390 nan 0.1000 -0.0040
## 300 2.8089 nan 0.1000 -0.0156
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.2207 nan 0.1000 7.4919
## 2 49.3055 nan 0.1000 7.0614
## 3 44.1252 nan 0.1000 5.2466
## 4 39.0990 nan 0.1000 4.3420
## 5 35.3445 nan 0.1000 3.5938
## 6 32.2056 nan 0.1000 3.3937
## 7 29.2746 nan 0.1000 2.5903
## 8 26.6296 nan 0.1000 2.4777
## 9 24.0747 nan 0.1000 2.5288
## 10 22.0669 nan 0.1000 1.8022
## 20 11.4280 nan 0.1000 0.5227
## 40 5.5032 nan 0.1000 0.0536
## 60 4.2231 nan 0.1000 0.0032
## 80 3.8656 nan 0.1000 -0.0162
## 100 3.6289 nan 0.1000 -0.0257
## 120 3.5064 nan 0.1000 -0.0480
## 140 3.3960 nan 0.1000 -0.0164
## 160 3.3056 nan 0.1000 -0.0402
## 180 3.2261 nan 0.1000 -0.0099
## 200 3.1557 nan 0.1000 -0.0148
## 220 3.0827 nan 0.1000 -0.0052
## 240 3.0329 nan 0.1000 -0.0001
## 260 2.9908 nan 0.1000 -0.0213
## 280 2.9422 nan 0.1000 -0.0101
## 300 2.8850 nan 0.1000 -0.0082
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.7292 nan 0.1000 7.4132
## 2 48.9532 nan 0.1000 5.9585
## 3 43.1526 nan 0.1000 5.4617
## 4 39.0325 nan 0.1000 3.9653
## 5 34.7299 nan 0.1000 4.3476
## 6 31.3903 nan 0.1000 3.4092
## 7 28.3504 nan 0.1000 2.8893
## 8 25.9729 nan 0.1000 2.2227
## 9 23.8565 nan 0.1000 1.6207
## 10 21.9310 nan 0.1000 2.0034
## 20 11.2952 nan 0.1000 0.5602
## 40 5.6801 nan 0.1000 0.0951
## 60 4.3684 nan 0.1000 -0.0058
## 80 4.0509 nan 0.1000 -0.0023
## 100 3.8592 nan 0.1000 -0.0393
## 120 3.7005 nan 0.1000 -0.0297
## 140 3.6225 nan 0.1000 -0.0094
## 160 3.5269 nan 0.1000 0.0061
## 180 3.4300 nan 0.1000 -0.0104
## 200 3.3479 nan 0.1000 -0.0067
## 220 3.3077 nan 0.1000 -0.0331
## 240 3.2482 nan 0.1000 -0.0085
## 260 3.1953 nan 0.1000 -0.0066
## 280 3.1469 nan 0.1000 -0.0215
## 300 3.1032 nan 0.1000 -0.0313
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 54.3270 nan 0.1000 9.0819
## 2 47.1392 nan 0.1000 6.6043
## 3 39.9644 nan 0.1000 7.2879
## 4 34.3592 nan 0.1000 4.8366
## 5 29.5060 nan 0.1000 4.8310
## 6 25.7510 nan 0.1000 3.7869
## 7 23.0138 nan 0.1000 2.6363
## 8 20.4468 nan 0.1000 2.9760
## 9 18.0784 nan 0.1000 1.8520
## 10 16.0114 nan 0.1000 1.9658
## 20 7.2342 nan 0.1000 0.4119
## 40 3.7900 nan 0.1000 0.0074
## 60 3.1928 nan 0.1000 0.0253
## 80 2.9038 nan 0.1000 -0.0263
## 100 2.6707 nan 0.1000 -0.0268
## 120 2.4715 nan 0.1000 -0.0114
## 140 2.2991 nan 0.1000 -0.0244
## 160 2.1687 nan 0.1000 -0.0322
## 180 2.0346 nan 0.1000 -0.0112
## 200 1.9457 nan 0.1000 -0.0023
## 220 1.8656 nan 0.1000 -0.0071
## 240 1.7740 nan 0.1000 -0.0150
## 260 1.6980 nan 0.1000 -0.0094
## 280 1.6220 nan 0.1000 -0.0210
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 53.6831 nan 0.1000 9.0721
## 2 46.4280 nan 0.1000 7.0710
## 3 39.8552 nan 0.1000 7.3381
## 4 34.5378 nan 0.1000 5.3747
## 5 29.9505 nan 0.1000 4.4544
## 6 25.9402 nan 0.1000 3.5993
## 7 23.1706 nan 0.1000 2.9278
## 8 20.3547 nan 0.1000 2.7626
## 9 18.0146 nan 0.1000 2.4197
## 10 16.1002 nan 0.1000 1.7348
## 20 6.9855 nan 0.1000 0.2788
## 40 3.7808 nan 0.1000 -0.0154
## 60 3.2258 nan 0.1000 -0.0082
## 80 2.9397 nan 0.1000 -0.0210
## 100 2.7509 nan 0.1000 -0.0394
## 120 2.5953 nan 0.1000 -0.0192
## 140 2.4550 nan 0.1000 -0.0146
## 160 2.3373 nan 0.1000 -0.0175
## 180 2.2584 nan 0.1000 -0.0140
## 200 2.1437 nan 0.1000 -0.0220
## 220 2.0670 nan 0.1000 -0.0281
## 240 1.9765 nan 0.1000 -0.0002
## 260 1.9128 nan 0.1000 -0.0126
## 280 1.8364 nan 0.1000 -0.0091
## 300 1.7651 nan 0.1000 -0.0140
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 54.1603 nan 0.1000 9.7853
## 2 45.8323 nan 0.1000 7.3745
## 3 39.3094 nan 0.1000 6.3458
## 4 33.3852 nan 0.1000 5.8287
## 5 29.0606 nan 0.1000 4.2428
## 6 25.3724 nan 0.1000 3.6521
## 7 22.3883 nan 0.1000 2.8241
## 8 19.6891 nan 0.1000 2.7569
## 9 17.5758 nan 0.1000 2.0377
## 10 15.7740 nan 0.1000 1.8435
## 20 7.4286 nan 0.1000 0.2978
## 40 4.1604 nan 0.1000 -0.0267
## 60 3.5721 nan 0.1000 -0.0399
## 80 3.2842 nan 0.1000 -0.0379
## 100 3.1012 nan 0.1000 -0.0738
## 120 2.9069 nan 0.1000 -0.0128
## 140 2.7739 nan 0.1000 -0.0108
## 160 2.6722 nan 0.1000 -0.0226
## 180 2.5828 nan 0.1000 -0.0277
## 200 2.4907 nan 0.1000 -0.0163
## 220 2.3875 nan 0.1000 -0.0284
## 240 2.2929 nan 0.1000 -0.0152
## 260 2.2223 nan 0.1000 -0.0203
## 280 2.1446 nan 0.1000 -0.0220
## 300 2.0825 nan 0.1000 -0.0114
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.8156 nan 0.1000 10.5213
## 2 44.8592 nan 0.1000 7.9080
## 3 38.2722 nan 0.1000 6.6073
## 4 32.7236 nan 0.1000 5.9504
## 5 27.8298 nan 0.1000 4.7160
## 6 23.9039 nan 0.1000 3.5995
## 7 20.7601 nan 0.1000 3.0650
## 8 18.2769 nan 0.1000 2.4035
## 9 16.2765 nan 0.1000 1.7914
## 10 14.3314 nan 0.1000 2.0182
## 20 5.7663 nan 0.1000 0.3200
## 40 3.1898 nan 0.1000 -0.0166
## 60 2.7281 nan 0.1000 -0.0246
## 80 2.3428 nan 0.1000 -0.0151
## 100 2.0928 nan 0.1000 -0.0294
## 120 1.8788 nan 0.1000 -0.0201
## 140 1.6880 nan 0.1000 -0.0123
## 160 1.5617 nan 0.1000 -0.0189
## 180 1.4270 nan 0.1000 -0.0184
## 200 1.2965 nan 0.1000 -0.0212
## 220 1.1970 nan 0.1000 -0.0220
## 240 1.1205 nan 0.1000 -0.0156
## 260 1.0302 nan 0.1000 -0.0126
## 280 0.9657 nan 0.1000 -0.0097
## 300 0.8995 nan 0.1000 -0.0099
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 53.5023 nan 0.1000 9.7758
## 2 45.2671 nan 0.1000 8.8786
## 3 38.1544 nan 0.1000 7.1530
## 4 32.2568 nan 0.1000 5.1619
## 5 27.6907 nan 0.1000 4.6288
## 6 24.0925 nan 0.1000 3.4815
## 7 20.9987 nan 0.1000 2.9457
## 8 18.3045 nan 0.1000 2.6403
## 9 16.0962 nan 0.1000 2.3654
## 10 14.2805 nan 0.1000 1.6842
## 20 5.9841 nan 0.1000 0.3651
## 40 3.4505 nan 0.1000 0.0137
## 60 2.9644 nan 0.1000 -0.0216
## 80 2.6487 nan 0.1000 -0.0272
## 100 2.3888 nan 0.1000 -0.0368
## 120 2.2058 nan 0.1000 -0.0095
## 140 2.0607 nan 0.1000 -0.0310
## 160 1.9291 nan 0.1000 -0.0345
## 180 1.7701 nan 0.1000 -0.0175
## 200 1.6591 nan 0.1000 -0.0224
## 220 1.5634 nan 0.1000 -0.0205
## 240 1.4723 nan 0.1000 -0.0247
## 260 1.4070 nan 0.1000 -0.0203
## 280 1.3344 nan 0.1000 -0.0234
## 300 1.2593 nan 0.1000 -0.0111
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 53.1113 nan 0.1000 9.7405
## 2 44.6201 nan 0.1000 8.4768
## 3 37.5756 nan 0.1000 6.2506
## 4 32.4154 nan 0.1000 5.5917
## 5 27.7011 nan 0.1000 4.4278
## 6 23.7170 nan 0.1000 3.6125
## 7 20.6222 nan 0.1000 2.8187
## 8 17.9091 nan 0.1000 2.7193
## 9 15.9112 nan 0.1000 2.0140
## 10 14.0945 nan 0.1000 1.7599
## 20 5.8625 nan 0.1000 0.2498
## 40 3.5433 nan 0.1000 0.0199
## 60 3.0875 nan 0.1000 -0.0250
## 80 2.8222 nan 0.1000 -0.0201
## 100 2.5823 nan 0.1000 -0.0427
## 120 2.4317 nan 0.1000 -0.0483
## 140 2.2946 nan 0.1000 -0.0405
## 160 2.1290 nan 0.1000 -0.0229
## 180 1.9872 nan 0.1000 -0.0137
## 200 1.8534 nan 0.1000 -0.0134
## 220 1.7533 nan 0.1000 -0.0130
## 240 1.6620 nan 0.1000 -0.0158
## 260 1.5805 nan 0.1000 -0.0099
## 280 1.5122 nan 0.1000 -0.0156
## 300 1.4484 nan 0.1000 -0.0080
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.5714 nan 0.0010 0.0765
## 2 59.4889 nan 0.0010 0.0660
## 3 59.4069 nan 0.0010 0.0735
## 4 59.3334 nan 0.0010 0.0758
## 5 59.2577 nan 0.0010 0.0763
## 6 59.1820 nan 0.0010 0.0750
## 7 59.1121 nan 0.0010 0.0748
## 8 59.0301 nan 0.0010 0.0738
## 9 58.9530 nan 0.0010 0.0733
## 10 58.8731 nan 0.0010 0.0811
## 20 58.1518 nan 0.0010 0.0742
## 40 56.7037 nan 0.0010 0.0694
## 60 55.3138 nan 0.0010 0.0685
## 80 53.9618 nan 0.0010 0.0663
## 100 52.6756 nan 0.0010 0.0613
## 120 51.4299 nan 0.0010 0.0566
## 140 50.2703 nan 0.0010 0.0590
## 160 49.1187 nan 0.0010 0.0557
## 180 48.0066 nan 0.0010 0.0555
## 200 46.9486 nan 0.0010 0.0526
## 220 45.9101 nan 0.0010 0.0541
## 240 44.8878 nan 0.0010 0.0460
## 260 43.9263 nan 0.0010 0.0445
## 280 42.9937 nan 0.0010 0.0447
## 300 42.0759 nan 0.0010 0.0411
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.5788 nan 0.0010 0.0742
## 2 59.5058 nan 0.0010 0.0752
## 3 59.4326 nan 0.0010 0.0727
## 4 59.3584 nan 0.0010 0.0743
## 5 59.2874 nan 0.0010 0.0719
## 6 59.2162 nan 0.0010 0.0739
## 7 59.1371 nan 0.0010 0.0732
## 8 59.0673 nan 0.0010 0.0740
## 9 58.9950 nan 0.0010 0.0732
## 10 58.9181 nan 0.0010 0.0726
## 20 58.1898 nan 0.0010 0.0711
## 40 56.7483 nan 0.0010 0.0682
## 60 55.3633 nan 0.0010 0.0667
## 80 54.0276 nan 0.0010 0.0631
## 100 52.7573 nan 0.0010 0.0640
## 120 51.5615 nan 0.0010 0.0558
## 140 50.3498 nan 0.0010 0.0551
## 160 49.1852 nan 0.0010 0.0570
## 180 48.0943 nan 0.0010 0.0528
## 200 47.0333 nan 0.0010 0.0511
## 220 46.0055 nan 0.0010 0.0534
## 240 44.9920 nan 0.0010 0.0493
## 260 44.0198 nan 0.0010 0.0402
## 280 43.0598 nan 0.0010 0.0467
## 300 42.1469 nan 0.0010 0.0407
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.5681 nan 0.0010 0.0702
## 2 59.4927 nan 0.0010 0.0755
## 3 59.4211 nan 0.0010 0.0815
## 4 59.3580 nan 0.0010 0.0708
## 5 59.2801 nan 0.0010 0.0716
## 6 59.1989 nan 0.0010 0.0798
## 7 59.1289 nan 0.0010 0.0762
## 8 59.0489 nan 0.0010 0.0758
## 9 58.9711 nan 0.0010 0.0759
## 10 58.8950 nan 0.0010 0.0743
## 20 58.1650 nan 0.0010 0.0718
## 40 56.7193 nan 0.0010 0.0712
## 60 55.3498 nan 0.0010 0.0671
## 80 54.0319 nan 0.0010 0.0509
## 100 52.7360 nan 0.0010 0.0628
## 120 51.4889 nan 0.0010 0.0611
## 140 50.2933 nan 0.0010 0.0596
## 160 49.1561 nan 0.0010 0.0546
## 180 48.0278 nan 0.0010 0.0585
## 200 46.9688 nan 0.0010 0.0486
## 220 45.9148 nan 0.0010 0.0515
## 240 44.9133 nan 0.0010 0.0477
## 260 43.9302 nan 0.0010 0.0517
## 280 42.9541 nan 0.0010 0.0430
## 300 42.0399 nan 0.0010 0.0462
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.5599 nan 0.0010 0.0872
## 2 59.4716 nan 0.0010 0.0909
## 3 59.3815 nan 0.0010 0.0891
## 4 59.2960 nan 0.0010 0.0813
## 5 59.2043 nan 0.0010 0.0849
## 6 59.1155 nan 0.0010 0.0949
## 7 59.0204 nan 0.0010 0.0832
## 8 58.9256 nan 0.0010 0.0904
## 9 58.8387 nan 0.0010 0.0942
## 10 58.7488 nan 0.0010 0.0889
## 20 57.8567 nan 0.0010 0.0956
## 40 56.1372 nan 0.0010 0.0795
## 60 54.4342 nan 0.0010 0.0849
## 80 52.8150 nan 0.0010 0.0763
## 100 51.2411 nan 0.0010 0.0787
## 120 49.7433 nan 0.0010 0.0694
## 140 48.2853 nan 0.0010 0.0736
## 160 46.8812 nan 0.0010 0.0692
## 180 45.5423 nan 0.0010 0.0652
## 200 44.2322 nan 0.0010 0.0573
## 220 42.9570 nan 0.0010 0.0567
## 240 41.7240 nan 0.0010 0.0602
## 260 40.5274 nan 0.0010 0.0556
## 280 39.4111 nan 0.0010 0.0510
## 300 38.3169 nan 0.0010 0.0490
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.5576 nan 0.0010 0.0905
## 2 59.4684 nan 0.0010 0.0917
## 3 59.3722 nan 0.0010 0.0843
## 4 59.2781 nan 0.0010 0.0943
## 5 59.1898 nan 0.0010 0.0856
## 6 59.0984 nan 0.0010 0.0923
## 7 59.0122 nan 0.0010 0.0855
## 8 58.9184 nan 0.0010 0.0857
## 9 58.8309 nan 0.0010 0.0865
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.5658 nan 0.0010 0.0939
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.5470 nan 0.0010 0.0908
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.5537 nan 0.0010 0.0984
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.5513 nan 0.0010 0.1110
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.3025 nan 0.0050 0.3688
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.2661 nan 0.0050 0.3922
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.2640 nan 0.0050 0.3846
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.1847 nan 0.0050 0.4977
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.1783 nan 0.0050 0.4364
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.2243 nan 0.0050 0.4371
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.1471 nan 0.0050 0.4522
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.1459 nan 0.0050 0.4408
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.1654 nan 0.0050 0.4830
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.8503 nan 0.0100 0.7371
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.9315 nan 0.0100 0.7804
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.8695 nan 0.0100 0.7053
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.7597 nan 0.0100 0.8734
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.6951 nan 0.0100 1.0009
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.7840 nan 0.0100 0.9425
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.6934 nan 0.0100 0.9663
## 2 57.6878 nan 0.0100 0.9790
## 3 56.7059 nan 0.0100 1.0982
## 4 55.7813 nan 0.0100 0.9209
## 5 54.8446 nan 0.0100 0.8619
## 6 53.9289 nan 0.0100 0.8014
## 7 53.0513 nan 0.0100 0.9268
## 8 52.1992 nan 0.0100 0.9063
## 9 51.3464 nan 0.0100 0.8025
## 10 50.5034 nan 0.0100 0.7730
## 20 42.9669 nan 0.0100 0.6467
## 40 31.5783 nan 0.0100 0.4589
## 60 23.5904 nan 0.0100 0.3473
## 80 17.9990 nan 0.0100 0.2071
## 100 14.1539 nan 0.0100 0.1701
## 120 11.3130 nan 0.0100 0.1110
## 140 9.2651 nan 0.0100 0.0761
## 160 7.7626 nan 0.0100 0.0638
## 180 6.6730 nan 0.0100 0.0279
## 200 5.8247 nan 0.0100 0.0386
## 220 5.1549 nan 0.0100 0.0182
## 240 4.6707 nan 0.0100 0.0155
## 260 4.2734 nan 0.0100 0.0128
## 280 3.9623 nan 0.0100 0.0143
## 300 3.7172 nan 0.0100 0.0059
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.6774 nan 0.0100 0.9844
## 2 57.6782 nan 0.0100 0.9327
## 3 56.7397 nan 0.0100 0.9015
## 4 55.7948 nan 0.0100 1.0461
## 5 54.8234 nan 0.0100 0.8805
## 6 53.9229 nan 0.0100 0.9492
## 7 53.0343 nan 0.0100 0.7720
## 8 52.1536 nan 0.0100 0.8320
## 9 51.2699 nan 0.0100 0.8025
## 10 50.4389 nan 0.0100 0.8372
## 20 42.9050 nan 0.0100 0.6959
## 40 31.4256 nan 0.0100 0.4952
## 60 23.4013 nan 0.0100 0.2298
## 80 17.9653 nan 0.0100 0.1820
## 100 14.1099 nan 0.0100 0.1340
## 120 11.3123 nan 0.0100 0.0961
## 140 9.2883 nan 0.0100 0.0758
## 160 7.8166 nan 0.0100 0.0545
## 180 6.6861 nan 0.0100 0.0416
## 200 5.8556 nan 0.0100 0.0383
## 220 5.1912 nan 0.0100 0.0246
## 240 4.6930 nan 0.0100 0.0161
## 260 4.3214 nan 0.0100 0.0053
## 280 4.0395 nan 0.0100 0.0064
## 300 3.8086 nan 0.0100 0.0066
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.6064 nan 0.0100 0.8560
## 2 57.6024 nan 0.0100 0.9689
## 3 56.6807 nan 0.0100 0.9434
## 4 55.7853 nan 0.0100 0.9291
## 5 54.9180 nan 0.0100 0.8725
## 6 54.0584 nan 0.0100 0.8657
## 7 53.1538 nan 0.0100 0.9383
## 8 52.2965 nan 0.0100 0.8360
## 9 51.4440 nan 0.0100 0.8220
## 10 50.6101 nan 0.0100 0.7213
## 20 43.0651 nan 0.0100 0.6688
## 40 31.6880 nan 0.0100 0.4242
## 60 23.6973 nan 0.0100 0.2871
## 80 18.1670 nan 0.0100 0.1946
## 100 14.2736 nan 0.0100 0.1532
## 120 11.5861 nan 0.0100 0.0978
## 140 9.5584 nan 0.0100 0.0771
## 160 8.0359 nan 0.0100 0.0622
## 180 6.9369 nan 0.0100 0.0403
## 200 6.0977 nan 0.0100 0.0309
## 220 5.4603 nan 0.0100 0.0183
## 240 4.9941 nan 0.0100 0.0111
## 260 4.6054 nan 0.0100 0.0096
## 280 4.3446 nan 0.0100 0.0016
## 300 4.1289 nan 0.0100 0.0043
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.7986 nan 0.0500 3.8679
## 2 53.3147 nan 0.0500 2.1023
## 3 50.1649 nan 0.0500 3.2145
## 4 47.3472 nan 0.0500 2.7631
## 5 44.4748 nan 0.0500 2.6487
## 6 41.9895 nan 0.0500 2.3947
## 7 39.5767 nan 0.0500 2.3417
## 8 37.6887 nan 0.0500 1.8809
## 9 35.7839 nan 0.0500 1.9975
## 10 33.9672 nan 0.0500 1.7112
## 20 21.3141 nan 0.0500 0.7023
## 40 11.0635 nan 0.0500 0.2854
## 60 7.1167 nan 0.0500 0.0927
## 80 5.3513 nan 0.0500 0.0404
## 100 4.4498 nan 0.0500 0.0125
## 120 4.0179 nan 0.0500 0.0021
## 140 3.8013 nan 0.0500 -0.0036
## 160 3.6832 nan 0.0500 -0.0020
## 180 3.5935 nan 0.0500 -0.0066
## 200 3.5123 nan 0.0500 -0.0062
## 220 3.4427 nan 0.0500 -0.0022
## 240 3.3829 nan 0.0500 -0.0060
## 260 3.3260 nan 0.0500 -0.0037
## 280 3.2716 nan 0.0500 -0.0092
## 300 3.2232 nan 0.0500 -0.0054
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.1052 nan 0.0500 3.7811
## 2 52.7838 nan 0.0500 3.4427
## 3 49.9888 nan 0.0500 3.1056
## 4 47.1342 nan 0.0500 2.8877
## 5 44.7924 nan 0.0500 2.1100
## 6 42.4371 nan 0.0500 2.1411
## 7 40.1394 nan 0.0500 2.1918
## 8 37.9276 nan 0.0500 2.0878
## 9 36.0751 nan 0.0500 1.7324
## 10 34.3491 nan 0.0500 1.6561
## 20 21.5960 nan 0.0500 0.9516
## 40 11.0862 nan 0.0500 0.2998
## 60 7.1061 nan 0.0500 0.0958
## 80 5.2770 nan 0.0500 0.0629
## 100 4.4383 nan 0.0500 0.0037
## 120 3.9883 nan 0.0500 0.0076
## 140 3.7822 nan 0.0500 0.0044
## 160 3.6610 nan 0.0500 -0.0072
## 180 3.5677 nan 0.0500 -0.0096
## 200 3.4887 nan 0.0500 -0.0117
## 220 3.4273 nan 0.0500 -0.0050
## 240 3.3611 nan 0.0500 -0.0089
## 260 3.3133 nan 0.0500 -0.0127
## 280 3.2729 nan 0.0500 -0.0128
## 300 3.2229 nan 0.0500 -0.0113
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.5064 nan 0.0500 2.4904
## 2 53.2053 nan 0.0500 3.6197
## 3 49.9153 nan 0.0500 3.2209
## 4 46.8455 nan 0.0500 2.7264
## 5 44.4453 nan 0.0500 2.6662
## 6 42.1787 nan 0.0500 2.3988
## 7 39.8152 nan 0.0500 2.2722
## 8 37.6397 nan 0.0500 2.0297
## 9 35.6649 nan 0.0500 1.7540
## 10 33.8947 nan 0.0500 1.4580
## 20 21.2277 nan 0.0500 0.7368
## 40 10.9772 nan 0.0500 0.2418
## 60 7.1149 nan 0.0500 0.0837
## 80 5.3890 nan 0.0500 0.0414
## 100 4.5994 nan 0.0500 0.0190
## 120 4.1752 nan 0.0500 -0.0017
## 140 3.9478 nan 0.0500 0.0003
## 160 3.8239 nan 0.0500 0.0015
## 180 3.7375 nan 0.0500 -0.0019
## 200 3.6503 nan 0.0500 0.0024
## 220 3.5874 nan 0.0500 -0.0067
## 240 3.5257 nan 0.0500 -0.0045
## 260 3.4669 nan 0.0500 -0.0132
## 280 3.4213 nan 0.0500 -0.0068
## 300 3.3709 nan 0.0500 -0.0069
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.1620 nan 0.0500 5.0655
## 2 51.0959 nan 0.0500 3.9277
## 3 47.1748 nan 0.0500 3.6034
## 4 43.7815 nan 0.0500 3.0503
## 5 40.7692 nan 0.0500 3.1918
## 6 37.8439 nan 0.0500 2.8220
## 7 35.5383 nan 0.0500 2.4943
## 8 33.1914 nan 0.0500 2.2203
## 9 31.1688 nan 0.0500 1.7723
## 10 29.0892 nan 0.0500 2.0486
## 20 16.0024 nan 0.0500 0.8313
## 40 7.1533 nan 0.0500 0.1995
## 60 4.5544 nan 0.0500 0.0412
## 80 3.6875 nan 0.0500 0.0125
## 100 3.2757 nan 0.0500 -0.0281
## 120 3.0219 nan 0.0500 0.0019
## 140 2.8492 nan 0.0500 -0.0059
## 160 2.7393 nan 0.0500 -0.0034
## 180 2.6045 nan 0.0500 -0.0104
## 200 2.5132 nan 0.0500 -0.0121
## 220 2.4188 nan 0.0500 -0.0007
## 240 2.3272 nan 0.0500 -0.0054
## 260 2.2449 nan 0.0500 -0.0071
## 280 2.1794 nan 0.0500 -0.0070
## 300 2.1131 nan 0.0500 -0.0078
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.5142 nan 0.0500 4.2308
## 2 51.5200 nan 0.0500 4.3398
## 3 47.4252 nan 0.0500 4.4951
## 4 44.2181 nan 0.0500 3.5408
## 5 41.0811 nan 0.0500 3.1503
## 6 38.3071 nan 0.0500 2.7498
## 7 35.7095 nan 0.0500 2.5640
## 8 33.4049 nan 0.0500 2.4072
## 9 31.1691 nan 0.0500 1.8059
## 10 29.0447 nan 0.0500 1.8717
## 20 15.8205 nan 0.0500 0.7104
## 40 7.0577 nan 0.0500 0.1395
## 60 4.5449 nan 0.0500 0.0638
## 80 3.7821 nan 0.0500 0.0132
## 100 3.4644 nan 0.0500 -0.0007
## 120 3.2606 nan 0.0500 -0.0060
## 140 3.1329 nan 0.0500 0.0015
## 160 3.0010 nan 0.0500 -0.0138
## 180 2.9037 nan 0.0500 -0.0067
## 200 2.8164 nan 0.0500 -0.0071
## 220 2.7461 nan 0.0500 -0.0269
## 240 2.6728 nan 0.0500 -0.0121
## 260 2.6071 nan 0.0500 -0.0205
## 280 2.5368 nan 0.0500 -0.0034
## 300 2.4740 nan 0.0500 -0.0098
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.2258 nan 0.0500 4.5104
## 2 51.3597 nan 0.0500 4.1836
## 3 47.8141 nan 0.0500 3.6867
## 4 44.3092 nan 0.0500 3.8225
## 5 41.2351 nan 0.0500 3.1737
## 6 38.4684 nan 0.0500 2.8401
## 7 35.7428 nan 0.0500 2.5644
## 8 33.3835 nan 0.0500 2.4454
## 9 31.0325 nan 0.0500 2.0494
## 10 28.9473 nan 0.0500 2.0106
## 20 15.8430 nan 0.0500 0.8303
## 40 7.1832 nan 0.0500 0.1871
## 60 4.6657 nan 0.0500 0.0335
## 80 3.9353 nan 0.0500 0.0094
## 100 3.6434 nan 0.0500 -0.0053
## 120 3.4392 nan 0.0500 -0.0135
## 140 3.2833 nan 0.0500 -0.0112
## 160 3.1500 nan 0.0500 -0.0091
## 180 3.0611 nan 0.0500 -0.0139
## 200 2.9628 nan 0.0500 -0.0099
## 220 2.9036 nan 0.0500 -0.0066
## 240 2.8233 nan 0.0500 -0.0097
## 260 2.7619 nan 0.0500 -0.0150
## 280 2.7054 nan 0.0500 -0.0131
## 300 2.6551 nan 0.0500 -0.0094
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.0528 nan 0.0500 4.7554
## 2 50.7458 nan 0.0500 4.0408
## 3 46.6107 nan 0.0500 3.7203
## 4 42.9040 nan 0.0500 3.7333
## 5 39.5581 nan 0.0500 3.3377
## 6 36.6423 nan 0.0500 2.6218
## 7 33.8319 nan 0.0500 2.9567
## 8 31.1860 nan 0.0500 2.4240
## 9 28.7470 nan 0.0500 2.6280
## 10 26.7831 nan 0.0500 2.2524
## 20 13.9519 nan 0.0500 0.7861
## 40 5.6283 nan 0.0500 0.1213
## 60 3.6480 nan 0.0500 0.0297
## 80 3.0997 nan 0.0500 -0.0214
## 100 2.8050 nan 0.0500 -0.0044
## 120 2.6232 nan 0.0500 -0.0221
## 140 2.4542 nan 0.0500 -0.0077
## 160 2.3110 nan 0.0500 -0.0167
## 180 2.1943 nan 0.0500 -0.0032
## 200 2.0976 nan 0.0500 -0.0125
## 220 1.9886 nan 0.0500 -0.0125
## 240 1.8730 nan 0.0500 -0.0061
## 260 1.7637 nan 0.0500 -0.0138
## 280 1.6968 nan 0.0500 -0.0041
## 300 1.6241 nan 0.0500 -0.0135
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 54.7289 nan 0.0500 4.6093
## 2 50.2329 nan 0.0500 4.3619
## 3 46.3616 nan 0.0500 3.8176
## 4 42.6564 nan 0.0500 3.5858
## 5 39.1024 nan 0.0500 3.7746
## 6 36.2297 nan 0.0500 3.1421
## 7 33.4449 nan 0.0500 2.5422
## 8 30.7653 nan 0.0500 2.2533
## 9 28.4560 nan 0.0500 2.1769
## 10 26.5012 nan 0.0500 2.0048
## 20 13.6393 nan 0.0500 0.8258
## 40 5.7009 nan 0.0500 0.1683
## 60 3.7860 nan 0.0500 0.0192
## 80 3.2758 nan 0.0500 0.0005
## 100 3.0206 nan 0.0500 -0.0099
## 120 2.8367 nan 0.0500 -0.0159
## 140 2.6654 nan 0.0500 -0.0193
## 160 2.5615 nan 0.0500 -0.0119
## 180 2.4506 nan 0.0500 -0.0161
## 200 2.3620 nan 0.0500 -0.0071
## 220 2.2536 nan 0.0500 -0.0154
## 240 2.1633 nan 0.0500 -0.0130
## 260 2.0827 nan 0.0500 -0.0146
## 280 2.0156 nan 0.0500 -0.0122
## 300 1.9445 nan 0.0500 -0.0174
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 54.7315 nan 0.0500 4.6801
## 2 50.0436 nan 0.0500 4.5964
## 3 46.0848 nan 0.0500 3.4795
## 4 42.7370 nan 0.0500 3.4789
## 5 39.5424 nan 0.0500 3.2035
## 6 36.7130 nan 0.0500 2.5667
## 7 33.9377 nan 0.0500 2.8607
## 8 31.5607 nan 0.0500 2.6515
## 9 29.2278 nan 0.0500 2.3905
## 10 27.2331 nan 0.0500 1.9801
## 20 14.2619 nan 0.0500 0.8776
## 40 6.1707 nan 0.0500 0.1589
## 60 4.1949 nan 0.0500 0.0444
## 80 3.5735 nan 0.0500 -0.0006
## 100 3.2728 nan 0.0500 0.0010
## 120 3.0675 nan 0.0500 -0.0235
## 140 2.9141 nan 0.0500 -0.0081
## 160 2.7929 nan 0.0500 -0.0132
## 180 2.7006 nan 0.0500 -0.0133
## 200 2.5867 nan 0.0500 -0.0216
## 220 2.4892 nan 0.0500 -0.0070
## 240 2.3948 nan 0.0500 -0.0103
## 260 2.3370 nan 0.0500 -0.0155
## 280 2.2661 nan 0.0500 -0.0064
## 300 2.1946 nan 0.0500 -0.0132
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.6492 nan 0.1000 6.6088
## 2 46.7282 nan 0.1000 6.3337
## 3 41.9816 nan 0.1000 5.0165
## 4 38.0328 nan 0.1000 3.4439
## 5 33.2840 nan 0.1000 4.2033
## 6 30.3330 nan 0.1000 2.9563
## 7 27.4567 nan 0.1000 2.4438
## 8 25.0494 nan 0.1000 3.0866
## 9 22.7678 nan 0.1000 2.3257
## 10 21.2039 nan 0.1000 1.1158
## 20 10.9991 nan 0.1000 0.6453
## 40 5.4567 nan 0.1000 0.0209
## 60 4.1300 nan 0.1000 0.0071
## 80 3.7590 nan 0.1000 -0.0226
## 100 3.5752 nan 0.1000 -0.0096
## 120 3.4589 nan 0.1000 -0.0221
## 140 3.3778 nan 0.1000 -0.0104
## 160 3.2847 nan 0.1000 -0.0186
## 180 3.2048 nan 0.1000 -0.0134
## 200 3.1568 nan 0.1000 -0.0336
## 220 3.1043 nan 0.1000 -0.0215
## 240 3.0367 nan 0.1000 -0.0037
## 260 2.9780 nan 0.1000 -0.0049
## 280 2.9482 nan 0.1000 -0.0226
## 300 2.9005 nan 0.1000 -0.0133
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.1566 nan 0.1000 7.5262
## 2 46.0565 nan 0.1000 5.3690
## 3 41.2095 nan 0.1000 5.0009
## 4 37.0191 nan 0.1000 3.6095
## 5 33.2801 nan 0.1000 3.5941
## 6 30.5109 nan 0.1000 2.5915
## 7 27.1862 nan 0.1000 2.9254
## 8 24.8311 nan 0.1000 2.0353
## 9 22.9399 nan 0.1000 1.8004
## 10 20.8144 nan 0.1000 1.8479
## 20 10.7750 nan 0.1000 0.6284
## 40 5.3679 nan 0.1000 0.1061
## 60 4.2066 nan 0.1000 0.0161
## 80 3.8680 nan 0.1000 0.0016
## 100 3.6720 nan 0.1000 -0.0140
## 120 3.5252 nan 0.1000 -0.0160
## 140 3.4117 nan 0.1000 -0.0083
## 160 3.3112 nan 0.1000 -0.0184
## 180 3.2424 nan 0.1000 -0.0247
## 200 3.1727 nan 0.1000 -0.0141
## 220 3.1197 nan 0.1000 -0.0127
## 240 3.0537 nan 0.1000 -0.0066
## 260 2.9819 nan 0.1000 -0.0102
## 280 2.9507 nan 0.1000 -0.0147
## 300 2.9140 nan 0.1000 -0.0505
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.4166 nan 0.1000 6.7994
## 2 46.1015 nan 0.1000 5.6284
## 3 41.8985 nan 0.1000 4.4637
## 4 37.0611 nan 0.1000 4.6415
## 5 33.5874 nan 0.1000 3.7693
## 6 30.4580 nan 0.1000 3.0871
## 7 27.8549 nan 0.1000 2.6185
## 8 25.4735 nan 0.1000 2.3308
## 9 23.1769 nan 0.1000 2.2162
## 10 21.2951 nan 0.1000 1.6230
## 20 11.0563 nan 0.1000 0.3328
## 40 5.6066 nan 0.1000 0.0601
## 60 4.4037 nan 0.1000 0.0134
## 80 4.1298 nan 0.1000 -0.0320
## 100 3.9002 nan 0.1000 -0.0327
## 120 3.7311 nan 0.1000 -0.0031
## 140 3.5930 nan 0.1000 0.0006
## 160 3.4885 nan 0.1000 -0.0479
## 180 3.3940 nan 0.1000 -0.0182
## 200 3.3089 nan 0.1000 -0.0210
## 220 3.2389 nan 0.1000 -0.0025
## 240 3.1835 nan 0.1000 -0.0267
## 260 3.1374 nan 0.1000 -0.0381
## 280 3.0917 nan 0.1000 -0.0051
## 300 3.0165 nan 0.1000 -0.0164
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.9629 nan 0.1000 8.3524
## 2 45.1382 nan 0.1000 6.9513
## 3 39.4094 nan 0.1000 5.3773
## 4 34.0295 nan 0.1000 5.6664
## 5 29.6293 nan 0.1000 3.9833
## 6 26.0513 nan 0.1000 3.8766
## 7 22.5509 nan 0.1000 3.2369
## 8 20.4022 nan 0.1000 2.3937
## 9 18.6089 nan 0.1000 1.5739
## 10 16.9086 nan 0.1000 1.4503
## 20 7.2399 nan 0.1000 0.4464
## 40 3.8305 nan 0.1000 -0.0003
## 60 3.2032 nan 0.1000 -0.0018
## 80 2.8607 nan 0.1000 0.0023
## 100 2.6814 nan 0.1000 -0.0050
## 120 2.4544 nan 0.1000 -0.0102
## 140 2.3354 nan 0.1000 -0.0138
## 160 2.2178 nan 0.1000 -0.0169
## 180 2.1155 nan 0.1000 -0.0513
## 200 2.0189 nan 0.1000 -0.0409
## 220 1.9069 nan 0.1000 -0.0018
## 240 1.8171 nan 0.1000 -0.0157
## 260 1.7087 nan 0.1000 -0.0149
## 280 1.6325 nan 0.1000 -0.0142
## 300 1.5869 nan 0.1000 -0.0189
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 50.8131 nan 0.1000 8.4391
## 2 44.2397 nan 0.1000 7.2852
## 3 38.0332 nan 0.1000 6.6342
## 4 33.1136 nan 0.1000 4.5136
## 5 28.9646 nan 0.1000 4.3288
## 6 25.7153 nan 0.1000 3.1572
## 7 22.5713 nan 0.1000 2.7024
## 8 20.0624 nan 0.1000 2.1572
## 9 17.8230 nan 0.1000 1.9878
## 10 15.8480 nan 0.1000 2.1032
## 20 6.8749 nan 0.1000 0.3159
## 40 3.8476 nan 0.1000 0.0298
## 60 3.3034 nan 0.1000 -0.0185
## 80 3.0018 nan 0.1000 -0.0388
## 100 2.8162 nan 0.1000 -0.0311
## 120 2.6487 nan 0.1000 -0.0223
## 140 2.4891 nan 0.1000 -0.0240
## 160 2.3496 nan 0.1000 -0.0296
## 180 2.2497 nan 0.1000 -0.0091
## 200 2.1517 nan 0.1000 -0.0385
## 220 2.0746 nan 0.1000 -0.0192
## 240 2.0025 nan 0.1000 -0.0239
## 260 1.9303 nan 0.1000 -0.0100
## 280 1.8532 nan 0.1000 -0.0099
## 300 1.8108 nan 0.1000 -0.0180
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.0718 nan 0.1000 9.0098
## 2 44.3032 nan 0.1000 7.2810
## 3 37.9179 nan 0.1000 6.1666
## 4 33.0849 nan 0.1000 4.7564
## 5 28.6652 nan 0.1000 4.4392
## 6 24.9974 nan 0.1000 3.0720
## 7 22.1668 nan 0.1000 2.5081
## 8 19.8961 nan 0.1000 2.2252
## 9 17.6588 nan 0.1000 2.0929
## 10 15.8546 nan 0.1000 1.8732
## 20 7.6767 nan 0.1000 0.4151
## 40 4.1126 nan 0.1000 -0.0018
## 60 3.4913 nan 0.1000 -0.0534
## 80 3.1750 nan 0.1000 -0.0110
## 100 2.9718 nan 0.1000 -0.0087
## 120 2.7725 nan 0.1000 -0.0231
## 140 2.6348 nan 0.1000 -0.0116
## 160 2.4904 nan 0.1000 -0.0537
## 180 2.4163 nan 0.1000 -0.0117
## 200 2.3268 nan 0.1000 -0.0200
## 220 2.2621 nan 0.1000 -0.0176
## 240 2.1954 nan 0.1000 -0.0288
## 260 2.1083 nan 0.1000 -0.0063
## 280 2.0528 nan 0.1000 -0.0296
## 300 1.9942 nan 0.1000 -0.0138
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 50.5587 nan 0.1000 9.7414
## 2 42.2869 nan 0.1000 8.0399
## 3 36.0708 nan 0.1000 6.4832
## 4 30.9783 nan 0.1000 4.8195
## 5 26.6741 nan 0.1000 4.3823
## 6 23.0961 nan 0.1000 3.3437
## 7 20.5164 nan 0.1000 2.6984
## 8 17.8371 nan 0.1000 2.5631
## 9 15.6695 nan 0.1000 2.1614
## 10 14.0200 nan 0.1000 1.6510
## 20 5.5289 nan 0.1000 0.2164
## 40 3.2157 nan 0.1000 -0.0152
## 60 2.6939 nan 0.1000 -0.0356
## 80 2.3908 nan 0.1000 -0.0173
## 100 2.1639 nan 0.1000 -0.0088
## 120 1.9734 nan 0.1000 -0.0279
## 140 1.7908 nan 0.1000 -0.0098
## 160 1.6276 nan 0.1000 -0.0135
## 180 1.4960 nan 0.1000 -0.0162
## 200 1.3789 nan 0.1000 -0.0127
## 220 1.2674 nan 0.1000 -0.0095
## 240 1.1742 nan 0.1000 -0.0094
## 260 1.1089 nan 0.1000 -0.0212
## 280 1.0310 nan 0.1000 -0.0131
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 50.1099 nan 0.1000 8.5182
## 2 42.5973 nan 0.1000 7.6629
## 3 36.2938 nan 0.1000 6.2767
## 4 30.9391 nan 0.1000 4.8675
## 5 26.4392 nan 0.1000 4.0551
## 6 22.6503 nan 0.1000 3.3578
## 7 19.7384 nan 0.1000 3.0101
## 8 17.1336 nan 0.1000 2.2121
## 9 15.1410 nan 0.1000 1.9040
## 10 13.3741 nan 0.1000 1.6802
## 20 5.7223 nan 0.1000 0.2792
## 40 3.3763 nan 0.1000 0.0166
## 60 2.8494 nan 0.1000 -0.0130
## 80 2.5471 nan 0.1000 -0.0143
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## 140 1.9896 nan 0.1000 -0.0132
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## 180 1.7481 nan 0.1000 -0.0268
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## 280 1.3428 nan 0.1000 -0.0147
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 50.4312 nan 0.1000 8.6752
## 2 42.6376 nan 0.1000 7.3731
## 3 36.2058 nan 0.1000 6.3062
## 4 30.8987 nan 0.1000 4.4518
## 5 26.4779 nan 0.1000 4.5392
## 6 22.8577 nan 0.1000 3.1431
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## 8 17.7638 nan 0.1000 2.3428
## 9 15.8844 nan 0.1000 1.7483
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## 20 6.1019 nan 0.1000 0.3326
## 40 3.6255 nan 0.1000 -0.0207
## 60 3.1304 nan 0.1000 -0.0262
## 80 2.8411 nan 0.1000 -0.0353
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## 280 1.6111 nan 0.1000 -0.0146
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.5819 nan 0.0010 0.0840
## 2 61.5096 nan 0.0010 0.0818
## 3 61.4262 nan 0.0010 0.0785
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.5914 nan 0.0010 0.0776
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## 3 61.4362 nan 0.0010 0.0786
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.5883 nan 0.0010 0.0911
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## 3 61.4337 nan 0.0010 0.0785
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.5786 nan 0.0010 0.0976
## 2 61.4821 nan 0.0010 0.1000
## 3 61.3838 nan 0.0010 0.0965
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## 60 56.2415 nan 0.0010 0.0838
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## 180 46.9186 nan 0.0010 0.0718
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.5809 nan 0.0010 0.0903
## 2 61.4846 nan 0.0010 0.0962
## 3 61.3886 nan 0.0010 0.0904
## 4 61.2959 nan 0.0010 0.0896
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## 8 60.9245 nan 0.0010 0.0918
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## 20 59.8287 nan 0.0010 0.0873
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## 60 56.2879 nan 0.0010 0.0826
## 80 54.5968 nan 0.0010 0.0755
## 100 52.9811 nan 0.0010 0.0731
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## 140 49.8936 nan 0.0010 0.0659
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## 260 41.8072 nan 0.0010 0.0552
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.5766 nan 0.0010 0.0947
## 2 61.4815 nan 0.0010 0.0895
## 3 61.3844 nan 0.0010 0.0923
## 4 61.2856 nan 0.0010 0.0882
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## 40 57.9701 nan 0.0010 0.0889
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## 180 46.9266 nan 0.0010 0.0685
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.5697 nan 0.0010 0.1048
## 2 61.4666 nan 0.0010 0.0914
## 3 61.3595 nan 0.0010 0.0965
## 4 61.2535 nan 0.0010 0.1005
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## 80 53.9686 nan 0.0010 0.0891
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## 140 48.9301 nan 0.0010 0.0732
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## 180 45.8852 nan 0.0010 0.0632
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## 240 41.7068 nan 0.0010 0.0649
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.5685 nan 0.0010 0.1070
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## 3 61.3581 nan 0.0010 0.0959
## 4 61.2470 nan 0.0010 0.1093
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## 20 59.6389 nan 0.0010 0.1004
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## 140 48.9195 nan 0.0010 0.0728
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## 240 41.7031 nan 0.0010 0.0584
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.5752 nan 0.0010 0.0918
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## 3 61.3694 nan 0.0010 0.1002
## 4 61.2602 nan 0.0010 0.1075
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## 10 60.6509 nan 0.0010 0.1004
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## 60 55.8277 nan 0.0010 0.0923
## 80 54.0062 nan 0.0010 0.0846
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## 140 49.0017 nan 0.0010 0.0755
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.2783 nan 0.0050 0.3971
## 2 60.9015 nan 0.0050 0.3813
## 3 60.5092 nan 0.0050 0.3856
## 4 60.1043 nan 0.0050 0.3929
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## 20 54.4708 nan 0.0050 0.3298
## 40 48.4795 nan 0.0050 0.2272
## 60 43.3999 nan 0.0050 0.2242
## 80 39.0252 nan 0.0050 0.1907
## 100 35.2994 nan 0.0050 0.1672
## 120 31.9936 nan 0.0050 0.1323
## 140 29.1616 nan 0.0050 0.1132
## 160 26.6106 nan 0.0050 0.1247
## 180 24.4169 nan 0.0050 0.0942
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.2776 nan 0.0050 0.4051
## 2 60.8472 nan 0.0050 0.4086
## 3 60.4971 nan 0.0050 0.3753
## 4 60.1151 nan 0.0050 0.3856
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## 20 54.5136 nan 0.0050 0.3409
## 40 48.3992 nan 0.0050 0.2631
## 60 43.2174 nan 0.0050 0.2064
## 80 38.8553 nan 0.0050 0.1993
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## 140 28.8808 nan 0.0050 0.1301
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.2697 nan 0.0050 0.4000
## 2 60.8698 nan 0.0050 0.3973
## 3 60.4941 nan 0.0050 0.3869
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## 180 24.3734 nan 0.0050 0.0964
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.1830 nan 0.0050 0.4654
## 2 60.7067 nan 0.0050 0.4476
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## 20 52.8591 nan 0.0050 0.3744
## 40 45.5390 nan 0.0050 0.3305
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## 140 23.1070 nan 0.0050 0.1303
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.2151 nan 0.0050 0.4906
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## 20 52.8812 nan 0.0050 0.4257
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## 140 23.0655 nan 0.0050 0.1695
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## 180 18.1880 nan 0.0050 0.0998
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.1808 nan 0.0050 0.4316
## 2 60.7659 nan 0.0050 0.4467
## 3 60.2800 nan 0.0050 0.4696
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.1330 nan 0.0050 0.5091
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.1602 nan 0.0050 0.5203
## 2 60.6272 nan 0.0050 0.4734
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.1812 nan 0.0050 0.5249
## 2 60.6427 nan 0.0050 0.5566
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## 10 56.7449 nan 0.0050 0.4880
## 20 52.2989 nan 0.0050 0.3770
## 40 44.5443 nan 0.0050 0.3144
## 60 38.0760 nan 0.0050 0.2804
## 80 32.7469 nan 0.0050 0.2337
## 100 28.2767 nan 0.0050 0.1800
## 120 24.5490 nan 0.0050 0.1628
## 140 21.4318 nan 0.0050 0.1291
## 160 18.8402 nan 0.0050 0.1113
## 180 16.6371 nan 0.0050 0.1004
## 200 14.7545 nan 0.0050 0.0864
## 220 13.1628 nan 0.0050 0.0588
## 240 11.8352 nan 0.0050 0.0558
## 260 10.7015 nan 0.0050 0.0469
## 280 9.7504 nan 0.0050 0.0394
## 300 8.8867 nan 0.0050 0.0399
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.9075 nan 0.0100 0.7719
## 2 60.1673 nan 0.0100 0.7468
## 3 59.5463 nan 0.0100 0.5723
## 4 58.7560 nan 0.0100 0.7225
## 5 57.9708 nan 0.0100 0.7583
## 6 57.2757 nan 0.0100 0.6932
## 7 56.5644 nan 0.0100 0.7256
## 8 55.8482 nan 0.0100 0.6384
## 9 55.1271 nan 0.0100 0.6793
## 10 54.3911 nan 0.0100 0.6805
## 20 48.3563 nan 0.0100 0.5674
## 40 38.8726 nan 0.0100 0.3889
## 60 31.8602 nan 0.0100 0.2683
## 80 26.5258 nan 0.0100 0.2438
## 100 22.3383 nan 0.0100 0.1609
## 120 19.1096 nan 0.0100 0.1399
## 140 16.5319 nan 0.0100 0.1029
## 160 14.4076 nan 0.0100 0.0796
## 180 12.7199 nan 0.0100 0.0627
## 200 11.3448 nan 0.0100 0.0524
## 220 10.2360 nan 0.0100 0.0487
## 240 9.2499 nan 0.0100 0.0291
## 260 8.4665 nan 0.0100 0.0201
## 280 7.7449 nan 0.0100 0.0233
## 300 7.1551 nan 0.0100 0.0153
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.8810 nan 0.0100 0.7627
## 2 60.1482 nan 0.0100 0.7512
## 3 59.3725 nan 0.0100 0.7462
## 4 58.6402 nan 0.0100 0.7107
## 5 57.8960 nan 0.0100 0.7169
## 6 57.0935 nan 0.0100 0.7421
## 7 56.3902 nan 0.0100 0.6938
## 8 55.6675 nan 0.0100 0.7050
## 9 54.9745 nan 0.0100 0.6438
## 10 54.2310 nan 0.0100 0.6057
## 20 48.0588 nan 0.0100 0.5211
## 40 38.8998 nan 0.0100 0.4072
## 60 31.7863 nan 0.0100 0.2860
## 80 26.3803 nan 0.0100 0.2056
## 100 22.3363 nan 0.0100 0.1652
## 120 19.0405 nan 0.0100 0.1184
## 140 16.5070 nan 0.0100 0.1081
## 160 14.4062 nan 0.0100 0.0854
## 180 12.7706 nan 0.0100 0.0607
## 200 11.3793 nan 0.0100 0.0418
## 220 10.2799 nan 0.0100 0.0479
## 240 9.2884 nan 0.0100 0.0276
## 260 8.4693 nan 0.0100 0.0315
## 280 7.7748 nan 0.0100 0.0262
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.8597 nan 0.0100 0.8147
## 2 60.0959 nan 0.0100 0.7461
## 3 59.3919 nan 0.0100 0.7224
## 4 58.6858 nan 0.0100 0.7244
## 5 57.9643 nan 0.0100 0.6853
## 6 57.2426 nan 0.0100 0.7211
## 7 56.5006 nan 0.0100 0.7536
## 8 55.8149 nan 0.0100 0.7068
## 9 55.1183 nan 0.0100 0.6647
## 10 54.4176 nan 0.0100 0.6585
## 20 48.4027 nan 0.0100 0.5115
## 40 39.0868 nan 0.0100 0.3812
## 60 32.1574 nan 0.0100 0.3059
## 80 26.7617 nan 0.0100 0.2117
## 100 22.5894 nan 0.0100 0.1831
## 120 19.3037 nan 0.0100 0.1291
## 140 16.6352 nan 0.0100 0.1092
## 160 14.5590 nan 0.0100 0.0874
## 180 12.8659 nan 0.0100 0.0782
## 200 11.4568 nan 0.0100 0.0454
## 220 10.3422 nan 0.0100 0.0312
## 240 9.4072 nan 0.0100 0.0359
## 260 8.5922 nan 0.0100 0.0211
## 280 7.9025 nan 0.0100 0.0310
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.6783 nan 0.0100 0.9775
## 2 59.6991 nan 0.0100 1.0078
## 3 58.8186 nan 0.0100 0.9251
## 4 57.8871 nan 0.0100 0.8702
## 5 57.0640 nan 0.0100 0.8302
## 6 56.1475 nan 0.0100 0.9264
## 7 55.3042 nan 0.0100 0.8706
## 8 54.5235 nan 0.0100 0.7700
## 9 53.7293 nan 0.0100 0.7438
## 10 52.8892 nan 0.0100 0.7783
## 20 45.4102 nan 0.0100 0.6396
## 40 34.1831 nan 0.0100 0.4932
## 60 26.2401 nan 0.0100 0.3355
## 80 20.4376 nan 0.0100 0.2002
## 100 16.3589 nan 0.0100 0.1633
## 120 13.4698 nan 0.0100 0.1374
## 140 11.2289 nan 0.0100 0.0678
## 160 9.5090 nan 0.0100 0.0729
## 180 8.1624 nan 0.0100 0.0557
## 200 7.1380 nan 0.0100 0.0414
## 220 6.3042 nan 0.0100 0.0342
## 240 5.6461 nan 0.0100 0.0218
## 260 5.1547 nan 0.0100 0.0196
## 280 4.7323 nan 0.0100 0.0133
## 300 4.4152 nan 0.0100 0.0122
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.6727 nan 0.0100 0.9012
## 2 59.7470 nan 0.0100 0.9457
## 3 58.7797 nan 0.0100 0.8480
## 4 57.9443 nan 0.0100 0.9127
## 5 57.0877 nan 0.0100 0.8463
## 6 56.2177 nan 0.0100 0.8476
## 7 55.3670 nan 0.0100 0.8475
## 8 54.5885 nan 0.0100 0.7700
## 9 53.7459 nan 0.0100 0.8317
## 10 52.9154 nan 0.0100 0.7974
## 20 45.6334 nan 0.0100 0.6307
## 40 34.1926 nan 0.0100 0.5088
## 60 26.1890 nan 0.0100 0.3524
## 80 20.3465 nan 0.0100 0.2354
## 100 16.2138 nan 0.0100 0.1529
## 120 13.2621 nan 0.0100 0.0917
## 140 11.1115 nan 0.0100 0.0949
## 160 9.4296 nan 0.0100 0.0879
## 180 8.1197 nan 0.0100 0.0446
## 200 7.1164 nan 0.0100 0.0309
## 220 6.3283 nan 0.0100 0.0296
## 240 5.7065 nan 0.0100 0.0167
## 260 5.2171 nan 0.0100 0.0093
## 280 4.8251 nan 0.0100 0.0112
## 300 4.5045 nan 0.0100 0.0105
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.7133 nan 0.0100 1.0327
## 2 59.7679 nan 0.0100 0.9286
## 3 58.8603 nan 0.0100 0.9304
## 4 57.9225 nan 0.0100 0.8650
## 5 57.0058 nan 0.0100 0.8081
## 6 56.1865 nan 0.0100 0.8451
## 7 55.3374 nan 0.0100 0.7808
## 8 54.5196 nan 0.0100 0.8598
## 9 53.6796 nan 0.0100 0.8297
## 10 52.8977 nan 0.0100 0.7434
## 20 45.4212 nan 0.0100 0.6695
## 40 33.8910 nan 0.0100 0.3997
## 60 25.9667 nan 0.0100 0.2979
## 80 20.3147 nan 0.0100 0.2107
## 100 16.2948 nan 0.0100 0.1688
## 120 13.4299 nan 0.0100 0.1209
## 140 11.2619 nan 0.0100 0.0814
## 160 9.6418 nan 0.0100 0.0523
## 180 8.4043 nan 0.0100 0.0527
## 200 7.4223 nan 0.0100 0.0326
## 220 6.6061 nan 0.0100 0.0261
## 240 6.0130 nan 0.0100 0.0181
## 260 5.5250 nan 0.0100 0.0218
## 280 5.1170 nan 0.0100 0.0132
## 300 4.8022 nan 0.0100 0.0114
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.6367 nan 0.0100 1.0849
## 2 59.6517 nan 0.0100 0.9519
## 3 58.6415 nan 0.0100 0.8643
## 4 57.6936 nan 0.0100 0.8567
## 5 56.7017 nan 0.0100 0.9336
## 6 55.8332 nan 0.0100 0.9269
## 7 54.9221 nan 0.0100 0.8480
## 8 53.9662 nan 0.0100 0.9649
## 9 53.1204 nan 0.0100 0.8142
## 10 52.2155 nan 0.0100 0.9533
## 20 44.4108 nan 0.0100 0.6595
## 40 32.3732 nan 0.0100 0.5001
## 60 24.1623 nan 0.0100 0.2891
## 80 18.4263 nan 0.0100 0.2347
## 100 14.2943 nan 0.0100 0.1618
## 120 11.3320 nan 0.0100 0.1275
## 140 9.2280 nan 0.0100 0.0800
## 160 7.6760 nan 0.0100 0.0634
## 180 6.5379 nan 0.0100 0.0414
## 200 5.6392 nan 0.0100 0.0255
## 220 4.9844 nan 0.0100 0.0273
## 240 4.5098 nan 0.0100 0.0121
## 260 4.1147 nan 0.0100 0.0124
## 280 3.8155 nan 0.0100 0.0062
## 300 3.5595 nan 0.0100 0.0003
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.5890 nan 0.0100 0.9595
## 2 59.5816 nan 0.0100 0.8968
## 3 58.5775 nan 0.0100 0.9467
## 4 57.6077 nan 0.0100 0.9741
## 5 56.6631 nan 0.0100 0.9679
## 6 55.6878 nan 0.0100 0.9826
## 7 54.7981 nan 0.0100 0.8726
## 8 53.9340 nan 0.0100 0.7607
## 9 53.0132 nan 0.0100 0.8446
## 10 52.1460 nan 0.0100 0.8106
## 20 44.3836 nan 0.0100 0.7440
## 40 32.4936 nan 0.0100 0.4909
## 60 24.2776 nan 0.0100 0.3345
## 80 18.5524 nan 0.0100 0.2482
## 100 14.5040 nan 0.0100 0.1620
## 120 11.5814 nan 0.0100 0.1209
## 140 9.4508 nan 0.0100 0.0575
## 160 7.8671 nan 0.0100 0.0605
## 180 6.6977 nan 0.0100 0.0416
## 200 5.8383 nan 0.0100 0.0286
## 220 5.1542 nan 0.0100 0.0248
## 240 4.6535 nan 0.0100 0.0131
## 260 4.2437 nan 0.0100 0.0136
## 280 3.9533 nan 0.0100 0.0087
## 300 3.7142 nan 0.0100 0.0066
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.6183 nan 0.0100 0.9044
## 2 59.5731 nan 0.0100 1.0981
## 3 58.6662 nan 0.0100 0.9714
## 4 57.7146 nan 0.0100 0.9618
## 5 56.8149 nan 0.0100 0.9901
## 6 55.8779 nan 0.0100 0.8153
## 7 54.9436 nan 0.0100 0.9303
## 8 53.9921 nan 0.0100 1.0013
## 9 53.0976 nan 0.0100 0.8245
## 10 52.2514 nan 0.0100 0.8046
## 20 44.4107 nan 0.0100 0.7137
## 40 32.6014 nan 0.0100 0.4834
## 60 24.5004 nan 0.0100 0.3067
## 80 18.6944 nan 0.0100 0.2504
## 100 14.6717 nan 0.0100 0.1576
## 120 11.7949 nan 0.0100 0.0935
## 140 9.7117 nan 0.0100 0.0870
## 160 8.1107 nan 0.0100 0.0480
## 180 6.9391 nan 0.0100 0.0374
## 200 6.0577 nan 0.0100 0.0225
## 220 5.3802 nan 0.0100 0.0260
## 240 4.8672 nan 0.0100 0.0173
## 260 4.4711 nan 0.0100 0.0111
## 280 4.1721 nan 0.0100 0.0085
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.9290 nan 0.0500 3.9218
## 2 54.6661 nan 0.0500 3.3910
## 3 51.6968 nan 0.0500 3.2650
## 4 48.8555 nan 0.0500 2.9078
## 5 46.2909 nan 0.0500 2.7390
## 6 43.6238 nan 0.0500 2.5137
## 7 41.3781 nan 0.0500 2.2461
## 8 39.2703 nan 0.0500 1.8191
## 9 37.2724 nan 0.0500 1.8410
## 10 35.4808 nan 0.0500 1.8330
## 20 22.6059 nan 0.0500 0.8573
## 40 11.3374 nan 0.0500 0.3004
## 60 7.2637 nan 0.0500 0.0901
## 80 5.2316 nan 0.0500 0.0444
## 100 4.2985 nan 0.0500 0.0031
## 120 3.8198 nan 0.0500 0.0030
## 140 3.6001 nan 0.0500 -0.0075
## 160 3.4479 nan 0.0500 -0.0197
## 180 3.3379 nan 0.0500 -0.0158
## 200 3.2366 nan 0.0500 -0.0166
## 220 3.1642 nan 0.0500 -0.0029
## 240 3.0982 nan 0.0500 -0.0024
## 260 3.0434 nan 0.0500 -0.0054
## 280 2.9935 nan 0.0500 -0.0118
## 300 2.9538 nan 0.0500 -0.0059
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.0208 nan 0.0500 4.1267
## 2 54.2695 nan 0.0500 3.3565
## 3 51.3393 nan 0.0500 3.0732
## 4 48.5633 nan 0.0500 2.8138
## 5 45.7020 nan 0.0500 2.7222
## 6 43.3309 nan 0.0500 2.5179
## 7 41.1164 nan 0.0500 1.9959
## 8 39.0289 nan 0.0500 1.6523
## 9 36.8639 nan 0.0500 1.9991
## 10 35.0592 nan 0.0500 1.8613
## 20 22.1900 nan 0.0500 0.9783
## 40 11.1411 nan 0.0500 0.2189
## 60 6.9161 nan 0.0500 0.1071
## 80 5.1912 nan 0.0500 0.0536
## 100 4.2815 nan 0.0500 0.0261
## 120 3.8364 nan 0.0500 -0.0010
## 140 3.5945 nan 0.0500 -0.0188
## 160 3.4477 nan 0.0500 0.0016
## 180 3.3750 nan 0.0500 -0.0020
## 200 3.2920 nan 0.0500 -0.0033
## 220 3.2279 nan 0.0500 -0.0056
## 240 3.1713 nan 0.0500 -0.0051
## 260 3.1272 nan 0.0500 -0.0011
## 280 3.0844 nan 0.0500 -0.0135
## 300 3.0449 nan 0.0500 -0.0071
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.6185 nan 0.0500 3.6999
## 2 54.2918 nan 0.0500 3.4201
## 3 51.3733 nan 0.0500 3.2330
## 4 48.4261 nan 0.0500 2.8030
## 5 45.8065 nan 0.0500 2.0887
## 6 43.2685 nan 0.0500 2.4809
## 7 40.9358 nan 0.0500 2.0922
## 8 38.8393 nan 0.0500 2.0829
## 9 37.1247 nan 0.0500 1.8230
## 10 35.2782 nan 0.0500 1.7651
## 20 21.8898 nan 0.0500 0.7843
## 40 11.3344 nan 0.0500 0.2862
## 60 7.3124 nan 0.0500 0.1312
## 80 5.4660 nan 0.0500 0.0372
## 100 4.5970 nan 0.0500 0.0145
## 120 4.1428 nan 0.0500 0.0085
## 140 3.9176 nan 0.0500 -0.0018
## 160 3.7573 nan 0.0500 -0.0030
## 180 3.6503 nan 0.0500 -0.0156
## 200 3.5509 nan 0.0500 -0.0104
## 220 3.4659 nan 0.0500 -0.0043
## 240 3.3905 nan 0.0500 -0.0031
## 260 3.3234 nan 0.0500 -0.0131
## 280 3.2740 nan 0.0500 -0.0068
## 300 3.2419 nan 0.0500 -0.0050
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.9041 nan 0.0500 4.5379
## 2 52.6425 nan 0.0500 4.2821
## 3 48.5497 nan 0.0500 3.9687
## 4 44.8775 nan 0.0500 3.8780
## 5 41.7358 nan 0.0500 3.1236
## 6 39.0221 nan 0.0500 2.8241
## 7 36.2595 nan 0.0500 2.9064
## 8 33.6910 nan 0.0500 2.4489
## 9 31.5008 nan 0.0500 1.9663
## 10 29.2807 nan 0.0500 2.1807
## 20 16.1838 nan 0.0500 0.9192
## 40 7.2604 nan 0.0500 0.2079
## 60 4.5476 nan 0.0500 0.0138
## 80 3.6329 nan 0.0500 -0.0078
## 100 3.2440 nan 0.0500 -0.0160
## 120 2.9911 nan 0.0500 -0.0105
## 140 2.8058 nan 0.0500 -0.0278
## 160 2.6569 nan 0.0500 -0.0076
## 180 2.5331 nan 0.0500 -0.0084
## 200 2.4369 nan 0.0500 -0.0060
## 220 2.3383 nan 0.0500 -0.0096
## 240 2.2542 nan 0.0500 -0.0115
## 260 2.1719 nan 0.0500 -0.0117
## 280 2.1032 nan 0.0500 -0.0015
## 300 2.0252 nan 0.0500 -0.0008
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.8312 nan 0.0500 4.9747
## 2 52.8577 nan 0.0500 4.1456
## 3 48.7576 nan 0.0500 3.6971
## 4 45.2963 nan 0.0500 3.4640
## 5 41.6868 nan 0.0500 3.5017
## 6 38.7881 nan 0.0500 2.8659
## 7 36.0464 nan 0.0500 2.2475
## 8 33.5846 nan 0.0500 2.2149
## 9 31.3158 nan 0.0500 2.5227
## 10 29.2207 nan 0.0500 2.0970
## 20 15.8127 nan 0.0500 0.6990
## 40 7.0275 nan 0.0500 0.1679
## 60 4.5008 nan 0.0500 0.0398
## 80 3.7005 nan 0.0500 0.0031
## 100 3.3385 nan 0.0500 -0.0072
## 120 3.1661 nan 0.0500 0.0002
## 140 3.0046 nan 0.0500 -0.0054
## 160 2.8677 nan 0.0500 -0.0062
## 180 2.7345 nan 0.0500 -0.0038
## 200 2.6368 nan 0.0500 -0.0115
## 220 2.5515 nan 0.0500 -0.0105
## 240 2.4681 nan 0.0500 -0.0138
## 260 2.3986 nan 0.0500 -0.0082
## 280 2.3388 nan 0.0500 -0.0152
## 300 2.2692 nan 0.0500 -0.0162
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.8431 nan 0.0500 4.8625
## 2 52.3324 nan 0.0500 4.1389
## 3 48.9190 nan 0.0500 3.5556
## 4 45.2067 nan 0.0500 4.1151
## 5 42.0855 nan 0.0500 3.3221
## 6 39.2637 nan 0.0500 2.6631
## 7 36.3941 nan 0.0500 2.5900
## 8 34.2319 nan 0.0500 2.2766
## 9 31.8765 nan 0.0500 2.3301
## 10 29.6670 nan 0.0500 2.1790
## 20 16.3291 nan 0.0500 0.7847
## 40 7.2622 nan 0.0500 0.2108
## 60 4.6258 nan 0.0500 0.0344
## 80 3.8425 nan 0.0500 0.0105
## 100 3.5106 nan 0.0500 -0.0065
## 120 3.3045 nan 0.0500 -0.0083
## 140 3.1337 nan 0.0500 -0.0107
## 160 3.0281 nan 0.0500 -0.0134
## 180 2.9150 nan 0.0500 -0.0153
## 200 2.8093 nan 0.0500 -0.0090
## 220 2.7182 nan 0.0500 -0.0034
## 240 2.6258 nan 0.0500 -0.0055
## 260 2.5556 nan 0.0500 -0.0110
## 280 2.4834 nan 0.0500 -0.0086
## 300 2.4192 nan 0.0500 -0.0057
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.6223 nan 0.0500 4.6068
## 2 52.0454 nan 0.0500 3.9362
## 3 47.7987 nan 0.0500 4.6124
## 4 44.0973 nan 0.0500 3.4244
## 5 40.7177 nan 0.0500 2.6974
## 6 37.7195 nan 0.0500 2.9861
## 7 34.9662 nan 0.0500 2.8790
## 8 32.4238 nan 0.0500 2.5195
## 9 30.2543 nan 0.0500 1.9335
## 10 28.0770 nan 0.0500 1.9197
## 20 14.2186 nan 0.0500 0.8444
## 40 5.7110 nan 0.0500 0.1690
## 60 3.5959 nan 0.0500 0.0323
## 80 2.9164 nan 0.0500 0.0051
## 100 2.5708 nan 0.0500 -0.0241
## 120 2.3789 nan 0.0500 -0.0129
## 140 2.1883 nan 0.0500 -0.0177
## 160 2.0574 nan 0.0500 -0.0091
## 180 1.9402 nan 0.0500 -0.0130
## 200 1.8390 nan 0.0500 -0.0056
## 220 1.7449 nan 0.0500 -0.0071
## 240 1.6560 nan 0.0500 -0.0126
## 260 1.5858 nan 0.0500 -0.0075
## 280 1.5177 nan 0.0500 -0.0201
## 300 1.4318 nan 0.0500 -0.0135
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.4385 nan 0.0500 4.9379
## 2 51.7482 nan 0.0500 4.4645
## 3 47.6996 nan 0.0500 3.3198
## 4 43.8418 nan 0.0500 3.8115
## 5 40.2513 nan 0.0500 3.6624
## 6 37.1164 nan 0.0500 3.0519
## 7 34.4391 nan 0.0500 2.8207
## 8 31.7655 nan 0.0500 2.4583
## 9 29.4455 nan 0.0500 2.1095
## 10 27.2464 nan 0.0500 2.1886
## 20 14.0516 nan 0.0500 0.8260
## 40 5.7598 nan 0.0500 0.1895
## 60 3.6920 nan 0.0500 0.0364
## 80 3.0467 nan 0.0500 -0.0043
## 100 2.7392 nan 0.0500 -0.0107
## 120 2.5398 nan 0.0500 -0.0094
## 140 2.3832 nan 0.0500 -0.0130
## 160 2.2694 nan 0.0500 -0.0144
## 180 2.1617 nan 0.0500 -0.0203
## 200 2.0633 nan 0.0500 -0.0156
## 220 1.9811 nan 0.0500 -0.0212
## 240 1.9088 nan 0.0500 -0.0078
## 260 1.8377 nan 0.0500 -0.0128
## 280 1.7720 nan 0.0500 -0.0127
## 300 1.7060 nan 0.0500 -0.0126
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.7177 nan 0.0500 5.4157
## 2 52.1714 nan 0.0500 4.2955
## 3 47.9535 nan 0.0500 4.2112
## 4 44.3927 nan 0.0500 3.4936
## 5 40.8297 nan 0.0500 3.2385
## 6 37.8011 nan 0.0500 3.0375
## 7 35.1115 nan 0.0500 2.7470
## 8 32.5726 nan 0.0500 2.5793
## 9 30.2418 nan 0.0500 2.2640
## 10 27.9758 nan 0.0500 2.2615
## 20 14.4773 nan 0.0500 0.7936
## 40 6.0493 nan 0.0500 0.1634
## 60 4.0228 nan 0.0500 0.0262
## 80 3.4006 nan 0.0500 -0.0057
## 100 3.1356 nan 0.0500 -0.0093
## 120 2.9304 nan 0.0500 -0.0084
## 140 2.7850 nan 0.0500 -0.0087
## 160 2.6224 nan 0.0500 -0.0067
## 180 2.5142 nan 0.0500 -0.0078
## 200 2.4106 nan 0.0500 -0.0208
## 220 2.3186 nan 0.0500 -0.0127
## 240 2.2317 nan 0.0500 -0.0085
## 260 2.1436 nan 0.0500 -0.0178
## 280 2.0814 nan 0.0500 -0.0205
## 300 2.0171 nan 0.0500 -0.0104
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 54.0598 nan 0.1000 7.2866
## 2 47.9948 nan 0.1000 6.3642
## 3 42.4859 nan 0.1000 4.8873
## 4 38.3385 nan 0.1000 4.2293
## 5 34.5869 nan 0.1000 3.8770
## 6 31.3167 nan 0.1000 3.1863
## 7 28.1506 nan 0.1000 2.7186
## 8 25.3070 nan 0.1000 2.3453
## 9 23.3236 nan 0.1000 2.1318
## 10 21.4285 nan 0.1000 1.8041
## 20 11.1266 nan 0.1000 0.4477
## 40 5.2272 nan 0.1000 0.0964
## 60 3.9630 nan 0.1000 0.0187
## 80 3.6233 nan 0.1000 -0.0003
## 100 3.4320 nan 0.1000 -0.0404
## 120 3.2812 nan 0.1000 -0.0169
## 140 3.1827 nan 0.1000 -0.0189
## 160 3.1222 nan 0.1000 -0.0055
## 180 3.0392 nan 0.1000 -0.0470
## 200 2.9652 nan 0.1000 -0.0340
## 220 2.8975 nan 0.1000 -0.0179
## 240 2.8218 nan 0.1000 -0.0223
## 260 2.7744 nan 0.1000 -0.0098
## 280 2.7264 nan 0.1000 -0.0152
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 54.2518 nan 0.1000 7.4043
## 2 47.7944 nan 0.1000 5.0742
## 3 42.7655 nan 0.1000 5.2990
## 4 38.2346 nan 0.1000 3.9695
## 5 34.3370 nan 0.1000 3.9339
## 6 31.1457 nan 0.1000 3.2594
## 7 28.5165 nan 0.1000 2.3665
## 8 25.8359 nan 0.1000 2.5402
## 9 23.6250 nan 0.1000 1.8117
## 10 21.8261 nan 0.1000 1.4364
## 20 11.0295 nan 0.1000 0.5868
## 40 5.2531 nan 0.1000 0.1007
## 60 3.9786 nan 0.1000 0.0030
## 80 3.5962 nan 0.1000 -0.0140
## 100 3.3644 nan 0.1000 -0.0046
## 120 3.2620 nan 0.1000 -0.0173
## 140 3.1390 nan 0.1000 -0.0248
## 160 3.0458 nan 0.1000 -0.0151
## 180 2.9810 nan 0.1000 -0.0224
## 200 2.9291 nan 0.1000 -0.0114
## 220 2.8816 nan 0.1000 -0.0096
## 240 2.8265 nan 0.1000 -0.0166
## 260 2.7695 nan 0.1000 -0.0260
## 280 2.7177 nan 0.1000 -0.0045
## 300 2.6865 nan 0.1000 -0.0080
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 54.1124 nan 0.1000 6.9832
## 2 47.9142 nan 0.1000 6.2010
## 3 42.3839 nan 0.1000 4.8740
## 4 38.2091 nan 0.1000 4.0357
## 5 34.3341 nan 0.1000 3.6121
## 6 30.5874 nan 0.1000 3.2451
## 7 27.8728 nan 0.1000 2.7444
## 8 25.5231 nan 0.1000 2.0417
## 9 23.2312 nan 0.1000 2.1470
## 10 21.2622 nan 0.1000 1.7860
## 20 10.9756 nan 0.1000 0.4736
## 40 5.4669 nan 0.1000 0.0957
## 60 4.4244 nan 0.1000 -0.0473
## 80 3.9725 nan 0.1000 -0.0260
## 100 3.7472 nan 0.1000 -0.0220
## 120 3.6015 nan 0.1000 -0.0139
## 140 3.4816 nan 0.1000 -0.0150
## 160 3.3760 nan 0.1000 -0.0254
## 180 3.2681 nan 0.1000 -0.0122
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## 220 3.0939 nan 0.1000 -0.0122
## 240 3.0515 nan 0.1000 -0.0227
## 260 2.9867 nan 0.1000 -0.0164
## 280 2.9525 nan 0.1000 -0.0126
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.9418 nan 0.1000 9.3326
## 2 45.1815 nan 0.1000 7.3196
## 3 39.5278 nan 0.1000 5.5540
## 4 33.5997 nan 0.1000 5.9612
## 5 29.4046 nan 0.1000 3.6667
## 6 26.2024 nan 0.1000 3.3637
## 7 22.7600 nan 0.1000 3.2535
## 8 20.0869 nan 0.1000 2.3040
## 9 18.1397 nan 0.1000 1.9147
## 10 16.2126 nan 0.1000 1.9464
## 20 7.3232 nan 0.1000 0.2543
## 40 3.7898 nan 0.1000 0.0363
## 60 3.0802 nan 0.1000 -0.0146
## 80 2.7411 nan 0.1000 -0.0123
## 100 2.4984 nan 0.1000 -0.0114
## 120 2.3317 nan 0.1000 -0.0115
## 140 2.1381 nan 0.1000 -0.0160
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## 180 1.9099 nan 0.1000 -0.0164
## 200 1.8182 nan 0.1000 -0.0130
## 220 1.7156 nan 0.1000 -0.0074
## 240 1.6612 nan 0.1000 -0.0254
## 260 1.5879 nan 0.1000 -0.0093
## 280 1.4984 nan 0.1000 -0.0059
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.6823 nan 0.1000 9.1523
## 2 45.3434 nan 0.1000 7.6009
## 3 38.8219 nan 0.1000 5.6742
## 4 33.6203 nan 0.1000 4.9950
## 5 29.0713 nan 0.1000 4.0821
## 6 25.7435 nan 0.1000 3.3386
## 7 22.8253 nan 0.1000 2.7281
## 8 19.9594 nan 0.1000 2.8263
## 9 17.9356 nan 0.1000 1.5026
## 10 16.1776 nan 0.1000 1.6883
## 20 7.1688 nan 0.1000 0.3313
## 40 3.8383 nan 0.1000 0.0322
## 60 3.3134 nan 0.1000 -0.0158
## 80 2.9797 nan 0.1000 -0.0518
## 100 2.7037 nan 0.1000 -0.0004
## 120 2.5440 nan 0.1000 -0.0292
## 140 2.3539 nan 0.1000 -0.0184
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## 180 2.1575 nan 0.1000 -0.0179
## 200 2.0480 nan 0.1000 -0.0375
## 220 1.9663 nan 0.1000 -0.0459
## 240 1.8793 nan 0.1000 -0.0123
## 260 1.8121 nan 0.1000 -0.0164
## 280 1.7382 nan 0.1000 -0.0305
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.4690 nan 0.1000 9.4976
## 2 44.8057 nan 0.1000 7.7418
## 3 38.5701 nan 0.1000 6.6249
## 4 33.2365 nan 0.1000 5.4920
## 5 29.3714 nan 0.1000 3.8651
## 6 25.3243 nan 0.1000 3.5589
## 7 22.5731 nan 0.1000 2.5740
## 8 19.8800 nan 0.1000 2.4364
## 9 17.6773 nan 0.1000 2.0926
## 10 15.8730 nan 0.1000 1.7744
## 20 7.2862 nan 0.1000 0.3651
## 40 4.0259 nan 0.1000 -0.0104
## 60 3.4738 nan 0.1000 -0.0254
## 80 3.2282 nan 0.1000 -0.0190
## 100 3.0004 nan 0.1000 -0.0467
## 120 2.8340 nan 0.1000 -0.0312
## 140 2.6902 nan 0.1000 -0.0123
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## 180 2.4674 nan 0.1000 -0.0137
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## 220 2.3198 nan 0.1000 -0.0169
## 240 2.2201 nan 0.1000 -0.0171
## 260 2.1508 nan 0.1000 -0.0164
## 280 2.0899 nan 0.1000 -0.0079
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.9906 nan 0.1000 9.6537
## 2 43.9974 nan 0.1000 8.6625
## 3 37.6306 nan 0.1000 6.4437
## 4 32.0207 nan 0.1000 5.1644
## 5 27.7357 nan 0.1000 4.3199
## 6 23.8435 nan 0.1000 3.4573
## 7 20.4407 nan 0.1000 3.2926
## 8 17.8327 nan 0.1000 2.4255
## 9 15.8026 nan 0.1000 1.9861
## 10 13.9106 nan 0.1000 1.6433
## 20 5.5909 nan 0.1000 0.2542
## 40 2.8854 nan 0.1000 -0.0114
## 60 2.3899 nan 0.1000 -0.0386
## 80 2.0836 nan 0.1000 -0.0149
## 100 1.8118 nan 0.1000 -0.0134
## 120 1.6492 nan 0.1000 -0.0235
## 140 1.4961 nan 0.1000 -0.0134
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## 180 1.2470 nan 0.1000 -0.0137
## 200 1.1363 nan 0.1000 -0.0134
## 220 1.0520 nan 0.1000 -0.0106
## 240 0.9864 nan 0.1000 -0.0090
## 260 0.9071 nan 0.1000 -0.0028
## 280 0.8331 nan 0.1000 -0.0110
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.3198 nan 0.1000 9.7593
## 2 43.2054 nan 0.1000 6.8824
## 3 36.6717 nan 0.1000 6.1212
## 4 31.0776 nan 0.1000 5.3124
## 5 26.7404 nan 0.1000 4.3729
## 6 23.2743 nan 0.1000 3.4932
## 7 19.9500 nan 0.1000 2.3761
## 8 17.5450 nan 0.1000 2.3272
## 9 15.3022 nan 0.1000 1.9909
## 10 13.5702 nan 0.1000 1.6874
## 20 5.5380 nan 0.1000 0.3129
## 40 2.9542 nan 0.1000 -0.0043
## 60 2.4476 nan 0.1000 -0.0081
## 80 2.2055 nan 0.1000 -0.0237
## 100 2.0474 nan 0.1000 -0.0287
## 120 1.8576 nan 0.1000 -0.0326
## 140 1.7082 nan 0.1000 -0.0088
## 160 1.5925 nan 0.1000 -0.0295
## 180 1.4972 nan 0.1000 -0.0180
## 200 1.4077 nan 0.1000 -0.0221
## 220 1.3142 nan 0.1000 -0.0208
## 240 1.2418 nan 0.1000 -0.0113
## 260 1.1849 nan 0.1000 -0.0089
## 280 1.1275 nan 0.1000 -0.0148
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.9398 nan 0.1000 8.2044
## 2 43.7461 nan 0.1000 8.0085
## 3 37.2025 nan 0.1000 5.7411
## 4 31.5718 nan 0.1000 4.9478
## 5 27.2026 nan 0.1000 4.7527
## 6 23.4781 nan 0.1000 3.8720
## 7 20.3650 nan 0.1000 3.3211
## 8 17.9138 nan 0.1000 2.7380
## 9 15.8855 nan 0.1000 1.9018
## 10 14.0019 nan 0.1000 1.7848
## 20 5.8361 nan 0.1000 0.3750
## 40 3.3499 nan 0.1000 -0.0010
## 60 2.9246 nan 0.1000 -0.0259
## 80 2.6657 nan 0.1000 -0.0322
## 100 2.4401 nan 0.1000 -0.0239
## 120 2.2681 nan 0.1000 -0.0340
## 140 2.1163 nan 0.1000 -0.0199
## 160 1.9838 nan 0.1000 -0.0313
## 180 1.8764 nan 0.1000 -0.0170
## 200 1.7663 nan 0.1000 -0.0281
## 220 1.6758 nan 0.1000 -0.0235
## 240 1.6178 nan 0.1000 -0.0220
## 260 1.5440 nan 0.1000 -0.0076
## 280 1.4613 nan 0.1000 -0.0117
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.9772 nan 0.0010 0.0788
## 2 62.9002 nan 0.0010 0.0802
## 3 62.8198 nan 0.0010 0.0820
## 4 62.7419 nan 0.0010 0.0781
## 5 62.6598 nan 0.0010 0.0788
## 6 62.5759 nan 0.0010 0.0774
## 7 62.5000 nan 0.0010 0.0773
## 8 62.4176 nan 0.0010 0.0805
## 9 62.3419 nan 0.0010 0.0785
## 10 62.2650 nan 0.0010 0.0757
## 20 61.4854 nan 0.0010 0.0759
## 40 59.9726 nan 0.0010 0.0710
## 60 58.5356 nan 0.0010 0.0623
## 80 57.1168 nan 0.0010 0.0671
## 100 55.7660 nan 0.0010 0.0642
## 120 54.4601 nan 0.0010 0.0635
## 140 53.1853 nan 0.0010 0.0687
## 160 51.9460 nan 0.0010 0.0633
## 180 50.7689 nan 0.0010 0.0608
## 200 49.6280 nan 0.0010 0.0571
## 220 48.5059 nan 0.0010 0.0546
## 240 47.4263 nan 0.0010 0.0477
## 260 46.4013 nan 0.0010 0.0438
## 280 45.3917 nan 0.0010 0.0477
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.9861 nan 0.0010 0.0732
## 2 62.9009 nan 0.0010 0.0810
## 3 62.8185 nan 0.0010 0.0684
## 4 62.7456 nan 0.0010 0.0759
## 5 62.6587 nan 0.0010 0.0830
## 6 62.5838 nan 0.0010 0.0756
## 7 62.4984 nan 0.0010 0.0774
## 8 62.4166 nan 0.0010 0.0807
## 9 62.3383 nan 0.0010 0.0792
## 10 62.2638 nan 0.0010 0.0759
## 20 61.4875 nan 0.0010 0.0732
## 40 59.9634 nan 0.0010 0.0766
## 60 58.4709 nan 0.0010 0.0682
## 80 57.0113 nan 0.0010 0.0678
## 100 55.6518 nan 0.0010 0.0587
## 120 54.3365 nan 0.0010 0.0643
## 140 53.0664 nan 0.0010 0.0630
## 160 51.8359 nan 0.0010 0.0575
## 180 50.6564 nan 0.0010 0.0552
## 200 49.5457 nan 0.0010 0.0516
## 220 48.4427 nan 0.0010 0.0538
## 240 47.3903 nan 0.0010 0.0511
## 260 46.3697 nan 0.0010 0.0487
## 280 45.3524 nan 0.0010 0.0501
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.9849 nan 0.0010 0.0843
## 2 62.9094 nan 0.0010 0.0822
## 3 62.8239 nan 0.0010 0.0832
## 4 62.7443 nan 0.0010 0.0779
## 5 62.6644 nan 0.0010 0.0803
## 6 62.5837 nan 0.0010 0.0761
## 7 62.5104 nan 0.0010 0.0743
## 8 62.4327 nan 0.0010 0.0808
## 9 62.3587 nan 0.0010 0.0730
## 10 62.2810 nan 0.0010 0.0723
## 20 61.4997 nan 0.0010 0.0779
## 40 59.9699 nan 0.0010 0.0761
## 60 58.5298 nan 0.0010 0.0693
## 80 57.1313 nan 0.0010 0.0694
## 100 55.7691 nan 0.0010 0.0658
## 120 54.4583 nan 0.0010 0.0657
## 140 53.1745 nan 0.0010 0.0625
## 160 51.9558 nan 0.0010 0.0619
## 180 50.7679 nan 0.0010 0.0634
## 200 49.6110 nan 0.0010 0.0496
## 220 48.5084 nan 0.0010 0.0546
## 240 47.4265 nan 0.0010 0.0515
## 260 46.3792 nan 0.0010 0.0474
## 280 45.3606 nan 0.0010 0.0449
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.9721 nan 0.0010 0.0910
## 2 62.8807 nan 0.0010 0.0904
## 3 62.7846 nan 0.0010 0.0969
## 4 62.6904 nan 0.0010 0.0942
## 5 62.5974 nan 0.0010 0.0952
## 6 62.5051 nan 0.0010 0.0943
## 7 62.4022 nan 0.0010 0.0908
## 8 62.3088 nan 0.0010 0.0899
## 9 62.2183 nan 0.0010 0.0966
## 10 62.1230 nan 0.0010 0.0971
## 20 61.1687 nan 0.0010 0.0897
## 40 59.3064 nan 0.0010 0.0960
## 60 57.5451 nan 0.0010 0.0877
## 80 55.8329 nan 0.0010 0.0780
## 100 54.1936 nan 0.0010 0.0777
## 120 52.6022 nan 0.0010 0.0718
## 140 51.0388 nan 0.0010 0.0799
## 160 49.5648 nan 0.0010 0.0778
## 180 48.1340 nan 0.0010 0.0636
## 200 46.7582 nan 0.0010 0.0620
## 220 45.4282 nan 0.0010 0.0642
## 240 44.1261 nan 0.0010 0.0620
## 260 42.8847 nan 0.0010 0.0646
## 280 41.6424 nan 0.0010 0.0538
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.9723 nan 0.0010 0.0898
## 2 62.8771 nan 0.0010 0.0926
## 3 62.7810 nan 0.0010 0.0941
## 4 62.6786 nan 0.0010 0.0972
## 5 62.5839 nan 0.0010 0.1042
## 6 62.4873 nan 0.0010 0.0904
## 7 62.3931 nan 0.0010 0.0894
## 8 62.3030 nan 0.0010 0.0738
## 9 62.2080 nan 0.0010 0.0806
## 10 62.1061 nan 0.0010 0.0997
## 20 61.1844 nan 0.0010 0.0879
## 40 59.3491 nan 0.0010 0.0898
## 60 57.5373 nan 0.0010 0.0804
## 80 55.8352 nan 0.0010 0.0816
## 100 54.1587 nan 0.0010 0.0802
## 120 52.5803 nan 0.0010 0.0789
## 140 51.0381 nan 0.0010 0.0798
## 160 49.5370 nan 0.0010 0.0718
## 180 48.1229 nan 0.0010 0.0708
## 200 46.7165 nan 0.0010 0.0727
## 220 45.3789 nan 0.0010 0.0732
## 240 44.0690 nan 0.0010 0.0636
## 260 42.8034 nan 0.0010 0.0572
## 280 41.6009 nan 0.0010 0.0593
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.9676 nan 0.0010 0.0927
## 2 62.8681 nan 0.0010 0.0916
## 3 62.7696 nan 0.0010 0.0969
## 4 62.6743 nan 0.0010 0.0984
## 5 62.5833 nan 0.0010 0.0906
## 6 62.4870 nan 0.0010 0.0992
## 7 62.3885 nan 0.0010 0.0950
## 8 62.2982 nan 0.0010 0.0957
## 9 62.2068 nan 0.0010 0.0883
## 10 62.1015 nan 0.0010 0.1068
## 20 61.1469 nan 0.0010 0.0943
## 40 59.3127 nan 0.0010 0.0956
## 60 57.5521 nan 0.0010 0.0855
## 80 55.8728 nan 0.0010 0.0961
## 100 54.2359 nan 0.0010 0.0872
## 120 52.6036 nan 0.0010 0.0768
## 140 51.0372 nan 0.0010 0.0673
## 160 49.5369 nan 0.0010 0.0845
## 180 48.0778 nan 0.0010 0.0742
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## 220 45.3382 nan 0.0010 0.0644
## 240 44.0314 nan 0.0010 0.0630
## 260 42.7675 nan 0.0010 0.0584
## 280 41.5496 nan 0.0010 0.0583
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.9567 nan 0.0010 0.1014
## 2 62.8510 nan 0.0010 0.1117
## 3 62.7437 nan 0.0010 0.1128
## 4 62.6371 nan 0.0010 0.1021
## 5 62.5391 nan 0.0010 0.0970
## 6 62.4388 nan 0.0010 0.1005
## 7 62.3349 nan 0.0010 0.1009
## 8 62.2322 nan 0.0010 0.0995
## 9 62.1331 nan 0.0010 0.1031
## 10 62.0302 nan 0.0010 0.1077
## 20 61.0054 nan 0.0010 0.0969
## 40 59.0211 nan 0.0010 0.0895
## 60 57.1032 nan 0.0010 0.0990
## 80 55.2613 nan 0.0010 0.1030
## 100 53.4862 nan 0.0010 0.0925
## 120 51.7350 nan 0.0010 0.0963
## 140 50.0874 nan 0.0010 0.0827
## 160 48.4954 nan 0.0010 0.0785
## 180 46.9455 nan 0.0010 0.0794
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## 220 44.0186 nan 0.0010 0.0683
## 240 42.6526 nan 0.0010 0.0636
## 260 41.3250 nan 0.0010 0.0591
## 280 40.0448 nan 0.0010 0.0567
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.9555 nan 0.0010 0.1066
## 2 62.8493 nan 0.0010 0.1062
## 3 62.7468 nan 0.0010 0.1032
## 4 62.6493 nan 0.0010 0.0930
## 5 62.5473 nan 0.0010 0.1145
## 6 62.4391 nan 0.0010 0.0982
## 7 62.3311 nan 0.0010 0.1020
## 8 62.2293 nan 0.0010 0.0957
## 9 62.1293 nan 0.0010 0.0878
## 10 62.0309 nan 0.0010 0.1063
## 20 61.0124 nan 0.0010 0.0922
## 40 59.0211 nan 0.0010 0.0935
## 60 57.0808 nan 0.0010 0.0815
## 80 55.2080 nan 0.0010 0.0907
## 100 53.3943 nan 0.0010 0.0831
## 120 51.6753 nan 0.0010 0.0821
## 140 50.0199 nan 0.0010 0.0776
## 160 48.4254 nan 0.0010 0.0733
## 180 46.8692 nan 0.0010 0.0734
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## 240 42.5842 nan 0.0010 0.0696
## 260 41.2757 nan 0.0010 0.0702
## 280 40.0141 nan 0.0010 0.0563
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.9598 nan 0.0010 0.1120
## 2 62.8557 nan 0.0010 0.0905
## 3 62.7501 nan 0.0010 0.1067
## 4 62.6509 nan 0.0010 0.0914
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## 6 62.4444 nan 0.0010 0.1057
## 7 62.3410 nan 0.0010 0.1003
## 8 62.2405 nan 0.0010 0.1035
## 9 62.1376 nan 0.0010 0.1106
## 10 62.0343 nan 0.0010 0.0976
## 20 61.0000 nan 0.0010 0.0947
## 40 58.9954 nan 0.0010 0.0909
## 60 57.0737 nan 0.0010 0.0857
## 80 55.2124 nan 0.0010 0.0784
## 100 53.4334 nan 0.0010 0.0898
## 120 51.7168 nan 0.0010 0.0837
## 140 50.0613 nan 0.0010 0.0841
## 160 48.4708 nan 0.0010 0.0798
## 180 46.9386 nan 0.0010 0.0679
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## 240 42.6874 nan 0.0010 0.0544
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## 280 40.0949 nan 0.0010 0.0576
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.6851 nan 0.0050 0.4062
## 2 62.3034 nan 0.0050 0.4051
## 3 61.9049 nan 0.0050 0.3839
## 4 61.5127 nan 0.0050 0.3657
## 5 61.1111 nan 0.0050 0.3840
## 6 60.7507 nan 0.0050 0.3768
## 7 60.3924 nan 0.0050 0.3776
## 8 59.9964 nan 0.0050 0.3612
## 9 59.6236 nan 0.0050 0.3781
## 10 59.2605 nan 0.0050 0.3671
## 20 55.7154 nan 0.0050 0.3249
## 40 49.5171 nan 0.0050 0.2847
## 60 44.3561 nan 0.0050 0.2445
## 80 39.9500 nan 0.0050 0.1936
## 100 36.0328 nan 0.0050 0.1836
## 120 32.6145 nan 0.0050 0.1557
## 140 29.6945 nan 0.0050 0.1324
## 160 27.1685 nan 0.0050 0.1085
## 180 24.9341 nan 0.0050 0.1077
## 200 22.9206 nan 0.0050 0.0876
## 220 21.1691 nan 0.0050 0.0775
## 240 19.5731 nan 0.0050 0.0658
## 260 18.1946 nan 0.0050 0.0554
## 280 16.9206 nan 0.0050 0.0583
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.6349 nan 0.0050 0.3949
## 2 62.2243 nan 0.0050 0.3918
## 3 61.8495 nan 0.0050 0.3992
## 4 61.4750 nan 0.0050 0.3628
## 5 61.1172 nan 0.0050 0.3992
## 6 60.7562 nan 0.0050 0.3791
## 7 60.3986 nan 0.0050 0.3826
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## 9 59.6519 nan 0.0050 0.3923
## 10 59.2908 nan 0.0050 0.3742
## 20 55.7391 nan 0.0050 0.3324
## 40 49.5081 nan 0.0050 0.2648
## 60 44.4301 nan 0.0050 0.2109
## 80 39.9034 nan 0.0050 0.2064
## 100 36.0382 nan 0.0050 0.1714
## 120 32.7463 nan 0.0050 0.1231
## 140 29.8129 nan 0.0050 0.1377
## 160 27.2332 nan 0.0050 0.1144
## 180 24.9951 nan 0.0050 0.0952
## 200 22.9899 nan 0.0050 0.0870
## 220 21.2041 nan 0.0050 0.0729
## 240 19.6306 nan 0.0050 0.0729
## 260 18.2385 nan 0.0050 0.0654
## 280 17.0032 nan 0.0050 0.0495
## 300 15.8439 nan 0.0050 0.0386
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.6575 nan 0.0050 0.3998
## 2 62.2451 nan 0.0050 0.3941
## 3 61.8397 nan 0.0050 0.3971
## 4 61.4595 nan 0.0050 0.3831
## 5 61.0975 nan 0.0050 0.3905
## 6 60.7015 nan 0.0050 0.3864
## 7 60.3225 nan 0.0050 0.3650
## 8 59.9630 nan 0.0050 0.3799
## 9 59.5926 nan 0.0050 0.3859
## 10 59.2163 nan 0.0050 0.3500
## 20 55.7430 nan 0.0050 0.3357
## 40 49.6278 nan 0.0050 0.2368
## 60 44.3970 nan 0.0050 0.2549
## 80 39.9309 nan 0.0050 0.1958
## 100 36.0821 nan 0.0050 0.1837
## 120 32.6403 nan 0.0050 0.1507
## 140 29.7181 nan 0.0050 0.1428
## 160 27.1231 nan 0.0050 0.1176
## 180 24.8533 nan 0.0050 0.1017
## 200 22.8613 nan 0.0050 0.0876
## 220 21.1231 nan 0.0050 0.0779
## 240 19.5137 nan 0.0050 0.0718
## 260 18.1180 nan 0.0050 0.0692
## 280 16.8518 nan 0.0050 0.0438
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.5605 nan 0.0050 0.4817
## 2 62.0636 nan 0.0050 0.5017
## 3 61.5633 nan 0.0050 0.4614
## 4 61.1254 nan 0.0050 0.4362
## 5 60.6900 nan 0.0050 0.4641
## 6 60.2751 nan 0.0050 0.4775
## 7 59.8056 nan 0.0050 0.4368
## 8 59.3725 nan 0.0050 0.4452
## 9 58.9001 nan 0.0050 0.4309
## 10 58.4605 nan 0.0050 0.4478
## 20 54.1163 nan 0.0050 0.4073
## 40 46.8531 nan 0.0050 0.3534
## 60 40.5963 nan 0.0050 0.3171
## 80 35.3357 nan 0.0050 0.2594
## 100 30.8028 nan 0.0050 0.1912
## 120 27.0026 nan 0.0050 0.1727
## 140 23.8681 nan 0.0050 0.1219
## 160 21.1862 nan 0.0050 0.0837
## 180 18.8327 nan 0.0050 0.1082
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## 220 15.1513 nan 0.0050 0.0743
## 240 13.7102 nan 0.0050 0.0586
## 260 12.4618 nan 0.0050 0.0543
## 280 11.3905 nan 0.0050 0.0409
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.6020 nan 0.0050 0.4339
## 2 62.1598 nan 0.0050 0.4388
## 3 61.6941 nan 0.0050 0.5006
## 4 61.2321 nan 0.0050 0.4635
## 5 60.7693 nan 0.0050 0.4297
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## 7 59.8496 nan 0.0050 0.4255
## 8 59.3662 nan 0.0050 0.4715
## 9 58.8964 nan 0.0050 0.4312
## 10 58.4514 nan 0.0050 0.4377
## 20 54.2805 nan 0.0050 0.3924
## 40 46.7217 nan 0.0050 0.3328
## 60 40.4133 nan 0.0050 0.2898
## 80 35.0189 nan 0.0050 0.2858
## 100 30.5835 nan 0.0050 0.2236
## 120 26.8397 nan 0.0050 0.1686
## 140 23.6455 nan 0.0050 0.1182
## 160 21.0097 nan 0.0050 0.1024
## 180 18.7503 nan 0.0050 0.1021
## 200 16.8019 nan 0.0050 0.0915
## 220 15.1397 nan 0.0050 0.0794
## 240 13.6748 nan 0.0050 0.0659
## 260 12.4348 nan 0.0050 0.0493
## 280 11.3883 nan 0.0050 0.0493
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.5584 nan 0.0050 0.5293
## 2 62.0778 nan 0.0050 0.4856
## 3 61.6208 nan 0.0050 0.4814
## 4 61.2057 nan 0.0050 0.4879
## 5 60.7424 nan 0.0050 0.4739
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## 7 59.8473 nan 0.0050 0.4456
## 8 59.4032 nan 0.0050 0.4188
## 9 59.0061 nan 0.0050 0.4557
## 10 58.5305 nan 0.0050 0.4491
## 20 54.3381 nan 0.0050 0.4363
## 40 46.8736 nan 0.0050 0.4113
## 60 40.6137 nan 0.0050 0.2905
## 80 35.1936 nan 0.0050 0.2276
## 100 30.7409 nan 0.0050 0.2067
## 120 27.0557 nan 0.0050 0.1706
## 140 23.9370 nan 0.0050 0.1464
## 160 21.2735 nan 0.0050 0.1333
## 180 19.0477 nan 0.0050 0.0892
## 200 17.0644 nan 0.0050 0.0877
## 220 15.4244 nan 0.0050 0.0708
## 240 13.9385 nan 0.0050 0.0611
## 260 12.7256 nan 0.0050 0.0511
## 280 11.7057 nan 0.0050 0.0513
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.5399 nan 0.0050 0.5474
## 2 62.0205 nan 0.0050 0.5043
## 3 61.5083 nan 0.0050 0.5518
## 4 61.0230 nan 0.0050 0.4798
## 5 60.5325 nan 0.0050 0.4728
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## 7 59.5142 nan 0.0050 0.4724
## 8 59.0403 nan 0.0050 0.4394
## 9 58.5800 nan 0.0050 0.4977
## 10 58.1053 nan 0.0050 0.4624
## 20 53.4971 nan 0.0050 0.4031
## 40 45.5169 nan 0.0050 0.3548
## 60 38.8347 nan 0.0050 0.2574
## 80 33.2636 nan 0.0050 0.2276
## 100 28.6068 nan 0.0050 0.1883
## 120 24.8016 nan 0.0050 0.1737
## 140 21.5715 nan 0.0050 0.1272
## 160 18.8972 nan 0.0050 0.1182
## 180 16.6434 nan 0.0050 0.1007
## 200 14.6962 nan 0.0050 0.0718
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## 240 11.7553 nan 0.0050 0.0505
## 260 10.5736 nan 0.0050 0.0498
## 280 9.5455 nan 0.0050 0.0414
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.5547 nan 0.0050 0.5189
## 2 62.0007 nan 0.0050 0.5162
## 3 61.4961 nan 0.0050 0.4987
## 4 60.9734 nan 0.0050 0.5048
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## 8 58.9605 nan 0.0050 0.5035
## 9 58.4682 nan 0.0050 0.5282
## 10 57.9789 nan 0.0050 0.4974
## 20 53.3108 nan 0.0050 0.4759
## 40 45.3842 nan 0.0050 0.3383
## 60 38.7152 nan 0.0050 0.3173
## 80 33.1353 nan 0.0050 0.2368
## 100 28.5152 nan 0.0050 0.2252
## 120 24.7551 nan 0.0050 0.1617
## 140 21.6098 nan 0.0050 0.1291
## 160 18.8810 nan 0.0050 0.1219
## 180 16.6481 nan 0.0050 0.0923
## 200 14.7583 nan 0.0050 0.0697
## 220 13.1385 nan 0.0050 0.0717
## 240 11.7667 nan 0.0050 0.0519
## 260 10.6081 nan 0.0050 0.0474
## 280 9.6064 nan 0.0050 0.0392
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.5239 nan 0.0050 0.5205
## 2 62.0050 nan 0.0050 0.5360
## 3 61.4834 nan 0.0050 0.5312
## 4 60.9881 nan 0.0050 0.4918
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## 6 59.9636 nan 0.0050 0.4475
## 7 59.4953 nan 0.0050 0.4820
## 8 59.0092 nan 0.0050 0.4931
## 9 58.5045 nan 0.0050 0.4342
## 10 58.0207 nan 0.0050 0.4775
## 20 53.4302 nan 0.0050 0.4699
## 40 45.4335 nan 0.0050 0.3413
## 60 38.8380 nan 0.0050 0.3203
## 80 33.3460 nan 0.0050 0.2453
## 100 28.8069 nan 0.0050 0.2086
## 120 25.0200 nan 0.0050 0.1971
## 140 21.8516 nan 0.0050 0.1431
## 160 19.1717 nan 0.0050 0.1140
## 180 16.9014 nan 0.0050 0.0895
## 200 15.0111 nan 0.0050 0.0775
## 220 13.4223 nan 0.0050 0.0684
## 240 12.0754 nan 0.0050 0.0535
## 260 10.9226 nan 0.0050 0.0450
## 280 9.9334 nan 0.0050 0.0421
## 300 9.0626 nan 0.0050 0.0290
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.2435 nan 0.0100 0.7869
## 2 61.5045 nan 0.0100 0.7899
## 3 60.7562 nan 0.0100 0.7604
## 4 60.0425 nan 0.0100 0.7498
## 5 59.2995 nan 0.0100 0.7819
## 6 58.5706 nan 0.0100 0.7384
## 7 57.8551 nan 0.0100 0.7269
## 8 57.1299 nan 0.0100 0.6729
## 9 56.4070 nan 0.0100 0.6745
## 10 55.7131 nan 0.0100 0.6431
## 20 49.4460 nan 0.0100 0.5883
## 40 39.8086 nan 0.0100 0.3986
## 60 32.4983 nan 0.0100 0.3092
## 80 27.0483 nan 0.0100 0.2481
## 100 22.8155 nan 0.0100 0.1560
## 120 19.4017 nan 0.0100 0.1262
## 140 16.7921 nan 0.0100 0.1264
## 160 14.6423 nan 0.0100 0.0777
## 180 12.9035 nan 0.0100 0.0816
## 200 11.5609 nan 0.0100 0.0470
## 220 10.3994 nan 0.0100 0.0434
## 240 9.4303 nan 0.0100 0.0336
## 260 8.6259 nan 0.0100 0.0313
## 280 7.9288 nan 0.0100 0.0141
## 300 7.3370 nan 0.0100 0.0244
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.2585 nan 0.0100 0.8301
## 2 61.5166 nan 0.0100 0.7757
## 3 60.7757 nan 0.0100 0.8009
## 4 59.9791 nan 0.0100 0.7176
## 5 59.2216 nan 0.0100 0.8296
## 6 58.4757 nan 0.0100 0.7389
## 7 57.7660 nan 0.0100 0.6983
## 8 57.0677 nan 0.0100 0.7025
## 9 56.3934 nan 0.0100 0.6861
## 10 55.7122 nan 0.0100 0.6880
## 20 49.5649 nan 0.0100 0.6358
## 40 39.7465 nan 0.0100 0.3827
## 60 32.4565 nan 0.0100 0.2990
## 80 26.9488 nan 0.0100 0.2376
## 100 22.7048 nan 0.0100 0.1776
## 120 19.4083 nan 0.0100 0.1306
## 140 16.8247 nan 0.0100 0.1008
## 160 14.7543 nan 0.0100 0.0920
## 180 13.0077 nan 0.0100 0.0644
## 200 11.6085 nan 0.0100 0.0537
## 220 10.4563 nan 0.0100 0.0375
## 240 9.5164 nan 0.0100 0.0333
## 260 8.7321 nan 0.0100 0.0287
## 280 8.0053 nan 0.0100 0.0268
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.2734 nan 0.0100 0.7702
## 2 61.5126 nan 0.0100 0.7853
## 3 60.7088 nan 0.0100 0.7805
## 4 59.9259 nan 0.0100 0.7542
## 5 59.2098 nan 0.0100 0.6704
## 6 58.4287 nan 0.0100 0.7033
## 7 57.7222 nan 0.0100 0.7287
## 8 57.0173 nan 0.0100 0.6972
## 9 56.3540 nan 0.0100 0.5614
## 10 55.6823 nan 0.0100 0.5926
## 20 49.5165 nan 0.0100 0.5657
## 40 39.9318 nan 0.0100 0.4009
## 60 32.6486 nan 0.0100 0.2912
## 80 27.2098 nan 0.0100 0.2224
## 100 22.9202 nan 0.0100 0.1563
## 120 19.5458 nan 0.0100 0.1445
## 140 16.8402 nan 0.0100 0.1117
## 160 14.7088 nan 0.0100 0.0750
## 180 13.0289 nan 0.0100 0.0655
## 200 11.6638 nan 0.0100 0.0466
## 220 10.5441 nan 0.0100 0.0412
## 240 9.5877 nan 0.0100 0.0331
## 260 8.7480 nan 0.0100 0.0221
## 280 8.0547 nan 0.0100 0.0269
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.0979 nan 0.0100 0.7865
## 2 61.1691 nan 0.0100 0.9918
## 3 60.2471 nan 0.0100 0.9804
## 4 59.4047 nan 0.0100 0.9063
## 5 58.4328 nan 0.0100 0.9295
## 6 57.5765 nan 0.0100 0.8250
## 7 56.6263 nan 0.0100 0.8396
## 8 55.7156 nan 0.0100 0.8250
## 9 54.9202 nan 0.0100 0.7518
## 10 54.1154 nan 0.0100 0.7442
## 20 46.6654 nan 0.0100 0.6889
## 40 35.0654 nan 0.0100 0.5300
## 60 26.9369 nan 0.0100 0.3522
## 80 20.9926 nan 0.0100 0.2148
## 100 16.7565 nan 0.0100 0.1765
## 120 13.6765 nan 0.0100 0.0902
## 140 11.3890 nan 0.0100 0.0786
## 160 9.7048 nan 0.0100 0.0592
## 180 8.3261 nan 0.0100 0.0438
## 200 7.2690 nan 0.0100 0.0341
## 220 6.4377 nan 0.0100 0.0341
## 240 5.8123 nan 0.0100 0.0224
## 260 5.2915 nan 0.0100 0.0198
## 280 4.9034 nan 0.0100 0.0100
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.1369 nan 0.0100 0.8606
## 2 61.1297 nan 0.0100 0.9188
## 3 60.2536 nan 0.0100 0.8952
## 4 59.3615 nan 0.0100 0.9120
## 5 58.5190 nan 0.0100 0.8267
## 6 57.6849 nan 0.0100 0.8418
## 7 56.8270 nan 0.0100 0.8363
## 8 55.9556 nan 0.0100 0.9189
## 9 55.1427 nan 0.0100 0.8770
## 10 54.3849 nan 0.0100 0.8272
## 20 46.7035 nan 0.0100 0.6358
## 40 35.0676 nan 0.0100 0.4596
## 60 26.9316 nan 0.0100 0.3784
## 80 21.0438 nan 0.0100 0.2164
## 100 16.8994 nan 0.0100 0.1766
## 120 13.7973 nan 0.0100 0.1299
## 140 11.5320 nan 0.0100 0.0798
## 160 9.7427 nan 0.0100 0.0677
## 180 8.4333 nan 0.0100 0.0526
## 200 7.4068 nan 0.0100 0.0326
## 220 6.5431 nan 0.0100 0.0281
## 240 5.8935 nan 0.0100 0.0220
## 260 5.4008 nan 0.0100 0.0214
## 280 4.9743 nan 0.0100 0.0115
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.1470 nan 0.0100 0.9614
## 2 61.1982 nan 0.0100 0.9385
## 3 60.1999 nan 0.0100 0.9230
## 4 59.3238 nan 0.0100 0.9274
## 5 58.4464 nan 0.0100 0.9488
## 6 57.5387 nan 0.0100 0.9324
## 7 56.6734 nan 0.0100 0.8208
## 8 55.7840 nan 0.0100 0.9035
## 9 54.9485 nan 0.0100 0.7938
## 10 54.0379 nan 0.0100 0.8238
## 20 46.6236 nan 0.0100 0.6969
## 40 35.1494 nan 0.0100 0.4373
## 60 26.9329 nan 0.0100 0.3304
## 80 21.2500 nan 0.0100 0.2290
## 100 17.0013 nan 0.0100 0.1789
## 120 13.9248 nan 0.0100 0.1110
## 140 11.5740 nan 0.0100 0.0903
## 160 9.9356 nan 0.0100 0.0620
## 180 8.5978 nan 0.0100 0.0504
## 200 7.5387 nan 0.0100 0.0422
## 220 6.7036 nan 0.0100 0.0311
## 240 6.0637 nan 0.0100 0.0212
## 260 5.5575 nan 0.0100 0.0209
## 280 5.1502 nan 0.0100 0.0138
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.0677 nan 0.0100 0.9927
## 2 61.1070 nan 0.0100 0.8863
## 3 60.1048 nan 0.0100 0.9546
## 4 59.0937 nan 0.0100 0.9177
## 5 58.1484 nan 0.0100 0.9650
## 6 57.2218 nan 0.0100 0.9912
## 7 56.2307 nan 0.0100 0.9871
## 8 55.3457 nan 0.0100 0.9614
## 9 54.4416 nan 0.0100 0.8438
## 10 53.5176 nan 0.0100 0.7842
## 20 45.4773 nan 0.0100 0.7451
## 40 33.0908 nan 0.0100 0.5087
## 60 24.7155 nan 0.0100 0.3274
## 80 18.8603 nan 0.0100 0.2393
## 100 14.7124 nan 0.0100 0.1705
## 120 11.7491 nan 0.0100 0.0978
## 140 9.6454 nan 0.0100 0.0873
## 160 8.0157 nan 0.0100 0.0568
## 180 6.7865 nan 0.0100 0.0396
## 200 5.8444 nan 0.0100 0.0208
## 220 5.1798 nan 0.0100 0.0168
## 240 4.6531 nan 0.0100 0.0175
## 260 4.2468 nan 0.0100 0.0098
## 280 3.9264 nan 0.0100 0.0106
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.0095 nan 0.0100 1.0144
## 2 60.9580 nan 0.0100 1.1364
## 3 59.9735 nan 0.0100 1.0367
## 4 58.9592 nan 0.0100 1.0433
## 5 57.9279 nan 0.0100 1.0403
## 6 56.9431 nan 0.0100 0.8858
## 7 55.9624 nan 0.0100 0.9379
## 8 55.0711 nan 0.0100 0.8256
## 9 54.1465 nan 0.0100 0.9141
## 10 53.1898 nan 0.0100 0.8359
## 20 45.2231 nan 0.0100 0.7400
## 40 33.0158 nan 0.0100 0.5100
## 60 24.6964 nan 0.0100 0.3428
## 80 18.8415 nan 0.0100 0.2504
## 100 14.6692 nan 0.0100 0.1595
## 120 11.6614 nan 0.0100 0.1331
## 140 9.4933 nan 0.0100 0.0877
## 160 7.9276 nan 0.0100 0.0530
## 180 6.7770 nan 0.0100 0.0377
## 200 5.8786 nan 0.0100 0.0356
## 220 5.1993 nan 0.0100 0.0201
## 240 4.6763 nan 0.0100 0.0200
## 260 4.2702 nan 0.0100 0.0050
## 280 3.9634 nan 0.0100 0.0066
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.9922 nan 0.0100 1.0260
## 2 60.9800 nan 0.0100 1.0586
## 3 59.9634 nan 0.0100 0.9294
## 4 59.0231 nan 0.0100 0.9318
## 5 58.0392 nan 0.0100 0.8607
## 6 57.1028 nan 0.0100 1.0221
## 7 56.1625 nan 0.0100 0.9658
## 8 55.2909 nan 0.0100 0.9054
## 9 54.3489 nan 0.0100 0.9446
## 10 53.4096 nan 0.0100 0.8695
## 20 45.3492 nan 0.0100 0.7784
## 40 33.4414 nan 0.0100 0.4817
## 60 24.9574 nan 0.0100 0.3455
## 80 19.0813 nan 0.0100 0.2437
## 100 14.9656 nan 0.0100 0.1229
## 120 12.0815 nan 0.0100 0.1042
## 140 9.9368 nan 0.0100 0.0803
## 160 8.3176 nan 0.0100 0.0536
## 180 7.1041 nan 0.0100 0.0455
## 200 6.1681 nan 0.0100 0.0390
## 220 5.5173 nan 0.0100 0.0259
## 240 5.0231 nan 0.0100 0.0171
## 260 4.6274 nan 0.0100 0.0106
## 280 4.3130 nan 0.0100 0.0087
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.9225 nan 0.0500 3.8951
## 2 55.3841 nan 0.0500 3.2621
## 3 52.5621 nan 0.0500 3.2808
## 4 49.6807 nan 0.0500 2.4797
## 5 46.8703 nan 0.0500 2.7765
## 6 44.0847 nan 0.0500 2.7743
## 7 41.4837 nan 0.0500 2.0418
## 8 39.2219 nan 0.0500 2.1373
## 9 37.1297 nan 0.0500 1.8864
## 10 35.3605 nan 0.0500 1.6945
## 20 22.3779 nan 0.0500 0.8073
## 40 11.3675 nan 0.0500 0.2447
## 60 7.3038 nan 0.0500 0.0493
## 80 5.4879 nan 0.0500 0.0659
## 100 4.5652 nan 0.0500 0.0143
## 120 4.0699 nan 0.0500 0.0010
## 140 3.8286 nan 0.0500 0.0096
## 160 3.6560 nan 0.0500 -0.0049
## 180 3.5241 nan 0.0500 -0.0018
## 200 3.4224 nan 0.0500 -0.0073
## 220 3.3305 nan 0.0500 -0.0091
## 240 3.2509 nan 0.0500 -0.0113
## 260 3.1560 nan 0.0500 -0.0031
## 280 3.1021 nan 0.0500 -0.0138
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.8677 nan 0.0500 3.7310
## 2 55.2605 nan 0.0500 3.3792
## 3 51.9458 nan 0.0500 3.2189
## 4 49.0196 nan 0.0500 2.9033
## 5 46.4713 nan 0.0500 2.5210
## 6 43.8873 nan 0.0500 2.4765
## 7 41.7062 nan 0.0500 2.1375
## 8 39.6457 nan 0.0500 1.8179
## 9 37.6867 nan 0.0500 1.9095
## 10 35.7315 nan 0.0500 1.8632
## 20 22.6660 nan 0.0500 0.7583
## 40 11.6429 nan 0.0500 0.2264
## 60 7.4843 nan 0.0500 0.1373
## 80 5.6275 nan 0.0500 0.0356
## 100 4.6918 nan 0.0500 0.0153
## 120 4.2179 nan 0.0500 0.0077
## 140 3.9430 nan 0.0500 0.0008
## 160 3.7848 nan 0.0500 -0.0068
## 180 3.6600 nan 0.0500 0.0016
## 200 3.5711 nan 0.0500 -0.0055
## 220 3.4721 nan 0.0500 -0.0018
## 240 3.3917 nan 0.0500 0.0000
## 260 3.3183 nan 0.0500 -0.0082
## 280 3.2438 nan 0.0500 -0.0046
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.2350 nan 0.0500 4.0732
## 2 55.4429 nan 0.0500 3.4303
## 3 52.2621 nan 0.0500 3.2178
## 4 49.5781 nan 0.0500 2.8125
## 5 46.7568 nan 0.0500 2.6740
## 6 44.2605 nan 0.0500 2.3875
## 7 42.0634 nan 0.0500 2.3446
## 8 39.8059 nan 0.0500 2.1980
## 9 38.0049 nan 0.0500 1.5175
## 10 36.1817 nan 0.0500 1.7165
## 20 22.9018 nan 0.0500 0.9219
## 40 11.6413 nan 0.0500 0.3010
## 60 7.5302 nan 0.0500 0.0677
## 80 5.6587 nan 0.0500 0.0417
## 100 4.6917 nan 0.0500 0.0102
## 120 4.2663 nan 0.0500 0.0045
## 140 4.0229 nan 0.0500 0.0021
## 160 3.8753 nan 0.0500 -0.0173
## 180 3.7430 nan 0.0500 -0.0028
## 200 3.6272 nan 0.0500 -0.0055
## 220 3.5430 nan 0.0500 -0.0037
## 240 3.4774 nan 0.0500 -0.0119
## 260 3.4037 nan 0.0500 -0.0061
## 280 3.3349 nan 0.0500 -0.0063
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.1141 nan 0.0500 4.5889
## 2 54.0823 nan 0.0500 4.0722
## 3 50.1933 nan 0.0500 4.1815
## 4 46.3683 nan 0.0500 3.4141
## 5 42.8779 nan 0.0500 3.1653
## 6 39.7175 nan 0.0500 2.8937
## 7 37.1556 nan 0.0500 2.7559
## 8 34.6357 nan 0.0500 2.5024
## 9 32.4365 nan 0.0500 2.0707
## 10 30.1385 nan 0.0500 2.0227
## 20 16.5111 nan 0.0500 0.8021
## 40 7.0895 nan 0.0500 0.2033
## 60 4.5946 nan 0.0500 0.0508
## 80 3.7091 nan 0.0500 0.0183
## 100 3.2541 nan 0.0500 0.0004
## 120 2.9878 nan 0.0500 -0.0162
## 140 2.7970 nan 0.0500 -0.0141
## 160 2.6399 nan 0.0500 0.0009
## 180 2.4864 nan 0.0500 -0.0048
## 200 2.3904 nan 0.0500 -0.0205
## 220 2.2982 nan 0.0500 -0.0065
## 240 2.2166 nan 0.0500 -0.0075
## 260 2.1286 nan 0.0500 -0.0052
## 280 2.0593 nan 0.0500 -0.0034
## 300 2.0058 nan 0.0500 -0.0062
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.7744 nan 0.0500 4.2356
## 2 54.4584 nan 0.0500 3.9659
## 3 50.5120 nan 0.0500 3.7956
## 4 46.7373 nan 0.0500 4.2426
## 5 43.4983 nan 0.0500 3.2240
## 6 40.4439 nan 0.0500 3.1049
## 7 37.8241 nan 0.0500 2.9411
## 8 35.1225 nan 0.0500 2.7024
## 9 32.7481 nan 0.0500 2.2673
## 10 30.4388 nan 0.0500 2.2020
## 20 16.5893 nan 0.0500 0.8829
## 40 7.1451 nan 0.0500 0.2801
## 60 4.5542 nan 0.0500 0.0006
## 80 3.6820 nan 0.0500 0.0126
## 100 3.2764 nan 0.0500 -0.0040
## 120 3.0542 nan 0.0500 -0.0142
## 140 2.8773 nan 0.0500 -0.0116
## 160 2.7413 nan 0.0500 -0.0192
## 180 2.6358 nan 0.0500 -0.0095
## 200 2.5447 nan 0.0500 -0.0131
## 220 2.4457 nan 0.0500 -0.0098
## 240 2.3410 nan 0.0500 -0.0069
## 260 2.2765 nan 0.0500 -0.0006
## 280 2.2186 nan 0.0500 -0.0092
## 300 2.1682 nan 0.0500 -0.0162
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.7821 nan 0.0500 4.8911
## 2 53.5577 nan 0.0500 4.3252
## 3 49.6252 nan 0.0500 3.7515
## 4 46.0784 nan 0.0500 3.7207
## 5 42.8917 nan 0.0500 3.1984
## 6 39.6332 nan 0.0500 3.0048
## 7 36.7458 nan 0.0500 2.9910
## 8 34.2008 nan 0.0500 2.2970
## 9 31.7751 nan 0.0500 2.3925
## 10 29.6931 nan 0.0500 1.8856
## 20 16.7333 nan 0.0500 0.8608
## 40 7.4618 nan 0.0500 0.2224
## 60 4.9018 nan 0.0500 0.0535
## 80 4.0473 nan 0.0500 0.0029
## 100 3.6745 nan 0.0500 0.0007
## 120 3.4140 nan 0.0500 -0.0044
## 140 3.2345 nan 0.0500 -0.0104
## 160 3.0627 nan 0.0500 -0.0073
## 180 2.9288 nan 0.0500 -0.0096
## 200 2.7985 nan 0.0500 -0.0099
## 220 2.6994 nan 0.0500 -0.0017
## 240 2.6215 nan 0.0500 -0.0039
## 260 2.5466 nan 0.0500 -0.0065
## 280 2.4737 nan 0.0500 -0.0096
## 300 2.4017 nan 0.0500 -0.0118
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.7437 nan 0.0500 5.1823
## 2 52.8912 nan 0.0500 4.6337
## 3 48.6834 nan 0.0500 4.6497
## 4 44.7312 nan 0.0500 3.7475
## 5 41.2665 nan 0.0500 3.7965
## 6 38.1005 nan 0.0500 3.5332
## 7 35.2320 nan 0.0500 3.1552
## 8 32.4554 nan 0.0500 2.6942
## 9 30.2999 nan 0.0500 2.5313
## 10 28.2957 nan 0.0500 2.1916
## 20 14.6008 nan 0.0500 0.8531
## 40 5.8068 nan 0.0500 0.1461
## 60 3.6100 nan 0.0500 0.0218
## 80 2.9798 nan 0.0500 -0.0017
## 100 2.6317 nan 0.0500 0.0006
## 120 2.3776 nan 0.0500 -0.0022
## 140 2.1719 nan 0.0500 -0.0086
## 160 2.0389 nan 0.0500 -0.0155
## 180 1.9160 nan 0.0500 -0.0210
## 200 1.8089 nan 0.0500 -0.0046
## 220 1.7051 nan 0.0500 -0.0071
## 240 1.6278 nan 0.0500 -0.0079
## 260 1.5512 nan 0.0500 -0.0039
## 280 1.4769 nan 0.0500 -0.0074
## 300 1.4067 nan 0.0500 -0.0042
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.8892 nan 0.0500 5.0801
## 2 53.1891 nan 0.0500 4.4378
## 3 48.7995 nan 0.0500 4.2671
## 4 44.9932 nan 0.0500 4.1260
## 5 41.5483 nan 0.0500 3.4567
## 6 38.3856 nan 0.0500 3.3647
## 7 35.5622 nan 0.0500 3.0173
## 8 32.9487 nan 0.0500 2.4053
## 9 30.5648 nan 0.0500 2.4723
## 10 28.3859 nan 0.0500 2.1107
## 20 14.8122 nan 0.0500 0.8391
## 40 5.9221 nan 0.0500 0.1835
## 60 3.7335 nan 0.0500 0.0412
## 80 3.1116 nan 0.0500 -0.0210
## 100 2.7618 nan 0.0500 -0.0143
## 120 2.5017 nan 0.0500 -0.0203
## 140 2.3354 nan 0.0500 -0.0137
## 160 2.1900 nan 0.0500 -0.0106
## 180 2.0583 nan 0.0500 -0.0101
## 200 1.9828 nan 0.0500 -0.0111
## 220 1.8784 nan 0.0500 -0.0088
## 240 1.8063 nan 0.0500 -0.0100
## 260 1.7315 nan 0.0500 -0.0114
## 280 1.6609 nan 0.0500 -0.0103
## 300 1.6037 nan 0.0500 -0.0088
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.8813 nan 0.0500 5.2839
## 2 53.3371 nan 0.0500 4.6904
## 3 49.0496 nan 0.0500 4.6023
## 4 45.0824 nan 0.0500 4.1415
## 5 41.5500 nan 0.0500 3.7558
## 6 38.4122 nan 0.0500 3.0629
## 7 35.7095 nan 0.0500 3.0510
## 8 33.2005 nan 0.0500 2.6959
## 9 30.7604 nan 0.0500 2.4886
## 10 28.7016 nan 0.0500 2.0400
## 20 14.9256 nan 0.0500 0.9068
## 40 6.2590 nan 0.0500 0.1522
## 60 4.1223 nan 0.0500 -0.0010
## 80 3.5152 nan 0.0500 -0.0088
## 100 3.1293 nan 0.0500 -0.0054
## 120 2.8869 nan 0.0500 -0.0223
## 140 2.7052 nan 0.0500 -0.0199
## 160 2.5557 nan 0.0500 -0.0215
## 180 2.4077 nan 0.0500 -0.0209
## 200 2.3099 nan 0.0500 -0.0105
## 220 2.2315 nan 0.0500 -0.0115
## 240 2.1320 nan 0.0500 -0.0169
## 260 2.0582 nan 0.0500 -0.0147
## 280 1.9771 nan 0.0500 -0.0098
## 300 1.9179 nan 0.0500 -0.0180
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.5592 nan 0.1000 7.2056
## 2 49.4507 nan 0.1000 6.2454
## 3 44.9878 nan 0.1000 4.6773
## 4 40.4450 nan 0.1000 4.7366
## 5 36.4740 nan 0.1000 3.5317
## 6 32.9616 nan 0.1000 3.4821
## 7 29.6976 nan 0.1000 3.1852
## 8 26.9124 nan 0.1000 2.4894
## 9 24.6731 nan 0.1000 2.2316
## 10 22.6749 nan 0.1000 1.8785
## 20 11.6485 nan 0.1000 0.8172
## 40 5.5192 nan 0.1000 0.0667
## 60 4.2399 nan 0.1000 0.0254
## 80 3.7789 nan 0.1000 -0.0096
## 100 3.5407 nan 0.1000 -0.0080
## 120 3.3788 nan 0.1000 -0.0213
## 140 3.2450 nan 0.1000 -0.0236
## 160 3.1223 nan 0.1000 -0.0254
## 180 3.0478 nan 0.1000 -0.0349
## 200 2.9765 nan 0.1000 -0.0312
## 220 2.9148 nan 0.1000 -0.0145
## 240 2.8509 nan 0.1000 -0.0105
## 260 2.7903 nan 0.1000 -0.0203
## 280 2.7413 nan 0.1000 -0.0170
## 300 2.6876 nan 0.1000 -0.0074
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.5172 nan 0.1000 7.9091
## 2 49.2460 nan 0.1000 6.0050
## 3 44.0925 nan 0.1000 5.1603
## 4 39.5639 nan 0.1000 4.1429
## 5 35.6149 nan 0.1000 3.6449
## 6 32.1879 nan 0.1000 3.3452
## 7 29.1690 nan 0.1000 2.8689
## 8 26.9872 nan 0.1000 1.4260
## 9 24.5380 nan 0.1000 2.4033
## 10 22.4896 nan 0.1000 1.8869
## 20 11.5772 nan 0.1000 0.5964
## 40 5.3327 nan 0.1000 0.0539
## 60 4.0717 nan 0.1000 0.0136
## 80 3.6564 nan 0.1000 0.0040
## 100 3.4494 nan 0.1000 -0.0085
## 120 3.2753 nan 0.1000 0.0015
## 140 3.1525 nan 0.1000 -0.0157
## 160 3.0611 nan 0.1000 -0.0221
## 180 2.9802 nan 0.1000 -0.0181
## 200 2.9100 nan 0.1000 -0.0217
## 220 2.8476 nan 0.1000 -0.0071
## 240 2.7877 nan 0.1000 -0.0139
## 260 2.7251 nan 0.1000 -0.0072
## 280 2.6739 nan 0.1000 -0.0142
## 300 2.6195 nan 0.1000 -0.0090
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 54.6790 nan 0.1000 7.1350
## 2 48.1823 nan 0.1000 5.7483
## 3 43.5998 nan 0.1000 4.9276
## 4 39.2330 nan 0.1000 4.0294
## 5 35.1486 nan 0.1000 4.0109
## 6 32.2055 nan 0.1000 3.0630
## 7 28.9351 nan 0.1000 2.3628
## 8 26.5850 nan 0.1000 2.6222
## 9 24.4507 nan 0.1000 2.0523
## 10 22.2555 nan 0.1000 2.0099
## 20 11.6335 nan 0.1000 0.6642
## 40 5.7993 nan 0.1000 0.0768
## 60 4.5320 nan 0.1000 -0.0203
## 80 4.2259 nan 0.1000 -0.0173
## 100 3.9692 nan 0.1000 0.0030
## 120 3.7457 nan 0.1000 -0.0160
## 140 3.5968 nan 0.1000 -0.0223
## 160 3.4562 nan 0.1000 0.0024
## 180 3.3520 nan 0.1000 -0.0135
## 200 3.3067 nan 0.1000 -0.0219
## 220 3.2335 nan 0.1000 -0.0105
## 240 3.1409 nan 0.1000 -0.0065
## 260 3.0931 nan 0.1000 -0.0098
## 280 3.0230 nan 0.1000 -0.0114
## 300 2.9618 nan 0.1000 -0.0192
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 53.3843 nan 0.1000 9.1318
## 2 45.8034 nan 0.1000 7.1961
## 3 39.3968 nan 0.1000 6.1120
## 4 33.8782 nan 0.1000 5.8476
## 5 29.3514 nan 0.1000 4.5068
## 6 25.9107 nan 0.1000 3.3347
## 7 22.9405 nan 0.1000 2.9449
## 8 20.0139 nan 0.1000 2.3924
## 9 17.6338 nan 0.1000 2.2205
## 10 15.7271 nan 0.1000 1.7723
## 20 6.9148 nan 0.1000 0.4317
## 40 3.6421 nan 0.1000 0.0048
## 60 3.0587 nan 0.1000 0.0015
## 80 2.7025 nan 0.1000 -0.0219
## 100 2.4415 nan 0.1000 -0.0081
## 120 2.2232 nan 0.1000 -0.0167
## 140 2.0697 nan 0.1000 0.0026
## 160 1.9610 nan 0.1000 -0.0059
## 180 1.8429 nan 0.1000 -0.0101
## 200 1.7591 nan 0.1000 -0.0212
## 220 1.6675 nan 0.1000 -0.0132
## 240 1.6116 nan 0.1000 -0.0190
## 260 1.5358 nan 0.1000 -0.0155
## 280 1.4640 nan 0.1000 -0.0078
## 300 1.4041 nan 0.1000 -0.0103
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 53.7819 nan 0.1000 9.1721
## 2 45.5996 nan 0.1000 7.9902
## 3 38.9345 nan 0.1000 6.4271
## 4 33.4925 nan 0.1000 4.4002
## 5 28.7884 nan 0.1000 4.1930
## 6 25.2573 nan 0.1000 3.4799
## 7 22.1783 nan 0.1000 2.8627
## 8 19.8720 nan 0.1000 2.2615
## 9 17.7070 nan 0.1000 1.9271
## 10 15.9230 nan 0.1000 1.8037
## 20 7.0199 nan 0.1000 0.3900
## 40 3.6359 nan 0.1000 -0.0012
## 60 3.0996 nan 0.1000 0.0018
## 80 2.7806 nan 0.1000 -0.0283
## 100 2.5150 nan 0.1000 -0.0111
## 120 2.3614 nan 0.1000 -0.0084
## 140 2.2095 nan 0.1000 -0.0159
## 160 2.0907 nan 0.1000 -0.0095
## 180 1.9944 nan 0.1000 -0.0154
## 200 1.8741 nan 0.1000 -0.0096
## 220 1.7875 nan 0.1000 -0.0215
## 240 1.7171 nan 0.1000 -0.0187
## 260 1.6560 nan 0.1000 -0.0247
## 280 1.5934 nan 0.1000 -0.0162
## 300 1.5309 nan 0.1000 -0.0068
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 54.1563 nan 0.1000 9.1890
## 2 47.1863 nan 0.1000 7.4860
## 3 40.5223 nan 0.1000 6.5973
## 4 35.0471 nan 0.1000 5.6348
## 5 30.5790 nan 0.1000 4.4031
## 6 26.9536 nan 0.1000 3.9411
## 7 23.7367 nan 0.1000 2.9762
## 8 21.0344 nan 0.1000 2.8311
## 9 18.6237 nan 0.1000 2.3509
## 10 16.9030 nan 0.1000 1.7766
## 20 7.5894 nan 0.1000 0.4691
## 40 4.2389 nan 0.1000 0.0239
## 60 3.5309 nan 0.1000 0.0059
## 80 3.0933 nan 0.1000 0.0011
## 100 2.8676 nan 0.1000 -0.0095
## 120 2.6494 nan 0.1000 -0.0116
## 140 2.4690 nan 0.1000 -0.0215
## 160 2.3283 nan 0.1000 -0.0267
## 180 2.2070 nan 0.1000 0.0005
## 200 2.1131 nan 0.1000 -0.0069
## 220 2.0109 nan 0.1000 -0.0085
## 240 1.9468 nan 0.1000 -0.0138
## 260 1.8766 nan 0.1000 -0.0273
## 280 1.8151 nan 0.1000 -0.0319
## 300 1.7568 nan 0.1000 -0.0061
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.9707 nan 0.1000 9.7861
## 2 44.6811 nan 0.1000 8.5758
## 3 38.0107 nan 0.1000 7.3145
## 4 32.6135 nan 0.1000 5.6445
## 5 27.9218 nan 0.1000 4.5815
## 6 24.1235 nan 0.1000 3.0533
## 7 20.9760 nan 0.1000 2.9903
## 8 18.0233 nan 0.1000 2.6554
## 9 15.8965 nan 0.1000 2.1371
## 10 13.8534 nan 0.1000 2.0530
## 20 5.5321 nan 0.1000 0.3555
## 40 2.9857 nan 0.1000 -0.0135
## 60 2.4533 nan 0.1000 -0.0049
## 80 2.0999 nan 0.1000 -0.0183
## 100 1.8238 nan 0.1000 -0.0019
## 120 1.6132 nan 0.1000 -0.0171
## 140 1.4482 nan 0.1000 -0.0144
## 160 1.3141 nan 0.1000 -0.0105
## 180 1.2308 nan 0.1000 -0.0128
## 200 1.1207 nan 0.1000 -0.0176
## 220 1.0379 nan 0.1000 -0.0112
## 240 0.9759 nan 0.1000 -0.0154
## 260 0.8945 nan 0.1000 -0.0102
## 280 0.8453 nan 0.1000 -0.0163
## 300 0.7891 nan 0.1000 -0.0083
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 53.2139 nan 0.1000 9.2980
## 2 44.6503 nan 0.1000 7.8656
## 3 38.0646 nan 0.1000 5.9041
## 4 32.6175 nan 0.1000 4.9176
## 5 27.9081 nan 0.1000 4.9373
## 6 24.3612 nan 0.1000 3.4839
## 7 21.0771 nan 0.1000 3.3119
## 8 18.2194 nan 0.1000 2.4167
## 9 16.0306 nan 0.1000 2.1591
## 10 14.1805 nan 0.1000 1.5558
## 20 5.7325 nan 0.1000 0.2454
## 40 3.2500 nan 0.1000 0.0113
## 60 2.6177 nan 0.1000 -0.0285
## 80 2.2816 nan 0.1000 -0.0196
## 100 2.0208 nan 0.1000 -0.0141
## 120 1.8317 nan 0.1000 -0.0233
## 140 1.6910 nan 0.1000 -0.0131
## 160 1.5778 nan 0.1000 -0.0106
## 180 1.4780 nan 0.1000 -0.0296
## 200 1.3784 nan 0.1000 -0.0274
## 220 1.2999 nan 0.1000 -0.0141
## 240 1.2365 nan 0.1000 -0.0123
## 260 1.1727 nan 0.1000 -0.0117
## 280 1.1066 nan 0.1000 -0.0061
## 300 1.0491 nan 0.1000 -0.0177
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.9383 nan 0.1000 8.6129
## 2 44.4343 nan 0.1000 8.8080
## 3 37.9714 nan 0.1000 6.4109
## 4 32.3280 nan 0.1000 6.3096
## 5 27.6423 nan 0.1000 4.1043
## 6 23.8484 nan 0.1000 3.1454
## 7 20.8135 nan 0.1000 3.1513
## 8 18.0614 nan 0.1000 2.4603
## 9 16.0158 nan 0.1000 1.8716
## 10 13.9073 nan 0.1000 2.1067
## 20 5.6968 nan 0.1000 0.2579
## 40 3.3720 nan 0.1000 -0.0007
## 60 2.8287 nan 0.1000 -0.0240
## 80 2.5373 nan 0.1000 -0.0132
## 100 2.2631 nan 0.1000 -0.0126
## 120 2.1034 nan 0.1000 -0.0166
## 140 1.9515 nan 0.1000 -0.0266
## 160 1.8362 nan 0.1000 -0.0282
## 180 1.7153 nan 0.1000 -0.0199
## 200 1.6567 nan 0.1000 -0.0148
## 220 1.5679 nan 0.1000 -0.0141
## 240 1.4944 nan 0.1000 -0.0118
## 260 1.4229 nan 0.1000 -0.0148
## 280 1.3772 nan 0.1000 -0.0179
## 300 1.3275 nan 0.1000 -0.0147
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.0754 nan 0.0010 0.0752
## 2 61.9944 nan 0.0010 0.0749
## 3 61.9148 nan 0.0010 0.0745
## 4 61.8449 nan 0.0010 0.0772
## 5 61.7667 nan 0.0010 0.0757
## 6 61.6902 nan 0.0010 0.0715
## 7 61.6166 nan 0.0010 0.0794
## 8 61.5397 nan 0.0010 0.0824
## 9 61.4628 nan 0.0010 0.0789
## 10 61.3853 nan 0.0010 0.0717
## 20 60.6293 nan 0.0010 0.0757
## 40 59.1500 nan 0.0010 0.0752
## 60 57.7111 nan 0.0010 0.0667
## 80 56.3488 nan 0.0010 0.0683
## 100 55.0360 nan 0.0010 0.0636
## 120 53.7516 nan 0.0010 0.0647
## 140 52.5220 nan 0.0010 0.0580
## 160 51.3006 nan 0.0010 0.0645
## 180 50.1342 nan 0.0010 0.0540
## 200 49.0267 nan 0.0010 0.0529
## 220 47.9267 nan 0.0010 0.0511
## 240 46.8907 nan 0.0010 0.0504
## 260 45.8579 nan 0.0010 0.0477
## 280 44.8637 nan 0.0010 0.0408
## 300 43.9017 nan 0.0010 0.0472
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.0732 nan 0.0010 0.0847
## 2 61.9964 nan 0.0010 0.0809
## 3 61.9168 nan 0.0010 0.0783
## 4 61.8338 nan 0.0010 0.0767
## 5 61.7634 nan 0.0010 0.0719
## 6 61.6853 nan 0.0010 0.0748
## 7 61.6082 nan 0.0010 0.0767
## 8 61.5272 nan 0.0010 0.0793
## 9 61.4484 nan 0.0010 0.0762
## 10 61.3695 nan 0.0010 0.0737
## 20 60.5923 nan 0.0010 0.0756
## 40 59.1248 nan 0.0010 0.0728
## 60 57.7153 nan 0.0010 0.0668
## 80 56.3447 nan 0.0010 0.0657
## 100 55.0031 nan 0.0010 0.0646
## 120 53.7289 nan 0.0010 0.0598
## 140 52.4527 nan 0.0010 0.0600
## 160 51.2282 nan 0.0010 0.0564
## 180 50.0588 nan 0.0010 0.0531
## 200 48.9181 nan 0.0010 0.0562
## 220 47.8340 nan 0.0010 0.0513
## 240 46.7881 nan 0.0010 0.0466
## 260 45.7916 nan 0.0010 0.0475
## 280 44.8138 nan 0.0010 0.0450
## 300 43.8557 nan 0.0010 0.0447
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.0735 nan 0.0010 0.0789
## 2 61.9908 nan 0.0010 0.0718
## 3 61.9064 nan 0.0010 0.0772
## 4 61.8243 nan 0.0010 0.0781
## 5 61.7413 nan 0.0010 0.0768
## 6 61.6634 nan 0.0010 0.0827
## 7 61.5913 nan 0.0010 0.0773
## 8 61.5169 nan 0.0010 0.0743
## 9 61.4387 nan 0.0010 0.0768
## 10 61.3648 nan 0.0010 0.0730
## 20 60.5969 nan 0.0010 0.0777
## 40 59.1104 nan 0.0010 0.0785
## 60 57.6582 nan 0.0010 0.0720
## 80 56.2899 nan 0.0010 0.0702
## 100 54.9670 nan 0.0010 0.0645
## 120 53.6760 nan 0.0010 0.0677
## 140 52.4357 nan 0.0010 0.0632
## 160 51.2431 nan 0.0010 0.0562
## 180 50.0923 nan 0.0010 0.0513
## 200 48.9661 nan 0.0010 0.0513
## 220 47.8768 nan 0.0010 0.0478
## 240 46.8224 nan 0.0010 0.0528
## 260 45.7887 nan 0.0010 0.0512
## 280 44.8007 nan 0.0010 0.0487
## 300 43.8565 nan 0.0010 0.0461
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.0569 nan 0.0010 0.0932
## 2 61.9628 nan 0.0010 0.0914
## 3 61.8697 nan 0.0010 0.0942
## 4 61.7765 nan 0.0010 0.0890
## 5 61.6767 nan 0.0010 0.0957
## 6 61.5796 nan 0.0010 0.0888
## 7 61.4921 nan 0.0010 0.0835
## 8 61.4007 nan 0.0010 0.0924
## 9 61.3097 nan 0.0010 0.0998
## 10 61.2124 nan 0.0010 0.0911
## 20 60.2761 nan 0.0010 0.0920
## 40 58.4661 nan 0.0010 0.0877
## 60 56.7267 nan 0.0010 0.0886
## 80 55.0407 nan 0.0010 0.0769
## 100 53.4041 nan 0.0010 0.0794
## 120 51.8241 nan 0.0010 0.0733
## 140 50.3060 nan 0.0010 0.0657
## 160 48.8152 nan 0.0010 0.0754
## 180 47.4212 nan 0.0010 0.0800
## 200 46.0678 nan 0.0010 0.0632
## 220 44.7154 nan 0.0010 0.0605
## 240 43.4422 nan 0.0010 0.0578
## 260 42.1901 nan 0.0010 0.0558
## 280 40.9969 nan 0.0010 0.0550
## 300 39.8451 nan 0.0010 0.0538
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.0615 nan 0.0010 0.0869
## 2 61.9636 nan 0.0010 0.0919
## 3 61.8716 nan 0.0010 0.0934
## 4 61.7685 nan 0.0010 0.0908
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.0533 nan 0.0010 0.0953
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## 3 61.8628 nan 0.0010 0.0897
## 4 61.7658 nan 0.0010 0.1000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.0535 nan 0.0010 0.0949
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## 3 61.8497 nan 0.0010 0.1027
## 4 61.7501 nan 0.0010 0.1037
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## 80 54.4396 nan 0.0010 0.0928
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.0484 nan 0.0010 0.0982
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## 3 61.8399 nan 0.0010 0.0963
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.0482 nan 0.0010 0.0885
## 2 61.9460 nan 0.0010 0.0887
## 3 61.8374 nan 0.0010 0.0976
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.8013 nan 0.0050 0.3940
## 2 61.4273 nan 0.0050 0.3905
## 3 61.0467 nan 0.0050 0.3918
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## 280 16.7899 nan 0.0050 0.0554
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.7913 nan 0.0050 0.3838
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## 3 61.0056 nan 0.0050 0.4250
## 4 60.6271 nan 0.0050 0.3433
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## 280 16.9240 nan 0.0050 0.0535
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.7560 nan 0.0050 0.3837
## 2 61.3484 nan 0.0050 0.3826
## 3 60.9640 nan 0.0050 0.3581
## 4 60.5900 nan 0.0050 0.3724
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## 180 24.8866 nan 0.0050 0.0940
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.6587 nan 0.0050 0.4437
## 2 61.2363 nan 0.0050 0.4538
## 3 60.7644 nan 0.0050 0.4669
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.6371 nan 0.0050 0.5024
## 2 61.1131 nan 0.0050 0.4550
## 3 60.6149 nan 0.0050 0.5436
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.7045 nan 0.0050 0.4555
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.6353 nan 0.0050 0.5519
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## 3 60.6312 nan 0.0050 0.5632
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## 60 38.4985 nan 0.0050 0.2877
## 80 33.0659 nan 0.0050 0.2245
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.5865 nan 0.0050 0.5113
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## 3 60.5360 nan 0.0050 0.4920
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## 40 44.6545 nan 0.0050 0.3537
## 60 38.1660 nan 0.0050 0.2886
## 80 32.7930 nan 0.0050 0.1976
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## 140 21.3893 nan 0.0050 0.1292
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.6303 nan 0.0050 0.5324
## 2 61.1122 nan 0.0050 0.4753
## 3 60.6015 nan 0.0050 0.5356
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## 60 38.2726 nan 0.0050 0.2798
## 80 32.7781 nan 0.0050 0.2245
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## 140 21.5323 nan 0.0050 0.1353
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## 180 16.6798 nan 0.0050 0.0957
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.4231 nan 0.0100 0.8007
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## 3 59.9001 nan 0.0100 0.7900
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## 20 48.6874 nan 0.0100 0.5202
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## 80 26.8401 nan 0.0100 0.2053
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.3600 nan 0.0100 0.7558
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## 3 59.8105 nan 0.0100 0.6519
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.3586 nan 0.0100 0.8241
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## 3 59.8011 nan 0.0100 0.7361
## 4 59.0550 nan 0.0100 0.6870
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## 80 26.9362 nan 0.0100 0.2254
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.1748 nan 0.0100 0.9225
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## 20 46.0659 nan 0.0100 0.6700
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.1454 nan 0.0100 0.9078
## 2 60.2830 nan 0.0100 0.7886
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.2702 nan 0.0100 0.9618
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## 200 7.4665 nan 0.0100 0.0333
## 220 6.6889 nan 0.0100 0.0318
## 240 6.0548 nan 0.0100 0.0232
## 260 5.5558 nan 0.0100 0.0139
## 280 5.1671 nan 0.0100 0.0080
## 300 4.8670 nan 0.0100 0.0109
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.1397 nan 0.0100 0.9481
## 2 60.1652 nan 0.0100 0.9431
## 3 59.1255 nan 0.0100 1.0902
## 4 58.1310 nan 0.0100 0.9447
## 5 57.1268 nan 0.0100 1.0498
## 6 56.1676 nan 0.0100 0.9156
## 7 55.1973 nan 0.0100 0.8430
## 8 54.3437 nan 0.0100 0.9172
## 9 53.4160 nan 0.0100 0.8079
## 10 52.5049 nan 0.0100 0.8152
## 20 44.5611 nan 0.0100 0.6963
## 40 32.6146 nan 0.0100 0.4615
## 60 24.3069 nan 0.0100 0.3361
## 80 18.5712 nan 0.0100 0.2420
## 100 14.5428 nan 0.0100 0.1673
## 120 11.5675 nan 0.0100 0.1213
## 140 9.4294 nan 0.0100 0.0781
## 160 7.8655 nan 0.0100 0.0512
## 180 6.7116 nan 0.0100 0.0410
## 200 5.8183 nan 0.0100 0.0333
## 220 5.1399 nan 0.0100 0.0197
## 240 4.6297 nan 0.0100 0.0157
## 260 4.2213 nan 0.0100 0.0136
## 280 3.9168 nan 0.0100 0.0047
## 300 3.6971 nan 0.0100 0.0035
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.1740 nan 0.0100 1.0519
## 2 60.1188 nan 0.0100 1.0716
## 3 59.1260 nan 0.0100 0.9244
## 4 58.1686 nan 0.0100 1.0022
## 5 57.2757 nan 0.0100 0.9846
## 6 56.3190 nan 0.0100 0.9525
## 7 55.4157 nan 0.0100 0.8928
## 8 54.5039 nan 0.0100 0.8966
## 9 53.5836 nan 0.0100 0.8538
## 10 52.7147 nan 0.0100 0.8452
## 20 44.8009 nan 0.0100 0.7050
## 40 32.8674 nan 0.0100 0.4990
## 60 24.5232 nan 0.0100 0.3374
## 80 18.7407 nan 0.0100 0.2479
## 100 14.6645 nan 0.0100 0.1871
## 120 11.6529 nan 0.0100 0.1183
## 140 9.4896 nan 0.0100 0.0790
## 160 7.8928 nan 0.0100 0.0618
## 180 6.7143 nan 0.0100 0.0515
## 200 5.8534 nan 0.0100 0.0226
## 220 5.1790 nan 0.0100 0.0203
## 240 4.6851 nan 0.0100 0.0218
## 260 4.3072 nan 0.0100 0.0118
## 280 4.0149 nan 0.0100 0.0108
## 300 3.7968 nan 0.0100 0.0037
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.0917 nan 0.0100 0.9678
## 2 60.0817 nan 0.0100 0.9216
## 3 59.0580 nan 0.0100 0.9021
## 4 58.0686 nan 0.0100 0.9090
## 5 57.1175 nan 0.0100 0.9171
## 6 56.1692 nan 0.0100 0.8902
## 7 55.2549 nan 0.0100 0.7393
## 8 54.3358 nan 0.0100 0.9887
## 9 53.4711 nan 0.0100 0.7550
## 10 52.5489 nan 0.0100 0.9925
## 20 44.7001 nan 0.0100 0.7138
## 40 32.8364 nan 0.0100 0.5034
## 60 24.6005 nan 0.0100 0.3416
## 80 18.7804 nan 0.0100 0.1957
## 100 14.7232 nan 0.0100 0.1479
## 120 11.8868 nan 0.0100 0.1117
## 140 9.7532 nan 0.0100 0.0749
## 160 8.1380 nan 0.0100 0.0572
## 180 6.9660 nan 0.0100 0.0390
## 200 6.0923 nan 0.0100 0.0238
## 220 5.4283 nan 0.0100 0.0267
## 240 4.9539 nan 0.0100 0.0133
## 260 4.5813 nan 0.0100 0.0067
## 280 4.2816 nan 0.0100 0.0120
## 300 4.0674 nan 0.0100 0.0072
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.2534 nan 0.0500 3.6030
## 2 54.8797 nan 0.0500 3.4165
## 3 51.6796 nan 0.0500 3.5804
## 4 48.5075 nan 0.0500 2.5901
## 5 45.6180 nan 0.0500 2.6803
## 6 43.3479 nan 0.0500 2.3455
## 7 41.3246 nan 0.0500 1.8857
## 8 39.2339 nan 0.0500 2.1609
## 9 37.3741 nan 0.0500 1.8928
## 10 35.6579 nan 0.0500 1.7235
## 20 22.8101 nan 0.0500 0.7521
## 40 11.6224 nan 0.0500 0.2937
## 60 7.2572 nan 0.0500 0.1362
## 80 5.4200 nan 0.0500 0.0318
## 100 4.5073 nan 0.0500 0.0226
## 120 4.0736 nan 0.0500 0.0134
## 140 3.8111 nan 0.0500 0.0018
## 160 3.6503 nan 0.0500 -0.0048
## 180 3.5491 nan 0.0500 -0.0096
## 200 3.4563 nan 0.0500 -0.0176
## 220 3.3759 nan 0.0500 -0.0038
## 240 3.3072 nan 0.0500 -0.0066
## 260 3.2591 nan 0.0500 -0.0192
## 280 3.2157 nan 0.0500 -0.0172
## 300 3.1639 nan 0.0500 -0.0119
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.2560 nan 0.0500 3.5064
## 2 54.5276 nan 0.0500 3.5875
## 3 51.3591 nan 0.0500 3.1184
## 4 48.7859 nan 0.0500 2.7443
## 5 46.1874 nan 0.0500 2.9085
## 6 43.7173 nan 0.0500 2.1134
## 7 41.4212 nan 0.0500 2.0850
## 8 39.1853 nan 0.0500 1.8946
## 9 37.0825 nan 0.0500 2.0729
## 10 35.1468 nan 0.0500 1.7580
## 20 22.5776 nan 0.0500 0.8031
## 40 11.6206 nan 0.0500 0.2715
## 60 7.4707 nan 0.0500 0.0856
## 80 5.5864 nan 0.0500 0.0548
## 100 4.7037 nan 0.0500 0.0244
## 120 4.3023 nan 0.0500 -0.0080
## 140 4.0861 nan 0.0500 -0.0147
## 160 3.9502 nan 0.0500 -0.0048
## 180 3.8571 nan 0.0500 -0.0060
## 200 3.7622 nan 0.0500 -0.0150
## 220 3.6900 nan 0.0500 -0.0074
## 240 3.6125 nan 0.0500 -0.0118
## 260 3.5447 nan 0.0500 -0.0112
## 280 3.4844 nan 0.0500 -0.0074
## 300 3.4308 nan 0.0500 -0.0057
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.2425 nan 0.0500 3.6402
## 2 54.8341 nan 0.0500 3.5455
## 3 51.7669 nan 0.0500 3.2467
## 4 48.6089 nan 0.0500 2.8844
## 5 45.8432 nan 0.0500 2.4904
## 6 43.2550 nan 0.0500 2.4954
## 7 41.0027 nan 0.0500 2.1900
## 8 38.8791 nan 0.0500 2.1152
## 9 36.9871 nan 0.0500 1.9220
## 10 35.0691 nan 0.0500 1.7293
## 20 22.2124 nan 0.0500 0.8149
## 40 11.5265 nan 0.0500 0.1819
## 60 7.3282 nan 0.0500 0.1251
## 80 5.6070 nan 0.0500 0.0244
## 100 4.7250 nan 0.0500 0.0150
## 120 4.3044 nan 0.0500 0.0200
## 140 4.0935 nan 0.0500 0.0040
## 160 3.9500 nan 0.0500 -0.0147
## 180 3.8562 nan 0.0500 -0.0123
## 200 3.7650 nan 0.0500 -0.0117
## 220 3.6867 nan 0.0500 -0.0127
## 240 3.6261 nan 0.0500 -0.0037
## 260 3.5463 nan 0.0500 -0.0204
## 280 3.4972 nan 0.0500 -0.0127
## 300 3.4403 nan 0.0500 -0.0092
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.4434 nan 0.0500 4.5156
## 2 53.2742 nan 0.0500 4.4939
## 3 49.1448 nan 0.0500 3.3021
## 4 45.5540 nan 0.0500 3.1519
## 5 42.1434 nan 0.0500 2.9959
## 6 39.4618 nan 0.0500 2.8469
## 7 36.7950 nan 0.0500 2.8181
## 8 34.1095 nan 0.0500 2.3468
## 9 31.7386 nan 0.0500 2.3273
## 10 29.7039 nan 0.0500 1.9393
## 20 16.1708 nan 0.0500 0.8734
## 40 7.1538 nan 0.0500 0.1835
## 60 4.6287 nan 0.0500 0.0427
## 80 3.8657 nan 0.0500 0.0184
## 100 3.5266 nan 0.0500 -0.0049
## 120 3.2687 nan 0.0500 -0.0006
## 140 3.0761 nan 0.0500 -0.0121
## 160 2.9144 nan 0.0500 -0.0268
## 180 2.7923 nan 0.0500 -0.0160
## 200 2.6766 nan 0.0500 -0.0114
## 220 2.5744 nan 0.0500 -0.0122
## 240 2.4883 nan 0.0500 -0.0117
## 260 2.4030 nan 0.0500 -0.0101
## 280 2.3266 nan 0.0500 -0.0097
## 300 2.2621 nan 0.0500 -0.0025
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.4215 nan 0.0500 4.7022
## 2 53.3549 nan 0.0500 4.0126
## 3 49.7375 nan 0.0500 3.6852
## 4 46.0470 nan 0.0500 3.7257
## 5 42.6467 nan 0.0500 3.1709
## 6 39.6984 nan 0.0500 3.0781
## 7 36.8379 nan 0.0500 2.5795
## 8 34.2619 nan 0.0500 2.4993
## 9 31.9214 nan 0.0500 2.3402
## 10 29.9630 nan 0.0500 1.9617
## 20 16.6153 nan 0.0500 0.8801
## 40 7.2706 nan 0.0500 0.1712
## 60 4.6658 nan 0.0500 0.0499
## 80 3.8380 nan 0.0500 0.0122
## 100 3.5136 nan 0.0500 -0.0095
## 120 3.2897 nan 0.0500 -0.0095
## 140 3.1280 nan 0.0500 -0.0066
## 160 2.9705 nan 0.0500 -0.0041
## 180 2.8401 nan 0.0500 -0.0093
## 200 2.7415 nan 0.0500 -0.0201
## 220 2.6642 nan 0.0500 -0.0099
## 240 2.5819 nan 0.0500 -0.0080
## 260 2.4961 nan 0.0500 -0.0059
## 280 2.4364 nan 0.0500 -0.0130
## 300 2.3777 nan 0.0500 -0.0156
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.4194 nan 0.0500 4.5859
## 2 53.3151 nan 0.0500 4.0257
## 3 49.2358 nan 0.0500 3.7990
## 4 45.7718 nan 0.0500 3.4503
## 5 42.4331 nan 0.0500 3.3947
## 6 39.5492 nan 0.0500 2.7945
## 7 36.6082 nan 0.0500 2.8091
## 8 34.0460 nan 0.0500 2.5424
## 9 31.6251 nan 0.0500 2.1926
## 10 29.4588 nan 0.0500 1.8479
## 20 16.3896 nan 0.0500 0.7353
## 40 7.3636 nan 0.0500 0.1819
## 60 5.0105 nan 0.0500 0.0612
## 80 4.1535 nan 0.0500 -0.0134
## 100 3.8049 nan 0.0500 -0.0029
## 120 3.5872 nan 0.0500 -0.0050
## 140 3.4155 nan 0.0500 -0.0062
## 160 3.2761 nan 0.0500 -0.0034
## 180 3.1730 nan 0.0500 -0.0067
## 200 3.0738 nan 0.0500 -0.0294
## 220 3.0040 nan 0.0500 -0.0159
## 240 2.9184 nan 0.0500 -0.0045
## 260 2.8470 nan 0.0500 -0.0215
## 280 2.7782 nan 0.0500 -0.0089
## 300 2.7206 nan 0.0500 -0.0025
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.1493 nan 0.0500 5.2840
## 2 52.7253 nan 0.0500 4.6258
## 3 48.3251 nan 0.0500 4.0881
## 4 44.4757 nan 0.0500 4.0895
## 5 40.7156 nan 0.0500 3.3888
## 6 37.4532 nan 0.0500 3.2934
## 7 34.6238 nan 0.0500 2.7885
## 8 32.0164 nan 0.0500 2.3052
## 9 29.6723 nan 0.0500 2.0828
## 10 27.5223 nan 0.0500 1.5096
## 20 14.3407 nan 0.0500 0.8260
## 40 5.7545 nan 0.0500 0.1174
## 60 3.7773 nan 0.0500 -0.0004
## 80 3.1813 nan 0.0500 0.0023
## 100 2.8447 nan 0.0500 -0.0035
## 120 2.6232 nan 0.0500 -0.0167
## 140 2.4141 nan 0.0500 -0.0141
## 160 2.2726 nan 0.0500 -0.0046
## 180 2.1268 nan 0.0500 -0.0158
## 200 2.0063 nan 0.0500 -0.0075
## 220 1.9087 nan 0.0500 -0.0072
## 240 1.8250 nan 0.0500 -0.0148
## 260 1.7466 nan 0.0500 -0.0121
## 280 1.6703 nan 0.0500 -0.0028
## 300 1.5950 nan 0.0500 -0.0132
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.9273 nan 0.0500 4.9560
## 2 52.4925 nan 0.0500 4.0922
## 3 48.4572 nan 0.0500 3.7872
## 4 44.6431 nan 0.0500 3.8221
## 5 41.0599 nan 0.0500 3.4251
## 6 38.0074 nan 0.0500 3.3214
## 7 35.3221 nan 0.0500 2.8608
## 8 32.4084 nan 0.0500 2.5341
## 9 29.9304 nan 0.0500 2.2079
## 10 27.7384 nan 0.0500 1.7318
## 20 13.9907 nan 0.0500 0.7589
## 40 5.8195 nan 0.0500 0.1374
## 60 3.7736 nan 0.0500 0.0294
## 80 3.2198 nan 0.0500 -0.0111
## 100 2.9317 nan 0.0500 -0.0085
## 120 2.7332 nan 0.0500 -0.0119
## 140 2.5712 nan 0.0500 -0.0231
## 160 2.4465 nan 0.0500 -0.0048
## 180 2.3270 nan 0.0500 -0.0084
## 200 2.2186 nan 0.0500 -0.0097
## 220 2.1390 nan 0.0500 -0.0075
## 240 2.0454 nan 0.0500 -0.0121
## 260 1.9721 nan 0.0500 -0.0173
## 280 1.9142 nan 0.0500 -0.0077
## 300 1.8510 nan 0.0500 -0.0082
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.2062 nan 0.0500 5.2387
## 2 52.4758 nan 0.0500 4.6808
## 3 48.3354 nan 0.0500 3.6956
## 4 44.6485 nan 0.0500 3.7159
## 5 41.2736 nan 0.0500 3.2717
## 6 38.1773 nan 0.0500 2.9922
## 7 35.1989 nan 0.0500 2.9896
## 8 32.7926 nan 0.0500 2.4158
## 9 30.4482 nan 0.0500 2.1849
## 10 28.3454 nan 0.0500 2.0143
## 20 14.8611 nan 0.0500 0.7733
## 40 6.1952 nan 0.0500 0.1526
## 60 4.1152 nan 0.0500 0.0192
## 80 3.5772 nan 0.0500 -0.0132
## 100 3.2644 nan 0.0500 0.0089
## 120 3.0493 nan 0.0500 -0.0218
## 140 2.8741 nan 0.0500 -0.0316
## 160 2.7429 nan 0.0500 -0.0241
## 180 2.6357 nan 0.0500 -0.0275
## 200 2.5340 nan 0.0500 -0.0157
## 220 2.4447 nan 0.0500 -0.0112
## 240 2.3516 nan 0.0500 -0.0086
## 260 2.2655 nan 0.0500 -0.0161
## 280 2.2006 nan 0.0500 -0.0092
## 300 2.1331 nan 0.0500 -0.0183
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 54.2519 nan 0.1000 7.2808
## 2 48.8385 nan 0.1000 5.3792
## 3 43.1370 nan 0.1000 4.8991
## 4 38.7180 nan 0.1000 3.9991
## 5 34.6270 nan 0.1000 3.8094
## 6 31.1423 nan 0.1000 3.3074
## 7 28.3977 nan 0.1000 2.3973
## 8 25.8556 nan 0.1000 2.7475
## 9 23.7521 nan 0.1000 1.6160
## 10 21.6798 nan 0.1000 2.0473
## 20 11.3759 nan 0.1000 0.5226
## 40 5.5797 nan 0.1000 0.0719
## 60 4.1778 nan 0.1000 0.0239
## 80 3.8465 nan 0.1000 -0.0045
## 100 3.6747 nan 0.1000 -0.0146
## 120 3.5126 nan 0.1000 -0.0338
## 140 3.4040 nan 0.1000 -0.0182
## 160 3.2990 nan 0.1000 -0.0456
## 180 3.2227 nan 0.1000 -0.0378
## 200 3.1460 nan 0.1000 -0.0185
## 220 3.0808 nan 0.1000 -0.0153
## 240 3.0104 nan 0.1000 -0.0078
## 260 2.9527 nan 0.1000 -0.0396
## 280 2.8942 nan 0.1000 -0.0176
## 300 2.8480 nan 0.1000 -0.0083
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 54.4694 nan 0.1000 8.0310
## 2 47.9874 nan 0.1000 5.7711
## 3 42.9927 nan 0.1000 5.1930
## 4 38.6800 nan 0.1000 4.3663
## 5 34.7808 nan 0.1000 3.8020
## 6 31.4920 nan 0.1000 3.1924
## 7 28.7341 nan 0.1000 2.5443
## 8 26.3758 nan 0.1000 2.0459
## 9 24.1956 nan 0.1000 2.0098
## 10 22.1605 nan 0.1000 1.9664
## 20 11.3139 nan 0.1000 0.6063
## 40 5.5003 nan 0.1000 0.0833
## 60 4.2280 nan 0.1000 -0.0127
## 80 3.8615 nan 0.1000 -0.0060
## 100 3.7079 nan 0.1000 -0.0082
## 120 3.5857 nan 0.1000 -0.0140
## 140 3.4467 nan 0.1000 -0.0024
## 160 3.3587 nan 0.1000 -0.0279
## 180 3.2646 nan 0.1000 -0.0062
## 200 3.1941 nan 0.1000 0.0036
## 220 3.1277 nan 0.1000 -0.0144
## 240 3.0489 nan 0.1000 -0.0119
## 260 2.9967 nan 0.1000 -0.0102
## 280 2.9404 nan 0.1000 -0.0122
## 300 2.8758 nan 0.1000 -0.0137
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 54.4587 nan 0.1000 7.6187
## 2 47.9437 nan 0.1000 6.2158
## 3 43.1259 nan 0.1000 4.6328
## 4 38.5314 nan 0.1000 4.4688
## 5 34.6663 nan 0.1000 3.7476
## 6 31.3711 nan 0.1000 3.5992
## 7 28.5502 nan 0.1000 2.7294
## 8 26.0573 nan 0.1000 2.4504
## 9 23.6538 nan 0.1000 2.3747
## 10 21.9882 nan 0.1000 1.6029
## 20 11.3682 nan 0.1000 0.5170
## 40 5.7459 nan 0.1000 0.1119
## 60 4.5166 nan 0.1000 0.0139
## 80 4.2393 nan 0.1000 0.0082
## 100 4.0394 nan 0.1000 -0.0150
## 120 3.8616 nan 0.1000 -0.0010
## 140 3.7425 nan 0.1000 -0.0924
## 160 3.6142 nan 0.1000 -0.0031
## 180 3.5234 nan 0.1000 -0.0044
## 200 3.4781 nan 0.1000 -0.0529
## 220 3.3943 nan 0.1000 -0.0102
## 240 3.3243 nan 0.1000 -0.0173
## 260 3.2759 nan 0.1000 -0.0120
## 280 3.2320 nan 0.1000 -0.0114
## 300 3.1724 nan 0.1000 -0.0102
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 53.3096 nan 0.1000 8.5314
## 2 45.4998 nan 0.1000 6.7912
## 3 38.7594 nan 0.1000 6.5171
## 4 33.8719 nan 0.1000 5.0580
## 5 29.0378 nan 0.1000 4.1145
## 6 25.4573 nan 0.1000 3.3477
## 7 22.6303 nan 0.1000 2.8379
## 8 19.9825 nan 0.1000 2.3083
## 9 17.9873 nan 0.1000 1.9695
## 10 16.1727 nan 0.1000 1.5339
## 20 7.2310 nan 0.1000 0.4441
## 40 3.8311 nan 0.1000 -0.0245
## 60 3.2329 nan 0.1000 -0.0186
## 80 2.9147 nan 0.1000 -0.0448
## 100 2.6836 nan 0.1000 -0.0201
## 120 2.4390 nan 0.1000 -0.0113
## 140 2.3024 nan 0.1000 -0.0216
## 160 2.1839 nan 0.1000 -0.0127
## 180 2.0588 nan 0.1000 -0.0063
## 200 1.9567 nan 0.1000 -0.0079
## 220 1.8538 nan 0.1000 -0.0232
## 240 1.7650 nan 0.1000 -0.0150
## 260 1.6779 nan 0.1000 -0.0216
## 280 1.6037 nan 0.1000 -0.0072
## 300 1.5452 nan 0.1000 -0.0209
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 53.8403 nan 0.1000 8.6354
## 2 46.1187 nan 0.1000 7.9219
## 3 39.5187 nan 0.1000 5.8935
## 4 34.1482 nan 0.1000 4.8524
## 5 29.9186 nan 0.1000 4.3602
## 6 26.0963 nan 0.1000 3.5270
## 7 22.7955 nan 0.1000 2.7100
## 8 20.1212 nan 0.1000 2.7340
## 9 18.1685 nan 0.1000 2.0491
## 10 16.0809 nan 0.1000 1.9504
## 20 7.3458 nan 0.1000 0.4680
## 40 3.9806 nan 0.1000 -0.0433
## 60 3.4534 nan 0.1000 -0.0373
## 80 3.1162 nan 0.1000 -0.0349
## 100 2.8808 nan 0.1000 -0.0286
## 120 2.6949 nan 0.1000 -0.0313
## 140 2.5612 nan 0.1000 -0.0274
## 160 2.4452 nan 0.1000 -0.0118
## 180 2.3093 nan 0.1000 -0.0110
## 200 2.1774 nan 0.1000 -0.0211
## 220 2.0661 nan 0.1000 -0.0149
## 240 1.9620 nan 0.1000 -0.0153
## 260 1.8949 nan 0.1000 -0.0340
## 280 1.8361 nan 0.1000 -0.0092
## 300 1.7833 nan 0.1000 -0.0426
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 53.2139 nan 0.1000 9.4245
## 2 45.6649 nan 0.1000 7.5648
## 3 39.0282 nan 0.1000 6.1342
## 4 33.5679 nan 0.1000 4.7540
## 5 29.3337 nan 0.1000 4.4004
## 6 25.6204 nan 0.1000 3.2573
## 7 22.4464 nan 0.1000 3.1265
## 8 19.8619 nan 0.1000 2.7744
## 9 17.8559 nan 0.1000 1.6435
## 10 16.0163 nan 0.1000 1.5528
## 20 6.9525 nan 0.1000 0.3251
## 40 3.9836 nan 0.1000 0.0039
## 60 3.4919 nan 0.1000 -0.0002
## 80 3.2220 nan 0.1000 -0.0073
## 100 3.0092 nan 0.1000 -0.0309
## 120 2.8362 nan 0.1000 -0.0085
## 140 2.6935 nan 0.1000 -0.0212
## 160 2.5758 nan 0.1000 -0.0162
## 180 2.4543 nan 0.1000 -0.0250
## 200 2.3784 nan 0.1000 -0.0245
## 220 2.2779 nan 0.1000 -0.0212
## 240 2.2115 nan 0.1000 -0.0294
## 260 2.1506 nan 0.1000 -0.0178
## 280 2.0607 nan 0.1000 -0.0197
## 300 2.0014 nan 0.1000 -0.0006
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.6153 nan 0.1000 8.4321
## 2 44.2954 nan 0.1000 8.3473
## 3 37.4082 nan 0.1000 6.3862
## 4 31.9636 nan 0.1000 5.3846
## 5 27.2449 nan 0.1000 4.5080
## 6 23.2462 nan 0.1000 3.6538
## 7 20.1999 nan 0.1000 2.8267
## 8 17.6731 nan 0.1000 2.2228
## 9 15.5209 nan 0.1000 1.9011
## 10 13.8604 nan 0.1000 1.6398
## 20 5.6648 nan 0.1000 0.2788
## 40 3.1494 nan 0.1000 -0.0405
## 60 2.6485 nan 0.1000 -0.0182
## 80 2.3594 nan 0.1000 -0.0319
## 100 2.1243 nan 0.1000 -0.0320
## 120 1.9257 nan 0.1000 -0.0339
## 140 1.7458 nan 0.1000 -0.0097
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## 180 1.4227 nan 0.1000 -0.0151
## 200 1.3388 nan 0.1000 -0.0307
## 220 1.2382 nan 0.1000 -0.0045
## 240 1.1606 nan 0.1000 -0.0154
## 260 1.0775 nan 0.1000 -0.0059
## 280 1.0092 nan 0.1000 -0.0176
## 300 0.9604 nan 0.1000 -0.0101
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.5860 nan 0.1000 10.9157
## 2 44.1911 nan 0.1000 7.4573
## 3 37.4138 nan 0.1000 6.0457
## 4 31.8673 nan 0.1000 4.4237
## 5 27.1001 nan 0.1000 4.8438
## 6 23.2851 nan 0.1000 3.6523
## 7 20.1336 nan 0.1000 2.9804
## 8 17.7856 nan 0.1000 2.3090
## 9 15.5223 nan 0.1000 2.0453
## 10 13.8175 nan 0.1000 1.6056
## 20 5.6989 nan 0.1000 0.2499
## 40 3.4330 nan 0.1000 0.0047
## 60 2.8632 nan 0.1000 -0.0266
## 80 2.5942 nan 0.1000 -0.0343
## 100 2.3447 nan 0.1000 -0.0230
## 120 2.1551 nan 0.1000 -0.0233
## 140 2.0124 nan 0.1000 -0.0231
## 160 1.8754 nan 0.1000 -0.0199
## 180 1.7682 nan 0.1000 -0.0349
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## 220 1.5559 nan 0.1000 -0.0212
## 240 1.4795 nan 0.1000 -0.0255
## 260 1.3988 nan 0.1000 -0.0312
## 280 1.3280 nan 0.1000 -0.0245
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.2222 nan 0.1000 9.7520
## 2 44.2249 nan 0.1000 6.6681
## 3 37.8111 nan 0.1000 6.5418
## 4 32.1561 nan 0.1000 5.9792
## 5 27.6798 nan 0.1000 4.0953
## 6 24.2646 nan 0.1000 3.5137
## 7 21.0487 nan 0.1000 3.6537
## 8 18.4753 nan 0.1000 2.5513
## 9 16.0060 nan 0.1000 2.3638
## 10 14.2300 nan 0.1000 1.7477
## 20 5.9290 nan 0.1000 0.3327
## 40 3.5796 nan 0.1000 -0.0425
## 60 3.1047 nan 0.1000 -0.0211
## 80 2.7799 nan 0.1000 -0.0433
## 100 2.5470 nan 0.1000 -0.0328
## 120 2.3886 nan 0.1000 -0.0143
## 140 2.2461 nan 0.1000 -0.0225
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## 180 1.9705 nan 0.1000 -0.0369
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## 220 1.7932 nan 0.1000 -0.0199
## 240 1.7224 nan 0.1000 -0.0095
## 260 1.6360 nan 0.1000 -0.0303
## 280 1.5634 nan 0.1000 -0.0255
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 63.7976 nan 0.0010 0.0825
## 2 63.7191 nan 0.0010 0.0814
## 3 63.6293 nan 0.0010 0.0772
## 4 63.5522 nan 0.0010 0.0815
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## 10 63.0633 nan 0.0010 0.0824
## 20 62.2502 nan 0.0010 0.0743
## 40 60.6882 nan 0.0010 0.0743
## 60 59.2090 nan 0.0010 0.0693
## 80 57.7725 nan 0.0010 0.0691
## 100 56.3916 nan 0.0010 0.0754
## 120 55.0718 nan 0.0010 0.0671
## 140 53.8158 nan 0.0010 0.0624
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## 180 51.3826 nan 0.0010 0.0491
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## 260 46.9600 nan 0.0010 0.0551
## 280 45.9263 nan 0.0010 0.0441
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 63.7868 nan 0.0010 0.0803
## 2 63.7015 nan 0.0010 0.0790
## 3 63.6202 nan 0.0010 0.0788
## 4 63.5370 nan 0.0010 0.0812
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## 60 59.2214 nan 0.0010 0.0852
## 80 57.7879 nan 0.0010 0.0728
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## 140 53.7960 nan 0.0010 0.0633
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 63.7893 nan 0.0010 0.0821
## 2 63.7105 nan 0.0010 0.0778
## 3 63.6335 nan 0.0010 0.0816
## 4 63.5519 nan 0.0010 0.0791
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## 80 57.7977 nan 0.0010 0.0672
## 100 56.4198 nan 0.0010 0.0704
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## 140 53.7477 nan 0.0010 0.0643
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## 260 46.8751 nan 0.0010 0.0446
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 63.7761 nan 0.0010 0.0962
## 2 63.6782 nan 0.0010 0.0884
## 3 63.5730 nan 0.0010 0.0929
## 4 63.4830 nan 0.0010 0.0893
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## 10 62.9121 nan 0.0010 0.0874
## 20 61.9500 nan 0.0010 0.0995
## 40 60.1057 nan 0.0010 0.0911
## 60 58.3349 nan 0.0010 0.0708
## 80 56.6241 nan 0.0010 0.0716
## 100 54.9640 nan 0.0010 0.0826
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## 140 51.7707 nan 0.0010 0.0792
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## 180 48.8283 nan 0.0010 0.0702
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## 240 44.7518 nan 0.0010 0.0623
## 260 43.4903 nan 0.0010 0.0607
## 280 42.2750 nan 0.0010 0.0648
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 63.7803 nan 0.0010 0.1021
## 2 63.6833 nan 0.0010 0.0916
## 3 63.5894 nan 0.0010 0.0983
## 4 63.4947 nan 0.0010 0.0934
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## 6 63.3129 nan 0.0010 0.0958
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## 9 63.0287 nan 0.0010 0.0992
## 10 62.9235 nan 0.0010 0.1007
## 20 61.9919 nan 0.0010 0.0940
## 40 60.1210 nan 0.0010 0.0844
## 60 58.3906 nan 0.0010 0.0927
## 80 56.6686 nan 0.0010 0.0806
## 100 55.0006 nan 0.0010 0.0787
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## 140 51.8487 nan 0.0010 0.0732
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## 180 48.8563 nan 0.0010 0.0730
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## 240 44.7460 nan 0.0010 0.0566
## 260 43.4945 nan 0.0010 0.0673
## 280 42.2294 nan 0.0010 0.0606
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 63.7747 nan 0.0010 0.0963
## 2 63.6808 nan 0.0010 0.0853
## 3 63.5824 nan 0.0010 0.1033
## 4 63.4807 nan 0.0010 0.0980
## 5 63.3816 nan 0.0010 0.1024
## 6 63.2818 nan 0.0010 0.1021
## 7 63.1951 nan 0.0010 0.0963
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## 9 63.0037 nan 0.0010 0.1019
## 10 62.9097 nan 0.0010 0.0878
## 20 61.9506 nan 0.0010 0.0865
## 40 60.0968 nan 0.0010 0.1002
## 60 58.3258 nan 0.0010 0.0810
## 80 56.5582 nan 0.0010 0.0933
## 100 54.8983 nan 0.0010 0.0795
## 120 53.2809 nan 0.0010 0.0820
## 140 51.7103 nan 0.0010 0.0667
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## 180 48.7652 nan 0.0010 0.0671
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## 240 44.6825 nan 0.0010 0.0625
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## 280 42.1308 nan 0.0010 0.0617
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 63.7664 nan 0.0010 0.1154
## 2 63.6601 nan 0.0010 0.1074
## 3 63.5505 nan 0.0010 0.1121
## 4 63.4409 nan 0.0010 0.1114
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## 7 63.1336 nan 0.0010 0.1009
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## 9 62.9262 nan 0.0010 0.1004
## 10 62.8166 nan 0.0010 0.0877
## 20 61.7764 nan 0.0010 0.1098
## 40 59.7509 nan 0.0010 0.1003
## 60 57.8079 nan 0.0010 0.1053
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## 100 54.1147 nan 0.0010 0.0789
## 120 52.3772 nan 0.0010 0.0837
## 140 50.7008 nan 0.0010 0.0742
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## 240 43.2277 nan 0.0010 0.0610
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 63.7696 nan 0.0010 0.0984
## 2 63.6612 nan 0.0010 0.1021
## 3 63.5487 nan 0.0010 0.1016
## 4 63.4423 nan 0.0010 0.1058
## 5 63.3373 nan 0.0010 0.1036
## 6 63.2385 nan 0.0010 0.0959
## 7 63.1402 nan 0.0010 0.0996
## 8 63.0363 nan 0.0010 0.1072
## 9 62.9320 nan 0.0010 0.1012
## 10 62.8284 nan 0.0010 0.1010
## 20 61.7994 nan 0.0010 0.0943
## 40 59.7983 nan 0.0010 0.1018
## 60 57.8627 nan 0.0010 0.0907
## 80 55.9810 nan 0.0010 0.0876
## 100 54.1896 nan 0.0010 0.0788
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## 140 50.8092 nan 0.0010 0.0848
## 160 49.2015 nan 0.0010 0.0742
## 180 47.6802 nan 0.0010 0.0788
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## 240 43.3808 nan 0.0010 0.0590
## 260 42.0442 nan 0.0010 0.0685
## 280 40.7677 nan 0.0010 0.0586
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 63.7746 nan 0.0010 0.1062
## 2 63.6663 nan 0.0010 0.0910
## 3 63.5527 nan 0.0010 0.1027
## 4 63.4461 nan 0.0010 0.0992
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## 6 63.2336 nan 0.0010 0.1059
## 7 63.1283 nan 0.0010 0.1070
## 8 63.0259 nan 0.0010 0.1086
## 9 62.9242 nan 0.0010 0.0914
## 10 62.8105 nan 0.0010 0.1010
## 20 61.7623 nan 0.0010 0.1061
## 40 59.7313 nan 0.0010 0.0967
## 60 57.7695 nan 0.0010 0.0875
## 80 55.9153 nan 0.0010 0.0931
## 100 54.1133 nan 0.0010 0.0849
## 120 52.3695 nan 0.0010 0.0795
## 140 50.6930 nan 0.0010 0.0752
## 160 49.0910 nan 0.0010 0.0796
## 180 47.5136 nan 0.0010 0.0746
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## 220 44.5939 nan 0.0010 0.0681
## 240 43.1947 nan 0.0010 0.0650
## 260 41.8713 nan 0.0010 0.0704
## 280 40.5937 nan 0.0010 0.0472
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 63.4674 nan 0.0050 0.3942
## 2 63.0404 nan 0.0050 0.3799
## 3 62.6887 nan 0.0050 0.3692
## 4 62.2734 nan 0.0050 0.3956
## 5 61.8859 nan 0.0050 0.4082
## 6 61.5086 nan 0.0050 0.3512
## 7 61.1209 nan 0.0050 0.3848
## 8 60.7714 nan 0.0050 0.3452
## 9 60.4047 nan 0.0050 0.3670
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## 20 56.5705 nan 0.0050 0.3240
## 40 50.2310 nan 0.0050 0.2640
## 60 44.9187 nan 0.0050 0.1900
## 80 40.3511 nan 0.0050 0.1941
## 100 36.4239 nan 0.0050 0.1758
## 120 33.0351 nan 0.0050 0.1602
## 140 30.0522 nan 0.0050 0.1319
## 160 27.4180 nan 0.0050 0.1043
## 180 25.1644 nan 0.0050 0.1023
## 200 23.1370 nan 0.0050 0.0873
## 220 21.3319 nan 0.0050 0.0730
## 240 19.7347 nan 0.0050 0.0837
## 260 18.3810 nan 0.0050 0.0572
## 280 17.1238 nan 0.0050 0.0549
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 63.5019 nan 0.0050 0.3648
## 2 63.1174 nan 0.0050 0.4059
## 3 62.7012 nan 0.0050 0.4015
## 4 62.3150 nan 0.0050 0.3714
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## 6 61.5673 nan 0.0050 0.3764
## 7 61.1731 nan 0.0050 0.3916
## 8 60.8034 nan 0.0050 0.3576
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## 20 56.5657 nan 0.0050 0.3557
## 40 50.3314 nan 0.0050 0.2928
## 60 44.9119 nan 0.0050 0.2382
## 80 40.4034 nan 0.0050 0.1981
## 100 36.4403 nan 0.0050 0.1845
## 120 32.9311 nan 0.0050 0.1328
## 140 29.9776 nan 0.0050 0.1362
## 160 27.3738 nan 0.0050 0.1318
## 180 25.1451 nan 0.0050 0.1154
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## 240 19.7733 nan 0.0050 0.0602
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## 280 17.1463 nan 0.0050 0.0529
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 63.4644 nan 0.0050 0.3810
## 2 63.0547 nan 0.0050 0.3635
## 3 62.6711 nan 0.0050 0.3577
## 4 62.2519 nan 0.0050 0.3982
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## 6 61.4540 nan 0.0050 0.3919
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## 8 60.6760 nan 0.0050 0.3555
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## 20 56.3401 nan 0.0050 0.3026
## 40 50.1040 nan 0.0050 0.2380
## 60 44.8081 nan 0.0050 0.2419
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## 100 36.3501 nan 0.0050 0.1694
## 120 32.9973 nan 0.0050 0.1670
## 140 30.0120 nan 0.0050 0.1215
## 160 27.4002 nan 0.0050 0.1185
## 180 25.0618 nan 0.0050 0.1073
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## 240 19.7367 nan 0.0050 0.0673
## 260 18.3070 nan 0.0050 0.0704
## 280 17.0554 nan 0.0050 0.0500
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 63.4203 nan 0.0050 0.4590
## 2 62.9699 nan 0.0050 0.4433
## 3 62.5010 nan 0.0050 0.4675
## 4 62.0154 nan 0.0050 0.4805
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## 20 54.9169 nan 0.0050 0.4404
## 40 47.3257 nan 0.0050 0.3872
## 60 40.9852 nan 0.0050 0.2847
## 80 35.5579 nan 0.0050 0.2823
## 100 31.1244 nan 0.0050 0.1859
## 120 27.3711 nan 0.0050 0.1676
## 140 24.1833 nan 0.0050 0.1545
## 160 21.4516 nan 0.0050 0.1324
## 180 19.1332 nan 0.0050 0.1029
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## 240 14.0430 nan 0.0050 0.0701
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## 280 11.7073 nan 0.0050 0.0466
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 63.3896 nan 0.0050 0.5014
## 2 62.9053 nan 0.0050 0.3826
## 3 62.4609 nan 0.0050 0.4897
## 4 61.9466 nan 0.0050 0.5186
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## 20 54.9342 nan 0.0050 0.4478
## 40 47.3982 nan 0.0050 0.3320
## 60 41.0255 nan 0.0050 0.2735
## 80 35.6536 nan 0.0050 0.2746
## 100 31.2776 nan 0.0050 0.1940
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## 140 24.1430 nan 0.0050 0.1436
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## 180 18.9854 nan 0.0050 0.1093
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## 280 11.6279 nan 0.0050 0.0482
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 63.3946 nan 0.0050 0.4739
## 2 62.9228 nan 0.0050 0.3986
## 3 62.4229 nan 0.0050 0.4925
## 4 61.9190 nan 0.0050 0.5063
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## 20 55.0161 nan 0.0050 0.4072
## 40 47.5413 nan 0.0050 0.3075
## 60 41.1528 nan 0.0050 0.2720
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## 140 24.4298 nan 0.0050 0.1414
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 63.3539 nan 0.0050 0.5291
## 2 62.8472 nan 0.0050 0.5148
## 3 62.3256 nan 0.0050 0.5615
## 4 61.8231 nan 0.0050 0.5085
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## 20 54.2201 nan 0.0050 0.4669
## 40 46.2110 nan 0.0050 0.3711
## 60 39.5405 nan 0.0050 0.3122
## 80 33.9269 nan 0.0050 0.2346
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## 140 22.0389 nan 0.0050 0.1338
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## 280 9.6920 nan 0.0050 0.0354
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 63.3550 nan 0.0050 0.4955
## 2 62.8441 nan 0.0050 0.4425
## 3 62.3170 nan 0.0050 0.5559
## 4 61.8043 nan 0.0050 0.5415
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## 40 46.1248 nan 0.0050 0.3574
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## 140 22.1409 nan 0.0050 0.1401
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 63.3233 nan 0.0050 0.5511
## 2 62.7912 nan 0.0050 0.5421
## 3 62.2845 nan 0.0050 0.5355
## 4 61.7936 nan 0.0050 0.4570
## 5 61.3079 nan 0.0050 0.5103
## 6 60.7982 nan 0.0050 0.4911
## 7 60.3074 nan 0.0050 0.4782
## 8 59.8178 nan 0.0050 0.4445
## 9 59.2946 nan 0.0050 0.5320
## 10 58.8115 nan 0.0050 0.4910
## 20 54.1326 nan 0.0050 0.4659
## 40 46.0654 nan 0.0050 0.3498
## 60 39.4205 nan 0.0050 0.3172
## 80 33.8791 nan 0.0050 0.2172
## 100 29.2109 nan 0.0050 0.1956
## 120 25.3015 nan 0.0050 0.1794
## 140 22.1023 nan 0.0050 0.1435
## 160 19.4280 nan 0.0050 0.1149
## 180 17.1721 nan 0.0050 0.0994
## 200 15.2451 nan 0.0050 0.0777
## 220 13.5884 nan 0.0050 0.0636
## 240 12.1977 nan 0.0050 0.0569
## 260 10.9707 nan 0.0050 0.0535
## 280 9.9619 nan 0.0050 0.0388
## 300 9.0910 nan 0.0050 0.0332
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 63.1187 nan 0.0100 0.7301
## 2 62.2898 nan 0.0100 0.8245
## 3 61.4983 nan 0.0100 0.7790
## 4 60.7161 nan 0.0100 0.7725
## 5 59.9853 nan 0.0100 0.7290
## 6 59.2002 nan 0.0100 0.7101
## 7 58.4913 nan 0.0100 0.7128
## 8 57.7196 nan 0.0100 0.7222
## 9 56.9701 nan 0.0100 0.6871
## 10 56.2561 nan 0.0100 0.6631
## 20 50.2336 nan 0.0100 0.5501
## 40 40.4117 nan 0.0100 0.4194
## 60 33.1186 nan 0.0100 0.3145
## 80 27.5209 nan 0.0100 0.2553
## 100 23.1767 nan 0.0100 0.1926
## 120 19.8104 nan 0.0100 0.1574
## 140 17.1786 nan 0.0100 0.1159
## 160 15.0377 nan 0.0100 0.0959
## 180 13.2857 nan 0.0100 0.0611
## 200 11.8797 nan 0.0100 0.0547
## 220 10.6674 nan 0.0100 0.0480
## 240 9.6904 nan 0.0100 0.0395
## 260 8.8408 nan 0.0100 0.0333
## 280 8.1298 nan 0.0100 0.0225
## 300 7.5182 nan 0.0100 0.0179
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 63.0534 nan 0.0100 0.7959
## 2 62.2782 nan 0.0100 0.6823
## 3 61.5161 nan 0.0100 0.7751
## 4 60.7051 nan 0.0100 0.7443
## 5 59.9362 nan 0.0100 0.7616
## 6 59.2488 nan 0.0100 0.7596
## 7 58.5195 nan 0.0100 0.7143
## 8 57.8121 nan 0.0100 0.7362
## 9 57.1589 nan 0.0100 0.7098
## 10 56.4465 nan 0.0100 0.6425
## 20 50.2163 nan 0.0100 0.5976
## 40 40.4308 nan 0.0100 0.3120
## 60 32.9868 nan 0.0100 0.2833
## 80 27.3578 nan 0.0100 0.2339
## 100 23.0574 nan 0.0100 0.1505
## 120 19.7009 nan 0.0100 0.1491
## 140 17.0911 nan 0.0100 0.1184
## 160 14.9543 nan 0.0100 0.0744
## 180 13.2090 nan 0.0100 0.0613
## 200 11.7883 nan 0.0100 0.0569
## 220 10.6489 nan 0.0100 0.0561
## 240 9.6363 nan 0.0100 0.0455
## 260 8.7935 nan 0.0100 0.0286
## 280 8.0879 nan 0.0100 0.0207
## 300 7.4775 nan 0.0100 0.0106
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 63.0395 nan 0.0100 0.7873
## 2 62.2575 nan 0.0100 0.7611
## 3 61.4700 nan 0.0100 0.7086
## 4 60.8097 nan 0.0100 0.7749
## 5 60.0545 nan 0.0100 0.6756
## 6 59.2917 nan 0.0100 0.7249
## 7 58.5824 nan 0.0100 0.7228
## 8 57.8311 nan 0.0100 0.6818
## 9 57.1021 nan 0.0100 0.6993
## 10 56.4598 nan 0.0100 0.6588
## 20 50.0881 nan 0.0100 0.5736
## 40 40.2976 nan 0.0100 0.4073
## 60 32.8515 nan 0.0100 0.2946
## 80 27.3661 nan 0.0100 0.2204
## 100 23.1187 nan 0.0100 0.1399
## 120 19.7358 nan 0.0100 0.1462
## 140 17.0784 nan 0.0100 0.1272
## 160 15.0129 nan 0.0100 0.0848
## 180 13.3216 nan 0.0100 0.0666
## 200 11.9383 nan 0.0100 0.0436
## 220 10.7497 nan 0.0100 0.0360
## 240 9.7622 nan 0.0100 0.0354
## 260 8.9095 nan 0.0100 0.0313
## 280 8.1754 nan 0.0100 0.0335
## 300 7.5702 nan 0.0100 0.0196
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.8684 nan 0.0100 1.0937
## 2 61.8236 nan 0.0100 1.0068
## 3 60.9776 nan 0.0100 0.7579
## 4 60.0451 nan 0.0100 0.9114
## 5 59.2121 nan 0.0100 0.8572
## 6 58.3310 nan 0.0100 0.7819
## 7 57.4558 nan 0.0100 0.7237
## 8 56.5984 nan 0.0100 0.8191
## 9 55.7826 nan 0.0100 0.8060
## 10 54.9996 nan 0.0100 0.8372
## 20 47.3398 nan 0.0100 0.7404
## 40 35.5824 nan 0.0100 0.4760
## 60 27.2966 nan 0.0100 0.3232
## 80 21.3859 nan 0.0100 0.2249
## 100 17.0897 nan 0.0100 0.1579
## 120 13.9455 nan 0.0100 0.1185
## 140 11.5940 nan 0.0100 0.1109
## 160 9.8650 nan 0.0100 0.0711
## 180 8.5463 nan 0.0100 0.0471
## 200 7.4863 nan 0.0100 0.0493
## 220 6.6695 nan 0.0100 0.0295
## 240 6.0197 nan 0.0100 0.0273
## 260 5.5223 nan 0.0100 0.0202
## 280 5.1134 nan 0.0100 0.0089
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.9040 nan 0.0100 1.0194
## 2 61.9881 nan 0.0100 0.9725
## 3 61.0589 nan 0.0100 0.9402
## 4 60.0695 nan 0.0100 0.9331
## 5 59.1250 nan 0.0100 0.9717
## 6 58.2694 nan 0.0100 0.9120
## 7 57.4192 nan 0.0100 0.7778
## 8 56.5699 nan 0.0100 0.8716
## 9 55.7622 nan 0.0100 0.7789
## 10 54.9841 nan 0.0100 0.8133
## 20 47.2715 nan 0.0100 0.5918
## 40 35.5678 nan 0.0100 0.4558
## 60 27.1774 nan 0.0100 0.3432
## 80 21.4160 nan 0.0100 0.2214
## 100 17.1433 nan 0.0100 0.1913
## 120 13.9807 nan 0.0100 0.1016
## 140 11.6055 nan 0.0100 0.0729
## 160 9.9555 nan 0.0100 0.0534
## 180 8.5879 nan 0.0100 0.0541
## 200 7.5576 nan 0.0100 0.0355
## 220 6.6955 nan 0.0100 0.0321
## 240 6.0455 nan 0.0100 0.0209
## 260 5.5154 nan 0.0100 0.0180
## 280 5.0800 nan 0.0100 0.0153
## 300 4.7549 nan 0.0100 0.0093
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.9697 nan 0.0100 0.9658
## 2 61.9916 nan 0.0100 0.8860
## 3 61.0790 nan 0.0100 0.8960
## 4 60.1895 nan 0.0100 0.9014
## 5 59.2605 nan 0.0100 0.9590
## 6 58.4464 nan 0.0100 0.8741
## 7 57.5006 nan 0.0100 0.8192
## 8 56.6507 nan 0.0100 0.8712
## 9 55.8626 nan 0.0100 0.7036
## 10 55.0868 nan 0.0100 0.8383
## 20 47.5600 nan 0.0100 0.6302
## 40 35.6210 nan 0.0100 0.4697
## 60 27.4234 nan 0.0100 0.3309
## 80 21.3492 nan 0.0100 0.2327
## 100 17.0267 nan 0.0100 0.1512
## 120 13.9216 nan 0.0100 0.1208
## 140 11.5711 nan 0.0100 0.0898
## 160 9.8578 nan 0.0100 0.0724
## 180 8.5418 nan 0.0100 0.0509
## 200 7.5090 nan 0.0100 0.0345
## 220 6.6761 nan 0.0100 0.0257
## 240 6.0298 nan 0.0100 0.0237
## 260 5.5247 nan 0.0100 0.0176
## 280 5.1512 nan 0.0100 0.0091
## 300 4.8475 nan 0.0100 0.0085
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.7797 nan 0.0100 1.0344
## 2 61.7457 nan 0.0100 0.9608
## 3 60.7251 nan 0.0100 1.0004
## 4 59.8299 nan 0.0100 0.9580
## 5 58.8490 nan 0.0100 0.9777
## 6 57.9327 nan 0.0100 1.0278
## 7 56.9927 nan 0.0100 0.9260
## 8 56.0847 nan 0.0100 0.9441
## 9 55.1767 nan 0.0100 0.9194
## 10 54.2412 nan 0.0100 0.9972
## 20 46.1715 nan 0.0100 0.6311
## 40 33.8257 nan 0.0100 0.4720
## 60 25.2068 nan 0.0100 0.3131
## 80 19.1087 nan 0.0100 0.2366
## 100 14.8729 nan 0.0100 0.1732
## 120 11.8418 nan 0.0100 0.1162
## 140 9.6806 nan 0.0100 0.0766
## 160 8.1006 nan 0.0100 0.0552
## 180 6.9074 nan 0.0100 0.0475
## 200 5.9815 nan 0.0100 0.0351
## 220 5.2542 nan 0.0100 0.0231
## 240 4.7328 nan 0.0100 0.0154
## 260 4.3297 nan 0.0100 0.0156
## 280 4.0200 nan 0.0100 0.0061
## 300 3.7901 nan 0.0100 0.0036
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.8220 nan 0.0100 1.0386
## 2 61.7777 nan 0.0100 1.0572
## 3 60.7368 nan 0.0100 1.0330
## 4 59.7392 nan 0.0100 0.9455
## 5 58.7486 nan 0.0100 0.9190
## 6 57.7722 nan 0.0100 0.9013
## 7 56.8135 nan 0.0100 0.8688
## 8 55.8820 nan 0.0100 1.0466
## 9 54.9864 nan 0.0100 0.8636
## 10 54.1164 nan 0.0100 0.9750
## 20 46.0173 nan 0.0100 0.7293
## 40 33.8254 nan 0.0100 0.5208
## 60 25.2884 nan 0.0100 0.3212
## 80 19.3457 nan 0.0100 0.2365
## 100 15.0832 nan 0.0100 0.1521
## 120 12.0464 nan 0.0100 0.1337
## 140 9.7907 nan 0.0100 0.0941
## 160 8.1618 nan 0.0100 0.0681
## 180 6.9723 nan 0.0100 0.0452
## 200 6.0862 nan 0.0100 0.0298
## 220 5.3935 nan 0.0100 0.0246
## 240 4.8362 nan 0.0100 0.0203
## 260 4.4571 nan 0.0100 0.0073
## 280 4.1633 nan 0.0100 0.0088
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.7771 nan 0.0100 1.0694
## 2 61.7205 nan 0.0100 1.0368
## 3 60.6971 nan 0.0100 0.9702
## 4 59.7020 nan 0.0100 0.9066
## 5 58.7453 nan 0.0100 0.8955
## 6 57.7855 nan 0.0100 0.9076
## 7 56.8222 nan 0.0100 0.9006
## 8 55.8464 nan 0.0100 0.9693
## 9 54.9444 nan 0.0100 0.9771
## 10 54.0380 nan 0.0100 0.9134
## 20 46.1611 nan 0.0100 0.7059
## 40 33.9471 nan 0.0100 0.5206
## 60 25.2988 nan 0.0100 0.3359
## 80 19.2295 nan 0.0100 0.2558
## 100 15.0580 nan 0.0100 0.1623
## 120 12.1206 nan 0.0100 0.1088
## 140 9.9437 nan 0.0100 0.0848
## 160 8.3790 nan 0.0100 0.0594
## 180 7.1751 nan 0.0100 0.0547
## 200 6.2726 nan 0.0100 0.0327
## 220 5.5944 nan 0.0100 0.0168
## 240 5.0873 nan 0.0100 0.0161
## 260 4.6986 nan 0.0100 0.0160
## 280 4.4069 nan 0.0100 0.0125
## 300 4.1750 nan 0.0100 0.0074
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.1873 nan 0.0500 4.1264
## 2 56.3682 nan 0.0500 3.5490
## 3 53.0606 nan 0.0500 3.1280
## 4 49.8666 nan 0.0500 2.9589
## 5 47.1936 nan 0.0500 2.3663
## 6 44.6219 nan 0.0500 2.6201
## 7 42.0441 nan 0.0500 2.7006
## 8 39.9026 nan 0.0500 2.1551
## 9 37.9972 nan 0.0500 1.9812
## 10 36.1740 nan 0.0500 1.8826
## 20 22.7090 nan 0.0500 0.8494
## 40 11.5902 nan 0.0500 0.2499
## 60 7.3828 nan 0.0500 0.1074
## 80 5.4229 nan 0.0500 0.0325
## 100 4.5896 nan 0.0500 0.0088
## 120 4.0883 nan 0.0500 0.0042
## 140 3.8639 nan 0.0500 -0.0029
## 160 3.7204 nan 0.0500 -0.0101
## 180 3.6142 nan 0.0500 -0.0081
## 200 3.5337 nan 0.0500 -0.0023
## 220 3.4545 nan 0.0500 -0.0065
## 240 3.3958 nan 0.0500 -0.0160
## 260 3.3418 nan 0.0500 -0.0062
## 280 3.2945 nan 0.0500 -0.0084
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.6722 nan 0.0500 3.9408
## 2 56.0382 nan 0.0500 3.5966
## 3 52.6950 nan 0.0500 3.2117
## 4 49.8672 nan 0.0500 2.6333
## 5 46.9628 nan 0.0500 2.5370
## 6 44.5233 nan 0.0500 2.3469
## 7 42.2052 nan 0.0500 2.3142
## 8 40.2895 nan 0.0500 1.8495
## 9 38.3073 nan 0.0500 1.9342
## 10 36.3038 nan 0.0500 1.9545
## 20 22.9704 nan 0.0500 0.9358
## 40 11.7293 nan 0.0500 0.2743
## 60 7.3802 nan 0.0500 0.0924
## 80 5.5698 nan 0.0500 0.0598
## 100 4.6871 nan 0.0500 0.0165
## 120 4.2413 nan 0.0500 0.0110
## 140 4.0242 nan 0.0500 0.0047
## 160 3.8707 nan 0.0500 -0.0039
## 180 3.7637 nan 0.0500 -0.0049
## 200 3.6714 nan 0.0500 -0.0030
## 220 3.5920 nan 0.0500 -0.0078
## 240 3.5326 nan 0.0500 -0.0124
## 260 3.4740 nan 0.0500 -0.0109
## 280 3.4205 nan 0.0500 -0.0002
## 300 3.3756 nan 0.0500 -0.0081
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.8951 nan 0.0500 3.9937
## 2 56.2020 nan 0.0500 3.4622
## 3 52.9403 nan 0.0500 3.0880
## 4 49.9655 nan 0.0500 3.0908
## 5 47.2631 nan 0.0500 2.8906
## 6 44.6638 nan 0.0500 2.7407
## 7 42.4214 nan 0.0500 2.2730
## 8 40.3551 nan 0.0500 2.1814
## 9 38.2207 nan 0.0500 1.9950
## 10 36.2813 nan 0.0500 1.8771
## 20 22.7886 nan 0.0500 0.9181
## 40 11.8105 nan 0.0500 0.2482
## 60 7.6381 nan 0.0500 0.1255
## 80 5.7600 nan 0.0500 0.0090
## 100 4.8983 nan 0.0500 0.0202
## 120 4.5222 nan 0.0500 0.0002
## 140 4.3043 nan 0.0500 -0.0028
## 160 4.1627 nan 0.0500 -0.0133
## 180 4.0494 nan 0.0500 -0.0005
## 200 3.9521 nan 0.0500 -0.0044
## 220 3.8681 nan 0.0500 -0.0059
## 240 3.8060 nan 0.0500 -0.0120
## 260 3.7394 nan 0.0500 -0.0025
## 280 3.6865 nan 0.0500 -0.0088
## 300 3.6372 nan 0.0500 -0.0070
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.2457 nan 0.0500 4.5520
## 2 54.9560 nan 0.0500 3.6346
## 3 50.8252 nan 0.0500 4.2503
## 4 47.2164 nan 0.0500 3.3636
## 5 43.7899 nan 0.0500 3.2541
## 6 40.7973 nan 0.0500 3.0524
## 7 38.2580 nan 0.0500 2.6974
## 8 35.7681 nan 0.0500 2.4008
## 9 33.3210 nan 0.0500 2.5542
## 10 31.0363 nan 0.0500 2.2947
## 20 16.7185 nan 0.0500 0.7959
## 40 7.2782 nan 0.0500 0.1742
## 60 4.6169 nan 0.0500 0.0563
## 80 3.6608 nan 0.0500 0.0104
## 100 3.3270 nan 0.0500 -0.0022
## 120 3.0830 nan 0.0500 -0.0285
## 140 2.9157 nan 0.0500 -0.0183
## 160 2.7773 nan 0.0500 -0.0104
## 180 2.6744 nan 0.0500 -0.0080
## 200 2.5679 nan 0.0500 -0.0103
## 220 2.4606 nan 0.0500 -0.0144
## 240 2.3717 nan 0.0500 -0.0088
## 260 2.3153 nan 0.0500 -0.0061
## 280 2.2474 nan 0.0500 -0.0103
## 300 2.1844 nan 0.0500 -0.0078
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.3365 nan 0.0500 4.6431
## 2 55.0075 nan 0.0500 4.0446
## 3 51.2266 nan 0.0500 3.9180
## 4 47.2848 nan 0.0500 3.3316
## 5 44.1710 nan 0.0500 3.3286
## 6 40.9356 nan 0.0500 2.9639
## 7 38.1772 nan 0.0500 2.6446
## 8 35.5012 nan 0.0500 2.4875
## 9 33.2951 nan 0.0500 1.8721
## 10 30.8166 nan 0.0500 2.3438
## 20 16.6566 nan 0.0500 0.7818
## 40 7.3475 nan 0.0500 0.1306
## 60 4.8227 nan 0.0500 0.0647
## 80 3.9124 nan 0.0500 -0.0204
## 100 3.5693 nan 0.0500 -0.0072
## 120 3.3367 nan 0.0500 -0.0004
## 140 3.1752 nan 0.0500 -0.0012
## 160 3.0551 nan 0.0500 -0.0075
## 180 2.8942 nan 0.0500 -0.0113
## 200 2.7901 nan 0.0500 -0.0047
## 220 2.7068 nan 0.0500 -0.0124
## 240 2.6359 nan 0.0500 -0.0093
## 260 2.5788 nan 0.0500 -0.0033
## 280 2.5069 nan 0.0500 -0.0129
## 300 2.4526 nan 0.0500 -0.0053
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.1854 nan 0.0500 4.8339
## 2 54.7981 nan 0.0500 4.3418
## 3 50.8347 nan 0.0500 4.1350
## 4 47.0637 nan 0.0500 3.6262
## 5 43.9761 nan 0.0500 3.0775
## 6 41.0451 nan 0.0500 2.8421
## 7 38.3019 nan 0.0500 2.4921
## 8 35.4977 nan 0.0500 2.7606
## 9 33.1351 nan 0.0500 2.2459
## 10 30.9980 nan 0.0500 1.9753
## 20 16.5976 nan 0.0500 0.7380
## 40 7.3894 nan 0.0500 0.1322
## 60 4.7284 nan 0.0500 0.0308
## 80 3.9501 nan 0.0500 0.0207
## 100 3.5972 nan 0.0500 -0.0083
## 120 3.3892 nan 0.0500 -0.0162
## 140 3.2307 nan 0.0500 -0.0091
## 160 3.1292 nan 0.0500 -0.0138
## 180 3.0200 nan 0.0500 -0.0313
## 200 2.9121 nan 0.0500 -0.0112
## 220 2.8335 nan 0.0500 -0.0060
## 240 2.7669 nan 0.0500 -0.0136
## 260 2.6929 nan 0.0500 -0.0052
## 280 2.6126 nan 0.0500 -0.0048
## 300 2.5632 nan 0.0500 -0.0074
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.7382 nan 0.0500 5.1757
## 2 54.1948 nan 0.0500 4.4922
## 3 50.0890 nan 0.0500 3.9259
## 4 46.3220 nan 0.0500 3.6182
## 5 42.6545 nan 0.0500 3.5540
## 6 39.5324 nan 0.0500 3.2442
## 7 36.6310 nan 0.0500 3.0783
## 8 33.8894 nan 0.0500 2.6078
## 9 31.2713 nan 0.0500 1.9894
## 10 29.0404 nan 0.0500 2.1621
## 20 14.6515 nan 0.0500 0.7654
## 40 5.9713 nan 0.0500 0.1517
## 60 3.8588 nan 0.0500 0.0211
## 80 3.2128 nan 0.0500 -0.0136
## 100 2.8959 nan 0.0500 -0.0141
## 120 2.6305 nan 0.0500 -0.0065
## 140 2.4551 nan 0.0500 -0.0032
## 160 2.2807 nan 0.0500 -0.0056
## 180 2.1382 nan 0.0500 -0.0111
## 200 2.0324 nan 0.0500 -0.0075
## 220 1.9414 nan 0.0500 -0.0064
## 240 1.8509 nan 0.0500 -0.0149
## 260 1.7563 nan 0.0500 -0.0127
## 280 1.6663 nan 0.0500 -0.0056
## 300 1.5943 nan 0.0500 -0.0085
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.4620 nan 0.0500 5.2450
## 2 53.5525 nan 0.0500 4.5384
## 3 49.3747 nan 0.0500 3.9132
## 4 45.4858 nan 0.0500 4.0586
## 5 41.8914 nan 0.0500 3.5271
## 6 39.0139 nan 0.0500 3.3394
## 7 35.9564 nan 0.0500 2.8440
## 8 33.1692 nan 0.0500 2.4592
## 9 30.7593 nan 0.0500 2.1300
## 10 28.4374 nan 0.0500 2.2211
## 20 14.4924 nan 0.0500 0.8855
## 40 6.0720 nan 0.0500 0.2179
## 60 3.9571 nan 0.0500 0.0390
## 80 3.3912 nan 0.0500 0.0038
## 100 3.1032 nan 0.0500 -0.0099
## 120 2.8811 nan 0.0500 -0.0208
## 140 2.6726 nan 0.0500 -0.0179
## 160 2.5368 nan 0.0500 -0.0190
## 180 2.4068 nan 0.0500 -0.0211
## 200 2.3112 nan 0.0500 -0.0105
## 220 2.2195 nan 0.0500 -0.0131
## 240 2.1232 nan 0.0500 -0.0060
## 260 2.0437 nan 0.0500 -0.0209
## 280 1.9714 nan 0.0500 -0.0121
## 300 1.8877 nan 0.0500 -0.0154
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.5419 nan 0.0500 5.0344
## 2 54.0088 nan 0.0500 4.5100
## 3 49.5820 nan 0.0500 4.3312
## 4 45.6695 nan 0.0500 3.7617
## 5 42.1316 nan 0.0500 3.6102
## 6 39.0149 nan 0.0500 3.2880
## 7 36.0435 nan 0.0500 2.8719
## 8 33.2602 nan 0.0500 2.7663
## 9 30.9096 nan 0.0500 2.4396
## 10 28.7352 nan 0.0500 1.9050
## 20 14.8826 nan 0.0500 0.8164
## 40 6.0535 nan 0.0500 0.1701
## 60 4.0629 nan 0.0500 0.0223
## 80 3.5069 nan 0.0500 0.0015
## 100 3.2431 nan 0.0500 -0.0158
## 120 3.0746 nan 0.0500 -0.0287
## 140 2.9361 nan 0.0500 -0.0123
## 160 2.7747 nan 0.0500 -0.0202
## 180 2.6410 nan 0.0500 -0.0211
## 200 2.5159 nan 0.0500 -0.0037
## 220 2.4197 nan 0.0500 -0.0064
## 240 2.3467 nan 0.0500 -0.0122
## 260 2.2579 nan 0.0500 -0.0137
## 280 2.1957 nan 0.0500 -0.0084
## 300 2.1330 nan 0.0500 -0.0097
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.1202 nan 0.1000 8.0301
## 2 50.4675 nan 0.1000 5.2155
## 3 44.7587 nan 0.1000 5.5338
## 4 39.8436 nan 0.1000 4.9209
## 5 35.6929 nan 0.1000 4.0811
## 6 32.1379 nan 0.1000 3.2839
## 7 29.1961 nan 0.1000 2.7432
## 8 26.6098 nan 0.1000 2.2173
## 9 24.2577 nan 0.1000 2.0747
## 10 22.0856 nan 0.1000 2.0069
## 20 11.6940 nan 0.1000 0.5429
## 40 5.7729 nan 0.1000 0.1433
## 60 4.4393 nan 0.1000 0.0220
## 80 4.0652 nan 0.1000 -0.0448
## 100 3.7847 nan 0.1000 0.0047
## 120 3.5983 nan 0.1000 0.0003
## 140 3.4868 nan 0.1000 -0.0182
## 160 3.3847 nan 0.1000 -0.0030
## 180 3.2876 nan 0.1000 -0.0192
## 200 3.2041 nan 0.1000 -0.0221
## 220 3.1324 nan 0.1000 -0.0135
## 240 3.0725 nan 0.1000 -0.0108
## 260 3.0088 nan 0.1000 -0.0113
## 280 2.9479 nan 0.1000 -0.0148
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.9969 nan 0.1000 7.5328
## 2 49.5695 nan 0.1000 5.4782
## 3 44.6735 nan 0.1000 5.2042
## 4 40.1271 nan 0.1000 4.5760
## 5 36.0738 nan 0.1000 4.0403
## 6 32.3825 nan 0.1000 3.5299
## 7 29.6804 nan 0.1000 2.3444
## 8 26.7630 nan 0.1000 2.6804
## 9 24.6898 nan 0.1000 2.0587
## 10 22.7428 nan 0.1000 1.7268
## 20 11.8709 nan 0.1000 0.6001
## 40 5.7245 nan 0.1000 0.0920
## 60 4.3709 nan 0.1000 0.0019
## 80 4.0358 nan 0.1000 -0.0113
## 100 3.8609 nan 0.1000 -0.0073
## 120 3.7288 nan 0.1000 -0.0171
## 140 3.5954 nan 0.1000 -0.0239
## 160 3.4990 nan 0.1000 -0.0045
## 180 3.4148 nan 0.1000 -0.0114
## 200 3.3412 nan 0.1000 -0.0120
## 220 3.2774 nan 0.1000 -0.0114
## 240 3.2172 nan 0.1000 -0.0095
## 260 3.1427 nan 0.1000 -0.0189
## 280 3.0964 nan 0.1000 -0.0069
## 300 3.0392 nan 0.1000 -0.0190
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.1640 nan 0.1000 7.5770
## 2 49.2641 nan 0.1000 6.4141
## 3 44.0752 nan 0.1000 4.7745
## 4 39.6976 nan 0.1000 4.2276
## 5 35.5207 nan 0.1000 3.8692
## 6 32.3879 nan 0.1000 2.8614
## 7 29.0951 nan 0.1000 2.9609
## 8 26.5306 nan 0.1000 2.7709
## 9 24.3528 nan 0.1000 1.8141
## 10 22.3293 nan 0.1000 1.9860
## 20 11.7621 nan 0.1000 0.6568
## 40 5.7106 nan 0.1000 0.0869
## 60 4.4755 nan 0.1000 0.0016
## 80 4.1515 nan 0.1000 -0.0148
## 100 3.9749 nan 0.1000 -0.0163
## 120 3.7960 nan 0.1000 -0.0477
## 140 3.6784 nan 0.1000 -0.0011
## 160 3.5580 nan 0.1000 -0.0014
## 180 3.4762 nan 0.1000 -0.0149
## 200 3.4115 nan 0.1000 -0.0137
## 220 3.3653 nan 0.1000 -0.0163
## 240 3.3025 nan 0.1000 -0.0167
## 260 3.2565 nan 0.1000 -0.0069
## 280 3.2238 nan 0.1000 -0.0072
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 54.9901 nan 0.1000 10.1981
## 2 47.8890 nan 0.1000 7.4647
## 3 40.9534 nan 0.1000 7.1913
## 4 35.7510 nan 0.1000 4.4552
## 5 30.7941 nan 0.1000 4.7447
## 6 26.9355 nan 0.1000 3.7898
## 7 23.6228 nan 0.1000 3.4735
## 8 21.1905 nan 0.1000 2.4656
## 9 18.5172 nan 0.1000 2.5268
## 10 16.4628 nan 0.1000 1.6927
## 20 7.0552 nan 0.1000 0.3199
## 40 3.8720 nan 0.1000 0.0092
## 60 3.1492 nan 0.1000 -0.0323
## 80 2.8081 nan 0.1000 -0.0147
## 100 2.5705 nan 0.1000 -0.0214
## 120 2.3937 nan 0.1000 -0.0087
## 140 2.2210 nan 0.1000 -0.0308
## 160 2.0942 nan 0.1000 -0.0350
## 180 1.9889 nan 0.1000 -0.0416
## 200 1.8985 nan 0.1000 -0.0128
## 220 1.8203 nan 0.1000 -0.0169
## 240 1.7347 nan 0.1000 -0.0353
## 260 1.6521 nan 0.1000 -0.0164
## 280 1.5828 nan 0.1000 -0.0182
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 54.1717 nan 0.1000 8.7761
## 2 46.3164 nan 0.1000 8.0179
## 3 40.3913 nan 0.1000 5.6585
## 4 35.5041 nan 0.1000 5.3546
## 5 30.6585 nan 0.1000 4.3823
## 6 26.9349 nan 0.1000 3.4264
## 7 23.7000 nan 0.1000 3.5626
## 8 21.0122 nan 0.1000 2.7902
## 9 18.4413 nan 0.1000 2.4077
## 10 16.4957 nan 0.1000 1.8312
## 20 7.1085 nan 0.1000 0.3845
## 40 3.9351 nan 0.1000 0.0344
## 60 3.3948 nan 0.1000 -0.0265
## 80 3.0477 nan 0.1000 -0.0058
## 100 2.8156 nan 0.1000 -0.0119
## 120 2.6299 nan 0.1000 -0.0055
## 140 2.5145 nan 0.1000 -0.0341
## 160 2.4142 nan 0.1000 -0.0187
## 180 2.2878 nan 0.1000 -0.0089
## 200 2.1921 nan 0.1000 -0.0241
## 220 2.1146 nan 0.1000 -0.0369
## 240 2.0301 nan 0.1000 -0.0162
## 260 1.9273 nan 0.1000 -0.0195
## 280 1.8637 nan 0.1000 -0.0254
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 54.2193 nan 0.1000 8.5623
## 2 46.1174 nan 0.1000 6.9997
## 3 39.9801 nan 0.1000 5.1663
## 4 34.3154 nan 0.1000 5.3349
## 5 30.1830 nan 0.1000 4.0992
## 6 26.1250 nan 0.1000 4.0987
## 7 23.1763 nan 0.1000 3.1158
## 8 20.2859 nan 0.1000 2.3976
## 9 18.0310 nan 0.1000 2.1460
## 10 16.1756 nan 0.1000 1.5967
## 20 7.2317 nan 0.1000 0.3488
## 40 4.0914 nan 0.1000 0.0253
## 60 3.5108 nan 0.1000 -0.0206
## 80 3.2002 nan 0.1000 -0.0167
## 100 2.9695 nan 0.1000 0.0055
## 120 2.8404 nan 0.1000 -0.0232
## 140 2.7114 nan 0.1000 -0.0247
## 160 2.5754 nan 0.1000 -0.0135
## 180 2.4901 nan 0.1000 -0.0260
## 200 2.3899 nan 0.1000 -0.0047
## 220 2.3037 nan 0.1000 -0.0270
## 240 2.2291 nan 0.1000 -0.0154
## 260 2.1607 nan 0.1000 -0.0105
## 280 2.0755 nan 0.1000 -0.0054
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 54.3483 nan 0.1000 9.7868
## 2 45.5288 nan 0.1000 8.8127
## 3 38.8180 nan 0.1000 7.1202
## 4 33.1333 nan 0.1000 5.2940
## 5 28.4913 nan 0.1000 3.9627
## 6 24.4988 nan 0.1000 3.6822
## 7 21.2260 nan 0.1000 3.1019
## 8 18.3302 nan 0.1000 2.7653
## 9 15.9967 nan 0.1000 2.0661
## 10 14.1034 nan 0.1000 1.8126
## 20 5.5898 nan 0.1000 0.2816
## 40 3.1196 nan 0.1000 -0.0180
## 60 2.5170 nan 0.1000 -0.0408
## 80 2.1583 nan 0.1000 -0.0358
## 100 1.9170 nan 0.1000 -0.0045
## 120 1.7576 nan 0.1000 -0.0087
## 140 1.6117 nan 0.1000 -0.0155
## 160 1.4706 nan 0.1000 -0.0118
## 180 1.3591 nan 0.1000 -0.0189
## 200 1.2533 nan 0.1000 -0.0115
## 220 1.1723 nan 0.1000 -0.0114
## 240 1.0939 nan 0.1000 -0.0169
## 260 1.0204 nan 0.1000 -0.0146
## 280 0.9520 nan 0.1000 -0.0125
## 300 0.8952 nan 0.1000 -0.0121
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 53.2911 nan 0.1000 9.8543
## 2 44.6798 nan 0.1000 7.8135
## 3 38.1791 nan 0.1000 6.1061
## 4 32.5972 nan 0.1000 5.1100
## 5 28.1571 nan 0.1000 4.2547
## 6 24.3310 nan 0.1000 3.6034
## 7 20.9073 nan 0.1000 3.2402
## 8 18.2344 nan 0.1000 2.4535
## 9 15.9229 nan 0.1000 2.2876
## 10 14.2336 nan 0.1000 1.5907
## 20 5.8674 nan 0.1000 0.3149
## 40 3.4749 nan 0.1000 -0.0290
## 60 2.9610 nan 0.1000 -0.0251
## 80 2.6886 nan 0.1000 -0.0215
## 100 2.4512 nan 0.1000 -0.0241
## 120 2.2463 nan 0.1000 -0.0022
## 140 2.0790 nan 0.1000 -0.0186
## 160 1.9524 nan 0.1000 -0.0347
## 180 1.8162 nan 0.1000 -0.0226
## 200 1.7069 nan 0.1000 -0.0269
## 220 1.6161 nan 0.1000 -0.0153
## 240 1.5303 nan 0.1000 -0.0187
## 260 1.4326 nan 0.1000 -0.0209
## 280 1.3782 nan 0.1000 -0.0201
## 300 1.3169 nan 0.1000 -0.0099
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 54.3016 nan 0.1000 8.8383
## 2 45.7606 nan 0.1000 7.9709
## 3 38.5692 nan 0.1000 6.4787
## 4 33.0389 nan 0.1000 5.2313
## 5 28.4766 nan 0.1000 3.9661
## 6 24.2143 nan 0.1000 3.9407
## 7 20.8897 nan 0.1000 2.5696
## 8 18.2209 nan 0.1000 2.5567
## 9 16.0597 nan 0.1000 2.1353
## 10 14.2399 nan 0.1000 1.5543
## 20 6.0245 nan 0.1000 0.3129
## 40 3.4904 nan 0.1000 -0.0470
## 60 3.0247 nan 0.1000 -0.0006
## 80 2.7839 nan 0.1000 -0.0434
## 100 2.6055 nan 0.1000 -0.0339
## 120 2.3969 nan 0.1000 -0.0354
## 140 2.2629 nan 0.1000 -0.0136
## 160 2.0980 nan 0.1000 -0.0231
## 180 1.9848 nan 0.1000 -0.0135
## 200 1.8598 nan 0.1000 -0.0233
## 220 1.7641 nan 0.1000 -0.0180
## 240 1.6690 nan 0.1000 -0.0172
## 260 1.5996 nan 0.1000 -0.0100
## 280 1.5215 nan 0.1000 -0.0048
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.3050 nan 0.0010 0.0778
## 2 61.2296 nan 0.0010 0.0740
## 3 61.1509 nan 0.0010 0.0806
## 4 61.0768 nan 0.0010 0.0740
## 5 61.0005 nan 0.0010 0.0750
## 6 60.9189 nan 0.0010 0.0710
## 7 60.8404 nan 0.0010 0.0782
## 8 60.7653 nan 0.0010 0.0670
## 9 60.6903 nan 0.0010 0.0790
## 10 60.6161 nan 0.0010 0.0731
## 20 59.8420 nan 0.0010 0.0772
## 40 58.3605 nan 0.0010 0.0682
## 60 56.9177 nan 0.0010 0.0694
## 80 55.5344 nan 0.0010 0.0673
## 100 54.1889 nan 0.0010 0.0634
## 120 52.9019 nan 0.0010 0.0600
## 140 51.6700 nan 0.0010 0.0639
## 160 50.4857 nan 0.0010 0.0565
## 180 49.3303 nan 0.0010 0.0566
## 200 48.2094 nan 0.0010 0.0522
## 220 47.1338 nan 0.0010 0.0512
## 240 46.0838 nan 0.0010 0.0478
## 260 45.0865 nan 0.0010 0.0478
## 280 44.1253 nan 0.0010 0.0504
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.2980 nan 0.0010 0.0834
## 2 61.2210 nan 0.0010 0.0791
## 3 61.1407 nan 0.0010 0.0714
## 4 61.0653 nan 0.0010 0.0781
## 5 60.9884 nan 0.0010 0.0761
## 6 60.9114 nan 0.0010 0.0747
## 7 60.8313 nan 0.0010 0.0782
## 8 60.7514 nan 0.0010 0.0777
## 9 60.6809 nan 0.0010 0.0760
## 10 60.6132 nan 0.0010 0.0741
## 20 59.8617 nan 0.0010 0.0682
## 40 58.3935 nan 0.0010 0.0751
## 60 56.9617 nan 0.0010 0.0675
## 80 55.6059 nan 0.0010 0.0627
## 100 54.2835 nan 0.0010 0.0630
## 120 52.9883 nan 0.0010 0.0629
## 140 51.7736 nan 0.0010 0.0606
## 160 50.5749 nan 0.0010 0.0559
## 180 49.4081 nan 0.0010 0.0538
## 200 48.3129 nan 0.0010 0.0507
## 220 47.2333 nan 0.0010 0.0531
## 240 46.1895 nan 0.0010 0.0444
## 260 45.1857 nan 0.0010 0.0492
## 280 44.2116 nan 0.0010 0.0443
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.2998 nan 0.0010 0.0816
## 2 61.2184 nan 0.0010 0.0792
## 3 61.1440 nan 0.0010 0.0802
## 4 61.0650 nan 0.0010 0.0697
## 5 60.9873 nan 0.0010 0.0739
## 6 60.9088 nan 0.0010 0.0741
## 7 60.8348 nan 0.0010 0.0790
## 8 60.7560 nan 0.0010 0.0700
## 9 60.6792 nan 0.0010 0.0775
## 10 60.5994 nan 0.0010 0.0817
## 20 59.8237 nan 0.0010 0.0714
## 40 58.3549 nan 0.0010 0.0734
## 60 56.9221 nan 0.0010 0.0639
## 80 55.5557 nan 0.0010 0.0634
## 100 54.2309 nan 0.0010 0.0680
## 120 52.9574 nan 0.0010 0.0605
## 140 51.7152 nan 0.0010 0.0573
## 160 50.4959 nan 0.0010 0.0525
## 180 49.3455 nan 0.0010 0.0533
## 200 48.2470 nan 0.0010 0.0548
## 220 47.1678 nan 0.0010 0.0509
## 240 46.1238 nan 0.0010 0.0471
## 260 45.1104 nan 0.0010 0.0489
## 280 44.1320 nan 0.0010 0.0408
## 300 43.2098 nan 0.0010 0.0443
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.2829 nan 0.0010 0.0917
## 2 61.1820 nan 0.0010 0.0890
## 3 61.0973 nan 0.0010 0.0883
## 4 61.0034 nan 0.0010 0.0962
## 5 60.9151 nan 0.0010 0.0921
## 6 60.8172 nan 0.0010 0.0972
## 7 60.7248 nan 0.0010 0.0908
## 8 60.6322 nan 0.0010 0.0867
## 9 60.5417 nan 0.0010 0.0778
## 10 60.4524 nan 0.0010 0.0816
## 20 59.5537 nan 0.0010 0.0877
## 40 57.7859 nan 0.0010 0.0896
## 60 56.0704 nan 0.0010 0.0845
## 80 54.4164 nan 0.0010 0.0705
## 100 52.7857 nan 0.0010 0.0758
## 120 51.2454 nan 0.0010 0.0724
## 140 49.7628 nan 0.0010 0.0779
## 160 48.2994 nan 0.0010 0.0727
## 180 46.9065 nan 0.0010 0.0654
## 200 45.5620 nan 0.0010 0.0577
## 220 44.2629 nan 0.0010 0.0591
## 240 43.0104 nan 0.0010 0.0620
## 260 41.7648 nan 0.0010 0.0542
## 280 40.5854 nan 0.0010 0.0510
## 300 39.4477 nan 0.0010 0.0570
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.2811 nan 0.0010 0.0955
## 2 61.1862 nan 0.0010 0.0948
## 3 61.0964 nan 0.0010 0.0893
## 4 61.0040 nan 0.0010 0.0835
## 5 60.9079 nan 0.0010 0.0941
## 6 60.8102 nan 0.0010 0.1022
## 7 60.7157 nan 0.0010 0.0883
## 8 60.6254 nan 0.0010 0.0922
## 9 60.5333 nan 0.0010 0.0912
## 10 60.4331 nan 0.0010 0.0872
## 20 59.4970 nan 0.0010 0.0811
## 40 57.7082 nan 0.0010 0.0876
## 60 56.0004 nan 0.0010 0.0830
## 80 54.3509 nan 0.0010 0.0762
## 100 52.7391 nan 0.0010 0.0803
## 120 51.1755 nan 0.0010 0.0818
## 140 49.6804 nan 0.0010 0.0630
## 160 48.2248 nan 0.0010 0.0781
## 180 46.8062 nan 0.0010 0.0704
## 200 45.4408 nan 0.0010 0.0610
## 220 44.1263 nan 0.0010 0.0580
## 240 42.8840 nan 0.0010 0.0562
## 260 41.6287 nan 0.0010 0.0636
## 280 40.4512 nan 0.0010 0.0528
## 300 39.3205 nan 0.0010 0.0535
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.2876 nan 0.0010 0.1010
## 2 61.1912 nan 0.0010 0.0938
## 3 61.0965 nan 0.0010 0.0999
## 4 61.0030 nan 0.0010 0.0911
## 5 60.9114 nan 0.0010 0.0934
## 6 60.8187 nan 0.0010 0.0947
## 7 60.7266 nan 0.0010 0.0889
## 8 60.6323 nan 0.0010 0.0968
## 9 60.5362 nan 0.0010 0.1096
## 10 60.4437 nan 0.0010 0.0956
## 20 59.5319 nan 0.0010 0.0859
## 40 57.7624 nan 0.0010 0.0842
## 60 56.0241 nan 0.0010 0.0865
## 80 54.3543 nan 0.0010 0.0841
## 100 52.7535 nan 0.0010 0.0746
## 120 51.2186 nan 0.0010 0.0732
## 140 49.7484 nan 0.0010 0.0694
## 160 48.2550 nan 0.0010 0.0721
## 180 46.8744 nan 0.0010 0.0665
## 200 45.5248 nan 0.0010 0.0708
## 220 44.2176 nan 0.0010 0.0648
## 240 42.9163 nan 0.0010 0.0636
## 260 41.6940 nan 0.0010 0.0610
## 280 40.5209 nan 0.0010 0.0575
## 300 39.3925 nan 0.0010 0.0554
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.2738 nan 0.0010 0.1078
## 2 61.1689 nan 0.0010 0.0953
## 3 61.0652 nan 0.0010 0.0876
## 4 60.9650 nan 0.0010 0.1067
## 5 60.8693 nan 0.0010 0.1031
## 6 60.7688 nan 0.0010 0.0943
## 7 60.6686 nan 0.0010 0.1011
## 8 60.5672 nan 0.0010 0.1050
## 9 60.4695 nan 0.0010 0.1073
## 10 60.3710 nan 0.0010 0.1014
## 20 59.3773 nan 0.0010 0.0924
## 40 57.4741 nan 0.0010 0.0872
## 60 55.6074 nan 0.0010 0.0845
## 80 53.8231 nan 0.0010 0.0820
## 100 52.1141 nan 0.0010 0.0889
## 120 50.4489 nan 0.0010 0.0798
## 140 48.8407 nan 0.0010 0.0740
## 160 47.2862 nan 0.0010 0.0759
## 180 45.7918 nan 0.0010 0.0668
## 200 44.3511 nan 0.0010 0.0712
## 220 42.9692 nan 0.0010 0.0592
## 240 41.6461 nan 0.0010 0.0563
## 260 40.3613 nan 0.0010 0.0658
## 280 39.1243 nan 0.0010 0.0689
## 300 37.9270 nan 0.0010 0.0519
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.2763 nan 0.0010 0.1135
## 2 61.1751 nan 0.0010 0.1077
## 3 61.0786 nan 0.0010 0.1021
## 4 60.9765 nan 0.0010 0.0995
## 5 60.8743 nan 0.0010 0.0974
## 6 60.7735 nan 0.0010 0.1028
## 7 60.6742 nan 0.0010 0.0895
## 8 60.5671 nan 0.0010 0.1080
## 9 60.4690 nan 0.0010 0.0977
## 10 60.3661 nan 0.0010 0.0984
## 20 59.3781 nan 0.0010 0.0919
## 40 57.4356 nan 0.0010 0.1019
## 60 55.5655 nan 0.0010 0.0834
## 80 53.7784 nan 0.0010 0.0871
## 100 52.0701 nan 0.0010 0.0818
## 120 50.4246 nan 0.0010 0.0795
## 140 48.8167 nan 0.0010 0.0816
## 160 47.2672 nan 0.0010 0.0691
## 180 45.8046 nan 0.0010 0.0682
## 200 44.3870 nan 0.0010 0.0594
## 220 42.9841 nan 0.0010 0.0691
## 240 41.6727 nan 0.0010 0.0656
## 260 40.3962 nan 0.0010 0.0632
## 280 39.1536 nan 0.0010 0.0557
## 300 37.9587 nan 0.0010 0.0562
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.2832 nan 0.0010 0.1058
## 2 61.1835 nan 0.0010 0.1027
## 3 61.0788 nan 0.0010 0.1062
## 4 60.9828 nan 0.0010 0.0968
## 5 60.8795 nan 0.0010 0.1003
## 6 60.7720 nan 0.0010 0.0957
## 7 60.6763 nan 0.0010 0.0941
## 8 60.5640 nan 0.0010 0.1129
## 9 60.4657 nan 0.0010 0.0961
## 10 60.3672 nan 0.0010 0.1001
## 20 59.3763 nan 0.0010 0.0992
## 40 57.4659 nan 0.0010 0.0934
## 60 55.5953 nan 0.0010 0.0867
## 80 53.8031 nan 0.0010 0.0819
## 100 52.0904 nan 0.0010 0.0790
## 120 50.4284 nan 0.0010 0.0835
## 140 48.8247 nan 0.0010 0.0787
## 160 47.2855 nan 0.0010 0.0742
## 180 45.8110 nan 0.0010 0.0737
## 200 44.3832 nan 0.0010 0.0570
## 220 42.9910 nan 0.0010 0.0693
## 240 41.6482 nan 0.0010 0.0582
## 260 40.3751 nan 0.0010 0.0644
## 280 39.1772 nan 0.0010 0.0605
## 300 38.0023 nan 0.0010 0.0559
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.9915 nan 0.0050 0.3792
## 2 60.5795 nan 0.0050 0.4111
## 3 60.1972 nan 0.0050 0.3884
## 4 59.8127 nan 0.0050 0.4141
## 5 59.4312 nan 0.0050 0.3711
## 6 59.0679 nan 0.0050 0.3462
## 7 58.6962 nan 0.0050 0.3360
## 8 58.3144 nan 0.0050 0.3495
## 9 57.9228 nan 0.0050 0.3329
## 10 57.5712 nan 0.0050 0.3251
## 20 54.1479 nan 0.0050 0.3013
## 40 48.1420 nan 0.0050 0.2784
## 60 43.1125 nan 0.0050 0.2073
## 80 38.8450 nan 0.0050 0.1771
## 100 35.1038 nan 0.0050 0.1509
## 120 31.8389 nan 0.0050 0.1375
## 140 28.9750 nan 0.0050 0.1259
## 160 26.5377 nan 0.0050 0.1085
## 180 24.3557 nan 0.0050 0.1005
## 200 22.4261 nan 0.0050 0.0827
## 220 20.7116 nan 0.0050 0.0583
## 240 19.2394 nan 0.0050 0.0611
## 260 17.9091 nan 0.0050 0.0521
## 280 16.7094 nan 0.0050 0.0482
## 300 15.6210 nan 0.0050 0.0304
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.9800 nan 0.0050 0.3594
## 2 60.5867 nan 0.0050 0.3928
## 3 60.2024 nan 0.0050 0.3788
## 4 59.8375 nan 0.0050 0.3750
## 5 59.4911 nan 0.0050 0.3496
## 6 59.0921 nan 0.0050 0.3720
## 7 58.7135 nan 0.0050 0.3483
## 8 58.3367 nan 0.0050 0.3438
## 9 58.0176 nan 0.0050 0.3482
## 10 57.6604 nan 0.0050 0.3419
## 20 54.1990 nan 0.0050 0.3346
## 40 48.2499 nan 0.0050 0.2712
## 60 43.3316 nan 0.0050 0.2327
## 80 38.9384 nan 0.0050 0.1942
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## 120 31.9451 nan 0.0050 0.1569
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.9911 nan 0.0050 0.3906
## 2 60.6381 nan 0.0050 0.3866
## 3 60.2510 nan 0.0050 0.3714
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## 20 54.2628 nan 0.0050 0.3277
## 40 48.2028 nan 0.0050 0.2614
## 60 43.1401 nan 0.0050 0.2153
## 80 38.9957 nan 0.0050 0.1738
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## 120 31.9945 nan 0.0050 0.1404
## 140 29.1494 nan 0.0050 0.1231
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.8980 nan 0.0050 0.4720
## 2 60.4101 nan 0.0050 0.4099
## 3 59.9456 nan 0.0050 0.4498
## 4 59.4628 nan 0.0050 0.4461
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## 20 52.6712 nan 0.0050 0.3813
## 40 45.4392 nan 0.0050 0.3604
## 60 39.3128 nan 0.0050 0.2320
## 80 34.1754 nan 0.0050 0.2237
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## 140 23.3418 nan 0.0050 0.0955
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## 180 18.4239 nan 0.0050 0.1060
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## 280 11.4737 nan 0.0050 0.0428
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.8814 nan 0.0050 0.4389
## 2 60.4169 nan 0.0050 0.4691
## 3 59.9516 nan 0.0050 0.4893
## 4 59.5156 nan 0.0050 0.4397
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## 60 39.4441 nan 0.0050 0.2900
## 80 34.2711 nan 0.0050 0.2337
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## 140 23.2530 nan 0.0050 0.1367
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## 180 18.4180 nan 0.0050 0.0981
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## 280 11.3889 nan 0.0050 0.0367
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.8846 nan 0.0050 0.4423
## 2 60.3917 nan 0.0050 0.5044
## 3 59.9230 nan 0.0050 0.4774
## 4 59.4540 nan 0.0050 0.4627
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## 20 52.7017 nan 0.0050 0.3559
## 40 45.4237 nan 0.0050 0.3033
## 60 39.3463 nan 0.0050 0.2895
## 80 34.2820 nan 0.0050 0.2023
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## 140 23.2574 nan 0.0050 0.1370
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## 180 18.5704 nan 0.0050 0.0993
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.8741 nan 0.0050 0.5048
## 2 60.3483 nan 0.0050 0.5068
## 3 59.8592 nan 0.0050 0.5249
## 4 59.3537 nan 0.0050 0.4952
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## 40 44.3915 nan 0.0050 0.3141
## 60 37.9449 nan 0.0050 0.2728
## 80 32.5522 nan 0.0050 0.2410
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## 120 24.3749 nan 0.0050 0.1651
## 140 21.2973 nan 0.0050 0.1263
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## 240 11.7030 nan 0.0050 0.0603
## 260 10.5691 nan 0.0050 0.0573
## 280 9.6007 nan 0.0050 0.0359
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.8440 nan 0.0050 0.5194
## 2 60.3472 nan 0.0050 0.4589
## 3 59.8497 nan 0.0050 0.4421
## 4 59.3548 nan 0.0050 0.5125
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## 40 44.4238 nan 0.0050 0.3894
## 60 38.0353 nan 0.0050 0.2775
## 80 32.7611 nan 0.0050 0.2204
## 100 28.3105 nan 0.0050 0.1951
## 120 24.5087 nan 0.0050 0.1819
## 140 21.4324 nan 0.0050 0.1391
## 160 18.8351 nan 0.0050 0.1243
## 180 16.5972 nan 0.0050 0.1103
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## 220 13.1985 nan 0.0050 0.0653
## 240 11.8523 nan 0.0050 0.0533
## 260 10.7044 nan 0.0050 0.0491
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.8695 nan 0.0050 0.4972
## 2 60.3537 nan 0.0050 0.4919
## 3 59.8600 nan 0.0050 0.4463
## 4 59.3775 nan 0.0050 0.5035
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## 20 52.1363 nan 0.0050 0.4306
## 40 44.4333 nan 0.0050 0.4044
## 60 38.0620 nan 0.0050 0.2801
## 80 32.6732 nan 0.0050 0.2534
## 100 28.3360 nan 0.0050 0.2119
## 120 24.6022 nan 0.0050 0.1411
## 140 21.4487 nan 0.0050 0.1487
## 160 18.8256 nan 0.0050 0.1145
## 180 16.6508 nan 0.0050 0.0915
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## 240 11.9385 nan 0.0050 0.0644
## 260 10.8122 nan 0.0050 0.0460
## 280 9.8490 nan 0.0050 0.0388
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.6087 nan 0.0100 0.7814
## 2 59.9348 nan 0.0100 0.7172
## 3 59.2057 nan 0.0100 0.6888
## 4 58.4760 nan 0.0100 0.7339
## 5 57.7460 nan 0.0100 0.7225
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## 8 55.6847 nan 0.0100 0.6703
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## 20 48.4091 nan 0.0100 0.5627
## 40 38.9320 nan 0.0100 0.4027
## 60 32.1655 nan 0.0100 0.3030
## 80 26.7627 nan 0.0100 0.2374
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## 140 16.9917 nan 0.0100 0.0691
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## 240 9.6395 nan 0.0100 0.0397
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.5846 nan 0.0100 0.7242
## 2 59.7189 nan 0.0100 0.7393
## 3 59.0176 nan 0.0100 0.7543
## 4 58.3235 nan 0.0100 0.7454
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## 7 56.1039 nan 0.0100 0.6678
## 8 55.4683 nan 0.0100 0.6032
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## 20 47.9552 nan 0.0100 0.4831
## 40 38.6719 nan 0.0100 0.3784
## 60 31.9243 nan 0.0100 0.2498
## 80 26.5724 nan 0.0100 0.2274
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## 140 16.7553 nan 0.0100 0.1061
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## 180 13.0101 nan 0.0100 0.0610
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## 220 10.5231 nan 0.0100 0.0436
## 240 9.5749 nan 0.0100 0.0335
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## 280 8.0925 nan 0.0100 0.0247
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.6512 nan 0.0100 0.7558
## 2 59.8782 nan 0.0100 0.7704
## 3 59.1220 nan 0.0100 0.7379
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## 7 56.1957 nan 0.0100 0.6645
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## 9 54.8911 nan 0.0100 0.6590
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## 20 48.3741 nan 0.0100 0.5458
## 40 38.8227 nan 0.0100 0.3727
## 60 31.8189 nan 0.0100 0.2026
## 80 26.6205 nan 0.0100 0.1783
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## 140 16.8582 nan 0.0100 0.1096
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## 180 13.1824 nan 0.0100 0.0578
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## 220 10.6462 nan 0.0100 0.0474
## 240 9.6826 nan 0.0100 0.0268
## 260 8.8830 nan 0.0100 0.0319
## 280 8.2203 nan 0.0100 0.0226
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.3731 nan 0.0100 0.8727
## 2 59.4232 nan 0.0100 0.9901
## 3 58.5814 nan 0.0100 0.8225
## 4 57.6375 nan 0.0100 0.8170
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## 8 54.3080 nan 0.0100 0.7694
## 9 53.5831 nan 0.0100 0.7170
## 10 52.8120 nan 0.0100 0.7951
## 20 45.4330 nan 0.0100 0.6382
## 40 34.1513 nan 0.0100 0.4739
## 60 26.1179 nan 0.0100 0.3541
## 80 20.5181 nan 0.0100 0.2119
## 100 16.4831 nan 0.0100 0.1636
## 120 13.5907 nan 0.0100 0.1170
## 140 11.3483 nan 0.0100 0.0788
## 160 9.6996 nan 0.0100 0.0774
## 180 8.4230 nan 0.0100 0.0506
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## 220 6.6393 nan 0.0100 0.0255
## 240 6.0196 nan 0.0100 0.0168
## 260 5.4898 nan 0.0100 0.0164
## 280 5.1034 nan 0.0100 0.0167
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.4031 nan 0.0100 0.8763
## 2 59.5255 nan 0.0100 0.9611
## 3 58.6023 nan 0.0100 0.8128
## 4 57.6573 nan 0.0100 0.8575
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## 8 54.3317 nan 0.0100 0.7571
## 9 53.5131 nan 0.0100 0.7943
## 10 52.6691 nan 0.0100 0.8224
## 20 45.7139 nan 0.0100 0.5879
## 40 34.4832 nan 0.0100 0.4407
## 60 26.4799 nan 0.0100 0.3230
## 80 20.7467 nan 0.0100 0.2424
## 100 16.6708 nan 0.0100 0.1602
## 120 13.6659 nan 0.0100 0.1141
## 140 11.3802 nan 0.0100 0.0959
## 160 9.7364 nan 0.0100 0.0581
## 180 8.4653 nan 0.0100 0.0337
## 200 7.4330 nan 0.0100 0.0352
## 220 6.6554 nan 0.0100 0.0285
## 240 6.0159 nan 0.0100 0.0216
## 260 5.5248 nan 0.0100 0.0127
## 280 5.1283 nan 0.0100 0.0149
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.4083 nan 0.0100 0.8183
## 2 59.5480 nan 0.0100 0.9328
## 3 58.6494 nan 0.0100 0.8520
## 4 57.8535 nan 0.0100 0.8869
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## 6 56.0789 nan 0.0100 0.7483
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## 9 53.5671 nan 0.0100 0.7891
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## 20 45.4237 nan 0.0100 0.6637
## 40 34.0460 nan 0.0100 0.4780
## 60 26.1934 nan 0.0100 0.3477
## 80 20.6276 nan 0.0100 0.2209
## 100 16.6789 nan 0.0100 0.1532
## 120 13.8552 nan 0.0100 0.1197
## 140 11.7327 nan 0.0100 0.0697
## 160 10.0283 nan 0.0100 0.0567
## 180 8.7399 nan 0.0100 0.0428
## 200 7.7108 nan 0.0100 0.0348
## 220 6.9022 nan 0.0100 0.0283
## 240 6.2768 nan 0.0100 0.0202
## 260 5.7934 nan 0.0100 0.0134
## 280 5.3973 nan 0.0100 0.0110
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.2910 nan 0.0100 0.9729
## 2 59.2984 nan 0.0100 0.9945
## 3 58.2858 nan 0.0100 1.0976
## 4 57.3115 nan 0.0100 0.8716
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## 7 54.6408 nan 0.0100 0.8683
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## 20 44.1672 nan 0.0100 0.6810
## 40 32.4008 nan 0.0100 0.4626
## 60 24.2603 nan 0.0100 0.3069
## 80 18.5819 nan 0.0100 0.2240
## 100 14.5456 nan 0.0100 0.1405
## 120 11.7106 nan 0.0100 0.1213
## 140 9.6018 nan 0.0100 0.0862
## 160 8.0393 nan 0.0100 0.0472
## 180 6.8978 nan 0.0100 0.0353
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## 240 4.7986 nan 0.0100 0.0146
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## 280 4.0904 nan 0.0100 0.0050
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.3339 nan 0.0100 1.0052
## 2 59.3097 nan 0.0100 0.9652
## 3 58.3582 nan 0.0100 0.8627
## 4 57.3773 nan 0.0100 0.9509
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## 9 52.8990 nan 0.0100 0.8947
## 10 52.0355 nan 0.0100 0.8474
## 20 44.3375 nan 0.0100 0.7369
## 40 32.6241 nan 0.0100 0.5083
## 60 24.3435 nan 0.0100 0.3356
## 80 18.6181 nan 0.0100 0.2336
## 100 14.6389 nan 0.0100 0.1528
## 120 11.7542 nan 0.0100 0.1102
## 140 9.6929 nan 0.0100 0.0712
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## 180 6.9334 nan 0.0100 0.0474
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## 220 5.3947 nan 0.0100 0.0205
## 240 4.8969 nan 0.0100 0.0182
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## 280 4.2243 nan 0.0100 0.0084
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.3801 nan 0.0100 1.0206
## 2 59.4205 nan 0.0100 0.8794
## 3 58.4217 nan 0.0100 0.9464
## 4 57.4588 nan 0.0100 1.0547
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## 7 54.6354 nan 0.0100 0.9901
## 8 53.6872 nan 0.0100 1.0242
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## 10 51.9890 nan 0.0100 0.8386
## 20 44.4202 nan 0.0100 0.7184
## 40 32.6710 nan 0.0100 0.4728
## 60 24.4048 nan 0.0100 0.3197
## 80 18.6802 nan 0.0100 0.2441
## 100 14.7475 nan 0.0100 0.1466
## 120 11.9100 nan 0.0100 0.1106
## 140 9.8708 nan 0.0100 0.0700
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## 180 7.2178 nan 0.0100 0.0500
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## 240 5.1741 nan 0.0100 0.0161
## 260 4.7836 nan 0.0100 0.0120
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.6882 nan 0.0500 3.7264
## 2 54.1214 nan 0.0500 3.6922
## 3 51.0108 nan 0.0500 3.0275
## 4 48.0237 nan 0.0500 2.9646
## 5 45.4477 nan 0.0500 1.9907
## 6 43.0769 nan 0.0500 2.0015
## 7 40.6490 nan 0.0500 2.2378
## 8 38.4082 nan 0.0500 2.2304
## 9 36.2285 nan 0.0500 2.2400
## 10 34.4423 nan 0.0500 1.7992
## 20 22.1371 nan 0.0500 0.8527
## 40 11.4004 nan 0.0500 0.2431
## 60 7.4596 nan 0.0500 0.1114
## 80 5.6228 nan 0.0500 0.0667
## 100 4.7329 nan 0.0500 -0.0051
## 120 4.3002 nan 0.0500 0.0049
## 140 4.0450 nan 0.0500 -0.0077
## 160 3.9214 nan 0.0500 -0.0149
## 180 3.8183 nan 0.0500 -0.0022
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## 220 3.6477 nan 0.0500 0.0002
## 240 3.6053 nan 0.0500 -0.0041
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## 280 3.4957 nan 0.0500 -0.0153
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.9618 nan 0.0500 3.7907
## 2 54.5807 nan 0.0500 3.3574
## 3 51.2795 nan 0.0500 3.0127
## 4 48.2549 nan 0.0500 2.8350
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## 7 40.9553 nan 0.0500 2.1129
## 8 38.8228 nan 0.0500 2.0239
## 9 36.6671 nan 0.0500 1.9774
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## 20 22.5753 nan 0.0500 0.5825
## 40 11.7541 nan 0.0500 0.2609
## 60 7.5536 nan 0.0500 0.0978
## 80 5.5796 nan 0.0500 0.0501
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## 120 4.2357 nan 0.0500 0.0023
## 140 4.0180 nan 0.0500 -0.0197
## 160 3.9100 nan 0.0500 -0.0004
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## 240 3.6391 nan 0.0500 -0.0120
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## 280 3.5282 nan 0.0500 -0.0025
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.7157 nan 0.0500 3.7902
## 2 54.4730 nan 0.0500 3.5571
## 3 51.2612 nan 0.0500 3.0139
## 4 48.4374 nan 0.0500 2.9287
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## 6 43.4560 nan 0.0500 2.4347
## 7 41.2749 nan 0.0500 2.1689
## 8 39.2471 nan 0.0500 2.0858
## 9 37.3553 nan 0.0500 1.9186
## 10 35.5429 nan 0.0500 1.4827
## 20 22.6926 nan 0.0500 0.8077
## 40 11.5969 nan 0.0500 0.2417
## 60 7.5349 nan 0.0500 0.1207
## 80 5.7729 nan 0.0500 0.0333
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## 140 4.4340 nan 0.0500 -0.0057
## 160 4.3125 nan 0.0500 -0.0034
## 180 4.2215 nan 0.0500 -0.0031
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## 280 3.8924 nan 0.0500 -0.0041
## 300 3.8494 nan 0.0500 -0.0056
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.1146 nan 0.0500 4.2013
## 2 52.7674 nan 0.0500 4.4111
## 3 48.5013 nan 0.0500 3.7474
## 4 44.8904 nan 0.0500 3.3747
## 5 41.5746 nan 0.0500 3.1724
## 6 38.7357 nan 0.0500 2.7897
## 7 35.9903 nan 0.0500 2.4036
## 8 33.6283 nan 0.0500 2.3189
## 9 31.2150 nan 0.0500 2.1451
## 10 29.1025 nan 0.0500 1.9996
## 20 16.0208 nan 0.0500 0.7012
## 40 7.2263 nan 0.0500 0.1697
## 60 4.7670 nan 0.0500 0.0496
## 80 3.9325 nan 0.0500 -0.0230
## 100 3.5795 nan 0.0500 -0.0041
## 120 3.3324 nan 0.0500 0.0015
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## 280 2.3663 nan 0.0500 -0.0045
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.1705 nan 0.0500 4.8351
## 2 52.4498 nan 0.0500 4.6638
## 3 48.7307 nan 0.0500 4.0931
## 4 45.4616 nan 0.0500 3.6567
## 5 42.2666 nan 0.0500 3.2523
## 6 39.1474 nan 0.0500 2.6656
## 7 36.5001 nan 0.0500 2.5567
## 8 33.8819 nan 0.0500 2.3102
## 9 31.6082 nan 0.0500 2.3739
## 10 29.6210 nan 0.0500 2.0300
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## 60 4.8171 nan 0.0500 0.0354
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## 100 3.7025 nan 0.0500 -0.0124
## 120 3.4790 nan 0.0500 -0.0070
## 140 3.2970 nan 0.0500 -0.0096
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## 280 2.5905 nan 0.0500 -0.0136
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.6348 nan 0.0500 4.6225
## 2 52.4694 nan 0.0500 3.7606
## 3 48.5963 nan 0.0500 4.0450
## 4 45.0871 nan 0.0500 3.5413
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## 20 16.3355 nan 0.0500 0.8080
## 40 7.6533 nan 0.0500 0.1513
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.3250 nan 0.0500 5.0922
## 2 51.7477 nan 0.0500 4.5414
## 3 47.6791 nan 0.0500 3.1199
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.6059 nan 0.0500 4.7659
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.1381 nan 0.0500 4.9662
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## 60 4.4009 nan 0.0500 0.0213
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 54.0511 nan 0.1000 7.4470
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 54.1760 nan 0.1000 7.5596
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## 3 43.2254 nan 0.1000 4.6278
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 53.6222 nan 0.1000 6.9997
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.4543 nan 0.1000 8.8288
## 2 45.1435 nan 0.1000 7.1661
## 3 39.9473 nan 0.1000 5.9227
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.7198 nan 0.1000 8.9661
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.5449 nan 0.1000 8.9548
## 2 45.0377 nan 0.1000 7.2808
## 3 38.3929 nan 0.1000 6.3621
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.9125 nan 0.1000 9.5619
## 2 44.4070 nan 0.1000 7.6436
## 3 37.3621 nan 0.1000 6.4708
## 4 32.3396 nan 0.1000 5.5120
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.4003 nan 0.1000 9.7223
## 2 43.0963 nan 0.1000 7.1128
## 3 36.4246 nan 0.1000 5.8032
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.8236 nan 0.1000 9.4686
## 2 44.1260 nan 0.1000 7.6190
## 3 37.3698 nan 0.1000 6.5661
## 4 31.9490 nan 0.1000 4.9936
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## 180 1.9581 nan 0.1000 -0.0254
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 53.1719 nan 0.1000 9.4665
## 2 45.9121 nan 0.1000 6.9589
## 3 39.7406 nan 0.1000 5.4936
## 4 34.3692 nan 0.1000 4.6713
## 5 29.9825 nan 0.1000 4.5305
## 6 25.9262 nan 0.1000 4.2774
## 7 22.9292 nan 0.1000 2.8950
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## 10 15.9527 nan 0.1000 1.8092
## 20 7.4563 nan 0.1000 0.2106
## 40 3.8987 nan 0.1000 0.0195
## 60 3.2508 nan 0.1000 -0.0078
## 80 2.9713 nan 0.1000 -0.0225
## 100 2.7113 nan 0.1000 -0.0084
## 120 2.5043 nan 0.1000 -0.0216
## 140 2.3299 nan 0.1000 -0.0166
## 160 2.2254 nan 0.1000 -0.0163
## 180 2.1006 nan 0.1000 -0.0129
## 200 1.9965 nan 0.1000 -0.0234
## 220 1.9027 nan 0.1000 -0.0049
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## 260 1.7373 nan 0.1000 -0.0210
## 280 1.6627 nan 0.1000 -0.0301
## 300 1.5958 nan 0.1000 -0.0100##################################
# Reporting the apparent results
# for the GBM model
##################################
GBM_DALEX <- DALEX::explain(GBM_Tune,
data = MD.Model.Predictors,
y = MD$LIFEXP,
verbose = FALSE,
label = "GBM")
(GBM_DALEX_Performance <- model_performance(GBM_DALEX))## Measures for: regression
## mse : 1.59583
## rmse : 1.263262
## r2 : 0.9742122
## mad : 0.6901519
##
## Residuals:
## 0% 10% 20% 30% 40% 50%
## -5.14895174 -1.37469335 -0.86109715 -0.58821296 -0.29421757 -0.02188095
## 60% 70% 80% 90% 100%
## 0.19929179 0.54567671 0.89833729 1.60070669 4.04954295(GBM_DALEX_Diagnostics <- model_diagnostics(GBM_DALEX))## GENDER CONTIN INFMOR PERCAP
## Male :139 Africa :89 Min. :0.3365 Min. :-1.4775
## Female:153 Asia :73 1st Qu.:1.7047 1st Qu.: 0.6183
## Europe :62 Median :2.6100 Median : 1.7960
## North America:31 Mean :2.5569 Mean : 1.7571
## Oceania :18 3rd Qu.:3.5025 3rd Qu.: 2.7938
## South America:19 Max. :4.4864 Max. : 4.7293
## CLTECH NCOMOR y y_hat
## Min. : 0.00 Min. :1.866 Min. :52.84 Min. :53.13
## 1st Qu.: 24.35 1st Qu.:3.944 1st Qu.:66.93 1st Qu.:66.84
## Median : 82.80 Median :4.717 Median :73.53 Median :73.46
## Mean : 64.60 Mean :4.652 Mean :72.47 Mean :72.47
## 3rd Qu.:100.00 3rd Qu.:5.347 3rd Qu.:78.54 3rd Qu.:78.63
## Max. :100.00 Max. :7.959 Max. :87.45 Max. :85.25
## residuals abs_residuals label ids
## Min. :-5.148952 Min. :0.00237 Length:292 Min. : 1.00
## 1st Qu.:-0.693640 1st Qu.:0.29973 Class :character 1st Qu.: 73.75
## Median :-0.021881 Median :0.69015 Mode :character Median :146.50
## Mean : 0.005138 Mean :0.93475 Mean :146.50
## 3rd Qu.: 0.687602 3rd Qu.:1.29094 3rd Qu.:219.25
## Max. : 4.049543 Max. :5.14895 Max. :292.00plot(GBM_DALEX_Diagnostics,
variable = "y",
yvariable = "y_hat") +
geom_point(size=3) +
scale_x_continuous("Observed LIFEXP") +
scale_y_continuous("Predicted LIFEXP") +
geom_abline(slope = 1) +
ggtitle("GBM: Observed and Predicted LIFEXP")
(GBM_DALEX_VariableImportance <- model_parts(GBM_DALEX,
loss_function = loss_root_mean_square,
B = 200,
N = NULL))## variable mean_dropout_loss label
## 1 _full_model_ 1.263262 GBM
## 2 PERCAP 1.524005 GBM
## 3 CLTECH 1.839900 GBM
## 4 GENDER 1.916960 GBM
## 5 CONTIN 1.928607 GBM
## 6 NCOMOR 3.569763 GBM
## 7 INFMOR 7.071063 GBM
## 8 _baseline_ 11.021426 GBMplot(GBM_DALEX_VariableImportance)
##################################
# Reporting the cross-validation results
# for the GBM model
##################################
GBM_Tune## Stochastic Gradient Boosting
##
## 292 samples
## 6 predictor
##
## No pre-processing
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 264, 263, 263, 262, 263, 264, ...
## Resampling results across tuning parameters:
##
## shrinkage interaction.depth n.minobsinnode n.trees RMSE Rsquared
## 0.001 1 5 100 7.377266 0.6960237
## 0.001 1 5 200 6.966222 0.7209826
## 0.001 1 5 300 6.597909 0.7441613
## 0.001 1 10 100 7.377949 0.6935879
## 0.001 1 10 200 6.966416 0.7208317
## 0.001 1 10 300 6.598121 0.7461646
## 0.001 1 15 100 7.377966 0.6917668
## 0.001 1 15 200 6.965878 0.7195112
## 0.001 1 15 300 6.596459 0.7448704
## 0.001 2 5 100 7.275870 0.8226732
## 0.001 2 5 200 6.762629 0.8370384
## 0.001 2 5 300 6.295943 0.8464683
## 0.001 2 10 100 7.275447 0.8240470
## 0.001 2 10 200 6.762739 0.8357535
## 0.001 2 10 300 6.299257 0.8457916
## 0.001 2 15 100 7.277399 0.8212853
## 0.001 2 15 200 6.760586 0.8360518
## 0.001 2 15 300 6.295997 0.8452784
## 0.001 3 5 100 7.231044 0.8682652
## 0.001 3 5 200 6.676028 0.8727234
## 0.001 3 5 300 6.182773 0.8763560
## 0.001 3 10 100 7.231374 0.8681444
## 0.001 3 10 200 6.680187 0.8716595
## 0.001 3 10 300 6.186029 0.8754754
## 0.001 3 15 100 7.233646 0.8658641
## 0.001 3 15 200 6.684882 0.8700754
## 0.001 3 15 300 6.193834 0.8724538
## 0.005 1 5 100 5.956976 0.7830051
## 0.005 1 5 200 4.792539 0.8329608
## 0.005 1 5 300 4.037847 0.8559526
## 0.005 1 10 100 5.963486 0.7866110
## 0.005 1 10 200 4.797846 0.8324357
## 0.005 1 10 300 4.050994 0.8556736
## 0.005 1 15 100 5.958365 0.7883867
## 0.005 1 15 200 4.797231 0.8331380
## 0.005 1 15 300 4.048700 0.8558011
## 0.005 2 5 100 5.500503 0.8577534
## 0.005 2 5 200 4.143717 0.8764977
## 0.005 2 5 300 3.357272 0.8909948
## 0.005 2 10 100 5.509140 0.8584730
## 0.005 2 10 200 4.143287 0.8761927
## 0.005 2 10 300 3.364624 0.8904997
## 0.005 2 15 100 5.504265 0.8577953
## 0.005 2 15 200 4.155921 0.8736943
## 0.005 2 15 300 3.388745 0.8881356
## 0.005 3 5 100 5.343316 0.8815099
## 0.005 3 5 200 3.909402 0.8962557
## 0.005 3 5 300 3.099679 0.9077613
## 0.005 3 10 100 5.342606 0.8825193
## 0.005 3 10 200 3.913986 0.8952557
## 0.005 3 10 300 3.104638 0.9066329
## 0.005 3 15 100 5.349350 0.8789039
## 0.005 3 15 200 3.942905 0.8909316
## 0.005 3 15 300 3.158405 0.9026099
## 0.010 1 5 100 4.785905 0.8365957
## 0.010 1 5 200 3.513753 0.8738534
## 0.010 1 5 300 2.880870 0.8972201
## 0.010 1 10 100 4.785535 0.8344965
## 0.010 1 10 200 3.519973 0.8734344
## 0.010 1 10 300 2.882445 0.8972143
## 0.010 1 15 100 4.794296 0.8332018
## 0.010 1 15 200 3.536103 0.8703625
## 0.010 1 15 300 2.926290 0.8926247
## 0.010 2 5 100 4.134028 0.8765837
## 0.010 2 5 200 2.885180 0.9023326
## 0.010 2 5 300 2.410639 0.9167652
## 0.010 2 10 100 4.146583 0.8765373
## 0.010 2 10 200 2.885874 0.9035827
## 0.010 2 10 300 2.401671 0.9178631
## 0.010 2 15 100 4.130967 0.8750381
## 0.010 2 15 200 2.918106 0.9006076
## 0.010 2 15 300 2.455977 0.9142649
## 0.010 3 5 100 3.909713 0.8952399
## 0.010 3 5 200 2.663648 0.9143332
## 0.010 3 5 300 2.274084 0.9230714
## 0.010 3 10 100 3.915620 0.8947741
## 0.010 3 10 200 2.660830 0.9144843
## 0.010 3 10 300 2.279239 0.9226394
## 0.010 3 15 100 3.919520 0.8927569
## 0.010 3 15 200 2.698192 0.9117988
## 0.010 3 15 300 2.323679 0.9202826
## 0.050 1 5 100 2.368670 0.9155076
## 0.050 1 5 200 2.194300 0.9242561
## 0.050 1 5 300 2.175057 0.9255193
## 0.050 1 10 100 2.356209 0.9166591
## 0.050 1 10 200 2.162508 0.9268045
## 0.050 1 10 300 2.145778 0.9276575
## 0.050 1 15 100 2.409980 0.9135821
## 0.050 1 15 200 2.255478 0.9217689
## 0.050 1 15 300 2.215643 0.9236305
## 0.050 2 5 100 2.223779 0.9224629
## 0.050 2 5 200 2.176185 0.9256471
## 0.050 2 5 300 2.156758 0.9263352
## 0.050 2 10 100 2.200024 0.9246320
## 0.050 2 10 200 2.174896 0.9256411
## 0.050 2 10 300 2.149895 0.9266258
## 0.050 2 15 100 2.229941 0.9222895
## 0.050 2 15 200 2.148962 0.9268146
## 0.050 2 15 300 2.140697 0.9269695
## 0.050 3 5 100 2.169569 0.9259392
## 0.050 3 5 200 2.172464 0.9251291
## 0.050 3 5 300 2.169582 0.9250868
## 0.050 3 10 100 2.185536 0.9249803
## 0.050 3 10 200 2.152994 0.9263177
## 0.050 3 10 300 2.148798 0.9262594
## 0.050 3 15 100 2.198534 0.9241099
## 0.050 3 15 200 2.169993 0.9255660
## 0.050 3 15 300 2.163552 0.9253829
## 0.100 1 5 100 2.208789 0.9219656
## 0.100 1 5 200 2.176595 0.9240653
## 0.100 1 5 300 2.176525 0.9235917
## 0.100 1 10 100 2.221531 0.9213379
## 0.100 1 10 200 2.165187 0.9250069
## 0.100 1 10 300 2.160528 0.9252803
## 0.100 1 15 100 2.222324 0.9220769
## 0.100 1 15 200 2.180369 0.9245016
## 0.100 1 15 300 2.150839 0.9263505
## 0.100 2 5 100 2.108652 0.9285686
## 0.100 2 5 200 2.110590 0.9279673
## 0.100 2 5 300 2.097843 0.9288204
## 0.100 2 10 100 2.184963 0.9240327
## 0.100 2 10 200 2.196965 0.9228255
## 0.100 2 10 300 2.201076 0.9221509
## 0.100 2 15 100 2.216480 0.9225563
## 0.100 2 15 200 2.192126 0.9231873
## 0.100 2 15 300 2.200571 0.9220875
## 0.100 3 5 100 2.205014 0.9239297
## 0.100 3 5 200 2.163422 0.9257538
## 0.100 3 5 300 2.210707 0.9218679
## 0.100 3 10 100 2.173034 0.9248470
## 0.100 3 10 200 2.165420 0.9244402
## 0.100 3 10 300 2.168710 0.9240569
## 0.100 3 15 100 2.206916 0.9238262
## 0.100 3 15 200 2.204289 0.9231056
## 0.100 3 15 300 2.192318 0.9234358
## MAE
## 6.147721
## 5.779464
## 5.455970
## 6.148010
## 5.780973
## 5.454359
## 6.149646
## 5.783358
## 5.454089
## 6.071049
## 5.621430
## 5.217508
## 6.070565
## 5.621452
## 5.220069
## 6.072811
## 5.619098
## 5.216703
## 6.059105
## 5.588782
## 5.169935
## 6.059887
## 5.593238
## 5.173991
## 6.060150
## 5.594574
## 5.174572
## 4.888478
## 3.873259
## 3.256497
## 4.894389
## 3.881221
## 3.270713
## 4.887381
## 3.879311
## 3.260956
## 4.542649
## 3.394213
## 2.730622
## 4.547507
## 3.382926
## 2.732000
## 4.538989
## 3.391199
## 2.737812
## 4.469254
## 3.246403
## 2.520197
## 4.459693
## 3.241521
## 2.517426
## 4.461381
## 3.257064
## 2.549622
## 3.873518
## 2.832869
## 2.284277
## 3.873192
## 2.835963
## 2.279827
## 3.873785
## 2.845547
## 2.312822
## 3.386359
## 2.300962
## 1.866370
## 3.389520
## 2.302218
## 1.851959
## 3.363637
## 2.304560
## 1.871229
## 3.245379
## 2.111343
## 1.731292
## 3.242745
## 2.101940
## 1.722644
## 3.238655
## 2.108107
## 1.737609
## 1.793937
## 1.615079
## 1.598225
## 1.782247
## 1.599494
## 1.568559
## 1.830035
## 1.682260
## 1.642767
## 1.654957
## 1.602377
## 1.589340
## 1.642846
## 1.597405
## 1.581444
## 1.638179
## 1.580946
## 1.580974
## 1.616842
## 1.607067
## 1.615612
## 1.591912
## 1.568460
## 1.567069
## 1.600684
## 1.587525
## 1.584272
## 1.635785
## 1.601813
## 1.608526
## 1.636636
## 1.588658
## 1.593587
## 1.637832
## 1.586292
## 1.569520
## 1.579722
## 1.578614
## 1.577171
## 1.602153
## 1.613135
## 1.620481
## 1.636962
## 1.612915
## 1.615130
## 1.602353
## 1.586244
## 1.644167
## 1.592651
## 1.595324
## 1.611267
## 1.613429
## 1.632831
## 1.617914
##
## RMSE was used to select the optimal model using the smallest value.
## The final values used for the model were n.trees = 300, interaction.depth =
## 2, shrinkage = 0.1 and n.minobsinnode = 5.GBM_Tune$finalModel## A gradient boosted model with gaussian loss function.
## 300 iterations were performed.
## There were 6 predictors of which 6 had non-zero influence.(GBM_Tune_RMSE <- GBM_Tune$results[GBM_Tune$results$shrinkage==GBM_Tune$bestTune$shrinkage &
GBM_Tune$results$interaction.depth==GBM_Tune$bestTune$interaction.depth &
GBM_Tune$results$n.minobsinnode==GBM_Tune$bestTune$n.minobsinnode &
GBM_Tune$results$n.trees==GBM_Tune$bestTune$n.trees,
c("RMSE")])## [1] 2.097843(GBM_Tune_Rsquared <- GBM_Tune$results[GBM_Tune$results$shrinkage==GBM_Tune$bestTune$shrinkage &
GBM_Tune$results$interaction.depth==GBM_Tune$bestTune$interaction.depth &
GBM_Tune$results$n.minobsinnode==GBM_Tune$bestTune$n.minobsinnode &
GBM_Tune$results$n.trees==GBM_Tune$bestTune$n.trees,
c("Rsquared")])## [1] 0.9288204(GBM_Tune_MAE <- GBM_Tune$results[GBM_Tune$results$shrinkage==GBM_Tune$bestTune$shrinkage &
GBM_Tune$results$interaction.depth==GBM_Tune$bestTune$interaction.depth &
GBM_Tune$results$n.minobsinnode==GBM_Tune$bestTune$n.minobsinnode &
GBM_Tune$results$n.trees==GBM_Tune$bestTune$n.trees,
c("MAE")])## [1] 1.5771711.3.6.3 Random Forest
[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 100 to 1000 with intervals of 100
[C] The 10-fold cross-validated model performance of the optimal model is summarized as follows:
[C.1] Optimal model used mtry=400 which demonstrated the lowest root mean square error
[C.2] Root Mean Square Error = 2.2390
[C.3] R-Squared = 0.9201
[D] The apparent model performance of the optimal model is summarized as follows:
[D.1] Root Mean Square Error = 0.9698
[D.2] R-Squared = 0.9848
[E] The top-performing predictors in the model ranked using the root mean square error loss after permutations are as follows:
[E.1] INFMOR (numeric) = 8.39
[E.2] NCOMOR (numeric) = 4.23
[E.3] CONTIN (factor) = 1.45
[E.4] GENDER (factor) = 1.36
[E.5] CLTECH (numeric) = 1.28
[E.6] PERCAP (numeric) = 1.25
Code Chunk | Output
##################################
# Defining the model hyperparameter values
# for the RF model
##################################
RF_Grid = data.frame(mtry = c(100, 200, 300, 400, 500,
600, 700, 800, 900, 1000))
##################################
# Running the RF model
# by setting the caret method to 'RF'
##################################
set.seed(12345678)
RF_Tune <- train(x = MD.Model.Predictors,
y = MD$LIFEXP,
method = "rf",
tuneGrid = RF_Grid,
trControl = KFold_Control)
##################################
# Reporting the apparent results
# for the RF model
##################################
RF_DALEX <- DALEX::explain(RF_Tune,
data = MD.Model.Predictors,
y = MD$LIFEXP,
verbose = FALSE,
label = "RF")
(RF_DALEX_Performance <- model_performance(RF_DALEX))## Measures for: regression
## mse : 0.9404626
## rmse : 0.9697745
## r2 : 0.9848026
## mad : 0.493929
##
## Residuals:
## 0% 10% 20% 30% 40% 50%
## -3.89311943 -1.04739946 -0.63797813 -0.37774333 -0.17489179 -0.02355452
## 60% 70% 80% 90% 100%
## 0.11686047 0.37368288 0.59428965 1.20572149 3.83678360(RF_DALEX_Diagnostics <- model_diagnostics(RF_DALEX))## GENDER CONTIN INFMOR PERCAP
## Male :139 Africa :89 Min. :0.3365 Min. :-1.4775
## Female:153 Asia :73 1st Qu.:1.7047 1st Qu.: 0.6183
## Europe :62 Median :2.6100 Median : 1.7960
## North America:31 Mean :2.5569 Mean : 1.7571
## Oceania :18 3rd Qu.:3.5025 3rd Qu.: 2.7938
## South America:19 Max. :4.4864 Max. : 4.7293
## CLTECH NCOMOR y y_hat
## Min. : 0.00 Min. :1.866 Min. :52.84 Min. :54.37
## 1st Qu.: 24.35 1st Qu.:3.944 1st Qu.:66.93 1st Qu.:67.15
## Median : 82.80 Median :4.717 Median :73.53 Median :73.58
## Mean : 64.60 Mean :4.652 Mean :72.47 Mean :72.49
## 3rd Qu.:100.00 3rd Qu.:5.347 3rd Qu.:78.54 3rd Qu.:78.49
## Max. :100.00 Max. :7.959 Max. :87.45 Max. :86.25
## residuals abs_residuals label ids
## Min. :-3.89312 Min. :0.004567 Length:292 Min. : 1.00
## 1st Qu.:-0.49373 1st Qu.:0.228644 Class :character 1st Qu.: 73.75
## Median :-0.02355 Median :0.493929 Mode :character Median :146.50
## Mean :-0.01104 Mean :0.695743 Mean :146.50
## 3rd Qu.: 0.49410 3rd Qu.:0.996858 3rd Qu.:219.25
## Max. : 3.83678 Max. :3.893119 Max. :292.00plot(RF_DALEX_Diagnostics,
variable = "y",
yvariable = "y_hat") +
geom_point(size=3) +
scale_x_continuous("Observed LIFEXP") +
scale_y_continuous("Predicted LIFEXP") +
geom_abline(slope = 1) +
ggtitle("RF: Observed and Predicted LIFEXP")
(RF_DALEX_VariableImportance <- model_parts(RF_DALEX,
loss_function = loss_root_mean_square,
B = 200,
N = NULL))## variable mean_dropout_loss label
## 1 _full_model_ 0.9697745 RF
## 2 PERCAP 1.2486580 RF
## 3 CLTECH 1.2756382 RF
## 4 GENDER 1.3578694 RF
## 5 CONTIN 1.4558017 RF
## 6 NCOMOR 4.2303790 RF
## 7 INFMOR 8.3874253 RF
## 8 _baseline_ 10.9599787 RFplot(RF_DALEX_VariableImportance)
##################################
# Reporting the cross-validation results
# for the RF model
##################################
RF_Tune## Random Forest
##
## 292 samples
## 6 predictor
##
## No pre-processing
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 264, 263, 263, 262, 263, 264, ...
## Resampling results across tuning parameters:
##
## mtry RMSE Rsquared MAE
## 100 2.244547 0.9196737 1.619943
## 200 2.249565 0.9196008 1.620459
## 300 2.251214 0.9194065 1.628754
## 400 2.239011 0.9201778 1.618476
## 500 2.244594 0.9199741 1.623557
## 600 2.250218 0.9195438 1.627748
## 700 2.250948 0.9195926 1.627966
## 800 2.252804 0.9194439 1.626298
## 900 2.256902 0.9191818 1.630857
## 1000 2.245525 0.9198362 1.618343
##
## RMSE was used to select the optimal model using the smallest value.
## The final value used for the model was mtry = 400.RF_Tune$finalModel##
## Call:
## randomForest(x = x, y = y, mtry = param$mtry)
## Type of random forest: regression
## Number of trees: 500
## No. of variables tried at each split: 6
##
## Mean of squared residuals: 5.126832
## % Var explained: 91.72(RF_Tune_RMSE <- RF_Tune$results[RF_Tune$results$mtry==RF_Tune$bestTune$mtry,
c("RMSE")])## [1] 2.239011(RF_Tune_Rsquared <- RF_Tune$results[RF_Tune$results$mtry==RF_Tune$bestTune$mtry,
c("Rsquared")])## [1] 0.9201778(RF_Tune_MAE <- RF_Tune$results[RF_Tune$results$mtry==RF_Tune$bestTune$mtry,
c("MAE")])## [1] 1.6184761.3.6.4 Neural Network
[A] The neural network regression model from the nnet package was implemented through the caret package.
[B] The model contains 2 hyperparameters:
[B.1] size = number of units in the hidden layer made to vary across a range of values equal to 2, 5, 10, 15 and 20
[B.2] decay = parameter for weight decay made to vary across a range of values equal to 0, 0.00001, 0.0001, 0.001 and 0.1
[C] The 10-fold cross-validated model performance of the optimal model is summarized as follows:
[C.1] Optimal model used size=2 and decay=0.1 which demonstrated the lowest root mean square error
[C.2] Root Mean Square Error = 2.0701
[C.3] R-Squared = 0.9321
[D] The apparent model performance of the optimal model is summarized as follows:
[D.1] Root Mean Square Error = 1.9473
[D.2] R-Squared = 0.9387
[E] The top-performing predictors in the model ranked using the root mean square error loss after permutations are as follows:
[E.1] INFMOR (numeric) = 7.70
[E.2] NCOMOR (numeric) = 3.67
[E.3] CLTECH (numeric) = 3.10
[E.4] CONTIN (factor) = 3.07
[E.5] GENDER (factor) = 2.51
[E.6] PERCAP (numeric) = 2.33
Code Chunk | Output
##################################
# Defining the model hyperparameter values
# for the NN model
##################################
NN_Grid = expand.grid(size = c(2, 5, 10, 15, 20),
decay = c(0, 0.00001, 0.0001, 0.001, 0.1))
##################################
# Running the NN model
# by setting the caret method to 'NN'
##################################
set.seed(12345678)
NN_Tune <- train(x = MD.Model.Predictors,
y = MD$LIFEXP,
method = "nnet",
linout = TRUE,
preProcess = c('center', 'scale'),
maxit = 500,
tuneGrid = NN_Grid,
trControl = KFold_Control)## # weights: 25
## initial value 1400339.778929
## iter 10 value 68496.910319
## iter 20 value 5956.174709
## iter 30 value 5844.262957
## iter 40 value 5352.739998
## iter 50 value 5259.580299
## iter 60 value 5258.019066
## iter 70 value 5205.090145
## iter 80 value 5196.195489
## iter 90 value 4833.831031
## iter 100 value 4569.510587
## iter 110 value 4553.721628
## iter 120 value 4553.045122
## iter 130 value 4553.015144
## iter 140 value 4552.876106
## iter 150 value 4550.489828
## iter 160 value 4550.208091
## iter 170 value 4550.152984
## iter 180 value 4550.031282
## iter 190 value 4549.764089
## iter 200 value 4549.444274
## iter 210 value 4548.348059
## iter 220 value 4548.157434
## iter 220 value 4548.157430
## iter 220 value 4548.157430
## final value 4548.157430
## converged
## # weights: 61
## initial value 1419402.900363
## iter 10 value 5466.160778
## iter 20 value 4136.215442
## iter 30 value 3233.377310
## iter 40 value 2184.748970
## iter 50 value 1482.694097
## iter 60 value 1341.742361
## iter 70 value 1297.232211
## iter 80 value 1213.738131
## iter 90 value 1146.376381
## iter 100 value 1070.241735
## iter 110 value 1030.232654
## iter 120 value 1015.217766
## iter 130 value 1005.639574
## iter 140 value 998.773821
## iter 150 value 992.324116
## iter 160 value 980.531489
## iter 170 value 978.495782
## iter 180 value 978.409744
## iter 190 value 978.361243
## iter 200 value 978.318025
## iter 210 value 978.296926
## iter 220 value 968.807011
## iter 230 value 939.853517
## iter 240 value 925.037978
## iter 250 value 919.646061
## iter 260 value 919.163802
## iter 270 value 917.547708
## iter 280 value 914.888136
## iter 290 value 911.397634
## iter 300 value 909.574590
## iter 310 value 908.742002
## iter 320 value 908.217371
## iter 330 value 907.307508
## iter 340 value 906.728904
## iter 350 value 906.413133
## iter 360 value 906.376723
## iter 370 value 906.329715
## iter 380 value 906.279582
## iter 390 value 906.271020
## iter 400 value 906.268970
## final value 906.268949
## converged
## # weights: 121
## initial value 1369973.913442
## iter 10 value 1456.280282
## iter 20 value 981.019262
## iter 30 value 820.891644
## iter 40 value 691.064010
## iter 50 value 615.847093
## iter 60 value 552.618476
## iter 70 value 501.933326
## iter 80 value 465.287566
## iter 90 value 435.639678
## iter 100 value 411.632837
## iter 110 value 377.319124
## iter 120 value 355.295277
## iter 130 value 332.770009
## iter 140 value 319.998174
## iter 150 value 311.670112
## iter 160 value 296.995967
## iter 170 value 289.673758
## iter 180 value 286.817395
## iter 190 value 285.327350
## iter 200 value 283.822117
## iter 210 value 281.823769
## iter 220 value 280.604183
## iter 230 value 279.960985
## iter 240 value 279.298747
## iter 250 value 278.767676
## iter 260 value 278.387227
## iter 270 value 277.570900
## iter 280 value 276.221841
## iter 290 value 273.862125
## iter 300 value 271.110331
## iter 310 value 269.686949
## iter 320 value 268.329167
## iter 330 value 266.353429
## iter 340 value 263.244321
## iter 350 value 262.025670
## iter 360 value 260.468974
## iter 370 value 259.082188
## iter 380 value 258.024935
## iter 390 value 257.709959
## iter 400 value 257.192116
## iter 410 value 256.946798
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## iter 450 value 256.763944
## iter 460 value 256.762805
## iter 470 value 256.759951
## iter 480 value 256.742706
## final value 256.731685
## converged
## # weights: 181
## initial value 1369859.779729
## iter 10 value 1218.791825
## iter 20 value 844.152299
## iter 30 value 679.366662
## iter 40 value 572.273305
## iter 50 value 466.657882
## iter 60 value 383.138573
## iter 70 value 339.951431
## iter 80 value 283.173217
## iter 90 value 257.527205
## iter 100 value 237.333601
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## iter 150 value 191.342838
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## iter 200 value 156.888476
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## iter 250 value 140.214188
## iter 260 value 137.517961
## iter 270 value 135.175708
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## iter 470 value 99.744133
## iter 480 value 97.351172
## iter 490 value 95.563516
## iter 500 value 93.616614
## final value 93.616614
## stopped after 500 iterations
## # weights: 241
## initial value 1362136.072048
## iter 10 value 1398.158403
## iter 20 value 813.986040
## iter 30 value 623.314450
## iter 40 value 451.572085
## iter 50 value 343.423349
## iter 60 value 299.481028
## iter 70 value 258.539324
## iter 80 value 218.722855
## iter 90 value 189.014309
## iter 100 value 172.421623
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## iter 150 value 114.523579
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## iter 220 value 74.404872
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## iter 240 value 69.350806
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## iter 260 value 63.476653
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## iter 280 value 59.602674
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## iter 300 value 55.931747
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## iter 320 value 52.788004
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## iter 390 value 49.915583
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## iter 420 value 49.385656
## iter 430 value 49.276835
## iter 440 value 49.181953
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## iter 460 value 48.923516
## iter 470 value 48.704723
## iter 480 value 48.485576
## iter 490 value 48.352389
## iter 500 value 48.312069
## final value 48.312069
## stopped after 500 iterations
## # weights: 25
## initial value 1384018.334099
## iter 10 value 14826.537515
## iter 20 value 5813.028843
## iter 30 value 4170.983208
## iter 40 value 2733.681626
## iter 50 value 1945.456766
## iter 60 value 1798.543289
## iter 70 value 1746.423017
## iter 80 value 1721.963093
## iter 90 value 1682.718888
## iter 100 value 1642.738696
## iter 110 value 1567.875475
## iter 120 value 1176.466252
## iter 130 value 940.171012
## iter 140 value 903.533021
## iter 150 value 899.315161
## iter 160 value 898.513967
## iter 170 value 888.514572
## iter 180 value 877.111204
## iter 190 value 875.630959
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## iter 210 value 875.220123
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## iter 250 value 873.524197
## iter 260 value 873.523434
## iter 270 value 873.508109
## iter 280 value 873.466496
## iter 290 value 873.412895
## iter 300 value 873.386788
## final value 873.386557
## converged
## # weights: 61
## initial value 1394097.657047
## iter 10 value 23565.379437
## iter 20 value 8413.870422
## iter 30 value 5669.603306
## iter 40 value 4961.340411
## iter 50 value 4636.688563
## iter 60 value 3698.652166
## iter 70 value 2908.454914
## iter 80 value 2733.688942
## iter 90 value 2432.062689
## iter 100 value 1825.325196
## iter 110 value 1387.504599
## iter 120 value 1257.609294
## iter 130 value 1216.167382
## iter 140 value 1114.359333
## iter 150 value 1087.913181
## iter 160 value 1071.689478
## iter 170 value 1065.937468
## iter 180 value 1043.151373
## iter 190 value 1033.117999
## iter 200 value 1026.374711
## iter 210 value 1026.256408
## iter 220 value 1024.932352
## iter 230 value 1016.981513
## iter 240 value 1011.378658
## iter 250 value 1008.021987
## iter 260 value 1007.075277
## iter 270 value 1005.158200
## iter 280 value 1002.378160
## iter 290 value 1000.597307
## iter 300 value 1000.366193
## iter 310 value 1000.243465
## iter 320 value 999.781314
## iter 330 value 999.689851
## iter 340 value 999.641768
## iter 350 value 998.495273
## iter 360 value 998.449789
## iter 370 value 996.522899
## iter 380 value 990.443838
## iter 390 value 990.276533
## iter 400 value 982.049946
## iter 410 value 978.892299
## iter 420 value 978.788357
## iter 430 value 978.419060
## iter 440 value 977.609602
## iter 450 value 977.277746
## iter 460 value 976.232075
## iter 470 value 975.840442
## iter 480 value 975.215593
## iter 490 value 974.549835
## iter 500 value 974.431056
## final value 974.431056
## stopped after 500 iterations
## # weights: 121
## initial value 1387426.161256
## iter 10 value 1363.935777
## iter 20 value 850.046899
## iter 30 value 721.609125
## iter 40 value 621.253964
## iter 50 value 563.129927
## iter 60 value 522.376227
## iter 70 value 494.041852
## iter 80 value 470.156676
## iter 90 value 451.146528
## iter 100 value 437.843172
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## iter 130 value 398.943760
## iter 140 value 383.669666
## iter 150 value 367.430345
## iter 160 value 352.351479
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## iter 180 value 334.098915
## iter 190 value 319.785746
## iter 200 value 310.996565
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## iter 230 value 295.645225
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## iter 300 value 270.836127
## iter 310 value 268.800980
## iter 320 value 265.811862
## iter 330 value 264.203125
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## iter 350 value 256.609959
## iter 360 value 252.285118
## iter 370 value 248.845421
## iter 380 value 247.669521
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## iter 400 value 246.209804
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## iter 460 value 244.481425
## iter 470 value 243.750911
## iter 480 value 243.460047
## iter 490 value 243.335108
## iter 500 value 243.299919
## final value 243.299919
## stopped after 500 iterations
## # weights: 181
## initial value 1353088.590211
## iter 10 value 1200.855364
## iter 20 value 862.423893
## iter 30 value 638.924966
## iter 40 value 515.881183
## iter 50 value 423.974738
## iter 60 value 363.855071
## iter 70 value 318.412839
## iter 80 value 279.564421
## iter 90 value 256.645780
## iter 100 value 234.226068
## iter 110 value 217.935518
## iter 120 value 208.240589
## iter 130 value 197.982033
## iter 140 value 190.884779
## iter 150 value 184.849018
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## iter 200 value 132.190096
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## iter 340 value 90.750629
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## iter 360 value 90.148519
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## iter 380 value 89.789402
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## iter 400 value 89.542679
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## iter 470 value 87.407789
## iter 480 value 87.159277
## iter 490 value 86.818416
## iter 500 value 85.405875
## final value 85.405875
## stopped after 500 iterations
## # weights: 241
## initial value 1379423.815877
## iter 10 value 1212.490627
## iter 20 value 826.304721
## iter 30 value 688.182359
## iter 40 value 562.121823
## iter 50 value 472.835657
## iter 60 value 350.842187
## iter 70 value 296.861777
## iter 80 value 256.663023
## iter 90 value 217.838346
## iter 100 value 186.250487
## iter 110 value 163.986479
## iter 120 value 144.961596
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## iter 300 value 62.045510
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## iter 320 value 56.536648
## iter 330 value 54.530216
## iter 340 value 52.949551
## iter 350 value 50.290994
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## iter 370 value 46.782066
## iter 380 value 45.044281
## iter 390 value 43.646564
## iter 400 value 42.074783
## iter 410 value 40.995858
## iter 420 value 39.924201
## iter 430 value 38.357228
## iter 440 value 36.913940
## iter 450 value 35.861560
## iter 460 value 35.161194
## iter 470 value 34.664702
## iter 480 value 34.192998
## iter 490 value 33.989409
## iter 500 value 33.915094
## final value 33.915094
## stopped after 500 iterations
## # weights: 25
## initial value 1423891.578229
## iter 10 value 12529.170147
## iter 20 value 8281.402632
## iter 30 value 5518.987759
## iter 40 value 1535.127285
## iter 50 value 1423.569703
## iter 60 value 1398.147001
## iter 70 value 1390.201194
## iter 80 value 1284.484692
## iter 90 value 1122.089275
## iter 100 value 1096.450793
## iter 110 value 1094.341697
## iter 120 value 1094.234374
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## iter 140 value 1089.733515
## iter 150 value 1087.422242
## iter 160 value 1083.523149
## iter 170 value 1081.839197
## iter 180 value 1080.256824
## iter 190 value 1063.195888
## iter 200 value 1056.974917
## iter 210 value 1056.822488
## final value 1056.820260
## converged
## # weights: 61
## initial value 1412917.860609
## iter 10 value 47303.008875
## iter 20 value 11755.994091
## iter 30 value 9630.615274
## iter 40 value 7061.767878
## iter 50 value 3671.746883
## iter 60 value 2269.630245
## iter 70 value 1566.915852
## iter 80 value 1154.112397
## iter 90 value 994.364982
## iter 100 value 920.055939
## iter 110 value 870.035266
## iter 120 value 851.376139
## iter 130 value 841.852135
## iter 140 value 835.441454
## iter 150 value 820.760988
## iter 160 value 806.356269
## iter 170 value 796.200061
## iter 180 value 785.582221
## iter 190 value 774.211518
## iter 200 value 767.656056
## iter 210 value 765.963927
## iter 220 value 763.602010
## iter 230 value 756.650247
## iter 240 value 746.249244
## iter 250 value 738.857296
## iter 260 value 733.862419
## iter 270 value 728.965572
## iter 280 value 725.766578
## iter 290 value 724.561077
## iter 300 value 723.246828
## iter 310 value 722.738505
## iter 320 value 722.028971
## iter 330 value 722.001350
## iter 340 value 721.866481
## iter 350 value 721.556103
## iter 360 value 721.041872
## iter 370 value 719.225066
## iter 380 value 710.243949
## iter 390 value 704.092263
## iter 400 value 703.679017
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## iter 420 value 702.610407
## iter 430 value 701.030334
## iter 440 value 696.191268
## iter 450 value 695.386565
## iter 460 value 694.796474
## iter 470 value 691.777508
## iter 480 value 679.412645
## iter 490 value 675.719101
## iter 500 value 674.866048
## final value 674.866048
## stopped after 500 iterations
## # weights: 121
## initial value 1433584.736785
## iter 10 value 2355.633008
## iter 20 value 1145.165498
## iter 30 value 888.745905
## iter 40 value 740.750670
## iter 50 value 652.352440
## iter 60 value 579.453174
## iter 70 value 533.512412
## iter 80 value 501.887607
## iter 90 value 480.145595
## iter 100 value 452.926209
## iter 110 value 418.571258
## iter 120 value 398.418778
## iter 130 value 367.963748
## iter 140 value 355.856524
## iter 150 value 344.709922
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## iter 180 value 288.218375
## iter 190 value 283.501525
## iter 200 value 278.529992
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## iter 220 value 271.984171
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## iter 240 value 266.940387
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## iter 280 value 262.008363
## iter 290 value 258.589585
## iter 300 value 254.944058
## iter 310 value 252.642300
## iter 320 value 250.436015
## iter 330 value 248.987485
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## iter 390 value 244.433892
## iter 400 value 244.271522
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## iter 460 value 242.719987
## iter 470 value 242.589912
## iter 480 value 242.524848
## iter 490 value 242.488644
## iter 500 value 242.486574
## final value 242.486574
## stopped after 500 iterations
## # weights: 181
## initial value 1394930.770230
## iter 10 value 1278.433344
## iter 20 value 867.435994
## iter 30 value 719.637140
## iter 40 value 560.797710
## iter 50 value 463.830026
## iter 60 value 413.533914
## iter 70 value 375.492235
## iter 80 value 325.290445
## iter 90 value 290.500881
## iter 100 value 264.171293
## iter 110 value 241.862985
## iter 120 value 220.347735
## iter 130 value 208.322945
## iter 140 value 201.847675
## iter 150 value 194.425185
## iter 160 value 186.123240
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## iter 180 value 169.763034
## iter 190 value 161.150333
## iter 200 value 154.748984
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## iter 220 value 148.303556
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## iter 320 value 124.833328
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## iter 400 value 116.967163
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## iter 470 value 113.342229
## iter 480 value 112.653800
## iter 490 value 111.503313
## iter 500 value 109.248090
## final value 109.248090
## stopped after 500 iterations
## # weights: 241
## initial value 1415446.964867
## iter 10 value 1559.904251
## iter 20 value 899.437584
## iter 30 value 645.422803
## iter 40 value 490.676667
## iter 50 value 402.901605
## iter 60 value 347.541350
## iter 70 value 305.427727
## iter 80 value 260.382951
## iter 90 value 226.823135
## iter 100 value 195.587129
## iter 110 value 172.105460
## iter 120 value 157.255997
## iter 130 value 141.977027
## iter 140 value 129.923376
## iter 150 value 119.082894
## iter 160 value 113.660080
## iter 170 value 105.773010
## iter 180 value 96.837696
## iter 190 value 90.623692
## iter 200 value 84.304154
## iter 210 value 79.525586
## iter 220 value 76.154848
## iter 230 value 72.182218
## iter 240 value 69.672286
## iter 250 value 67.164232
## iter 260 value 64.373258
## iter 270 value 61.866617
## iter 280 value 58.117691
## iter 290 value 56.229528
## iter 300 value 53.144085
## iter 310 value 50.844936
## iter 320 value 48.063442
## iter 330 value 46.222540
## iter 340 value 44.664173
## iter 350 value 43.717416
## iter 360 value 42.949110
## iter 370 value 42.474388
## iter 380 value 42.099466
## iter 390 value 41.512779
## iter 400 value 41.068240
## iter 410 value 40.688048
## iter 420 value 40.265663
## iter 430 value 39.806252
## iter 440 value 39.542293
## iter 450 value 39.401378
## iter 460 value 39.201674
## iter 470 value 38.968486
## iter 480 value 38.710956
## iter 490 value 38.610006
## iter 500 value 38.584482
## final value 38.584482
## stopped after 500 iterations
## # weights: 25
## initial value 1386870.685250
## iter 10 value 16096.471266
## iter 20 value 7895.328241
## iter 30 value 6087.506933
## iter 40 value 5104.670168
## iter 50 value 3630.386403
## iter 60 value 2773.128290
## iter 70 value 2393.977512
## iter 80 value 1607.519203
## iter 90 value 1475.617424
## iter 100 value 1387.224873
## iter 110 value 1345.284467
## iter 120 value 1331.915665
## iter 130 value 1303.517843
## iter 140 value 1301.718018
## iter 150 value 1301.679570
## iter 160 value 1297.529432
## iter 170 value 1287.716432
## iter 180 value 1286.356977
## iter 190 value 1285.930443
## iter 200 value 1285.776334
## iter 210 value 1283.497109
## iter 220 value 1273.992883
## iter 230 value 1271.547993
## iter 240 value 1271.544315
## iter 250 value 1270.779669
## iter 260 value 1269.735827
## iter 270 value 1265.694381
## iter 280 value 1207.879898
## iter 290 value 1205.349504
## iter 300 value 1204.744104
## iter 310 value 1204.514356
## iter 320 value 1204.508441
## iter 330 value 1204.473451
## final value 1204.461200
## converged
## # weights: 61
## initial value 1369944.319004
## iter 10 value 4815.533687
## iter 20 value 2684.248773
## iter 30 value 1954.654699
## iter 40 value 1542.342820
## iter 50 value 1289.962786
## iter 60 value 1045.067110
## iter 70 value 919.847384
## iter 80 value 896.148679
## iter 90 value 884.573744
## iter 100 value 873.210237
## iter 110 value 862.848267
## iter 120 value 855.586184
## iter 130 value 843.390593
## iter 140 value 830.357310
## iter 150 value 825.114538
## iter 160 value 808.230715
## iter 170 value 804.029122
## iter 180 value 796.540522
## iter 190 value 784.852192
## iter 200 value 731.280183
## iter 210 value 705.757076
## iter 220 value 700.473408
## iter 230 value 699.952963
## iter 240 value 690.589191
## iter 250 value 671.335386
## iter 260 value 668.833203
## iter 270 value 662.458878
## iter 280 value 645.331457
## iter 290 value 640.232688
## iter 300 value 639.088208
## iter 310 value 638.383079
## iter 320 value 638.068420
## iter 330 value 637.827135
## iter 340 value 637.665301
## iter 350 value 637.664812
## iter 360 value 637.658093
## iter 370 value 637.635611
## iter 380 value 637.571904
## iter 390 value 637.503709
## iter 400 value 637.496327
## iter 410 value 637.493522
## iter 420 value 637.493030
## iter 430 value 637.492572
## final value 637.492519
## converged
## # weights: 121
## initial value 1448634.135786
## iter 10 value 13365.381127
## iter 20 value 4048.021931
## iter 30 value 2851.895676
## iter 40 value 2734.337610
## iter 50 value 2371.632887
## iter 60 value 2094.972978
## iter 70 value 2009.984258
## iter 80 value 1963.551893
## iter 90 value 1885.140175
## iter 100 value 1599.503527
## iter 110 value 1436.861610
## iter 120 value 1156.369592
## iter 130 value 1021.150333
## iter 140 value 946.898959
## iter 150 value 924.618764
## iter 160 value 883.056511
## iter 170 value 821.074645
## iter 180 value 799.166493
## iter 190 value 793.100919
## iter 200 value 787.226783
## iter 210 value 773.556025
## iter 220 value 753.958909
## iter 230 value 726.659188
## iter 240 value 673.795286
## iter 250 value 629.018011
## iter 260 value 598.290840
## iter 270 value 580.995586
## iter 280 value 572.452078
## iter 290 value 562.352859
## iter 300 value 556.040845
## iter 310 value 540.950431
## iter 320 value 531.160877
## iter 330 value 523.745793
## iter 340 value 518.478188
## iter 350 value 515.889307
## iter 360 value 513.289931
## iter 370 value 512.436723
## iter 380 value 510.708493
## iter 390 value 509.897700
## iter 400 value 509.127533
## iter 410 value 507.906190
## iter 420 value 506.970431
## iter 430 value 502.539285
## iter 440 value 498.732152
## iter 450 value 497.380738
## iter 460 value 496.074611
## iter 470 value 495.443510
## iter 480 value 494.677278
## iter 490 value 494.097700
## iter 500 value 493.701644
## final value 493.701644
## stopped after 500 iterations
## # weights: 181
## initial value 1413917.792341
## iter 10 value 1209.209651
## iter 20 value 781.180139
## iter 30 value 660.681676
## iter 40 value 535.339485
## iter 50 value 439.783484
## iter 60 value 384.779747
## iter 70 value 350.780786
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## iter 490 value 139.585490
## iter 500 value 139.499069
## final value 139.499069
## stopped after 500 iterations
## # weights: 241
## initial value 1408462.479376
## iter 10 value 1697.627145
## iter 20 value 835.887614
## iter 30 value 645.274833
## iter 40 value 501.988260
## iter 50 value 397.050465
## iter 60 value 349.365470
## iter 70 value 298.601990
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## iter 100 value 218.350036
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## iter 160 value 144.441632
## iter 170 value 136.303637
## iter 180 value 127.347627
## iter 190 value 120.376089
## iter 200 value 114.277419
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## iter 220 value 102.802914
## iter 230 value 96.707401
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## iter 250 value 89.935334
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## iter 300 value 79.992722
## iter 310 value 77.602812
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## iter 330 value 73.800070
## iter 340 value 72.297664
## iter 350 value 70.973439
## iter 360 value 69.184427
## iter 370 value 67.649322
## iter 380 value 66.548076
## iter 390 value 65.234160
## iter 400 value 64.389556
## iter 410 value 63.675854
## iter 420 value 62.902501
## iter 430 value 61.969867
## iter 440 value 61.270260
## iter 450 value 60.348626
## iter 460 value 59.618829
## iter 470 value 59.076608
## iter 480 value 58.516615
## iter 490 value 58.353411
## iter 500 value 58.316900
## final value 58.316900
## stopped after 500 iterations
## # weights: 25
## initial value 1372477.240115
## iter 10 value 68000.716832
## iter 20 value 45384.622598
## iter 30 value 18763.404622
## iter 40 value 7417.209929
## iter 50 value 6881.877909
## iter 60 value 5819.556644
## iter 70 value 4081.634645
## iter 80 value 3165.419431
## iter 90 value 2575.796798
## iter 100 value 1911.298732
## iter 110 value 1606.869769
## iter 120 value 1511.470006
## iter 130 value 1473.188656
## iter 140 value 1433.762599
## iter 150 value 1401.289902
## iter 160 value 1386.813004
## iter 170 value 1380.408583
## iter 180 value 1379.317008
## iter 190 value 1373.748811
## iter 200 value 1372.146333
## final value 1372.145916
## converged
## # weights: 61
## initial value 1407939.343260
## iter 10 value 3092.687067
## iter 20 value 1931.688936
## iter 30 value 1432.669659
## iter 40 value 1205.261041
## iter 50 value 1077.690698
## iter 60 value 1013.913512
## iter 70 value 976.294824
## iter 80 value 963.875827
## iter 90 value 945.000181
## iter 100 value 935.902340
## iter 110 value 915.538520
## iter 120 value 888.588209
## iter 130 value 878.106035
## iter 140 value 869.710834
## iter 150 value 857.096332
## iter 160 value 839.747821
## iter 170 value 824.377096
## iter 180 value 799.610261
## iter 190 value 787.952188
## iter 200 value 779.409241
## iter 210 value 779.329603
## iter 220 value 779.316042
## iter 220 value 779.316035
## iter 220 value 779.316035
## final value 779.316035
## converged
## # weights: 121
## initial value 1400960.980227
## iter 10 value 4073.998877
## iter 20 value 2031.977876
## iter 30 value 1677.958612
## iter 40 value 1447.062631
## iter 50 value 1360.666864
## iter 60 value 1314.471303
## iter 70 value 1249.882354
## iter 80 value 1173.154791
## iter 90 value 1085.830693
## iter 100 value 976.662591
## iter 110 value 907.427418
## iter 120 value 860.814474
## iter 130 value 818.373187
## iter 140 value 792.797002
## iter 150 value 748.859847
## iter 160 value 720.918962
## iter 170 value 696.636924
## iter 180 value 682.767414
## iter 190 value 671.126480
## iter 200 value 665.804198
## iter 210 value 657.184339
## iter 220 value 633.140966
## iter 230 value 624.170437
## iter 240 value 617.709966
## iter 250 value 614.288745
## iter 260 value 610.853614
## iter 270 value 603.887383
## iter 280 value 596.819356
## iter 290 value 587.660064
## iter 300 value 582.085943
## iter 310 value 578.839603
## iter 320 value 577.192081
## iter 330 value 576.041276
## iter 340 value 574.957140
## iter 350 value 574.764805
## iter 360 value 574.627474
## iter 370 value 574.463076
## iter 380 value 574.375052
## iter 390 value 574.371756
## iter 400 value 574.371076
## final value 574.370901
## converged
## # weights: 181
## initial value 1436383.758015
## iter 10 value 1536.550524
## iter 20 value 919.896062
## iter 30 value 790.892651
## iter 40 value 674.554759
## iter 50 value 614.419624
## iter 60 value 583.936787
## iter 70 value 550.146021
## iter 80 value 530.297297
## iter 90 value 518.322629
## iter 100 value 510.657793
## iter 110 value 504.887218
## iter 120 value 499.473850
## iter 130 value 493.350208
## iter 140 value 486.415350
## iter 150 value 480.327480
## iter 160 value 475.618453
## iter 170 value 472.483206
## iter 180 value 469.491265
## iter 190 value 465.900970
## iter 200 value 462.907095
## iter 210 value 459.966260
## iter 220 value 456.353593
## iter 230 value 452.828305
## iter 240 value 450.624938
## iter 250 value 448.622059
## iter 260 value 445.648443
## iter 270 value 443.132678
## iter 280 value 439.970704
## iter 290 value 437.676754
## iter 300 value 436.330583
## iter 310 value 435.436812
## iter 320 value 433.281107
## iter 330 value 431.059264
## iter 340 value 428.688555
## iter 350 value 422.583928
## iter 360 value 418.544006
## iter 370 value 417.347830
## iter 380 value 416.674099
## iter 390 value 415.287281
## iter 400 value 414.275928
## iter 410 value 413.704906
## iter 420 value 412.242230
## iter 430 value 410.228318
## iter 440 value 408.551319
## iter 450 value 407.113968
## iter 460 value 405.964912
## iter 470 value 405.196618
## iter 480 value 404.779265
## iter 490 value 404.378579
## iter 500 value 402.785654
## final value 402.785654
## stopped after 500 iterations
## # weights: 241
## initial value 1361340.947693
## iter 10 value 1333.030230
## iter 20 value 961.495114
## iter 30 value 774.958407
## iter 40 value 684.194412
## iter 50 value 633.213059
## iter 60 value 569.100717
## iter 70 value 529.119334
## iter 80 value 505.940657
## iter 90 value 495.207525
## iter 100 value 487.456296
## iter 110 value 478.726657
## iter 120 value 472.177350
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## iter 150 value 454.710167
## iter 160 value 449.366294
## iter 170 value 444.724931
## iter 180 value 440.020484
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## iter 200 value 428.369074
## iter 210 value 422.549961
## iter 220 value 415.317116
## iter 230 value 409.829144
## iter 240 value 403.903396
## iter 250 value 397.399969
## iter 260 value 393.366711
## iter 270 value 389.615690
## iter 280 value 386.529896
## iter 290 value 384.005067
## iter 300 value 381.520446
## iter 310 value 379.813014
## iter 320 value 378.344202
## iter 330 value 376.984890
## iter 340 value 375.143021
## iter 350 value 373.548547
## iter 360 value 371.882479
## iter 370 value 370.084481
## iter 380 value 368.409556
## iter 390 value 366.629486
## iter 400 value 363.880307
## iter 410 value 360.051411
## iter 420 value 357.655096
## iter 430 value 355.559870
## iter 440 value 353.156278
## iter 450 value 350.548607
## iter 460 value 348.115628
## iter 470 value 346.204603
## iter 480 value 345.117220
## iter 490 value 344.408435
## iter 500 value 343.333897
## final value 343.333897
## stopped after 500 iterations
## # weights: 25
## initial value 1387099.627384
## iter 10 value 6884.863381
## iter 20 value 5742.708364
## iter 30 value 5648.924554
## iter 40 value 5489.348356
## iter 50 value 5294.227643
## iter 60 value 4191.619062
## iter 70 value 3439.820488
## iter 80 value 1865.159081
## iter 90 value 1354.174023
## iter 100 value 1230.275976
## iter 110 value 1213.606377
## iter 120 value 1203.457053
## iter 130 value 1172.423722
## iter 140 value 1157.156416
## iter 150 value 1149.554267
## iter 160 value 1148.385147
## iter 170 value 1145.328761
## iter 180 value 1133.782683
## iter 190 value 1115.050236
## iter 200 value 1098.741020
## iter 210 value 1071.941371
## iter 220 value 1069.565696
## iter 230 value 1068.972566
## iter 240 value 1065.824168
## iter 250 value 1063.384913
## iter 260 value 1061.492019
## iter 270 value 1061.043238
## iter 280 value 1061.026919
## iter 290 value 1060.383288
## iter 300 value 1059.598781
## iter 310 value 1058.796290
## iter 320 value 1058.548991
## iter 330 value 1058.546728
## iter 340 value 1058.471061
## iter 350 value 1057.953294
## iter 360 value 1057.525014
## iter 370 value 1057.265463
## iter 380 value 1057.261163
## iter 380 value 1057.261161
## iter 380 value 1057.261157
## final value 1057.261157
## converged
## # weights: 61
## initial value 1409061.029459
## iter 10 value 1454.501398
## iter 20 value 1173.662421
## iter 30 value 948.423994
## iter 40 value 818.321733
## iter 50 value 723.675688
## iter 60 value 669.952133
## iter 70 value 632.214352
## iter 80 value 621.930101
## iter 90 value 612.884284
## iter 100 value 610.410488
## iter 110 value 608.256095
## iter 120 value 607.334157
## iter 130 value 606.911674
## iter 140 value 606.777125
## iter 150 value 606.119864
## iter 160 value 604.317289
## iter 170 value 600.597374
## iter 180 value 593.442563
## iter 190 value 589.851119
## iter 200 value 587.664736
## iter 210 value 585.790198
## iter 220 value 584.125603
## iter 230 value 582.950025
## iter 240 value 582.386864
## iter 250 value 582.116854
## iter 260 value 582.086040
## iter 270 value 581.971393
## iter 280 value 581.690192
## iter 290 value 581.460932
## iter 300 value 581.353291
## iter 310 value 581.149949
## iter 320 value 580.848503
## iter 330 value 580.611379
## iter 340 value 580.393534
## iter 350 value 580.266394
## iter 360 value 580.141481
## iter 370 value 579.957066
## iter 380 value 579.953258
## iter 390 value 579.941049
## iter 400 value 579.880828
## iter 410 value 579.788238
## iter 420 value 579.685639
## iter 430 value 579.303637
## iter 440 value 579.152107
## iter 450 value 578.975600
## iter 460 value 578.868404
## iter 470 value 578.775307
## iter 480 value 578.687300
## iter 490 value 578.665848
## final value 578.663563
## converged
## # weights: 121
## initial value 1416636.374166
## iter 10 value 1671.478236
## iter 20 value 1000.629062
## iter 30 value 821.121997
## iter 40 value 713.814097
## iter 50 value 641.158097
## iter 60 value 608.680645
## iter 70 value 586.740869
## iter 80 value 567.069916
## iter 90 value 553.661699
## iter 100 value 545.983177
## iter 110 value 542.387548
## iter 120 value 538.795445
## iter 130 value 532.736078
## iter 140 value 527.976666
## iter 150 value 518.717146
## iter 160 value 509.550051
## iter 170 value 494.733760
## iter 180 value 487.147887
## iter 190 value 468.792136
## iter 200 value 454.176554
## iter 210 value 446.169427
## iter 220 value 438.455061
## iter 230 value 430.930929
## iter 240 value 415.480240
## iter 250 value 394.001123
## iter 260 value 387.039195
## iter 270 value 384.757568
## iter 280 value 382.687809
## iter 290 value 379.690969
## iter 300 value 378.106648
## iter 310 value 377.187713
## iter 320 value 375.975369
## iter 330 value 374.850924
## iter 340 value 374.210535
## iter 350 value 373.882347
## iter 360 value 373.618886
## iter 370 value 373.397226
## iter 380 value 373.393873
## iter 390 value 373.385757
## iter 400 value 373.367752
## iter 410 value 373.349413
## iter 420 value 373.338195
## iter 430 value 373.331903
## iter 440 value 373.321640
## iter 450 value 373.295236
## iter 460 value 373.258294
## iter 470 value 372.411457
## iter 480 value 371.348802
## iter 490 value 370.902117
## iter 500 value 370.609300
## final value 370.609300
## stopped after 500 iterations
## # weights: 181
## initial value 1381719.005892
## iter 10 value 1467.172498
## iter 20 value 786.635499
## iter 30 value 620.697677
## iter 40 value 515.593629
## iter 50 value 431.749592
## iter 60 value 362.889480
## iter 70 value 319.911837
## iter 80 value 275.422756
## iter 90 value 248.714420
## iter 100 value 230.661755
## iter 110 value 212.389696
## iter 120 value 200.509538
## iter 130 value 188.212657
## iter 140 value 178.478457
## iter 150 value 166.196181
## iter 160 value 155.862943
## iter 170 value 144.901767
## iter 180 value 134.984710
## iter 190 value 128.138283
## iter 200 value 123.781473
## iter 210 value 120.680937
## iter 220 value 116.928077
## iter 230 value 114.145371
## iter 240 value 112.195092
## iter 250 value 110.962329
## iter 260 value 108.867038
## iter 270 value 107.311021
## iter 280 value 106.083067
## iter 290 value 105.115520
## iter 300 value 104.299323
## iter 310 value 103.735284
## iter 320 value 103.177508
## iter 330 value 102.788422
## iter 340 value 102.494317
## iter 350 value 102.231510
## iter 360 value 102.058439
## iter 370 value 101.984354
## iter 380 value 101.943327
## iter 390 value 101.862886
## iter 400 value 101.732656
## iter 410 value 101.610271
## iter 420 value 101.527765
## iter 430 value 101.409239
## iter 440 value 101.299359
## iter 450 value 101.010618
## iter 460 value 100.526020
## iter 470 value 100.185451
## iter 480 value 99.605011
## iter 490 value 99.192383
## iter 500 value 98.901575
## final value 98.901575
## stopped after 500 iterations
## # weights: 241
## initial value 1406575.679480
## iter 10 value 1475.842726
## iter 20 value 810.414268
## iter 30 value 656.808281
## iter 40 value 565.655535
## iter 50 value 446.121098
## iter 60 value 353.930487
## iter 70 value 297.073127
## iter 80 value 264.388584
## iter 90 value 236.573331
## iter 100 value 202.989764
## iter 110 value 183.267716
## iter 120 value 169.714214
## iter 130 value 160.201311
## iter 140 value 151.958332
## iter 150 value 144.941023
## iter 160 value 139.131706
## iter 170 value 133.951261
## iter 180 value 129.112631
## iter 190 value 121.705868
## iter 200 value 114.814578
## iter 210 value 109.074391
## iter 220 value 104.472022
## iter 230 value 101.252522
## iter 240 value 97.580645
## iter 250 value 94.504699
## iter 260 value 92.713906
## iter 270 value 91.167732
## iter 280 value 88.978635
## iter 290 value 87.362266
## iter 300 value 86.082114
## iter 310 value 84.627525
## iter 320 value 83.374603
## iter 330 value 81.261878
## iter 340 value 79.186915
## iter 350 value 78.093975
## iter 360 value 76.713596
## iter 370 value 74.926649
## iter 380 value 72.850564
## iter 390 value 71.190179
## iter 400 value 69.651247
## iter 410 value 68.466478
## iter 420 value 67.145333
## iter 430 value 65.799797
## iter 440 value 64.284767
## iter 450 value 63.308524
## iter 460 value 62.314202
## iter 470 value 61.325149
## iter 480 value 60.164964
## iter 490 value 59.507512
## iter 500 value 59.326190
## final value 59.326190
## stopped after 500 iterations
## # weights: 25
## initial value 1383669.158062
## iter 10 value 12914.764786
## iter 20 value 10739.940816
## iter 30 value 10664.882768
## iter 40 value 10589.066325
## iter 50 value 10582.126502
## iter 60 value 10531.342944
## iter 70 value 10392.163059
## iter 80 value 10297.587548
## iter 90 value 10289.698008
## iter 100 value 10289.460185
## iter 110 value 10289.034671
## iter 120 value 10288.251586
## iter 130 value 6060.146755
## iter 140 value 6010.284788
## iter 150 value 5741.776331
## iter 160 value 5409.137393
## iter 170 value 5113.823906
## iter 180 value 5050.082562
## iter 190 value 5012.614178
## iter 200 value 4996.693418
## iter 210 value 4982.943420
## iter 220 value 4981.624847
## iter 230 value 4948.471680
## iter 240 value 4933.575068
## iter 250 value 4871.345741
## iter 260 value 4738.526092
## iter 270 value 4563.749275
## iter 280 value 3954.337876
## iter 290 value 2142.029339
## iter 300 value 1410.599572
## iter 310 value 1274.274054
## iter 320 value 1243.388607
## iter 330 value 1239.065502
## iter 340 value 1210.815776
## iter 350 value 1201.565346
## iter 360 value 1197.881404
## iter 370 value 1196.585397
## iter 380 value 1196.290971
## iter 390 value 1196.205017
## iter 400 value 1195.321295
## iter 410 value 1194.507769
## iter 420 value 1193.877934
## iter 430 value 1193.583957
## iter 440 value 1193.577372
## final value 1193.577021
## converged
## # weights: 61
## initial value 1386500.939009
## iter 10 value 7716.560988
## iter 20 value 4133.235806
## iter 30 value 3131.441764
## iter 40 value 2435.051421
## iter 50 value 2360.403970
## iter 60 value 2311.603489
## iter 70 value 2132.716946
## iter 80 value 2091.443471
## iter 90 value 1954.374295
## iter 100 value 1949.554188
## iter 110 value 1939.576503
## iter 120 value 1935.078174
## iter 130 value 1907.436000
## iter 140 value 1885.300534
## iter 150 value 1841.639207
## iter 160 value 1747.840731
## iter 170 value 1555.891742
## iter 180 value 1345.507381
## iter 190 value 1209.348706
## iter 200 value 1140.414671
## iter 210 value 1119.165944
## iter 220 value 1095.259044
## iter 230 value 1086.568009
## iter 240 value 1081.662160
## iter 250 value 1079.701230
## iter 260 value 1078.750457
## iter 270 value 1077.419206
## iter 280 value 1075.270745
## iter 290 value 1074.090393
## iter 300 value 1070.418840
## iter 310 value 1069.979448
## iter 320 value 1069.860002
## iter 330 value 1069.499510
## iter 340 value 1069.233642
## iter 350 value 1069.091124
## iter 360 value 1068.999060
## iter 370 value 1068.952981
## iter 380 value 1068.929235
## iter 390 value 1068.866437
## iter 390 value 1068.866433
## final value 1068.866347
## converged
## # weights: 121
## initial value 1413501.596255
## iter 10 value 3108.897693
## iter 20 value 1569.354039
## iter 30 value 1011.071874
## iter 40 value 734.254163
## iter 50 value 657.806415
## iter 60 value 599.191033
## iter 70 value 545.185862
## iter 80 value 499.605607
## iter 90 value 454.964681
## iter 100 value 438.276542
## iter 110 value 433.809153
## iter 120 value 429.942272
## iter 130 value 427.971234
## iter 140 value 424.416137
## iter 150 value 420.982493
## iter 160 value 411.806448
## iter 170 value 400.822048
## iter 180 value 390.764241
## iter 190 value 385.246443
## iter 200 value 375.438897
## iter 210 value 361.103408
## iter 220 value 348.599128
## iter 230 value 343.941908
## iter 240 value 342.419757
## iter 250 value 341.766065
## iter 260 value 340.991168
## iter 270 value 340.194500
## iter 280 value 339.441532
## iter 290 value 338.696976
## iter 300 value 337.991445
## iter 310 value 337.313739
## iter 320 value 336.953002
## iter 330 value 336.576572
## iter 340 value 336.450712
## iter 350 value 336.399707
## iter 360 value 336.378936
## iter 370 value 336.377771
## iter 380 value 336.375665
## iter 390 value 336.372203
## iter 400 value 336.362318
## iter 410 value 336.347331
## iter 420 value 336.303123
## iter 430 value 336.232174
## iter 440 value 336.077844
## iter 450 value 335.849954
## iter 460 value 335.726770
## iter 470 value 335.616470
## iter 480 value 335.526235
## iter 490 value 335.481676
## iter 500 value 335.461019
## final value 335.461019
## stopped after 500 iterations
## # weights: 181
## initial value 1341342.110612
## iter 10 value 1094.906120
## iter 20 value 779.320779
## iter 30 value 628.016802
## iter 40 value 494.235553
## iter 50 value 414.181204
## iter 60 value 343.785850
## iter 70 value 306.192745
## iter 80 value 275.896913
## iter 90 value 260.616736
## iter 100 value 249.761017
## iter 110 value 239.900241
## iter 120 value 229.187410
## iter 130 value 217.482503
## iter 140 value 209.750916
## iter 150 value 200.708648
## iter 160 value 190.775432
## iter 170 value 186.224202
## iter 180 value 182.475082
## iter 190 value 178.936579
## iter 200 value 175.656530
## iter 210 value 172.375262
## iter 220 value 167.273615
## iter 230 value 164.096168
## iter 240 value 161.203456
## iter 250 value 159.229025
## iter 260 value 157.504239
## iter 270 value 156.587575
## iter 280 value 155.531746
## iter 290 value 154.559980
## iter 300 value 153.656472
## iter 310 value 152.946523
## iter 320 value 152.485074
## iter 330 value 151.835283
## iter 340 value 151.434099
## iter 350 value 150.980317
## iter 360 value 150.521845
## iter 370 value 150.389598
## iter 380 value 150.329004
## iter 390 value 150.260145
## iter 400 value 150.121005
## iter 410 value 149.872528
## iter 420 value 149.606853
## iter 430 value 149.517933
## iter 440 value 149.389873
## iter 450 value 149.013867
## iter 460 value 148.653535
## iter 470 value 148.380377
## iter 480 value 148.058668
## iter 490 value 147.527118
## iter 500 value 146.731923
## final value 146.731923
## stopped after 500 iterations
## # weights: 241
## initial value 1423332.312985
## iter 10 value 1711.667087
## iter 20 value 868.528121
## iter 30 value 688.813149
## iter 40 value 587.147446
## iter 50 value 482.381212
## iter 60 value 411.170262
## iter 70 value 376.495682
## iter 80 value 334.255103
## iter 90 value 293.125713
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## iter 220 value 105.616896
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## iter 300 value 80.240551
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## iter 330 value 74.247712
## iter 340 value 72.619933
## iter 350 value 71.275721
## iter 360 value 69.793623
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## iter 380 value 65.957361
## iter 390 value 64.350427
## iter 400 value 62.802094
## iter 410 value 60.879695
## iter 420 value 58.978189
## iter 430 value 57.246998
## iter 440 value 55.370175
## iter 450 value 53.998019
## iter 460 value 52.439343
## iter 470 value 51.101911
## iter 480 value 50.347936
## iter 490 value 50.053463
## iter 500 value 49.895207
## final value 49.895207
## stopped after 500 iterations
## # weights: 25
## initial value 1375188.416442
## iter 10 value 16102.055508
## iter 20 value 15853.925861
## iter 30 value 15328.540602
## iter 40 value 14085.586455
## iter 50 value 12085.447237
## iter 60 value 11918.166806
## iter 70 value 11805.179982
## iter 80 value 11660.798775
## iter 90 value 11361.537171
## iter 100 value 10984.839138
## iter 110 value 9566.969003
## iter 120 value 6432.531781
## iter 130 value 5313.224592
## iter 140 value 4829.569025
## iter 150 value 4787.592748
## iter 160 value 4787.216112
## iter 170 value 4782.023428
## iter 180 value 4775.394195
## iter 190 value 4775.062232
## iter 200 value 4775.042270
## iter 210 value 4775.038984
## iter 210 value 4775.038958
## iter 210 value 4775.038929
## final value 4775.038929
## converged
## # weights: 61
## initial value 1399511.969281
## iter 10 value 3293.173425
## iter 20 value 1038.467858
## iter 30 value 862.750973
## iter 40 value 791.531697
## iter 50 value 730.586649
## iter 60 value 694.421961
## iter 70 value 659.394090
## iter 80 value 631.015748
## iter 90 value 618.964032
## iter 100 value 604.168017
## iter 110 value 586.138578
## iter 120 value 575.347708
## iter 130 value 573.700660
## iter 140 value 572.929843
## iter 150 value 571.471589
## iter 160 value 566.918886
## iter 170 value 553.565112
## iter 180 value 533.985698
## iter 190 value 525.757770
## iter 200 value 517.304148
## iter 210 value 512.639343
## iter 220 value 506.827291
## iter 230 value 501.505795
## iter 240 value 494.073676
## iter 250 value 491.070789
## iter 260 value 490.781011
## iter 270 value 489.768830
## iter 280 value 487.691058
## iter 290 value 485.771755
## iter 300 value 484.509492
## iter 310 value 483.772653
## iter 320 value 482.555982
## iter 330 value 481.813600
## iter 340 value 481.267303
## iter 350 value 480.700841
## iter 360 value 480.208391
## iter 370 value 480.113350
## iter 380 value 480.110838
## iter 390 value 480.106432
## iter 400 value 480.099015
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## iter 420 value 480.093227
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## iter 440 value 480.075095
## iter 450 value 480.021294
## iter 460 value 479.989083
## iter 470 value 479.949530
## iter 480 value 479.689558
## iter 490 value 478.469520
## iter 500 value 478.262142
## final value 478.262142
## stopped after 500 iterations
## # weights: 121
## initial value 1318817.031447
## iter 10 value 1452.690276
## iter 20 value 920.789955
## iter 30 value 705.021923
## iter 40 value 593.892554
## iter 50 value 547.201897
## iter 60 value 512.945591
## iter 70 value 469.972578
## iter 80 value 443.416354
## iter 90 value 421.526308
## iter 100 value 399.509812
## iter 110 value 378.941153
## iter 120 value 367.745631
## iter 130 value 358.122082
## iter 140 value 347.815019
## iter 150 value 339.654914
## iter 160 value 332.856470
## iter 170 value 329.203483
## iter 180 value 328.408538
## iter 190 value 327.836173
## iter 200 value 327.012733
## iter 210 value 325.731887
## iter 220 value 323.769168
## iter 230 value 322.509952
## iter 240 value 321.791758
## iter 250 value 321.645405
## iter 260 value 321.494315
## iter 270 value 321.292225
## iter 280 value 320.932134
## iter 290 value 320.131915
## iter 300 value 318.050179
## iter 310 value 315.271354
## iter 320 value 308.540896
## iter 330 value 303.641128
## iter 340 value 299.036088
## iter 350 value 297.768825
## iter 360 value 296.612252
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## iter 380 value 293.666531
## iter 390 value 291.298371
## iter 400 value 289.680911
## iter 410 value 287.424119
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## iter 450 value 281.002749
## iter 460 value 280.312862
## iter 470 value 280.151889
## iter 480 value 280.033600
## iter 490 value 279.956148
## iter 500 value 279.935351
## final value 279.935351
## stopped after 500 iterations
## # weights: 181
## initial value 1419080.385443
## iter 10 value 1099.176085
## iter 20 value 763.320358
## iter 30 value 627.198542
## iter 40 value 498.793937
## iter 50 value 428.821184
## iter 60 value 377.207061
## iter 70 value 326.546398
## iter 80 value 283.956340
## iter 90 value 249.944164
## iter 100 value 230.013294
## iter 110 value 212.683989
## iter 120 value 201.491946
## iter 130 value 194.927140
## iter 140 value 186.967490
## iter 150 value 178.334234
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## iter 180 value 165.760347
## iter 190 value 162.684110
## iter 200 value 160.301357
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## iter 240 value 147.263456
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## iter 260 value 142.104375
## iter 270 value 140.816288
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## iter 300 value 133.332483
## iter 310 value 131.647369
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## iter 400 value 125.539814
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## iter 420 value 124.368893
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## iter 450 value 122.573415
## iter 460 value 121.814210
## iter 470 value 119.929884
## iter 480 value 116.092060
## iter 490 value 114.143457
## iter 500 value 113.301216
## final value 113.301216
## stopped after 500 iterations
## # weights: 241
## initial value 1397038.253488
## iter 10 value 1210.230408
## iter 20 value 772.781019
## iter 30 value 588.911979
## iter 40 value 445.445295
## iter 50 value 354.771998
## iter 60 value 317.702059
## iter 70 value 279.779209
## iter 80 value 248.415090
## iter 90 value 223.218063
## iter 100 value 200.310837
## iter 110 value 184.394847
## iter 120 value 174.854907
## iter 130 value 165.330989
## iter 140 value 155.447832
## iter 150 value 151.102038
## iter 160 value 145.614241
## iter 170 value 140.925140
## iter 180 value 137.193774
## iter 190 value 132.915430
## iter 200 value 129.208818
## iter 210 value 126.324587
## iter 220 value 121.485535
## iter 230 value 117.497486
## iter 240 value 112.973179
## iter 250 value 109.497220
## iter 260 value 106.493104
## iter 270 value 103.366171
## iter 280 value 97.718667
## iter 290 value 92.993132
## iter 300 value 88.150764
## iter 310 value 84.340032
## iter 320 value 80.740909
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## iter 440 value 61.455906
## iter 450 value 59.484782
## iter 460 value 58.133184
## iter 470 value 56.931597
## iter 480 value 56.303462
## iter 490 value 56.017572
## iter 500 value 55.907166
## final value 55.907166
## stopped after 500 iterations
## # weights: 25
## initial value 1403525.149577
## iter 10 value 21697.277037
## iter 20 value 18083.651237
## iter 30 value 13265.431241
## iter 40 value 6016.758970
## iter 50 value 4798.537113
## iter 60 value 3094.872443
## iter 70 value 1812.653947
## iter 80 value 1388.665597
## iter 90 value 1285.069171
## iter 100 value 1252.334838
## iter 110 value 1125.059730
## iter 120 value 1027.811915
## iter 130 value 984.305717
## iter 140 value 967.528777
## iter 150 value 961.547041
## iter 160 value 948.668359
## iter 170 value 944.960938
## iter 180 value 943.767075
## iter 190 value 941.297172
## iter 200 value 940.509674
## iter 210 value 935.160095
## iter 220 value 927.458181
## iter 230 value 911.523977
## iter 240 value 883.110578
## iter 250 value 881.326236
## iter 260 value 873.092846
## iter 270 value 863.467400
## iter 280 value 860.166048
## iter 290 value 853.938736
## iter 300 value 853.683547
## iter 310 value 852.069672
## iter 320 value 848.330435
## iter 330 value 846.235303
## iter 340 value 846.101308
## iter 350 value 846.100112
## iter 360 value 845.986112
## iter 370 value 845.844756
## iter 380 value 845.813079
## final value 845.810777
## converged
## # weights: 61
## initial value 1366580.539952
## iter 10 value 4029.233090
## iter 20 value 3297.549745
## iter 30 value 2846.511707
## iter 40 value 1739.584966
## iter 50 value 1412.511289
## iter 60 value 1240.922593
## iter 70 value 1190.954967
## iter 80 value 1115.833784
## iter 90 value 1033.781017
## iter 100 value 979.058558
## iter 110 value 939.559569
## iter 120 value 903.399078
## iter 130 value 892.295059
## iter 140 value 887.091645
## iter 150 value 873.580442
## iter 160 value 835.563134
## iter 170 value 799.726986
## iter 180 value 795.371841
## iter 190 value 787.976033
## iter 200 value 778.654653
## iter 210 value 776.827259
## iter 220 value 771.742956
## iter 230 value 766.903550
## iter 240 value 756.423729
## iter 250 value 752.548226
## iter 260 value 749.999683
## iter 270 value 742.633434
## iter 280 value 733.156446
## iter 290 value 726.455629
## iter 300 value 710.369141
## iter 310 value 707.283238
## iter 320 value 698.066635
## iter 330 value 676.707596
## iter 340 value 668.695456
## iter 350 value 663.630547
## iter 360 value 662.375458
## iter 370 value 660.647564
## iter 380 value 660.499859
## iter 390 value 660.494775
## iter 400 value 660.399953
## iter 410 value 659.444728
## iter 420 value 656.576271
## iter 430 value 640.663787
## iter 440 value 631.993177
## iter 450 value 629.609871
## iter 460 value 629.198758
## iter 470 value 629.029796
## iter 480 value 628.984517
## iter 490 value 628.975119
## iter 500 value 628.848685
## final value 628.848685
## stopped after 500 iterations
## # weights: 121
## initial value 1428452.441692
## iter 10 value 6384.768307
## iter 20 value 1988.095661
## iter 30 value 1049.000430
## iter 40 value 802.580215
## iter 50 value 707.224527
## iter 60 value 608.402405
## iter 70 value 544.484021
## iter 80 value 510.788642
## iter 90 value 480.915831
## iter 100 value 442.152837
## iter 110 value 429.589723
## iter 120 value 419.771159
## iter 130 value 407.868940
## iter 140 value 401.898617
## iter 150 value 394.783838
## iter 160 value 387.245566
## iter 170 value 378.854766
## iter 180 value 374.491843
## iter 190 value 367.947580
## iter 200 value 358.880884
## iter 210 value 349.035210
## iter 220 value 340.740584
## iter 230 value 329.937497
## iter 240 value 325.733237
## iter 250 value 324.551150
## iter 260 value 321.971947
## iter 270 value 320.434203
## iter 280 value 319.729849
## iter 290 value 319.469373
## iter 300 value 319.431822
## iter 310 value 319.396934
## iter 320 value 319.387271
## iter 330 value 319.383439
## iter 340 value 319.382102
## iter 350 value 319.381910
## iter 360 value 319.381769
## iter 370 value 319.381622
## iter 380 value 319.381546
## final value 319.381539
## converged
## # weights: 181
## initial value 1404235.441782
## iter 10 value 1034.280273
## iter 20 value 732.884134
## iter 30 value 592.594287
## iter 40 value 450.857814
## iter 50 value 379.943521
## iter 60 value 339.925313
## iter 70 value 299.428528
## iter 80 value 264.566854
## iter 90 value 245.507529
## iter 100 value 226.728438
## iter 110 value 205.813100
## iter 120 value 185.088295
## iter 130 value 175.121314
## iter 140 value 167.679262
## iter 150 value 161.304719
## iter 160 value 156.056370
## iter 170 value 152.121413
## iter 180 value 148.396516
## iter 190 value 144.239963
## iter 200 value 139.574057
## iter 210 value 136.150019
## iter 220 value 132.371316
## iter 230 value 128.954634
## iter 240 value 126.587177
## iter 250 value 123.673748
## iter 260 value 120.347411
## iter 270 value 117.702960
## iter 280 value 115.982896
## iter 290 value 113.982161
## iter 300 value 112.848898
## iter 310 value 111.855295
## iter 320 value 110.924533
## iter 330 value 109.984496
## iter 340 value 109.312298
## iter 350 value 108.752643
## iter 360 value 108.396991
## iter 370 value 108.199091
## iter 380 value 108.129401
## iter 390 value 108.011112
## iter 400 value 107.811455
## iter 410 value 107.545364
## iter 420 value 106.687093
## iter 430 value 105.543324
## iter 440 value 104.858482
## iter 450 value 104.396878
## iter 460 value 103.728063
## iter 470 value 102.963561
## iter 480 value 102.582412
## iter 490 value 102.385875
## iter 500 value 102.220620
## final value 102.220620
## stopped after 500 iterations
## # weights: 241
## initial value 1297007.428320
## iter 10 value 1364.629397
## iter 20 value 846.744067
## iter 30 value 621.105766
## iter 40 value 508.430751
## iter 50 value 384.974974
## iter 60 value 307.208297
## iter 70 value 259.296425
## iter 80 value 228.767269
## iter 90 value 203.948715
## iter 100 value 187.451558
## iter 110 value 174.177514
## iter 120 value 166.003290
## iter 130 value 160.684567
## iter 140 value 153.146459
## iter 150 value 145.604708
## iter 160 value 137.190086
## iter 170 value 129.776538
## iter 180 value 124.785137
## iter 190 value 120.812841
## iter 200 value 114.762096
## iter 210 value 107.401399
## iter 220 value 100.549706
## iter 230 value 95.320181
## iter 240 value 91.206744
## iter 250 value 87.373292
## iter 260 value 84.474361
## iter 270 value 79.818937
## iter 280 value 75.926767
## iter 290 value 73.069538
## iter 300 value 71.151305
## iter 310 value 69.059229
## iter 320 value 67.217814
## iter 330 value 65.041050
## iter 340 value 63.765513
## iter 350 value 61.899823
## iter 360 value 60.433662
## iter 370 value 58.543606
## iter 380 value 57.194785
## iter 390 value 55.736398
## iter 400 value 54.427956
## iter 410 value 53.580331
## iter 420 value 52.714788
## iter 430 value 51.033733
## iter 440 value 48.907358
## iter 450 value 47.405408
## iter 460 value 46.246342
## iter 470 value 45.492758
## iter 480 value 44.957521
## iter 490 value 44.733160
## iter 500 value 44.644663
## final value 44.644663
## stopped after 500 iterations
## # weights: 25
## initial value 1401353.258151
## iter 10 value 53063.045787
## iter 20 value 17955.323222
## iter 30 value 11996.595551
## iter 40 value 6092.357487
## iter 50 value 3078.535761
## iter 60 value 2715.678783
## iter 70 value 2362.002626
## iter 80 value 1771.989386
## iter 90 value 1302.469755
## iter 100 value 1174.013261
## iter 110 value 1161.729185
## iter 120 value 1154.528299
## iter 130 value 1118.315849
## iter 140 value 1103.237555
## iter 150 value 1098.728427
## iter 160 value 1097.823329
## iter 170 value 1097.704053
## iter 180 value 1097.274693
## final value 1097.274586
## converged
## # weights: 61
## initial value 1390333.053511
## iter 10 value 55214.502560
## iter 20 value 12495.215956
## iter 30 value 8730.314220
## iter 40 value 6775.553821
## iter 50 value 4466.270691
## iter 60 value 2640.081380
## iter 70 value 2004.347231
## iter 80 value 1664.952214
## iter 90 value 1423.511818
## iter 100 value 1327.091562
## iter 110 value 1262.007427
## iter 120 value 1212.537456
## iter 130 value 1142.876234
## iter 140 value 1120.186174
## iter 150 value 1085.433733
## iter 160 value 1048.021857
## iter 170 value 1022.884528
## iter 180 value 992.164664
## iter 190 value 983.581385
## iter 200 value 966.950661
## iter 210 value 940.513744
## iter 220 value 911.125720
## iter 230 value 880.584376
## iter 240 value 860.620180
## iter 250 value 848.544184
## iter 260 value 830.379415
## iter 270 value 815.790142
## iter 280 value 807.709292
## iter 290 value 802.525052
## iter 300 value 798.023993
## iter 310 value 797.385334
## iter 320 value 797.088218
## iter 330 value 796.774697
## iter 340 value 796.556291
## iter 350 value 796.519108
## final value 796.517893
## converged
## # weights: 121
## initial value 1372743.445145
## iter 10 value 1505.299243
## iter 20 value 982.333079
## iter 30 value 853.635196
## iter 40 value 789.995905
## iter 50 value 737.244506
## iter 60 value 692.005357
## iter 70 value 669.265706
## iter 80 value 652.478759
## iter 90 value 631.808700
## iter 100 value 597.074935
## iter 110 value 585.942659
## iter 120 value 573.542749
## iter 130 value 564.486505
## iter 140 value 559.994245
## iter 150 value 553.160677
## iter 160 value 546.373547
## iter 170 value 542.095502
## iter 180 value 535.621822
## iter 190 value 528.001022
## iter 200 value 522.081695
## iter 210 value 518.381062
## iter 220 value 516.220621
## iter 230 value 513.946237
## iter 240 value 511.816142
## iter 250 value 509.637226
## iter 260 value 507.933784
## iter 270 value 504.512788
## iter 280 value 502.121514
## iter 290 value 499.634333
## iter 300 value 496.926506
## iter 310 value 495.407402
## iter 320 value 494.653847
## iter 330 value 493.408566
## iter 340 value 491.961507
## iter 350 value 490.941754
## iter 360 value 490.469934
## iter 370 value 490.026014
## iter 380 value 489.701960
## iter 390 value 489.535360
## iter 400 value 489.484940
## iter 410 value 489.475937
## final value 489.475601
## converged
## # weights: 181
## initial value 1378447.756449
## iter 10 value 2081.694421
## iter 20 value 901.641352
## iter 30 value 713.522440
## iter 40 value 599.756131
## iter 50 value 530.663255
## iter 60 value 501.452674
## iter 70 value 475.153144
## iter 80 value 461.554184
## iter 90 value 453.125121
## iter 100 value 444.849367
## iter 110 value 436.332107
## iter 120 value 429.072659
## iter 130 value 422.599480
## iter 140 value 417.436343
## iter 150 value 408.762541
## iter 160 value 399.278466
## iter 170 value 393.829839
## iter 180 value 390.116159
## iter 190 value 388.448405
## iter 200 value 387.175758
## iter 210 value 386.115151
## iter 220 value 384.820198
## iter 230 value 383.844095
## iter 240 value 382.783976
## iter 250 value 381.574879
## iter 260 value 380.247991
## iter 270 value 377.801323
## iter 280 value 376.605938
## iter 290 value 375.198512
## iter 300 value 374.047719
## iter 310 value 372.216405
## iter 320 value 369.718948
## iter 330 value 367.896975
## iter 340 value 365.678460
## iter 350 value 363.040314
## iter 360 value 361.475565
## iter 370 value 360.635766
## iter 380 value 360.092402
## iter 390 value 359.436765
## iter 400 value 359.172401
## iter 410 value 359.027522
## iter 420 value 358.945907
## iter 430 value 358.887058
## iter 440 value 358.862257
## iter 450 value 358.853670
## iter 460 value 358.845485
## iter 470 value 358.743233
## iter 480 value 358.462279
## iter 490 value 358.242103
## iter 500 value 358.099635
## final value 358.099635
## stopped after 500 iterations
## # weights: 241
## initial value 1365946.721509
## iter 10 value 1311.348535
## iter 20 value 868.368024
## iter 30 value 749.670659
## iter 40 value 650.977204
## iter 50 value 568.875882
## iter 60 value 516.537894
## iter 70 value 479.628075
## iter 80 value 455.872591
## iter 90 value 424.128935
## iter 100 value 402.299205
## iter 110 value 388.556264
## iter 120 value 381.076329
## iter 130 value 376.846879
## iter 140 value 373.218833
## iter 150 value 369.193003
## iter 160 value 366.254966
## iter 170 value 363.296215
## iter 180 value 360.223131
## iter 190 value 357.635851
## iter 200 value 354.192492
## iter 210 value 351.164814
## iter 220 value 348.757273
## iter 230 value 346.129622
## iter 240 value 343.440193
## iter 250 value 341.976460
## iter 260 value 340.046225
## iter 270 value 338.227225
## iter 280 value 336.389291
## iter 290 value 332.001434
## iter 300 value 325.998337
## iter 310 value 321.495704
## iter 320 value 318.018718
## iter 330 value 315.334151
## iter 340 value 312.652233
## iter 350 value 310.204898
## iter 360 value 307.965005
## iter 370 value 306.620540
## iter 380 value 305.425614
## iter 390 value 304.514942
## iter 400 value 303.868719
## iter 410 value 303.474343
## iter 420 value 303.136935
## iter 430 value 302.738887
## iter 440 value 302.258472
## iter 450 value 301.819825
## iter 460 value 301.281834
## iter 470 value 300.648678
## iter 480 value 300.156362
## iter 490 value 299.928554
## iter 500 value 299.631879
## final value 299.631879
## stopped after 500 iterations
## # weights: 25
## initial value 1369311.602739
## iter 10 value 6820.461951
## iter 20 value 5446.843389
## iter 30 value 5295.517703
## iter 40 value 4906.365573
## iter 50 value 4273.899997
## iter 60 value 3727.361713
## iter 70 value 1766.238133
## iter 80 value 1402.899845
## iter 90 value 1346.814811
## iter 100 value 1328.122741
## iter 110 value 1301.694360
## iter 120 value 1287.661388
## iter 130 value 1282.648408
## iter 140 value 1280.563819
## iter 150 value 1278.920853
## iter 160 value 1278.912377
## final value 1278.911363
## converged
## # weights: 61
## initial value 1377040.387857
## iter 10 value 8571.316210
## iter 20 value 6387.743023
## iter 30 value 5753.483085
## iter 40 value 5440.730456
## iter 50 value 4714.881211
## iter 60 value 3345.592322
## iter 70 value 2573.088092
## iter 80 value 2317.392581
## iter 90 value 2283.694066
## iter 100 value 2258.226956
## iter 110 value 2225.707491
## iter 120 value 2201.629906
## iter 130 value 2176.421395
## iter 140 value 2137.332954
## iter 150 value 2121.353478
## iter 160 value 2100.527564
## iter 170 value 2000.596293
## iter 180 value 1818.544666
## iter 190 value 1789.426629
## iter 200 value 1787.530658
## iter 210 value 1773.212944
## iter 220 value 1760.690107
## iter 230 value 1749.447440
## iter 240 value 1746.319286
## iter 250 value 1745.545835
## iter 260 value 1744.931662
## iter 270 value 1744.452723
## iter 280 value 1743.533941
## iter 290 value 1717.677648
## iter 300 value 1698.646441
## iter 310 value 1652.563573
## iter 320 value 1644.484688
## iter 330 value 1642.039126
## iter 340 value 1622.964985
## iter 350 value 1603.421679
## iter 360 value 1583.012274
## iter 370 value 1563.759331
## iter 380 value 1512.874144
## iter 390 value 1348.064613
## iter 400 value 1019.306111
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## iter 450 value 868.011342
## iter 460 value 865.222298
## iter 470 value 854.609702
## iter 480 value 848.468739
## iter 490 value 848.260851
## iter 500 value 844.915794
## final value 844.915794
## stopped after 500 iterations
## # weights: 121
## initial value 1426097.473036
## iter 10 value 3019.134982
## iter 20 value 1534.369030
## iter 30 value 1048.182910
## iter 40 value 762.735738
## iter 50 value 650.644900
## iter 60 value 591.275378
## iter 70 value 558.046179
## iter 80 value 531.574389
## iter 90 value 516.034614
## iter 100 value 491.111659
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## iter 120 value 463.287524
## iter 130 value 454.202281
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## iter 150 value 440.021249
## iter 160 value 421.566852
## iter 170 value 387.683424
## iter 180 value 363.415541
## iter 190 value 352.033622
## iter 200 value 339.449921
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## iter 230 value 311.420873
## iter 240 value 307.067459
## iter 250 value 304.823497
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## iter 290 value 300.125644
## iter 300 value 298.034447
## iter 310 value 295.206864
## iter 320 value 291.522073
## iter 330 value 285.937588
## iter 340 value 280.633593
## iter 350 value 274.581599
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## iter 460 value 264.807567
## iter 470 value 264.470707
## iter 480 value 264.101405
## iter 490 value 263.905791
## iter 500 value 263.901083
## final value 263.901083
## stopped after 500 iterations
## # weights: 181
## initial value 1386961.737673
## iter 10 value 1078.049854
## iter 20 value 742.927129
## iter 30 value 636.335191
## iter 40 value 520.049415
## iter 50 value 415.406605
## iter 60 value 363.600726
## iter 70 value 322.840754
## iter 80 value 288.649996
## iter 90 value 259.843976
## iter 100 value 241.239006
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## iter 120 value 216.959256
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## iter 140 value 194.045034
## iter 150 value 186.977582
## iter 160 value 180.402468
## iter 170 value 174.068486
## iter 180 value 167.872786
## iter 190 value 163.612426
## iter 200 value 160.190966
## iter 210 value 155.173493
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## iter 470 value 112.437231
## iter 480 value 111.998950
## iter 490 value 111.572933
## iter 500 value 110.859297
## final value 110.859297
## stopped after 500 iterations
## # weights: 241
## initial value 1330168.016794
## iter 10 value 1354.057588
## iter 20 value 735.288960
## iter 30 value 597.233265
## iter 40 value 461.802342
## iter 50 value 359.972501
## iter 60 value 289.409395
## iter 70 value 242.612727
## iter 80 value 209.506249
## iter 90 value 186.474628
## iter 100 value 169.072803
## iter 110 value 153.979989
## iter 120 value 141.134093
## iter 130 value 127.000045
## iter 140 value 116.141903
## iter 150 value 109.717238
## iter 160 value 104.958974
## iter 170 value 97.674274
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## iter 190 value 85.742121
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## iter 320 value 38.058901
## iter 330 value 35.478419
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## iter 460 value 21.338875
## iter 470 value 20.627857
## iter 480 value 20.050029
## iter 490 value 19.814863
## iter 500 value 19.753910
## final value 19.753910
## stopped after 500 iterations
## # weights: 25
## initial value 1356805.109099
## iter 10 value 59222.119487
## iter 20 value 9417.890232
## iter 30 value 9246.746354
## iter 40 value 6902.246588
## iter 50 value 6726.021739
## iter 60 value 6684.140998
## iter 70 value 6643.029436
## iter 80 value 6319.357460
## iter 90 value 6235.343275
## iter 100 value 6235.244068
## iter 110 value 6172.187747
## iter 120 value 6142.233453
## iter 130 value 6142.101684
## iter 140 value 6141.228948
## iter 150 value 6133.958899
## iter 160 value 5621.871668
## iter 170 value 5530.558889
## iter 180 value 5330.100058
## iter 190 value 5290.709249
## iter 200 value 5164.637480
## iter 210 value 5126.026976
## iter 220 value 5095.071735
## iter 230 value 5072.876329
## iter 240 value 5066.573246
## iter 250 value 5065.550255
## iter 260 value 5064.784612
## iter 270 value 5049.894687
## iter 280 value 5048.100747
## iter 290 value 5047.538351
## iter 300 value 4679.655423
## iter 310 value 4382.686243
## iter 320 value 4248.856572
## iter 330 value 4169.446145
## iter 340 value 4157.848439
## iter 350 value 4154.638534
## iter 360 value 3982.154433
## iter 370 value 3752.922755
## iter 380 value 3397.184381
## iter 390 value 3197.633765
## iter 400 value 2816.612607
## iter 410 value 2289.036268
## iter 420 value 2092.562306
## iter 430 value 2037.751338
## iter 440 value 2028.852138
## iter 450 value 2028.382772
## iter 460 value 2027.240235
## final value 2027.211106
## converged
## # weights: 61
## initial value 1366094.035726
## iter 10 value 14594.371374
## iter 20 value 3703.204313
## iter 30 value 2886.847629
## iter 40 value 2562.392877
## iter 50 value 2493.333089
## iter 60 value 2379.144228
## iter 70 value 2298.212668
## iter 80 value 2289.076637
## iter 90 value 2171.722245
## iter 100 value 2097.324863
## iter 110 value 1891.723820
## iter 120 value 1561.579193
## iter 130 value 1481.331588
## iter 140 value 1463.427491
## iter 150 value 1461.339548
## iter 160 value 1460.824174
## iter 170 value 1460.724207
## iter 180 value 1456.202838
## iter 190 value 1450.734090
## iter 200 value 1449.824705
## iter 210 value 1449.418029
## iter 220 value 1447.830406
## iter 230 value 1447.429410
## iter 240 value 1447.134912
## iter 250 value 1447.108561
## iter 260 value 1447.105605
## iter 270 value 1447.087742
## iter 280 value 1447.085697
## final value 1447.077655
## converged
## # weights: 121
## initial value 1409073.718393
## iter 10 value 1737.906640
## iter 20 value 951.608409
## iter 30 value 739.761715
## iter 40 value 644.752321
## iter 50 value 579.252648
## iter 60 value 548.734764
## iter 70 value 533.790576
## iter 80 value 523.944789
## iter 90 value 511.135769
## iter 100 value 493.955447
## iter 110 value 469.549577
## iter 120 value 453.538929
## iter 130 value 442.651043
## iter 140 value 434.989608
## iter 150 value 429.445562
## iter 160 value 418.673603
## iter 170 value 407.802071
## iter 180 value 396.497072
## iter 190 value 388.232433
## iter 200 value 383.878534
## iter 210 value 375.744446
## iter 220 value 366.697908
## iter 230 value 361.283529
## iter 240 value 359.598320
## iter 250 value 359.050364
## iter 260 value 358.788190
## iter 270 value 358.114289
## iter 280 value 357.462971
## iter 290 value 356.733742
## iter 300 value 356.038466
## iter 310 value 354.559289
## iter 320 value 352.557699
## iter 330 value 350.808445
## iter 340 value 346.500381
## iter 350 value 338.025057
## iter 360 value 328.605527
## iter 370 value 326.839992
## iter 380 value 326.195609
## iter 390 value 325.361250
## iter 400 value 325.094774
## iter 410 value 324.850723
## iter 420 value 324.705346
## iter 430 value 324.511860
## iter 440 value 324.134060
## iter 450 value 323.430420
## iter 460 value 320.903335
## iter 470 value 320.718395
## iter 480 value 320.527931
## iter 490 value 320.188330
## iter 500 value 320.025528
## final value 320.025528
## stopped after 500 iterations
## # weights: 181
## initial value 1420843.867292
## iter 10 value 1553.878919
## iter 20 value 783.985886
## iter 30 value 549.826976
## iter 40 value 440.165903
## iter 50 value 357.013814
## iter 60 value 311.903325
## iter 70 value 273.129877
## iter 80 value 250.112370
## iter 90 value 232.659705
## iter 100 value 220.296688
## iter 110 value 205.859366
## iter 120 value 191.913094
## iter 130 value 179.971842
## iter 140 value 171.584758
## iter 150 value 164.055834
## iter 160 value 157.771779
## iter 170 value 154.593005
## iter 180 value 152.389330
## iter 190 value 150.316412
## iter 200 value 147.779605
## iter 210 value 145.050627
## iter 220 value 142.083187
## iter 230 value 139.375397
## iter 240 value 137.427783
## iter 250 value 136.112916
## iter 260 value 134.252522
## iter 270 value 132.364169
## iter 280 value 131.302994
## iter 290 value 130.404729
## iter 300 value 128.421451
## iter 310 value 125.839618
## iter 320 value 124.094197
## iter 330 value 122.371954
## iter 340 value 120.926141
## iter 350 value 119.566828
## iter 360 value 118.600702
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## iter 380 value 118.325507
## iter 390 value 118.240402
## iter 400 value 118.128036
## iter 410 value 117.997047
## iter 420 value 117.803511
## iter 430 value 117.546840
## iter 440 value 117.266092
## iter 450 value 116.862985
## iter 460 value 116.293996
## iter 470 value 116.057361
## iter 480 value 115.938455
## iter 490 value 115.674717
## iter 500 value 115.343990
## final value 115.343990
## stopped after 500 iterations
## # weights: 241
## initial value 1440668.147904
## iter 10 value 1502.928511
## iter 20 value 835.596188
## iter 30 value 696.855528
## iter 40 value 498.244616
## iter 50 value 383.102391
## iter 60 value 318.736438
## iter 70 value 279.277861
## iter 80 value 238.541646
## iter 90 value 205.585552
## iter 100 value 175.467187
## iter 110 value 153.186976
## iter 120 value 135.226145
## iter 130 value 122.768024
## iter 140 value 113.093941
## iter 150 value 103.993975
## iter 160 value 94.817869
## iter 170 value 85.041852
## iter 180 value 74.534872
## iter 190 value 67.869491
## iter 200 value 62.272400
## iter 210 value 59.505032
## iter 220 value 56.959957
## iter 230 value 55.007171
## iter 240 value 53.477949
## iter 250 value 52.251961
## iter 260 value 50.989739
## iter 270 value 49.853158
## iter 280 value 48.386716
## iter 290 value 46.766237
## iter 300 value 45.142317
## iter 310 value 43.781358
## iter 320 value 42.418068
## iter 330 value 40.671757
## iter 340 value 38.593174
## iter 350 value 36.870450
## iter 360 value 36.042975
## iter 370 value 35.244790
## iter 380 value 34.699879
## iter 390 value 34.135125
## iter 400 value 33.376765
## iter 410 value 32.855154
## iter 420 value 32.373534
## iter 430 value 31.954689
## iter 440 value 31.314648
## iter 450 value 30.678883
## iter 460 value 29.672448
## iter 470 value 29.015203
## iter 480 value 28.446649
## iter 490 value 28.015522
## iter 500 value 27.865818
## final value 27.865818
## stopped after 500 iterations
## # weights: 25
## initial value 1402172.745191
## iter 10 value 63295.391174
## iter 20 value 15374.565679
## iter 30 value 8149.580330
## iter 40 value 7944.096117
## iter 50 value 7466.367668
## iter 60 value 7176.665746
## iter 70 value 7122.777303
## iter 80 value 6483.588255
## iter 90 value 4857.515929
## iter 100 value 4198.845859
## iter 110 value 4082.456045
## iter 120 value 4045.246330
## iter 130 value 3862.711111
## iter 140 value 3708.210015
## iter 150 value 3246.142211
## iter 160 value 3084.976258
## iter 170 value 3015.979565
## iter 180 value 3006.828731
## iter 190 value 2971.701250
## iter 200 value 2960.393376
## iter 210 value 2937.801232
## iter 220 value 2908.290003
## iter 230 value 2901.760090
## iter 240 value 2900.015925
## iter 250 value 2899.367701
## iter 260 value 2897.093539
## iter 270 value 2637.842843
## iter 280 value 2362.602928
## iter 290 value 1964.999855
## iter 300 value 1724.391243
## iter 310 value 1350.318145
## iter 320 value 1196.249954
## iter 330 value 1137.614444
## iter 340 value 1136.492860
## iter 350 value 1136.027795
## iter 360 value 1133.170187
## iter 370 value 1131.707272
## iter 380 value 1131.149459
## iter 390 value 1130.969477
## iter 400 value 1130.967831
## final value 1130.967783
## converged
## # weights: 61
## initial value 1400054.190012
## iter 10 value 4847.551767
## iter 20 value 1550.798989
## iter 30 value 1223.374323
## iter 40 value 947.169354
## iter 50 value 838.567825
## iter 60 value 790.987311
## iter 70 value 746.761026
## iter 80 value 718.900976
## iter 90 value 689.319768
## iter 100 value 650.472045
## iter 110 value 623.530055
## iter 120 value 610.924475
## iter 130 value 605.671804
## iter 140 value 604.391065
## iter 150 value 599.055364
## iter 160 value 590.904882
## iter 170 value 585.484653
## iter 180 value 582.880373
## iter 190 value 581.727608
## iter 200 value 579.933918
## iter 210 value 578.469396
## iter 220 value 578.065713
## iter 230 value 577.828920
## iter 240 value 577.628304
## iter 250 value 577.591399
## iter 260 value 577.589082
## iter 270 value 577.586269
## iter 280 value 577.579977
## iter 290 value 577.565120
## iter 300 value 577.550947
## iter 310 value 577.385000
## iter 320 value 577.275677
## iter 330 value 577.270027
## iter 340 value 576.970083
## iter 350 value 572.034129
## iter 360 value 570.931307
## iter 370 value 570.776523
## iter 380 value 570.677631
## iter 390 value 570.624846
## iter 400 value 570.596008
## iter 410 value 570.583740
## iter 420 value 570.582022
## final value 570.580708
## converged
## # weights: 121
## initial value 1358554.316834
## iter 10 value 1337.243323
## iter 20 value 850.364392
## iter 30 value 696.314543
## iter 40 value 616.218629
## iter 50 value 579.380382
## iter 60 value 536.830502
## iter 70 value 501.950865
## iter 80 value 473.882623
## iter 90 value 449.413838
## iter 100 value 406.869833
## iter 110 value 369.309203
## iter 120 value 347.648195
## iter 130 value 335.300049
## iter 140 value 323.425022
## iter 150 value 315.228826
## iter 160 value 305.182860
## iter 170 value 295.144182
## iter 180 value 290.205309
## iter 190 value 285.394013
## iter 200 value 280.228982
## iter 210 value 276.542393
## iter 220 value 272.233011
## iter 230 value 268.197440
## iter 240 value 266.264726
## iter 250 value 265.375063
## iter 260 value 264.867079
## iter 270 value 264.312464
## iter 280 value 263.597141
## iter 290 value 262.296595
## iter 300 value 260.210630
## iter 310 value 258.413407
## iter 320 value 256.326209
## iter 330 value 254.403211
## iter 340 value 249.939388
## iter 350 value 246.000583
## iter 360 value 242.325533
## iter 370 value 241.778214
## iter 380 value 241.409336
## iter 390 value 241.023325
## iter 400 value 240.730920
## iter 410 value 240.607571
## iter 420 value 240.511108
## iter 430 value 240.341820
## iter 440 value 240.219244
## iter 450 value 240.099095
## iter 460 value 240.046430
## iter 470 value 239.897886
## iter 480 value 239.854673
## iter 490 value 239.845293
## iter 500 value 239.844892
## final value 239.844892
## stopped after 500 iterations
## # weights: 181
## initial value 1389840.670094
## iter 10 value 1193.112946
## iter 20 value 772.948197
## iter 30 value 563.392355
## iter 40 value 463.042753
## iter 50 value 387.066107
## iter 60 value 334.413835
## iter 70 value 305.819553
## iter 80 value 261.772386
## iter 90 value 232.820311
## iter 100 value 215.460830
## iter 110 value 208.243489
## iter 120 value 198.912361
## iter 130 value 190.603035
## iter 140 value 181.922901
## iter 150 value 166.217408
## iter 160 value 157.503755
## iter 170 value 150.037209
## iter 180 value 144.289776
## iter 190 value 141.074906
## iter 200 value 138.498399
## iter 210 value 134.747672
## iter 220 value 132.047677
## iter 230 value 128.514963
## iter 240 value 123.386204
## iter 250 value 117.419340
## iter 260 value 114.207343
## iter 270 value 111.189121
## iter 280 value 108.889660
## iter 290 value 105.100057
## iter 300 value 101.704964
## iter 310 value 99.259772
## iter 320 value 97.655341
## iter 330 value 96.401790
## iter 340 value 95.338264
## iter 350 value 94.652644
## iter 360 value 94.290776
## iter 370 value 94.103856
## iter 380 value 94.032265
## iter 390 value 93.876083
## iter 400 value 93.709753
## iter 410 value 93.263465
## iter 420 value 92.831659
## iter 430 value 92.428810
## iter 440 value 92.085753
## iter 450 value 91.775611
## iter 460 value 91.354353
## iter 470 value 90.924158
## iter 480 value 90.499642
## iter 490 value 89.994242
## iter 500 value 89.724758
## final value 89.724758
## stopped after 500 iterations
## # weights: 241
## initial value 1372968.271570
## iter 10 value 1169.447193
## iter 20 value 734.379773
## iter 30 value 630.625105
## iter 40 value 540.218707
## iter 50 value 416.238418
## iter 60 value 341.865356
## iter 70 value 292.271601
## iter 80 value 258.782885
## iter 90 value 231.371683
## iter 100 value 210.301325
## iter 110 value 195.034096
## iter 120 value 179.928877
## iter 130 value 166.640731
## iter 140 value 155.425600
## iter 150 value 145.473971
## iter 160 value 135.095350
## iter 170 value 126.886617
## iter 180 value 121.279100
## iter 190 value 115.572185
## iter 200 value 109.950461
## iter 210 value 106.398344
## iter 220 value 102.904828
## iter 230 value 99.566074
## iter 240 value 94.888514
## iter 250 value 89.523929
## iter 260 value 84.624778
## iter 270 value 81.497959
## iter 280 value 78.965338
## iter 290 value 75.984896
## iter 300 value 73.173065
## iter 310 value 70.775972
## iter 320 value 68.617223
## iter 330 value 66.245945
## iter 340 value 64.914521
## iter 350 value 63.388993
## iter 360 value 62.185103
## iter 370 value 60.325627
## iter 380 value 58.460396
## iter 390 value 56.293493
## iter 400 value 54.345842
## iter 410 value 53.352981
## iter 420 value 52.169025
## iter 430 value 51.399806
## iter 440 value 50.752780
## iter 450 value 50.231516
## iter 460 value 49.471509
## iter 470 value 48.753595
## iter 480 value 48.020965
## iter 490 value 47.598918
## iter 500 value 47.475638
## final value 47.475638
## stopped after 500 iterations
## # weights: 25
## initial value 1430460.441402
## iter 10 value 6206.095093
## iter 20 value 5170.355729
## iter 30 value 5108.006007
## iter 40 value 5079.732627
## iter 50 value 5061.111948
## iter 60 value 5042.033389
## iter 70 value 4949.563582
## iter 80 value 4669.476968
## iter 90 value 4173.069659
## iter 100 value 3137.667881
## iter 110 value 1628.904006
## iter 120 value 1403.689002
## iter 130 value 1355.649351
## iter 140 value 1338.357360
## iter 150 value 1304.816752
## iter 160 value 1293.729286
## iter 170 value 1288.007615
## iter 180 value 1230.642831
## iter 190 value 1229.807419
## iter 200 value 1191.204463
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## iter 220 value 1067.320486
## iter 230 value 1049.763818
## iter 240 value 1049.089184
## iter 250 value 1045.635535
## iter 260 value 1041.206350
## iter 270 value 1041.182860
## iter 280 value 1039.589491
## iter 290 value 1036.335076
## iter 300 value 1027.348826
## iter 310 value 1019.563504
## iter 320 value 1013.542916
## iter 330 value 1013.154546
## iter 340 value 1013.112380
## iter 350 value 1012.465654
## iter 360 value 1012.113972
## iter 370 value 1012.092908
## iter 380 value 1012.090442
## iter 390 value 1012.090009
## final value 1012.089810
## converged
## # weights: 61
## initial value 1396403.619978
## iter 10 value 156807.623316
## iter 20 value 9277.858256
## iter 30 value 5841.608563
## iter 40 value 4158.880567
## iter 50 value 2694.421949
## iter 60 value 2311.134320
## iter 70 value 1879.328450
## iter 80 value 1722.876553
## iter 90 value 1506.950809
## iter 100 value 1267.473436
## iter 110 value 965.050359
## iter 120 value 858.584727
## iter 130 value 825.948972
## iter 140 value 807.065973
## iter 150 value 794.735339
## iter 160 value 781.821053
## iter 170 value 773.306241
## iter 180 value 765.169721
## iter 190 value 761.949115
## iter 200 value 759.635461
## iter 210 value 755.273682
## iter 220 value 746.580924
## iter 230 value 739.310766
## iter 240 value 734.232944
## iter 250 value 727.777104
## iter 260 value 721.177983
## iter 270 value 719.829543
## iter 280 value 717.217856
## iter 290 value 712.350097
## iter 300 value 707.065399
## iter 310 value 703.444815
## iter 320 value 701.629494
## iter 330 value 697.949614
## iter 340 value 692.080993
## iter 350 value 684.884469
## iter 360 value 681.653392
## iter 370 value 681.193241
## iter 380 value 680.827170
## iter 390 value 680.718647
## iter 400 value 680.704108
## iter 410 value 680.693648
## iter 420 value 680.692984
## iter 420 value 680.692980
## iter 420 value 680.692980
## final value 680.692980
## converged
## # weights: 121
## initial value 1388293.378420
## iter 10 value 1384.067018
## iter 20 value 886.202745
## iter 30 value 696.684586
## iter 40 value 593.566926
## iter 50 value 531.447636
## iter 60 value 481.552453
## iter 70 value 464.088052
## iter 80 value 451.420028
## iter 90 value 427.962696
## iter 100 value 412.762701
## iter 110 value 397.424848
## iter 120 value 388.062244
## iter 130 value 384.805558
## iter 140 value 381.432716
## iter 150 value 378.825993
## iter 160 value 370.598125
## iter 170 value 363.364242
## iter 180 value 353.103424
## iter 190 value 345.763187
## iter 200 value 339.653630
## iter 210 value 333.745967
## iter 220 value 328.467242
## iter 230 value 324.709951
## iter 240 value 319.860229
## iter 250 value 318.091221
## iter 260 value 316.764580
## iter 270 value 312.674001
## iter 280 value 308.156721
## iter 290 value 303.624590
## iter 300 value 299.714807
## iter 310 value 296.654030
## iter 320 value 293.340140
## iter 330 value 290.897347
## iter 340 value 288.778735
## iter 350 value 286.586079
## iter 360 value 284.743212
## iter 370 value 282.717461
## iter 380 value 280.720739
## iter 390 value 277.785514
## iter 400 value 272.768451
## iter 410 value 271.172047
## iter 420 value 269.870611
## iter 430 value 269.429404
## iter 440 value 269.309635
## iter 450 value 269.253951
## iter 460 value 269.219156
## iter 470 value 269.194542
## iter 480 value 269.153507
## iter 490 value 269.129900
## iter 500 value 269.129010
## final value 269.129010
## stopped after 500 iterations
## # weights: 181
## initial value 1359360.688678
## iter 10 value 1402.715958
## iter 20 value 876.057823
## iter 30 value 632.749836
## iter 40 value 518.746265
## iter 50 value 451.017909
## iter 60 value 392.669477
## iter 70 value 321.529549
## iter 80 value 285.980037
## iter 90 value 262.077291
## iter 100 value 236.912880
## iter 110 value 215.360026
## iter 120 value 202.064635
## iter 130 value 190.264386
## iter 140 value 179.088567
## iter 150 value 170.644241
## iter 160 value 162.666186
## iter 170 value 155.642596
## iter 180 value 151.367639
## iter 190 value 148.081351
## iter 200 value 144.464575
## iter 210 value 142.025098
## iter 220 value 138.710272
## iter 230 value 134.909411
## iter 240 value 132.614013
## iter 250 value 131.552435
## iter 260 value 130.885668
## iter 270 value 129.959879
## iter 280 value 129.060966
## iter 290 value 127.652744
## iter 300 value 125.945256
## iter 310 value 123.776784
## iter 320 value 122.299535
## iter 330 value 121.160575
## iter 340 value 120.365795
## iter 350 value 119.820059
## iter 360 value 119.390031
## iter 370 value 119.208085
## iter 380 value 119.136206
## iter 390 value 118.864128
## iter 400 value 118.593850
## iter 410 value 118.100189
## iter 420 value 117.633176
## iter 430 value 117.237786
## iter 440 value 116.973163
## iter 450 value 116.749047
## iter 460 value 116.539267
## iter 470 value 116.196349
## iter 480 value 115.850095
## iter 490 value 115.562332
## iter 500 value 115.401642
## final value 115.401642
## stopped after 500 iterations
## # weights: 241
## initial value 1339079.616359
## iter 10 value 2297.776587
## iter 20 value 944.942186
## iter 30 value 716.169257
## iter 40 value 569.136564
## iter 50 value 494.647146
## iter 60 value 426.861710
## iter 70 value 359.347475
## iter 80 value 323.851713
## iter 90 value 288.880175
## iter 100 value 265.437948
## iter 110 value 244.167808
## iter 120 value 231.157760
## iter 130 value 222.780869
## iter 140 value 212.869069
## iter 150 value 198.393105
## iter 160 value 185.579339
## iter 170 value 175.596738
## iter 180 value 166.264200
## iter 190 value 159.348511
## iter 200 value 153.635095
## iter 210 value 146.849711
## iter 220 value 141.412719
## iter 230 value 137.385671
## iter 240 value 133.888510
## iter 250 value 130.668026
## iter 260 value 126.500650
## iter 270 value 122.101962
## iter 280 value 118.204913
## iter 290 value 113.039082
## iter 300 value 109.943139
## iter 310 value 106.898056
## iter 320 value 104.740651
## iter 330 value 103.370125
## iter 340 value 102.140599
## iter 350 value 101.031816
## iter 360 value 99.749392
## iter 370 value 98.567229
## iter 380 value 97.202470
## iter 390 value 96.027127
## iter 400 value 95.006720
## iter 410 value 94.014557
## iter 420 value 93.220764
## iter 430 value 92.470245
## iter 440 value 91.492829
## iter 450 value 90.283563
## iter 460 value 89.039802
## iter 470 value 88.032305
## iter 480 value 87.201536
## iter 490 value 86.801475
## iter 500 value 86.682918
## final value 86.682918
## stopped after 500 iterations
## # weights: 25
## initial value 1371307.473023
## iter 10 value 15198.454565
## iter 20 value 13424.974703
## iter 30 value 7708.284801
## iter 40 value 7019.938824
## iter 50 value 6607.383671
## iter 60 value 4766.709751
## iter 70 value 3912.487889
## iter 80 value 2912.286175
## iter 90 value 2067.287807
## iter 100 value 1513.932988
## iter 110 value 1378.612337
## iter 120 value 1299.062479
## iter 130 value 1220.955045
## iter 140 value 1174.507340
## iter 150 value 1149.508775
## iter 160 value 1143.481910
## iter 170 value 1140.135914
## iter 180 value 1134.747246
## iter 190 value 1134.393424
## final value 1134.390374
## converged
## # weights: 61
## initial value 1356620.562293
## iter 10 value 2490.611049
## iter 20 value 1865.698456
## iter 30 value 1675.396022
## iter 40 value 1434.572826
## iter 50 value 1210.173809
## iter 60 value 1111.015977
## iter 70 value 1043.066295
## iter 80 value 1020.711397
## iter 90 value 977.000704
## iter 100 value 912.409472
## iter 110 value 892.135557
## iter 120 value 864.765033
## iter 130 value 835.987469
## iter 140 value 827.535275
## iter 150 value 817.588272
## iter 160 value 803.160297
## iter 170 value 795.643846
## iter 180 value 790.225914
## iter 190 value 780.234656
## iter 200 value 777.383537
## iter 210 value 762.666911
## iter 220 value 756.679514
## iter 230 value 755.367366
## iter 240 value 754.771889
## iter 250 value 754.692662
## iter 260 value 754.680180
## iter 270 value 754.669297
## iter 280 value 754.657558
## iter 290 value 754.645262
## iter 300 value 754.545669
## iter 310 value 751.733486
## iter 320 value 747.851729
## iter 330 value 747.124173
## iter 340 value 747.023182
## iter 350 value 746.971489
## iter 360 value 746.946542
## iter 370 value 746.945016
## iter 370 value 746.945015
## final value 746.945015
## converged
## # weights: 121
## initial value 1414506.620687
## iter 10 value 1373.171126
## iter 20 value 961.260999
## iter 30 value 843.875867
## iter 40 value 723.157828
## iter 50 value 648.466088
## iter 60 value 618.791851
## iter 70 value 602.481407
## iter 80 value 590.388422
## iter 90 value 573.283002
## iter 100 value 551.931943
## iter 110 value 536.687349
## iter 120 value 519.791853
## iter 130 value 506.877803
## iter 140 value 496.528787
## iter 150 value 489.187647
## iter 160 value 483.805888
## iter 170 value 479.614300
## iter 180 value 474.420378
## iter 190 value 470.713794
## iter 200 value 460.259525
## iter 210 value 452.796128
## iter 220 value 449.202228
## iter 230 value 446.281934
## iter 240 value 443.920325
## iter 250 value 442.077375
## iter 260 value 440.360018
## iter 270 value 438.347816
## iter 280 value 437.025350
## iter 290 value 436.381221
## iter 300 value 435.874778
## iter 310 value 435.339594
## iter 320 value 434.665166
## iter 330 value 433.591479
## iter 340 value 430.732099
## iter 350 value 427.611230
## iter 360 value 426.097113
## iter 370 value 425.671512
## iter 380 value 425.077531
## iter 390 value 424.409720
## iter 400 value 424.324645
## iter 410 value 424.316766
## iter 420 value 424.316240
## final value 424.316229
## converged
## # weights: 181
## initial value 1386843.327339
## iter 10 value 1192.188935
## iter 20 value 861.519545
## iter 30 value 715.760897
## iter 40 value 582.660882
## iter 50 value 534.227353
## iter 60 value 512.751215
## iter 70 value 481.538898
## iter 80 value 448.615759
## iter 90 value 425.691874
## iter 100 value 412.779702
## iter 110 value 399.709068
## iter 120 value 388.541904
## iter 130 value 383.513679
## iter 140 value 380.045142
## iter 150 value 377.548172
## iter 160 value 375.988572
## iter 170 value 374.114694
## iter 180 value 371.619410
## iter 190 value 368.502684
## iter 200 value 365.450238
## iter 210 value 363.257109
## iter 220 value 361.335223
## iter 230 value 358.309546
## iter 240 value 355.176908
## iter 250 value 351.531824
## iter 260 value 346.175896
## iter 270 value 341.818587
## iter 280 value 338.631150
## iter 290 value 336.500291
## iter 300 value 333.669243
## iter 310 value 329.850394
## iter 320 value 327.501830
## iter 330 value 326.122515
## iter 340 value 324.981953
## iter 350 value 324.022441
## iter 360 value 323.079683
## iter 370 value 322.658694
## iter 380 value 322.316042
## iter 390 value 321.925787
## iter 400 value 321.582726
## iter 410 value 321.380535
## iter 420 value 321.138927
## iter 430 value 320.806764
## iter 440 value 320.332402
## iter 450 value 319.750061
## iter 460 value 318.997866
## iter 470 value 318.308580
## iter 480 value 317.926626
## iter 490 value 317.494332
## iter 500 value 317.146502
## final value 317.146502
## stopped after 500 iterations
## # weights: 241
## initial value 1399983.448408
## iter 10 value 3386.743941
## iter 20 value 1123.405333
## iter 30 value 877.659532
## iter 40 value 691.585588
## iter 50 value 638.305708
## iter 60 value 587.475056
## iter 70 value 553.179688
## iter 80 value 531.812287
## iter 90 value 518.447979
## iter 100 value 505.691247
## iter 110 value 487.957638
## iter 120 value 477.140808
## iter 130 value 468.820133
## iter 140 value 461.697226
## iter 150 value 452.153126
## iter 160 value 441.513830
## iter 170 value 432.105296
## iter 180 value 421.540010
## iter 190 value 414.022875
## iter 200 value 407.354768
## iter 210 value 400.634649
## iter 220 value 393.918373
## iter 230 value 386.743917
## iter 240 value 381.672362
## iter 250 value 376.498965
## iter 260 value 372.833179
## iter 270 value 369.309377
## iter 280 value 367.095217
## iter 290 value 365.027830
## iter 300 value 362.006914
## iter 310 value 358.539945
## iter 320 value 356.021626
## iter 330 value 354.035638
## iter 340 value 351.639933
## iter 350 value 349.677160
## iter 360 value 347.789196
## iter 370 value 346.460745
## iter 380 value 344.922865
## iter 390 value 344.042027
## iter 400 value 343.328032
## iter 410 value 342.739669
## iter 420 value 342.088866
## iter 430 value 341.432676
## iter 440 value 340.839160
## iter 450 value 340.221096
## iter 460 value 339.357998
## iter 470 value 338.622392
## iter 480 value 337.361249
## iter 490 value 336.741196
## iter 500 value 336.175094
## final value 336.175094
## stopped after 500 iterations
## # weights: 25
## initial value 1396719.500964
## iter 10 value 14442.509209
## iter 20 value 8318.877569
## iter 30 value 2449.613836
## iter 40 value 1442.390049
## iter 50 value 1264.974398
## iter 60 value 1229.307221
## iter 70 value 1192.892924
## iter 80 value 1180.448678
## iter 90 value 1176.394756
## iter 100 value 1172.649592
## iter 110 value 1151.043775
## iter 120 value 1139.618268
## iter 130 value 1137.936695
## iter 140 value 1135.762766
## iter 150 value 1135.621121
## iter 160 value 1135.604907
## iter 170 value 1135.571068
## iter 180 value 1135.545407
## final value 1135.531521
## converged
## # weights: 61
## initial value 1395559.158788
## iter 10 value 2965.362259
## iter 20 value 1648.111629
## iter 30 value 1078.679235
## iter 40 value 858.968683
## iter 50 value 763.990663
## iter 60 value 728.855353
## iter 70 value 688.317954
## iter 80 value 659.053913
## iter 90 value 640.301413
## iter 100 value 622.044528
## iter 110 value 610.951452
## iter 120 value 600.766610
## iter 130 value 595.506993
## iter 140 value 593.075413
## iter 150 value 588.107703
## iter 160 value 579.301858
## iter 170 value 571.500934
## iter 180 value 565.002137
## iter 190 value 563.150493
## iter 200 value 561.290695
## iter 210 value 560.476340
## iter 220 value 560.281806
## iter 230 value 560.173274
## iter 240 value 560.024207
## iter 250 value 559.949920
## iter 260 value 559.567576
## iter 270 value 558.756010
## iter 280 value 558.186569
## iter 290 value 556.313216
## iter 300 value 555.890766
## iter 310 value 555.799140
## iter 320 value 555.623466
## iter 330 value 555.556972
## iter 340 value 555.511387
## iter 350 value 555.476564
## iter 360 value 555.468100
## iter 360 value 555.468096
## iter 360 value 555.468093
## final value 555.468093
## converged
## # weights: 121
## initial value 1367573.323344
## iter 10 value 1387.922761
## iter 20 value 902.438461
## iter 30 value 741.281949
## iter 40 value 620.089931
## iter 50 value 553.786006
## iter 60 value 507.241444
## iter 70 value 462.427228
## iter 80 value 433.559924
## iter 90 value 413.686746
## iter 100 value 395.764417
## iter 110 value 381.836097
## iter 120 value 374.803124
## iter 130 value 366.707894
## iter 140 value 362.038174
## iter 150 value 355.314352
## iter 160 value 349.264977
## iter 170 value 343.676292
## iter 180 value 337.771236
## iter 190 value 333.605960
## iter 200 value 329.821507
## iter 210 value 324.452005
## iter 220 value 320.154650
## iter 230 value 317.146476
## iter 240 value 315.115750
## iter 250 value 314.159772
## iter 260 value 313.338361
## iter 270 value 312.229241
## iter 280 value 309.663396
## iter 290 value 306.046459
## iter 300 value 302.279587
## iter 310 value 298.687355
## iter 320 value 294.957058
## iter 330 value 291.051472
## iter 340 value 289.002688
## iter 350 value 285.647698
## iter 360 value 282.719438
## iter 370 value 280.046920
## iter 380 value 277.528513
## iter 390 value 274.400966
## iter 400 value 271.914406
## iter 410 value 269.263666
## iter 420 value 266.516046
## iter 430 value 265.142300
## iter 440 value 263.885987
## iter 450 value 262.816387
## iter 460 value 261.020055
## iter 470 value 258.699086
## iter 480 value 255.618241
## iter 490 value 253.725632
## iter 500 value 253.034260
## final value 253.034260
## stopped after 500 iterations
## # weights: 181
## initial value 1376459.602667
## iter 10 value 1127.345852
## iter 20 value 832.945479
## iter 30 value 674.005026
## iter 40 value 583.374431
## iter 50 value 505.273381
## iter 60 value 469.719277
## iter 70 value 435.532127
## iter 80 value 389.899075
## iter 90 value 352.911699
## iter 100 value 323.992758
## iter 110 value 298.214756
## iter 120 value 274.799992
## iter 130 value 260.649590
## iter 140 value 248.773487
## iter 150 value 230.974150
## iter 160 value 213.545800
## iter 170 value 204.372007
## iter 180 value 198.059819
## iter 190 value 192.314134
## iter 200 value 185.412410
## iter 210 value 177.952262
## iter 220 value 170.755753
## iter 230 value 166.903620
## iter 240 value 163.051089
## iter 250 value 160.017337
## iter 260 value 157.171586
## iter 270 value 154.385850
## iter 280 value 152.290162
## iter 290 value 149.698070
## iter 300 value 147.543353
## iter 310 value 143.980594
## iter 320 value 141.781824
## iter 330 value 139.406858
## iter 340 value 136.832988
## iter 350 value 135.208600
## iter 360 value 134.298948
## iter 370 value 133.566552
## iter 380 value 133.280880
## iter 390 value 132.588687
## iter 400 value 131.662410
## iter 410 value 130.859474
## iter 420 value 129.970304
## iter 430 value 128.990268
## iter 440 value 127.352780
## iter 450 value 126.228489
## iter 460 value 123.407267
## iter 470 value 120.238405
## iter 480 value 118.793030
## iter 490 value 117.143551
## iter 500 value 113.849053
## final value 113.849053
## stopped after 500 iterations
## # weights: 241
## initial value 1389790.278838
## iter 10 value 1263.565629
## iter 20 value 793.948900
## iter 30 value 632.273659
## iter 40 value 520.049714
## iter 50 value 407.226424
## iter 60 value 347.868354
## iter 70 value 310.508556
## iter 80 value 279.572911
## iter 90 value 246.255188
## iter 100 value 212.968424
## iter 110 value 187.778570
## iter 120 value 169.578705
## iter 130 value 159.193569
## iter 140 value 151.159279
## iter 150 value 142.326458
## iter 160 value 134.003837
## iter 170 value 123.380027
## iter 180 value 116.739004
## iter 190 value 109.334043
## iter 200 value 104.365390
## iter 210 value 100.136316
## iter 220 value 97.064740
## iter 230 value 93.927804
## iter 240 value 88.875125
## iter 250 value 86.337503
## iter 260 value 83.020601
## iter 270 value 80.476937
## iter 280 value 78.064533
## iter 290 value 76.258054
## iter 300 value 74.680371
## iter 310 value 73.122328
## iter 320 value 71.469310
## iter 330 value 69.455467
## iter 340 value 67.549627
## iter 350 value 65.934425
## iter 360 value 64.218492
## iter 370 value 62.305616
## iter 380 value 60.605498
## iter 390 value 59.204247
## iter 400 value 57.526085
## iter 410 value 55.349930
## iter 420 value 53.616819
## iter 430 value 52.351348
## iter 440 value 51.393803
## iter 450 value 50.241243
## iter 460 value 48.883759
## iter 470 value 47.611422
## iter 480 value 46.592423
## iter 490 value 46.342161
## iter 500 value 46.248030
## final value 46.248030
## stopped after 500 iterations
## # weights: 25
## initial value 1386822.146307
## iter 10 value 14758.691254
## iter 20 value 13613.304801
## iter 30 value 8755.508514
## iter 40 value 4991.430592
## iter 50 value 3005.528418
## iter 60 value 2621.036090
## iter 70 value 2147.142279
## iter 80 value 1257.147981
## iter 90 value 1161.661720
## iter 100 value 1109.774475
## iter 110 value 1092.326060
## iter 120 value 1082.133413
## iter 130 value 1067.663090
## iter 140 value 1060.198884
## iter 150 value 1055.210596
## iter 160 value 1053.988824
## iter 170 value 1053.731418
## iter 180 value 1052.494035
## iter 190 value 1050.944192
## iter 200 value 1050.132522
## iter 210 value 1049.156963
## iter 220 value 1049.144095
## iter 230 value 1048.981006
## iter 240 value 1048.606674
## iter 250 value 1048.380446
## iter 260 value 1047.970885
## final value 1047.970373
## converged
## # weights: 61
## initial value 1364692.269724
## iter 10 value 14569.135330
## iter 20 value 1891.128270
## iter 30 value 1454.398815
## iter 40 value 1049.877971
## iter 50 value 859.491542
## iter 60 value 779.312336
## iter 70 value 755.070073
## iter 80 value 724.757249
## iter 90 value 714.987765
## iter 100 value 701.640617
## iter 110 value 685.953080
## iter 120 value 649.384215
## iter 130 value 635.876683
## iter 140 value 631.082722
## iter 150 value 624.505241
## iter 160 value 622.794205
## iter 170 value 621.416019
## iter 180 value 620.298981
## iter 190 value 619.023519
## iter 200 value 618.746234
## iter 210 value 618.672495
## iter 220 value 618.426620
## iter 230 value 618.277612
## iter 240 value 616.757977
## iter 250 value 610.268468
## iter 260 value 605.363652
## iter 270 value 602.949895
## iter 280 value 601.682288
## iter 290 value 600.983493
## iter 300 value 600.940224
## iter 310 value 600.870214
## iter 320 value 600.743880
## iter 330 value 600.732386
## iter 340 value 600.696382
## iter 350 value 600.211524
## iter 360 value 595.784453
## iter 370 value 594.303371
## iter 380 value 594.256917
## iter 390 value 594.252164
## iter 400 value 594.246596
## iter 410 value 594.144556
## iter 420 value 594.056982
## iter 430 value 594.029995
## iter 440 value 593.987343
## iter 450 value 593.747009
## iter 460 value 593.548432
## iter 470 value 593.465973
## iter 480 value 593.433626
## iter 490 value 593.366650
## iter 500 value 593.331932
## final value 593.331932
## stopped after 500 iterations
## # weights: 121
## initial value 1380306.925375
## iter 10 value 1796.975280
## iter 20 value 956.682140
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## final value 302.002043
## stopped after 500 iterations
## # weights: 181
## initial value 1374834.001642
## iter 10 value 1041.763264
## iter 20 value 753.462948
## iter 30 value 630.508967
## iter 40 value 536.934338
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## iter 490 value 119.570577
## iter 500 value 118.379093
## final value 118.379093
## stopped after 500 iterations
## # weights: 241
## initial value 1433425.845934
## iter 10 value 1418.347192
## iter 20 value 848.451721
## iter 30 value 651.940273
## iter 40 value 514.993324
## iter 50 value 428.626059
## iter 60 value 361.363296
## iter 70 value 307.808712
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## iter 400 value 56.186604
## iter 410 value 55.612506
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## iter 440 value 53.499389
## iter 450 value 52.896335
## iter 460 value 52.027712
## iter 470 value 51.287010
## iter 480 value 50.592392
## iter 490 value 50.206121
## iter 500 value 50.072026
## final value 50.072026
## stopped after 500 iterations
## # weights: 25
## initial value 1386845.219292
## iter 10 value 6234.479225
## iter 20 value 5406.964881
## iter 30 value 5356.124906
## iter 40 value 5350.207260
## iter 50 value 5340.630402
## iter 60 value 5297.997641
## iter 70 value 5276.670635
## iter 80 value 5214.360104
## iter 90 value 4663.959358
## iter 100 value 2984.359093
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## iter 200 value 1277.449697
## iter 210 value 1277.087888
## iter 220 value 1277.008591
## iter 220 value 1277.008589
## final value 1277.008543
## converged
## # weights: 61
## initial value 1398990.272598
## iter 10 value 300254.329701
## iter 20 value 17563.231329
## iter 30 value 10215.571366
## iter 40 value 6200.663432
## iter 50 value 3771.642449
## iter 60 value 1990.800145
## iter 70 value 1240.192049
## iter 80 value 976.883910
## iter 90 value 918.298142
## iter 100 value 867.222602
## iter 110 value 836.188787
## iter 120 value 784.152801
## iter 130 value 759.369803
## iter 140 value 754.520122
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## iter 170 value 711.333201
## iter 180 value 696.666318
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## iter 220 value 665.023372
## iter 230 value 660.756506
## iter 240 value 652.992470
## iter 250 value 649.435856
## iter 260 value 649.284499
## iter 270 value 648.703618
## iter 280 value 647.597440
## iter 290 value 646.265642
## iter 300 value 645.384172
## iter 310 value 644.820615
## iter 320 value 644.039012
## iter 330 value 643.728189
## iter 340 value 640.170921
## iter 350 value 637.539117
## iter 360 value 636.065879
## iter 370 value 633.465009
## iter 380 value 633.042814
## iter 390 value 632.782505
## iter 400 value 632.750505
## iter 410 value 632.660685
## iter 420 value 632.341191
## iter 430 value 631.787403
## iter 440 value 631.584815
## iter 450 value 630.813054
## iter 460 value 628.532258
## iter 470 value 624.910962
## iter 480 value 616.279701
## iter 490 value 609.244685
## iter 500 value 605.962555
## final value 605.962555
## stopped after 500 iterations
## # weights: 121
## initial value 1294845.386479
## iter 10 value 3143.101005
## iter 20 value 1378.632729
## iter 30 value 883.265741
## iter 40 value 657.668806
## iter 50 value 539.714600
## iter 60 value 481.895520
## iter 70 value 448.040850
## iter 80 value 428.097304
## iter 90 value 411.906951
## iter 100 value 394.012376
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## iter 120 value 366.802302
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## iter 180 value 308.023178
## iter 190 value 304.468976
## iter 200 value 299.497429
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## iter 220 value 293.486605
## iter 230 value 291.807898
## iter 240 value 290.595371
## iter 250 value 289.005105
## iter 260 value 287.824966
## iter 270 value 286.178377
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## iter 290 value 279.485224
## iter 300 value 274.483384
## iter 310 value 271.181324
## iter 320 value 266.693185
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## iter 340 value 260.122163
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## iter 390 value 255.993569
## iter 400 value 255.602315
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## iter 420 value 254.972708
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## iter 450 value 254.668313
## iter 460 value 254.612589
## iter 470 value 254.553764
## iter 480 value 254.507317
## iter 490 value 254.491763
## iter 500 value 254.491273
## final value 254.491273
## stopped after 500 iterations
## # weights: 181
## initial value 1398210.103005
## iter 10 value 1211.759364
## iter 20 value 789.957312
## iter 30 value 598.807709
## iter 40 value 460.262528
## iter 50 value 374.926380
## iter 60 value 326.548254
## iter 70 value 293.543312
## iter 80 value 248.195114
## iter 90 value 229.732562
## iter 100 value 211.842193
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## iter 390 value 106.289640
## iter 400 value 105.711425
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## iter 470 value 101.882753
## iter 480 value 101.559917
## iter 490 value 101.295982
## iter 500 value 101.069302
## final value 101.069302
## stopped after 500 iterations
## # weights: 241
## initial value 1459404.043101
## iter 10 value 1486.503902
## iter 20 value 856.155235
## iter 30 value 643.226072
## iter 40 value 465.143008
## iter 50 value 355.763037
## iter 60 value 298.920475
## iter 70 value 254.775556
## iter 80 value 218.204974
## iter 90 value 184.268593
## iter 100 value 166.339803
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## iter 120 value 138.054342
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## iter 160 value 105.083876
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## iter 180 value 95.433625
## iter 190 value 92.439980
## iter 200 value 89.161658
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## iter 220 value 79.555503
## iter 230 value 75.010279
## iter 240 value 72.023815
## iter 250 value 69.895407
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## iter 270 value 65.957895
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## iter 290 value 62.551615
## iter 300 value 60.535929
## iter 310 value 59.233305
## iter 320 value 58.170379
## iter 330 value 57.059287
## iter 340 value 55.942992
## iter 350 value 55.151995
## iter 360 value 54.619430
## iter 370 value 54.107868
## iter 380 value 53.779659
## iter 390 value 53.451496
## iter 400 value 53.128894
## iter 410 value 52.851728
## iter 420 value 52.613213
## iter 430 value 52.204536
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## iter 450 value 51.355028
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## iter 470 value 50.599515
## iter 480 value 50.340153
## iter 490 value 50.196699
## iter 500 value 50.151775
## final value 50.151775
## stopped after 500 iterations
## # weights: 25
## initial value 1348348.568952
## iter 10 value 16521.196490
## iter 20 value 16513.519187
## iter 30 value 16511.529537
## iter 40 value 16510.226362
## iter 50 value 16386.286072
## iter 60 value 11092.568026
## iter 70 value 4972.688104
## iter 80 value 4380.726480
## iter 90 value 2642.819615
## iter 100 value 1536.097036
## iter 110 value 1364.388184
## iter 120 value 1351.265650
## iter 130 value 1323.585662
## iter 140 value 1304.025848
## iter 150 value 1298.032747
## iter 160 value 1296.225809
## iter 170 value 1294.495974
## iter 180 value 1291.470218
## iter 190 value 1290.894359
## final value 1290.791926
## converged
## # weights: 61
## initial value 1396640.521541
## iter 10 value 4735.734594
## iter 20 value 3431.801461
## iter 30 value 2729.839993
## iter 40 value 1810.630954
## iter 50 value 1263.671301
## iter 60 value 1088.124646
## iter 70 value 1026.108606
## iter 80 value 955.404618
## iter 90 value 874.225475
## iter 100 value 848.252381
## iter 110 value 834.146056
## iter 120 value 812.637153
## iter 130 value 788.429505
## iter 140 value 761.760663
## iter 150 value 732.542376
## iter 160 value 712.281087
## iter 170 value 683.564423
## iter 180 value 649.834224
## iter 190 value 635.224130
## iter 200 value 617.509948
## iter 210 value 610.111253
## iter 220 value 607.551811
## iter 230 value 606.602745
## iter 240 value 603.385901
## iter 250 value 598.628657
## iter 260 value 592.881351
## iter 270 value 583.193944
## iter 280 value 574.091850
## iter 290 value 567.869939
## iter 300 value 563.742917
## iter 310 value 562.954203
## iter 320 value 561.805471
## iter 330 value 560.643147
## iter 340 value 560.401149
## iter 350 value 560.396960
## iter 360 value 560.383085
## iter 370 value 560.355335
## iter 380 value 560.275899
## iter 390 value 560.233462
## iter 400 value 560.228906
## iter 410 value 560.227107
## final value 560.226619
## converged
## # weights: 121
## initial value 1348256.371483
## iter 10 value 1482.302039
## iter 20 value 891.019299
## iter 30 value 729.297626
## iter 40 value 635.054310
## iter 50 value 576.609604
## iter 60 value 502.418666
## iter 70 value 470.147482
## iter 80 value 449.127288
## iter 90 value 419.145150
## iter 100 value 391.010236
## iter 110 value 377.403800
## iter 120 value 368.340699
## iter 130 value 360.325957
## iter 140 value 355.895272
## iter 150 value 348.027086
## iter 160 value 341.082669
## iter 170 value 337.704146
## iter 180 value 334.427184
## iter 190 value 331.657082
## iter 200 value 329.952044
## iter 210 value 327.942152
## iter 220 value 325.872857
## iter 230 value 323.828099
## iter 240 value 322.660892
## iter 250 value 322.151421
## iter 260 value 322.028181
## iter 270 value 321.750831
## iter 280 value 321.410148
## iter 290 value 320.444048
## iter 300 value 318.534927
## iter 310 value 316.373709
## iter 320 value 314.307332
## iter 330 value 309.381160
## iter 340 value 307.301700
## iter 350 value 306.296468
## iter 360 value 305.998798
## iter 370 value 305.846880
## iter 380 value 305.755962
## iter 390 value 305.735937
## iter 400 value 305.670328
## iter 410 value 305.031412
## iter 420 value 304.667012
## iter 430 value 304.610438
## iter 440 value 304.566569
## iter 450 value 304.497683
## iter 460 value 304.421004
## iter 470 value 304.087328
## iter 480 value 303.883454
## iter 490 value 303.816294
## iter 500 value 303.785664
## final value 303.785664
## stopped after 500 iterations
## # weights: 181
## initial value 1445155.065072
## iter 10 value 1612.195514
## iter 20 value 863.589961
## iter 30 value 611.124240
## iter 40 value 521.158031
## iter 50 value 420.536551
## iter 60 value 338.363160
## iter 70 value 308.064454
## iter 80 value 286.373679
## iter 90 value 272.674227
## iter 100 value 261.049460
## iter 110 value 253.363119
## iter 120 value 245.548995
## iter 130 value 239.247472
## iter 140 value 232.041641
## iter 150 value 225.784434
## iter 160 value 221.199671
## iter 170 value 217.937491
## iter 180 value 215.066282
## iter 190 value 211.069815
## iter 200 value 208.297822
## iter 210 value 206.954880
## iter 220 value 204.173804
## iter 230 value 200.044367
## iter 240 value 197.843177
## iter 250 value 196.753830
## iter 260 value 195.994183
## iter 270 value 195.094603
## iter 280 value 192.421969
## iter 290 value 190.297561
## iter 300 value 188.987167
## iter 310 value 188.065129
## iter 320 value 187.293543
## iter 330 value 186.867012
## iter 340 value 186.340055
## iter 350 value 185.687692
## iter 360 value 184.328675
## iter 370 value 182.653736
## iter 380 value 181.963533
## iter 390 value 180.267459
## iter 400 value 178.371729
## iter 410 value 174.800284
## iter 420 value 169.932697
## iter 430 value 165.504277
## iter 440 value 162.163749
## iter 450 value 160.198875
## iter 460 value 158.661723
## iter 470 value 157.245569
## iter 480 value 155.577815
## iter 490 value 154.271843
## iter 500 value 153.429292
## final value 153.429292
## stopped after 500 iterations
## # weights: 241
## initial value 1409936.961826
## iter 10 value 1074.598124
## iter 20 value 799.530464
## iter 30 value 673.827798
## iter 40 value 530.852029
## iter 50 value 436.929998
## iter 60 value 365.147352
## iter 70 value 327.504515
## iter 80 value 289.654408
## iter 90 value 251.539523
## iter 100 value 225.657049
## iter 110 value 205.715962
## iter 120 value 183.333877
## iter 130 value 170.383326
## iter 140 value 161.498266
## iter 150 value 152.583423
## iter 160 value 145.671386
## iter 170 value 139.467493
## iter 180 value 131.873794
## iter 190 value 120.861561
## iter 200 value 116.355138
## iter 210 value 110.624756
## iter 220 value 104.948002
## iter 230 value 99.151733
## iter 240 value 94.737263
## iter 250 value 89.409982
## iter 260 value 85.117986
## iter 270 value 81.435695
## iter 280 value 77.388827
## iter 290 value 74.122955
## iter 300 value 71.216731
## iter 310 value 67.632336
## iter 320 value 65.786146
## iter 330 value 64.191643
## iter 340 value 62.577057
## iter 350 value 61.492389
## iter 360 value 60.354464
## iter 370 value 58.582051
## iter 380 value 57.522936
## iter 390 value 56.611027
## iter 400 value 55.708567
## iter 410 value 54.498988
## iter 420 value 53.826479
## iter 430 value 53.271556
## iter 440 value 52.953259
## iter 450 value 52.727548
## iter 460 value 52.520889
## iter 470 value 52.370173
## iter 480 value 52.191273
## iter 490 value 52.086549
## iter 500 value 52.061239
## final value 52.061239
## stopped after 500 iterations
## # weights: 25
## initial value 1373823.877108
## iter 10 value 8346.833099
## iter 20 value 7317.507624
## iter 30 value 5736.358830
## iter 40 value 4837.763140
## iter 50 value 3976.395594
## iter 60 value 2609.910168
## iter 70 value 2061.369149
## iter 80 value 1589.035749
## iter 90 value 1377.114427
## iter 100 value 1265.699358
## iter 110 value 1215.465328
## iter 120 value 1203.826230
## iter 130 value 1158.823290
## iter 140 value 1132.622241
## iter 150 value 1131.544574
## iter 160 value 1131.440096
## iter 170 value 1131.436787
## iter 170 value 1131.436781
## final value 1131.436061
## converged
## # weights: 61
## initial value 1364399.719744
## iter 10 value 12411.792567
## iter 20 value 4385.524585
## iter 30 value 3306.080165
## iter 40 value 2381.877519
## iter 50 value 1800.919025
## iter 60 value 1450.703771
## iter 70 value 1281.875946
## iter 80 value 1169.404774
## iter 90 value 1093.636862
## iter 100 value 1050.375105
## iter 110 value 1016.363968
## iter 120 value 984.641656
## iter 130 value 943.291833
## iter 140 value 935.838769
## iter 150 value 926.006264
## iter 160 value 915.340078
## iter 170 value 905.719883
## iter 180 value 899.501033
## iter 190 value 889.741915
## iter 200 value 885.612599
## iter 210 value 880.076582
## iter 220 value 870.160674
## iter 230 value 854.023507
## iter 240 value 849.554827
## iter 250 value 847.219122
## iter 260 value 846.411616
## iter 270 value 845.334312
## iter 280 value 840.934147
## iter 290 value 836.448128
## iter 300 value 832.198823
## iter 310 value 829.872270
## iter 320 value 828.380135
## iter 330 value 828.191698
## final value 828.188391
## converged
## # weights: 121
## initial value 1356509.974064
## iter 10 value 1352.283819
## iter 20 value 1021.570701
## iter 30 value 852.018777
## iter 40 value 763.744886
## iter 50 value 705.815262
## iter 60 value 654.014585
## iter 70 value 629.357613
## iter 80 value 609.806058
## iter 90 value 592.569539
## iter 100 value 576.163000
## iter 110 value 568.861813
## iter 120 value 564.598316
## iter 130 value 559.927610
## iter 140 value 554.686089
## iter 150 value 544.723541
## iter 160 value 537.692386
## iter 170 value 531.633504
## iter 180 value 527.056981
## iter 190 value 525.129761
## iter 200 value 523.605036
## iter 210 value 522.857258
## iter 220 value 522.355931
## iter 230 value 521.993979
## iter 240 value 521.869306
## iter 250 value 521.825514
## iter 260 value 521.794785
## iter 270 value 521.754447
## iter 280 value 521.691793
## iter 290 value 521.275867
## iter 300 value 519.686553
## iter 310 value 518.397664
## iter 320 value 517.893704
## iter 330 value 517.409070
## iter 340 value 517.014686
## iter 350 value 516.600890
## iter 360 value 515.868488
## iter 370 value 515.790898
## iter 380 value 515.785372
## final value 515.785339
## converged
## # weights: 181
## initial value 1336743.119793
## iter 10 value 1271.872298
## iter 20 value 887.296210
## iter 30 value 734.820821
## iter 40 value 637.274875
## iter 50 value 595.848226
## iter 60 value 553.364815
## iter 70 value 523.914196
## iter 80 value 506.813947
## iter 90 value 494.619346
## iter 100 value 483.631667
## iter 110 value 476.547829
## iter 120 value 470.114906
## iter 130 value 461.301889
## iter 140 value 456.371675
## iter 150 value 451.288071
## iter 160 value 444.605983
## iter 170 value 439.253968
## iter 180 value 436.218404
## iter 190 value 434.361932
## iter 200 value 432.991508
## iter 210 value 431.420930
## iter 220 value 429.305871
## iter 230 value 426.505852
## iter 240 value 424.578481
## iter 250 value 422.395024
## iter 260 value 418.544733
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## iter 470 value 395.271910
## iter 480 value 393.549059
## iter 490 value 391.942472
## iter 500 value 390.676871
## final value 390.676871
## stopped after 500 iterations
## # weights: 241
## initial value 1399192.634958
## iter 10 value 1650.521607
## iter 20 value 975.523645
## iter 30 value 768.177963
## iter 40 value 665.977503
## iter 50 value 570.613723
## iter 60 value 517.907833
## iter 70 value 482.681104
## iter 80 value 463.018172
## iter 90 value 441.662705
## iter 100 value 427.635038
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## iter 460 value 346.179828
## iter 470 value 346.134334
## iter 480 value 346.098322
## iter 490 value 346.079459
## iter 500 value 346.043604
## final value 346.043604
## stopped after 500 iterations
## # weights: 25
## initial value 1403786.083865
## iter 10 value 109090.575854
## iter 20 value 12235.275131
## iter 30 value 10458.293873
## iter 40 value 7729.915632
## iter 50 value 7098.728154
## iter 60 value 6936.036218
## iter 70 value 6845.601719
## iter 80 value 6831.785691
## iter 90 value 5306.701518
## iter 100 value 5273.013103
## iter 110 value 5161.193596
## iter 120 value 5115.887293
## iter 130 value 5036.311116
## iter 140 value 4924.146262
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## iter 200 value 4874.809686
## iter 210 value 4874.799255
## iter 220 value 4851.453366
## iter 230 value 4849.099113
## iter 240 value 4846.060725
## iter 250 value 4843.024676
## iter 260 value 4842.887685
## final value 4842.871187
## converged
## # weights: 61
## initial value 1393088.021461
## iter 10 value 3260.795846
## iter 20 value 1384.820038
## iter 30 value 1155.267180
## iter 40 value 994.360390
## iter 50 value 869.922356
## iter 60 value 785.870009
## iter 70 value 731.120828
## iter 80 value 678.912984
## iter 90 value 659.770267
## iter 100 value 649.324934
## iter 110 value 643.098544
## iter 120 value 639.799592
## iter 130 value 639.135584
## iter 140 value 638.384518
## iter 150 value 636.736730
## iter 160 value 633.972176
## iter 170 value 628.206410
## iter 180 value 616.282653
## iter 190 value 595.848528
## iter 200 value 569.711816
## iter 210 value 553.979811
## iter 220 value 545.787447
## iter 230 value 534.945899
## iter 240 value 527.557535
## iter 250 value 525.554159
## iter 260 value 525.277297
## iter 270 value 523.367951
## iter 280 value 521.871232
## iter 290 value 520.658230
## iter 300 value 520.458818
## iter 310 value 518.787633
## iter 320 value 512.649611
## iter 330 value 512.040457
## iter 340 value 511.492133
## iter 350 value 509.990577
## iter 360 value 508.364601
## iter 370 value 508.250028
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## iter 390 value 508.231024
## iter 400 value 508.211727
## iter 410 value 508.064736
## iter 420 value 507.918188
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## iter 440 value 507.720711
## iter 450 value 507.574598
## iter 460 value 507.561893
## iter 470 value 507.561175
## iter 480 value 507.558771
## iter 490 value 507.550793
## final value 507.550416
## converged
## # weights: 121
## initial value 1412465.598450
## iter 10 value 3644.689711
## iter 20 value 1284.277922
## iter 30 value 927.686067
## iter 40 value 858.829070
## iter 50 value 826.437010
## iter 60 value 792.697973
## iter 70 value 734.664321
## iter 80 value 705.302068
## iter 90 value 669.431413
## iter 100 value 615.021180
## iter 110 value 585.162706
## iter 120 value 561.139588
## iter 130 value 531.179863
## iter 140 value 514.026613
## iter 150 value 504.161953
## iter 160 value 490.860952
## iter 170 value 482.631429
## iter 180 value 481.610414
## iter 190 value 479.876922
## iter 200 value 477.322793
## iter 210 value 476.408946
## iter 220 value 476.174850
## iter 230 value 475.395736
## iter 240 value 474.708554
## iter 250 value 473.786295
## iter 260 value 473.523383
## iter 270 value 473.304851
## iter 280 value 472.774154
## iter 290 value 472.398132
## iter 300 value 471.855370
## iter 310 value 470.562349
## iter 320 value 469.715299
## iter 330 value 469.553279
## iter 340 value 469.370841
## iter 350 value 468.942093
## iter 360 value 468.300864
## iter 370 value 468.043216
## iter 380 value 467.725542
## iter 390 value 467.626573
## iter 400 value 467.561666
## iter 410 value 467.513369
## iter 420 value 467.451402
## iter 430 value 467.431944
## iter 440 value 467.405126
## iter 450 value 467.400114
## iter 460 value 467.398315
## final value 467.397823
## converged
## # weights: 181
## initial value 1378212.034278
## iter 10 value 1703.541153
## iter 20 value 1101.212112
## iter 30 value 731.917070
## iter 40 value 568.798338
## iter 50 value 462.899902
## iter 60 value 398.317097
## iter 70 value 360.462422
## iter 80 value 327.909791
## iter 90 value 304.697749
## iter 100 value 282.805433
## iter 110 value 262.578836
## iter 120 value 245.889765
## iter 130 value 229.466097
## iter 140 value 215.270058
## iter 150 value 207.713023
## iter 160 value 200.510845
## iter 170 value 195.583108
## iter 180 value 192.284509
## iter 190 value 188.651419
## iter 200 value 185.605565
## iter 210 value 183.961976
## iter 220 value 182.321087
## iter 230 value 181.391753
## iter 240 value 180.220902
## iter 250 value 178.327857
## iter 260 value 176.647529
## iter 270 value 173.243753
## iter 280 value 170.589587
## iter 290 value 168.088739
## iter 300 value 165.664329
## iter 310 value 164.400616
## iter 320 value 163.630390
## iter 330 value 162.671111
## iter 340 value 161.375750
## iter 350 value 159.708574
## iter 360 value 158.172092
## iter 370 value 157.059211
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## iter 390 value 156.177545
## iter 400 value 154.012173
## iter 410 value 150.051945
## iter 420 value 143.356092
## iter 430 value 139.358932
## iter 440 value 137.001456
## iter 450 value 135.145501
## iter 460 value 133.658801
## iter 470 value 132.715739
## iter 480 value 131.910983
## iter 490 value 131.344263
## iter 500 value 130.791752
## final value 130.791752
## stopped after 500 iterations
## # weights: 241
## initial value 1414928.952290
## iter 10 value 1112.083537
## iter 20 value 820.314474
## iter 30 value 627.938497
## iter 40 value 471.243433
## iter 50 value 352.983889
## iter 60 value 304.959582
## iter 70 value 263.290683
## iter 80 value 200.116239
## iter 90 value 156.462294
## iter 100 value 126.919666
## iter 110 value 108.706463
## iter 120 value 99.617129
## iter 130 value 92.514948
## iter 140 value 86.640786
## iter 150 value 80.254759
## iter 160 value 74.218510
## iter 170 value 68.952178
## iter 180 value 65.317075
## iter 190 value 62.231048
## iter 200 value 59.476403
## iter 210 value 56.405730
## iter 220 value 52.293064
## iter 230 value 47.774677
## iter 240 value 45.708373
## iter 250 value 43.545619
## iter 260 value 41.375606
## iter 270 value 38.989487
## iter 280 value 36.060379
## iter 290 value 34.187959
## iter 300 value 32.880289
## iter 310 value 31.892459
## iter 320 value 31.052977
## iter 330 value 30.432835
## iter 340 value 29.467565
## iter 350 value 28.509170
## iter 360 value 27.523931
## iter 370 value 26.181882
## iter 380 value 25.403279
## iter 390 value 24.759019
## iter 400 value 24.351240
## iter 410 value 24.029185
## iter 420 value 23.818087
## iter 430 value 23.649284
## iter 440 value 23.488856
## iter 450 value 23.266357
## iter 460 value 22.540332
## iter 470 value 22.050322
## iter 480 value 21.749350
## iter 490 value 21.343178
## iter 500 value 21.085269
## final value 21.085269
## stopped after 500 iterations
## # weights: 25
## initial value 1403838.724969
## iter 10 value 467805.038397
## iter 20 value 11392.209253
## iter 30 value 8422.305423
## iter 40 value 7850.990075
## iter 50 value 6677.790107
## iter 60 value 6274.880099
## iter 70 value 6249.848331
## final value 6249.769698
## converged
## # weights: 61
## initial value 1355728.880421
## iter 10 value 10407.679513
## iter 20 value 5099.180005
## iter 30 value 3987.600455
## iter 40 value 3546.285167
## iter 50 value 2324.452152
## iter 60 value 1358.978036
## iter 70 value 1094.979872
## iter 80 value 1013.692245
## iter 90 value 982.523732
## iter 100 value 969.702595
## iter 110 value 957.274600
## iter 120 value 940.949276
## iter 130 value 916.360015
## iter 140 value 902.921530
## iter 150 value 871.075490
## iter 160 value 867.281607
## iter 170 value 860.376931
## iter 180 value 852.532379
## iter 190 value 844.571415
## iter 200 value 833.322692
## iter 210 value 831.413157
## iter 220 value 818.966745
## iter 230 value 803.113286
## iter 240 value 790.685463
## iter 250 value 781.799586
## iter 260 value 776.535270
## iter 270 value 773.907870
## iter 280 value 772.368616
## iter 290 value 770.129233
## iter 300 value 768.860095
## iter 310 value 767.879103
## iter 320 value 767.274230
## iter 330 value 767.147535
## iter 340 value 767.092600
## iter 350 value 766.972061
## iter 360 value 766.780158
## iter 370 value 765.178563
## iter 380 value 764.158257
## iter 390 value 759.055855
## iter 400 value 753.503197
## iter 410 value 751.863998
## iter 420 value 750.986147
## iter 430 value 750.935055
## iter 440 value 750.610583
## iter 450 value 750.197399
## iter 460 value 750.062897
## iter 470 value 749.888634
## iter 480 value 749.857000
## iter 490 value 749.829474
## final value 749.824813
## converged
## # weights: 121
## initial value 1403071.617497
## iter 10 value 1931.552172
## iter 20 value 1236.977604
## iter 30 value 933.765223
## iter 40 value 799.882182
## iter 50 value 696.721855
## iter 60 value 628.922876
## iter 70 value 578.377230
## iter 80 value 523.157891
## iter 90 value 467.835952
## iter 100 value 446.056316
## iter 110 value 438.865551
## iter 120 value 424.501050
## iter 130 value 393.026991
## iter 140 value 385.466125
## iter 150 value 380.896582
## iter 160 value 360.744278
## iter 170 value 354.704515
## iter 180 value 351.415033
## iter 190 value 348.229318
## iter 200 value 343.065371
## iter 210 value 339.240022
## iter 220 value 334.209677
## iter 230 value 329.941862
## iter 240 value 327.221762
## iter 250 value 326.427815
## iter 260 value 326.237711
## iter 270 value 325.745894
## iter 280 value 324.949633
## iter 290 value 323.136105
## iter 300 value 321.068616
## iter 310 value 317.176306
## iter 320 value 314.964486
## iter 330 value 314.187318
## iter 340 value 311.949995
## iter 350 value 310.957591
## iter 360 value 310.468637
## iter 370 value 309.789518
## iter 380 value 309.631159
## iter 390 value 309.578582
## iter 400 value 309.465207
## iter 410 value 309.406272
## iter 420 value 309.340425
## iter 430 value 309.306432
## iter 440 value 309.217946
## iter 450 value 309.205797
## iter 460 value 309.189461
## iter 470 value 309.143120
## iter 480 value 308.893846
## iter 490 value 308.740661
## iter 500 value 308.712782
## final value 308.712782
## stopped after 500 iterations
## # weights: 181
## initial value 1392522.887724
## iter 10 value 1234.036373
## iter 20 value 835.317109
## iter 30 value 629.904187
## iter 40 value 484.802041
## iter 50 value 397.423150
## iter 60 value 342.743211
## iter 70 value 294.914209
## iter 80 value 255.889993
## iter 90 value 229.810480
## iter 100 value 205.657024
## iter 110 value 189.604267
## iter 120 value 178.885032
## iter 130 value 170.538044
## iter 140 value 153.334458
## iter 150 value 140.965724
## iter 160 value 131.934706
## iter 170 value 124.964455
## iter 180 value 115.785500
## iter 190 value 111.080273
## iter 200 value 106.442400
## iter 210 value 102.847104
## iter 220 value 100.760044
## iter 230 value 98.912403
## iter 240 value 96.494754
## iter 250 value 93.042436
## iter 260 value 89.893218
## iter 270 value 88.623298
## iter 280 value 87.338593
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## iter 300 value 84.721301
## iter 310 value 83.772887
## iter 320 value 83.125523
## iter 330 value 82.695398
## iter 340 value 82.284161
## iter 350 value 82.000315
## iter 360 value 81.609023
## iter 370 value 81.434014
## iter 380 value 81.391211
## iter 390 value 81.308633
## iter 400 value 81.200809
## iter 410 value 81.117219
## iter 420 value 80.983710
## iter 430 value 80.834665
## iter 440 value 80.741919
## iter 450 value 80.648608
## iter 460 value 80.529615
## iter 470 value 80.436395
## iter 480 value 80.342289
## iter 490 value 80.155999
## iter 500 value 80.089463
## final value 80.089463
## stopped after 500 iterations
## # weights: 241
## initial value 1389315.386128
## iter 10 value 1233.160722
## iter 20 value 874.956845
## iter 30 value 681.976203
## iter 40 value 569.841441
## iter 50 value 458.040884
## iter 60 value 353.806906
## iter 70 value 300.486355
## iter 80 value 257.183185
## iter 90 value 218.254253
## iter 100 value 186.352017
## iter 110 value 159.571506
## iter 120 value 135.365425
## iter 130 value 119.477747
## iter 140 value 105.290692
## iter 150 value 93.070601
## iter 160 value 85.261614
## iter 170 value 81.178695
## iter 180 value 77.271974
## iter 190 value 72.789957
## iter 200 value 67.658961
## iter 210 value 63.573676
## iter 220 value 59.835160
## iter 230 value 55.788748
## iter 240 value 52.685638
## iter 250 value 50.079542
## iter 260 value 47.692557
## iter 270 value 45.667134
## iter 280 value 43.770170
## iter 290 value 42.297308
## iter 300 value 41.212650
## iter 310 value 40.027586
## iter 320 value 38.636684
## iter 330 value 36.758823
## iter 340 value 35.591527
## iter 350 value 34.971704
## iter 360 value 34.408698
## iter 370 value 33.734417
## iter 380 value 33.035674
## iter 390 value 31.887369
## iter 400 value 29.761628
## iter 410 value 28.346488
## iter 420 value 27.088922
## iter 430 value 26.001425
## iter 440 value 25.057206
## iter 450 value 24.308093
## iter 460 value 23.785298
## iter 470 value 23.192337
## iter 480 value 22.843276
## iter 490 value 22.683109
## iter 500 value 22.629903
## final value 22.629903
## stopped after 500 iterations
## # weights: 25
## initial value 1416069.580900
## iter 10 value 2233.288751
## iter 20 value 1733.766696
## iter 30 value 1278.531529
## iter 40 value 1124.724144
## iter 50 value 1072.460482
## iter 60 value 1058.060960
## iter 70 value 1041.528385
## iter 80 value 980.961994
## iter 90 value 961.364185
## iter 100 value 955.299157
## iter 110 value 953.152901
## iter 120 value 952.810428
## iter 130 value 946.424913
## iter 140 value 945.647620
## iter 150 value 945.221449
## iter 160 value 945.204951
## iter 160 value 945.204947
## final value 945.204852
## converged
## # weights: 61
## initial value 1365216.092234
## iter 10 value 6416.649421
## iter 20 value 2298.410356
## iter 30 value 1768.637657
## iter 40 value 1499.822271
## iter 50 value 1417.286127
## iter 60 value 1382.169762
## iter 70 value 1324.322800
## iter 80 value 1259.012024
## iter 90 value 1230.302167
## iter 100 value 1098.248477
## iter 110 value 892.895261
## iter 120 value 798.256199
## iter 130 value 784.031712
## iter 140 value 778.144316
## iter 150 value 772.597074
## iter 160 value 769.678746
## iter 170 value 760.945185
## iter 180 value 758.714528
## iter 190 value 758.664483
## iter 200 value 758.554190
## iter 210 value 757.883488
## iter 220 value 757.400110
## iter 230 value 757.102811
## iter 240 value 755.714949
## iter 250 value 754.530482
## iter 260 value 754.434587
## iter 270 value 754.428572
## iter 280 value 754.414156
## iter 290 value 753.831086
## iter 300 value 753.466332
## iter 310 value 753.005204
## iter 320 value 752.432792
## iter 330 value 752.277620
## iter 340 value 751.775657
## iter 350 value 751.530673
## iter 360 value 751.405878
## iter 370 value 751.339085
## iter 380 value 750.619538
## iter 390 value 749.455091
## iter 400 value 748.955694
## iter 410 value 748.907529
## iter 420 value 748.865658
## iter 430 value 748.850836
## iter 440 value 748.834891
## iter 450 value 748.794357
## iter 460 value 748.790097
## iter 470 value 748.773317
## iter 480 value 748.581506
## iter 490 value 748.465941
## iter 500 value 748.218082
## final value 748.218082
## stopped after 500 iterations
## # weights: 121
## initial value 1403325.747769
## iter 10 value 1121.625909
## iter 20 value 869.338122
## iter 30 value 725.860472
## iter 40 value 617.203567
## iter 50 value 574.767551
## iter 60 value 519.314607
## iter 70 value 477.314192
## iter 80 value 433.332822
## iter 90 value 412.396499
## iter 100 value 395.124042
## iter 110 value 379.798945
## iter 120 value 363.523277
## iter 130 value 351.861846
## iter 140 value 346.702130
## iter 150 value 339.096821
## iter 160 value 333.813317
## iter 170 value 327.940737
## iter 180 value 313.110562
## iter 190 value 304.635062
## iter 200 value 300.102053
## iter 210 value 293.662057
## iter 220 value 288.076665
## iter 230 value 282.438116
## iter 240 value 277.091824
## iter 250 value 274.074841
## iter 260 value 273.381229
## iter 270 value 272.478494
## iter 280 value 270.452217
## iter 290 value 268.112408
## iter 300 value 263.005908
## iter 310 value 259.991395
## iter 320 value 258.399237
## iter 330 value 256.420556
## iter 340 value 254.113383
## iter 350 value 250.984189
## iter 360 value 249.631940
## iter 370 value 248.573483
## iter 380 value 247.377351
## iter 390 value 245.788539
## iter 400 value 244.975518
## iter 410 value 244.115202
## iter 420 value 242.648870
## iter 430 value 241.532369
## iter 440 value 240.676464
## iter 450 value 239.682503
## iter 460 value 238.821715
## iter 470 value 238.354329
## iter 480 value 237.773365
## iter 490 value 237.364001
## iter 500 value 237.248085
## final value 237.248085
## stopped after 500 iterations
## # weights: 181
## initial value 1361206.644015
## iter 10 value 1295.081431
## iter 20 value 827.221187
## iter 30 value 639.083692
## iter 40 value 508.015842
## iter 50 value 426.223322
## iter 60 value 364.745698
## iter 70 value 315.248164
## iter 80 value 282.910192
## iter 90 value 262.415659
## iter 100 value 248.434733
## iter 110 value 231.487214
## iter 120 value 218.535737
## iter 130 value 209.914851
## iter 140 value 199.932877
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## iter 160 value 180.087381
## iter 170 value 172.108117
## iter 180 value 158.754977
## iter 190 value 148.500678
## iter 200 value 142.990147
## iter 210 value 137.999583
## iter 220 value 135.049784
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## iter 240 value 125.552931
## iter 250 value 123.077583
## iter 260 value 120.507028
## iter 270 value 118.254286
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## iter 290 value 112.565449
## iter 300 value 110.964645
## iter 310 value 110.339379
## iter 320 value 109.851580
## iter 330 value 109.547759
## iter 340 value 109.073022
## iter 350 value 108.679720
## iter 360 value 108.051798
## iter 370 value 107.829152
## iter 380 value 107.735420
## iter 390 value 107.623775
## iter 400 value 107.523135
## iter 410 value 107.278322
## iter 420 value 106.737017
## iter 430 value 106.184689
## iter 440 value 105.355085
## iter 450 value 104.499800
## iter 460 value 103.863369
## iter 470 value 103.205372
## iter 480 value 102.985298
## iter 490 value 102.776386
## iter 500 value 102.130350
## final value 102.130350
## stopped after 500 iterations
## # weights: 241
## initial value 1354588.702198
## iter 10 value 1082.053888
## iter 20 value 748.451437
## iter 30 value 610.417281
## iter 40 value 468.071077
## iter 50 value 358.605975
## iter 60 value 293.332382
## iter 70 value 248.282412
## iter 80 value 212.892238
## iter 90 value 182.904838
## iter 100 value 155.987415
## iter 110 value 132.865633
## iter 120 value 119.084999
## iter 130 value 109.374074
## iter 140 value 104.222856
## iter 150 value 101.997664
## iter 160 value 96.692292
## iter 170 value 92.318064
## iter 180 value 87.695033
## iter 190 value 82.931393
## iter 200 value 77.766094
## iter 210 value 74.283059
## iter 220 value 70.264362
## iter 230 value 65.418074
## iter 240 value 61.793995
## iter 250 value 58.344102
## iter 260 value 55.958299
## iter 270 value 53.631386
## iter 280 value 52.155395
## iter 290 value 50.820845
## iter 300 value 49.168196
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## iter 320 value 45.327784
## iter 330 value 43.980108
## iter 340 value 42.360933
## iter 350 value 40.517543
## iter 360 value 38.053190
## iter 370 value 36.578345
## iter 380 value 35.504093
## iter 390 value 34.388482
## iter 400 value 33.354074
## iter 410 value 32.423610
## iter 420 value 31.728371
## iter 430 value 31.104129
## iter 440 value 30.299798
## iter 450 value 29.053194
## iter 460 value 28.035386
## iter 470 value 27.522929
## iter 480 value 27.061861
## iter 490 value 26.898156
## iter 500 value 26.867678
## final value 26.867678
## stopped after 500 iterations
## # weights: 25
## initial value 1433122.294702
## iter 10 value 9360.827216
## iter 20 value 3548.104939
## iter 30 value 1828.757462
## iter 40 value 1538.019291
## iter 50 value 1434.983849
## iter 60 value 1401.227032
## iter 70 value 1296.548675
## iter 80 value 1232.864416
## iter 90 value 1230.064981
## iter 100 value 1230.029680
## iter 110 value 1227.170144
## iter 120 value 1222.964461
## iter 130 value 1206.582303
## iter 140 value 1203.023485
## iter 150 value 1163.534253
## iter 160 value 1151.910138
## iter 170 value 1149.139101
## iter 180 value 1147.662378
## iter 190 value 1147.608209
## iter 200 value 1147.481206
## iter 210 value 1147.251078
## iter 220 value 1144.997073
## iter 230 value 1144.986783
## iter 230 value 1144.986780
## final value 1144.986780
## converged
## # weights: 61
## initial value 1409890.584048
## iter 10 value 57202.181972
## iter 20 value 19856.984649
## iter 30 value 15185.460073
## iter 40 value 9031.224390
## iter 50 value 5765.498439
## iter 60 value 2569.549833
## iter 70 value 1416.345116
## iter 80 value 1182.619775
## iter 90 value 1035.880291
## iter 100 value 931.581225
## iter 110 value 918.483307
## iter 120 value 900.880758
## iter 130 value 892.034805
## iter 140 value 889.695598
## iter 150 value 887.347891
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## iter 170 value 861.712258
## iter 180 value 834.226458
## iter 190 value 813.551990
## iter 200 value 800.340154
## iter 210 value 786.045908
## iter 220 value 772.423239
## iter 230 value 764.745118
## iter 240 value 760.966738
## iter 250 value 754.974839
## iter 260 value 754.033978
## iter 270 value 752.664371
## iter 280 value 744.468779
## iter 290 value 733.804352
## iter 300 value 709.444997
## iter 310 value 676.724838
## iter 320 value 668.621720
## iter 330 value 654.375534
## iter 340 value 647.569663
## iter 350 value 642.553857
## iter 360 value 634.363850
## iter 370 value 628.350132
## iter 380 value 628.068889
## iter 390 value 627.642486
## iter 400 value 626.554954
## iter 410 value 624.504100
## iter 420 value 619.862308
## iter 430 value 612.605044
## iter 440 value 608.493222
## iter 450 value 606.476827
## iter 460 value 605.837539
## iter 470 value 603.843750
## iter 480 value 602.307989
## iter 490 value 602.163033
## iter 500 value 601.838512
## final value 601.838512
## stopped after 500 iterations
## # weights: 121
## initial value 1435570.118935
## iter 10 value 161053.626564
## iter 20 value 9356.626293
## iter 30 value 5964.638567
## iter 40 value 4918.458631
## iter 50 value 4060.683958
## iter 60 value 3637.247145
## iter 70 value 3120.965219
## iter 80 value 2595.030683
## iter 90 value 1960.011851
## iter 100 value 1479.037191
## iter 110 value 1128.203085
## iter 120 value 939.834594
## iter 130 value 900.295765
## iter 140 value 847.085840
## iter 150 value 828.311682
## iter 160 value 822.574233
## iter 170 value 806.075804
## iter 180 value 786.756193
## iter 190 value 774.643403
## iter 200 value 733.783020
## iter 210 value 715.188564
## iter 220 value 688.933997
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## iter 240 value 612.451996
## iter 250 value 580.869317
## iter 260 value 565.724973
## iter 270 value 556.472675
## iter 280 value 545.162574
## iter 290 value 538.051991
## iter 300 value 531.259746
## iter 310 value 527.112035
## iter 320 value 524.575159
## iter 330 value 521.273437
## iter 340 value 510.562388
## iter 350 value 494.490763
## iter 360 value 478.210956
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## iter 380 value 455.876400
## iter 390 value 454.142033
## iter 400 value 454.010827
## iter 410 value 453.178418
## iter 420 value 452.434551
## iter 430 value 450.979370
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## iter 450 value 448.647606
## iter 460 value 446.549175
## iter 470 value 443.935430
## iter 480 value 442.468451
## iter 490 value 441.512449
## iter 500 value 440.262670
## final value 440.262670
## stopped after 500 iterations
## # weights: 181
## initial value 1393214.632643
## iter 10 value 1045.133634
## iter 20 value 833.851758
## iter 30 value 694.559719
## iter 40 value 538.508881
## iter 50 value 430.378516
## iter 60 value 364.212597
## iter 70 value 316.351943
## iter 80 value 277.774322
## iter 90 value 249.256913
## iter 100 value 224.999845
## iter 110 value 207.658312
## iter 120 value 196.044953
## iter 130 value 183.778856
## iter 140 value 177.130141
## iter 150 value 168.884648
## iter 160 value 161.353432
## iter 170 value 148.116151
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## iter 300 value 127.970171
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## iter 330 value 125.878897
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## iter 360 value 122.947513
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## iter 400 value 121.004856
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## iter 420 value 118.725358
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## iter 460 value 114.042509
## iter 470 value 112.266588
## iter 480 value 111.513998
## iter 490 value 111.062775
## iter 500 value 110.770678
## final value 110.770678
## stopped after 500 iterations
## # weights: 241
## initial value 1375678.733273
## iter 10 value 1634.519642
## iter 20 value 854.592346
## iter 30 value 678.919219
## iter 40 value 556.385617
## iter 50 value 449.007278
## iter 60 value 381.418700
## iter 70 value 337.241582
## iter 80 value 309.725322
## iter 90 value 274.301871
## iter 100 value 254.062754
## iter 110 value 218.847808
## iter 120 value 194.616061
## iter 130 value 179.827021
## iter 140 value 169.418698
## iter 150 value 158.849237
## iter 160 value 149.890362
## iter 170 value 140.095389
## iter 180 value 132.933575
## iter 190 value 126.345840
## iter 200 value 121.023117
## iter 210 value 115.248283
## iter 220 value 108.457774
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## iter 240 value 98.550832
## iter 250 value 95.013244
## iter 260 value 91.556698
## iter 270 value 88.583953
## iter 280 value 86.245129
## iter 290 value 83.730638
## iter 300 value 81.020359
## iter 310 value 78.550774
## iter 320 value 76.723813
## iter 330 value 75.461131
## iter 340 value 73.590049
## iter 350 value 72.166695
## iter 360 value 70.925007
## iter 370 value 69.748412
## iter 380 value 68.596247
## iter 390 value 67.381465
## iter 400 value 66.433061
## iter 410 value 65.476551
## iter 420 value 64.537093
## iter 430 value 63.921520
## iter 440 value 63.288798
## iter 450 value 62.671571
## iter 460 value 62.132217
## iter 470 value 61.664097
## iter 480 value 61.146123
## iter 490 value 60.886427
## iter 500 value 60.755120
## final value 60.755120
## stopped after 500 iterations
## # weights: 25
## initial value 1393796.348214
## iter 10 value 19571.050607
## iter 20 value 9476.654328
## iter 30 value 5852.498933
## iter 40 value 5066.136792
## iter 50 value 3882.342399
## iter 60 value 2961.085838
## iter 70 value 1619.537789
## iter 80 value 1412.207797
## iter 90 value 1311.748099
## iter 100 value 1292.813827
## iter 110 value 1266.534189
## iter 120 value 1205.144314
## iter 130 value 1178.052809
## iter 140 value 1169.472609
## iter 150 value 1168.399745
## iter 160 value 1168.274983
## iter 170 value 1167.679891
## final value 1167.679280
## converged
## # weights: 61
## initial value 1415136.075140
## iter 10 value 70404.839772
## iter 20 value 24626.167051
## iter 30 value 16151.825018
## iter 40 value 9895.325113
## iter 50 value 7509.701126
## iter 60 value 5177.551323
## iter 70 value 4094.814305
## iter 80 value 3527.310647
## iter 90 value 3030.592918
## iter 100 value 2478.211603
## iter 110 value 1994.297486
## iter 120 value 1656.658641
## iter 130 value 1583.818421
## iter 140 value 1553.083453
## iter 150 value 1469.364920
## iter 160 value 1351.327976
## iter 170 value 1217.678827
## iter 180 value 1145.679797
## iter 190 value 1095.086160
## iter 200 value 1072.679163
## iter 210 value 1056.394533
## iter 220 value 1046.373876
## iter 230 value 1035.050997
## iter 240 value 1017.575045
## iter 250 value 1002.974967
## iter 260 value 997.641113
## iter 270 value 988.071609
## iter 280 value 978.952721
## iter 290 value 971.717646
## iter 300 value 964.053719
## iter 310 value 954.530484
## iter 320 value 946.602119
## iter 330 value 941.060731
## iter 340 value 937.317776
## iter 350 value 934.052520
## iter 360 value 931.021992
## iter 370 value 927.137414
## iter 380 value 925.913220
## iter 390 value 924.930438
## iter 400 value 923.917843
## iter 410 value 923.163259
## iter 420 value 921.408289
## iter 430 value 917.050750
## iter 440 value 911.172805
## iter 450 value 904.153786
## iter 460 value 884.694632
## iter 470 value 869.549345
## iter 480 value 865.349557
## iter 490 value 860.127791
## iter 500 value 858.773960
## final value 858.773960
## stopped after 500 iterations
## # weights: 121
## initial value 1400988.897317
## iter 10 value 2283.435955
## iter 20 value 1106.143541
## iter 30 value 968.917216
## iter 40 value 849.839066
## iter 50 value 775.685396
## iter 60 value 740.585542
## iter 70 value 717.303632
## iter 80 value 702.607206
## iter 90 value 695.947479
## iter 100 value 687.220125
## iter 110 value 680.283469
## iter 120 value 672.647119
## iter 130 value 664.858215
## iter 140 value 654.880438
## iter 150 value 647.431448
## iter 160 value 642.692633
## iter 170 value 629.454122
## iter 180 value 612.023490
## iter 190 value 604.072145
## iter 200 value 593.938572
## iter 210 value 578.917754
## iter 220 value 558.608445
## iter 230 value 548.051104
## iter 240 value 540.738814
## iter 250 value 537.857540
## iter 260 value 535.241481
## iter 270 value 531.884257
## iter 280 value 528.233239
## iter 290 value 522.434968
## iter 300 value 517.462780
## iter 310 value 515.458596
## iter 320 value 514.158911
## iter 330 value 511.656022
## iter 340 value 510.415382
## iter 350 value 503.576147
## iter 360 value 497.666672
## iter 370 value 494.781383
## iter 380 value 492.654376
## iter 390 value 491.997814
## iter 400 value 491.769525
## iter 410 value 491.633076
## iter 420 value 491.602675
## iter 430 value 491.595537
## final value 491.594925
## converged
## # weights: 181
## initial value 1389591.719339
## iter 10 value 1298.996607
## iter 20 value 903.170374
## iter 30 value 789.832417
## iter 40 value 665.928866
## iter 50 value 584.547994
## iter 60 value 535.706191
## iter 70 value 498.626787
## iter 80 value 465.717750
## iter 90 value 446.731586
## iter 100 value 430.921874
## iter 110 value 418.816181
## iter 120 value 407.152824
## iter 130 value 398.738609
## iter 140 value 391.778183
## iter 150 value 383.573883
## iter 160 value 377.977210
## iter 170 value 372.476959
## iter 180 value 368.714206
## iter 190 value 365.709175
## iter 200 value 362.432001
## iter 210 value 359.696889
## iter 220 value 357.746057
## iter 230 value 355.589074
## iter 240 value 354.215089
## iter 250 value 353.250236
## iter 260 value 352.215233
## iter 270 value 349.923612
## iter 280 value 346.874297
## iter 290 value 344.724721
## iter 300 value 342.755655
## iter 310 value 339.479435
## iter 320 value 337.475403
## iter 330 value 336.420979
## iter 340 value 335.563394
## iter 350 value 334.943699
## iter 360 value 334.646674
## iter 370 value 334.476884
## iter 380 value 334.084946
## iter 390 value 333.592646
## iter 400 value 333.126607
## iter 410 value 332.872565
## iter 420 value 332.609265
## iter 430 value 332.351947
## iter 440 value 332.246377
## iter 450 value 332.175295
## iter 460 value 332.126913
## iter 470 value 332.109201
## iter 480 value 332.105966
## iter 490 value 332.105232
## final value 332.105162
## converged
## # weights: 241
## initial value 1482969.531612
## iter 10 value 1286.452760
## iter 20 value 922.676067
## iter 30 value 799.564248
## iter 40 value 707.891174
## iter 50 value 587.338630
## iter 60 value 501.319504
## iter 70 value 458.269465
## iter 80 value 443.504672
## iter 90 value 425.893038
## iter 100 value 413.211067
## iter 110 value 400.196849
## iter 120 value 390.564602
## iter 130 value 383.224563
## iter 140 value 375.946459
## iter 150 value 369.842945
## iter 160 value 364.483673
## iter 170 value 358.131351
## iter 180 value 351.790671
## iter 190 value 346.219261
## iter 200 value 340.350476
## iter 210 value 335.726234
## iter 220 value 331.099034
## iter 230 value 326.221870
## iter 240 value 322.859162
## iter 250 value 320.379006
## iter 260 value 317.695285
## iter 270 value 315.293078
## iter 280 value 312.698703
## iter 290 value 310.604322
## iter 300 value 308.541830
## iter 310 value 306.100915
## iter 320 value 304.557246
## iter 330 value 303.126682
## iter 340 value 301.699122
## iter 350 value 300.768361
## iter 360 value 300.107587
## iter 370 value 299.559455
## iter 380 value 299.223095
## iter 390 value 298.974363
## iter 400 value 298.730892
## iter 410 value 298.537765
## iter 420 value 298.400348
## iter 430 value 298.337541
## iter 440 value 298.300917
## iter 450 value 298.265225
## iter 460 value 298.168940
## iter 470 value 297.963515
## iter 480 value 297.774846
## iter 490 value 297.715234
## iter 500 value 297.657659
## final value 297.657659
## stopped after 500 iterations
## # weights: 25
## initial value 1385756.953187
## iter 10 value 48172.563992
## iter 20 value 5626.046095
## iter 30 value 4827.184454
## iter 40 value 4684.994733
## iter 50 value 4676.847180
## iter 60 value 4411.624885
## iter 70 value 4337.236652
## iter 80 value 4305.803371
## iter 90 value 4189.023750
## iter 100 value 4111.053620
## iter 110 value 4097.388796
## iter 120 value 4077.868661
## iter 130 value 4058.186724
## iter 140 value 4024.363131
## iter 150 value 4001.599711
## iter 160 value 3992.031933
## iter 170 value 3985.928854
## iter 180 value 3844.621387
## iter 190 value 3731.034289
## iter 200 value 3079.219451
## iter 210 value 2651.403268
## iter 220 value 1869.076982
## iter 230 value 1447.981206
## iter 240 value 1348.279574
## iter 250 value 1315.701274
## iter 260 value 1311.012222
## iter 270 value 1296.442451
## iter 280 value 1286.097193
## iter 290 value 1282.492933
## iter 300 value 1280.901038
## iter 310 value 1280.461069
## iter 320 value 1280.174577
## iter 330 value 1278.815732
## iter 340 value 1277.895013
## iter 350 value 1277.342544
## iter 360 value 1277.152740
## final value 1277.152420
## converged
## # weights: 61
## initial value 1399122.577923
## iter 10 value 8492.495248
## iter 20 value 3737.440331
## iter 30 value 3164.521992
## iter 40 value 2751.922036
## iter 50 value 2329.764530
## iter 60 value 1958.142304
## iter 70 value 1797.442093
## iter 80 value 1754.922723
## iter 90 value 1736.232238
## iter 100 value 1695.533168
## iter 110 value 1665.867370
## iter 120 value 1661.013312
## iter 130 value 1658.112296
## iter 140 value 1625.844994
## iter 150 value 1467.383800
## iter 160 value 1287.902896
## iter 170 value 1170.546110
## iter 180 value 1132.947532
## iter 190 value 1105.897044
## iter 200 value 1067.354388
## iter 210 value 1050.529468
## iter 220 value 1040.752608
## iter 230 value 1033.392759
## iter 240 value 1025.014724
## iter 250 value 1020.459613
## iter 260 value 1019.574775
## iter 270 value 1013.798544
## iter 280 value 1011.793458
## iter 290 value 1011.217186
## iter 300 value 1009.965131
## iter 310 value 1008.592208
## iter 320 value 1007.325138
## iter 330 value 1005.693383
## iter 340 value 1005.025584
## iter 350 value 1004.531955
## iter 360 value 1004.318232
## iter 370 value 1003.417727
## iter 380 value 1002.715534
## iter 390 value 1002.463412
## iter 400 value 1002.380161
## final value 1002.380055
## converged
## # weights: 121
## initial value 1413298.565646
## iter 10 value 1430.562028
## iter 20 value 831.031857
## iter 30 value 658.690126
## iter 40 value 549.857548
## iter 50 value 492.301158
## iter 60 value 457.480271
## iter 70 value 433.811995
## iter 80 value 416.642943
## iter 90 value 398.975130
## iter 100 value 381.526442
## iter 110 value 361.337671
## iter 120 value 340.486234
## iter 130 value 330.183381
## iter 140 value 317.941881
## iter 150 value 309.763997
## iter 160 value 301.814000
## iter 170 value 296.264440
## iter 180 value 292.844882
## iter 190 value 288.592533
## iter 200 value 284.673743
## iter 210 value 278.572477
## iter 220 value 273.092553
## iter 230 value 265.569153
## iter 240 value 259.581992
## iter 250 value 257.438313
## iter 260 value 256.415332
## iter 270 value 253.732371
## iter 280 value 250.500840
## iter 290 value 247.289329
## iter 300 value 244.434309
## iter 310 value 241.818178
## iter 320 value 239.240053
## iter 330 value 236.289890
## iter 340 value 230.583178
## iter 350 value 224.751983
## iter 360 value 222.301082
## iter 370 value 220.534332
## iter 380 value 219.531307
## iter 390 value 219.156432
## iter 400 value 218.913938
## iter 410 value 218.709599
## iter 420 value 218.642769
## iter 430 value 218.467114
## iter 440 value 218.375785
## iter 450 value 218.158281
## iter 460 value 218.078756
## iter 470 value 217.957158
## iter 480 value 217.891003
## iter 490 value 217.872053
## iter 500 value 217.871202
## final value 217.871202
## stopped after 500 iterations
## # weights: 181
## initial value 1411394.011152
## iter 10 value 1138.201808
## iter 20 value 771.813077
## iter 30 value 609.921321
## iter 40 value 473.322909
## iter 50 value 382.024387
## iter 60 value 338.760105
## iter 70 value 295.291145
## iter 80 value 251.255562
## iter 90 value 214.970369
## iter 100 value 199.301783
## iter 110 value 188.836892
## iter 120 value 177.175882
## iter 130 value 167.993402
## iter 140 value 157.269320
## iter 150 value 143.186740
## iter 160 value 129.694431
## iter 170 value 121.078541
## iter 180 value 111.482796
## iter 190 value 102.003307
## iter 200 value 96.673977
## iter 210 value 91.251334
## iter 220 value 85.943837
## iter 230 value 82.719689
## iter 240 value 79.065838
## iter 250 value 76.309919
## iter 260 value 74.108705
## iter 270 value 73.123644
## iter 280 value 72.172961
## iter 290 value 71.759515
## iter 300 value 71.367803
## iter 310 value 70.968709
## iter 320 value 70.501713
## iter 330 value 70.255363
## iter 340 value 70.122502
## iter 350 value 69.962639
## iter 360 value 69.874514
## iter 370 value 69.825143
## iter 380 value 69.794042
## iter 390 value 69.718004
## iter 400 value 69.618456
## iter 410 value 69.513349
## iter 420 value 69.303430
## iter 430 value 69.182564
## iter 440 value 69.093405
## iter 450 value 68.982104
## iter 460 value 68.868635
## iter 470 value 68.715874
## iter 480 value 68.271789
## iter 490 value 66.847164
## iter 500 value 65.995674
## final value 65.995674
## stopped after 500 iterations
## # weights: 241
## initial value 1442424.607015
## iter 10 value 1308.114413
## iter 20 value 739.043313
## iter 30 value 607.718410
## iter 40 value 519.468246
## iter 50 value 433.357689
## iter 60 value 362.754740
## iter 70 value 324.148434
## iter 80 value 295.712863
## iter 90 value 263.848253
## iter 100 value 240.841996
## iter 110 value 212.429790
## iter 120 value 185.662652
## iter 130 value 164.931948
## iter 140 value 141.721610
## iter 150 value 126.710512
## iter 160 value 117.472936
## iter 170 value 108.069620
## iter 180 value 101.634520
## iter 190 value 90.423121
## iter 200 value 81.015868
## iter 210 value 73.964015
## iter 220 value 67.833524
## iter 230 value 64.017169
## iter 240 value 60.042701
## iter 250 value 56.459999
## iter 260 value 54.295065
## iter 270 value 52.049504
## iter 280 value 49.624346
## iter 290 value 48.404838
## iter 300 value 46.712110
## iter 310 value 44.551690
## iter 320 value 43.024047
## iter 330 value 41.128716
## iter 340 value 39.114657
## iter 350 value 36.356922
## iter 360 value 34.593786
## iter 370 value 33.541986
## iter 380 value 32.857202
## iter 390 value 32.321888
## iter 400 value 31.713845
## iter 410 value 31.328039
## iter 420 value 30.922180
## iter 430 value 30.410621
## iter 440 value 29.946491
## iter 450 value 29.559267
## iter 460 value 29.084275
## iter 470 value 28.642889
## iter 480 value 28.374166
## iter 490 value 28.220594
## iter 500 value 28.154890
## final value 28.154890
## stopped after 500 iterations
## # weights: 25
## initial value 1410405.364464
## iter 10 value 16664.137787
## iter 20 value 15096.748951
## iter 30 value 12549.236408
## iter 40 value 11488.462747
## iter 50 value 7730.009277
## iter 60 value 3793.641618
## iter 70 value 2986.832676
## iter 80 value 2114.680223
## iter 90 value 1279.480849
## iter 100 value 1028.456384
## iter 110 value 941.902481
## iter 120 value 926.447840
## iter 130 value 889.953454
## iter 140 value 867.020889
## iter 150 value 857.125815
## iter 160 value 854.331166
## iter 170 value 854.176895
## iter 180 value 852.988232
## iter 190 value 850.543506
## iter 200 value 848.972516
## iter 210 value 848.346179
## iter 220 value 848.333211
## iter 230 value 848.055292
## iter 240 value 847.497789
## iter 250 value 847.118298
## iter 260 value 846.716988
## iter 270 value 846.709071
## final value 846.708572
## converged
## # weights: 61
## initial value 1390494.402350
## iter 10 value 3920.683475
## iter 20 value 2679.867948
## iter 30 value 2268.604656
## iter 40 value 1804.375165
## iter 50 value 1464.608669
## iter 60 value 1305.769231
## iter 70 value 1210.473193
## iter 80 value 1160.840842
## iter 90 value 1027.435471
## iter 100 value 988.448282
## iter 110 value 960.952427
## iter 120 value 845.086061
## iter 130 value 794.561948
## iter 140 value 782.381227
## iter 150 value 765.159884
## iter 160 value 751.340148
## iter 170 value 739.901372
## iter 180 value 735.366379
## iter 190 value 731.712903
## iter 200 value 730.147909
## iter 210 value 728.299518
## iter 220 value 725.789405
## iter 230 value 724.661036
## iter 240 value 722.525929
## iter 250 value 721.248565
## iter 260 value 721.233440
## iter 270 value 721.205966
## iter 280 value 721.114458
## iter 290 value 720.876453
## iter 300 value 720.197837
## iter 310 value 718.291994
## iter 320 value 717.380814
## iter 330 value 717.104662
## iter 340 value 716.700801
## iter 350 value 716.162642
## iter 360 value 715.939400
## iter 370 value 715.851920
## iter 380 value 715.848299
## iter 390 value 715.845399
## iter 400 value 715.839034
## iter 410 value 715.830027
## iter 420 value 715.804616
## iter 430 value 715.765376
## iter 440 value 715.612316
## iter 450 value 715.555917
## iter 460 value 715.545258
## iter 470 value 715.539839
## iter 480 value 715.539297
## iter 490 value 715.530198
## final value 715.529994
## converged
## # weights: 121
## initial value 1428210.442718
## iter 10 value 3390.762202
## iter 20 value 1801.886234
## iter 30 value 1179.026094
## iter 40 value 813.285969
## iter 50 value 690.819183
## iter 60 value 625.521516
## iter 70 value 602.402370
## iter 80 value 548.350256
## iter 90 value 518.641264
## iter 100 value 503.641161
## iter 110 value 493.832452
## iter 120 value 478.040246
## iter 130 value 461.136961
## iter 140 value 440.975706
## iter 150 value 430.139750
## iter 160 value 421.354500
## iter 170 value 415.665601
## iter 180 value 406.506078
## iter 190 value 402.150036
## iter 200 value 398.078584
## iter 210 value 394.139184
## iter 220 value 392.732523
## iter 230 value 390.354931
## iter 240 value 384.309650
## iter 250 value 380.998841
## iter 260 value 379.519453
## iter 270 value 377.106422
## iter 280 value 375.036614
## iter 290 value 374.284669
## iter 300 value 373.637429
## iter 310 value 371.908679
## iter 320 value 367.877168
## iter 330 value 366.700537
## iter 340 value 366.463469
## iter 350 value 364.134473
## iter 360 value 363.452765
## iter 370 value 362.243557
## iter 380 value 360.870761
## iter 390 value 360.123664
## iter 400 value 359.881283
## iter 410 value 359.724910
## iter 420 value 359.644382
## iter 430 value 359.589874
## iter 440 value 359.518467
## iter 450 value 359.440554
## iter 460 value 359.377742
## iter 470 value 359.346419
## iter 480 value 359.327435
## iter 490 value 359.313169
## iter 500 value 359.312913
## final value 359.312913
## stopped after 500 iterations
## # weights: 181
## initial value 1345792.096119
## iter 10 value 1174.282494
## iter 20 value 768.282517
## iter 30 value 596.651961
## iter 40 value 507.606054
## iter 50 value 410.633526
## iter 60 value 360.014228
## iter 70 value 317.639030
## iter 80 value 271.423348
## iter 90 value 247.916813
## iter 100 value 234.825048
## iter 110 value 228.005411
## iter 120 value 219.566846
## iter 130 value 212.110614
## iter 140 value 200.605065
## iter 150 value 186.383900
## iter 160 value 177.815437
## iter 170 value 170.649608
## iter 180 value 163.958014
## iter 190 value 158.196504
## iter 200 value 153.983588
## iter 210 value 150.478467
## iter 220 value 147.349998
## iter 230 value 143.880609
## iter 240 value 140.625682
## iter 250 value 137.668604
## iter 260 value 135.583163
## iter 270 value 133.416740
## iter 280 value 130.527921
## iter 290 value 127.926023
## iter 300 value 124.752405
## iter 310 value 121.563491
## iter 320 value 117.780278
## iter 330 value 113.916933
## iter 340 value 111.516500
## iter 350 value 109.267477
## iter 360 value 106.957609
## iter 370 value 105.779048
## iter 380 value 105.306271
## iter 390 value 104.403557
## iter 400 value 103.748148
## iter 410 value 103.170995
## iter 420 value 102.460405
## iter 430 value 101.600687
## iter 440 value 100.956194
## iter 450 value 100.481635
## iter 460 value 99.830135
## iter 470 value 98.790680
## iter 480 value 97.709273
## iter 490 value 96.487754
## iter 500 value 95.160196
## final value 95.160196
## stopped after 500 iterations
## # weights: 241
## initial value 1442889.707335
## iter 10 value 1723.728390
## iter 20 value 801.483978
## iter 30 value 526.210784
## iter 40 value 404.632305
## iter 50 value 318.327677
## iter 60 value 267.449580
## iter 70 value 222.362614
## iter 80 value 187.403288
## iter 90 value 164.927384
## iter 100 value 143.823540
## iter 110 value 127.183956
## iter 120 value 112.526731
## iter 130 value 103.862060
## iter 140 value 99.006115
## iter 150 value 96.316981
## iter 160 value 93.536886
## iter 170 value 90.201088
## iter 180 value 86.080168
## iter 190 value 80.470265
## iter 200 value 75.733526
## iter 210 value 72.020386
## iter 220 value 68.936432
## iter 230 value 66.844755
## iter 240 value 65.211033
## iter 250 value 63.229894
## iter 260 value 61.913655
## iter 270 value 60.308873
## iter 280 value 59.018638
## iter 290 value 58.153902
## iter 300 value 57.429537
## iter 310 value 56.909952
## iter 320 value 56.047197
## iter 330 value 55.212237
## iter 340 value 54.564767
## iter 350 value 53.740030
## iter 360 value 52.964369
## iter 370 value 52.073067
## iter 380 value 51.326150
## iter 390 value 50.626134
## iter 400 value 49.878547
## iter 410 value 49.403758
## iter 420 value 49.079103
## iter 430 value 48.801552
## iter 440 value 48.528490
## iter 450 value 48.321530
## iter 460 value 48.155074
## iter 470 value 47.947754
## iter 480 value 47.707952
## iter 490 value 47.527944
## iter 500 value 47.488937
## final value 47.488937
## stopped after 500 iterations
## # weights: 25
## initial value 1376505.550904
## iter 10 value 13853.247941
## iter 20 value 8513.701664
## iter 30 value 6375.718420
## iter 40 value 5001.200360
## iter 50 value 4739.486851
## iter 60 value 2078.972254
## iter 70 value 1497.582259
## iter 80 value 1347.645026
## iter 90 value 1317.106995
## iter 100 value 1304.382577
## iter 110 value 1300.326450
## iter 120 value 1292.750085
## iter 130 value 1286.669082
## iter 140 value 1284.572262
## iter 150 value 1282.474793
## iter 160 value 1282.104395
## iter 170 value 1282.094926
## iter 180 value 1282.093685
## iter 180 value 1282.093679
## iter 190 value 1282.093443
## iter 190 value 1282.093443
## final value 1282.093417
## converged
## # weights: 61
## initial value 1408902.175492
## iter 10 value 12072.597288
## iter 20 value 5299.304668
## iter 30 value 3756.796458
## iter 40 value 2230.129503
## iter 50 value 1405.522609
## iter 60 value 1286.749558
## iter 70 value 1246.362382
## iter 80 value 1176.939889
## iter 90 value 1108.524103
## iter 100 value 1101.579532
## iter 110 value 1085.843427
## iter 120 value 1069.897277
## iter 130 value 1064.382055
## iter 140 value 1050.946462
## iter 150 value 1048.329715
## iter 160 value 1031.836250
## iter 170 value 993.830921
## iter 180 value 983.369739
## iter 190 value 982.161259
## iter 200 value 979.799929
## iter 210 value 979.017682
## iter 220 value 977.373647
## iter 230 value 976.804989
## iter 240 value 967.947720
## iter 250 value 959.977623
## iter 260 value 955.454946
## iter 270 value 949.031736
## iter 280 value 872.512769
## iter 290 value 782.945693
## iter 300 value 721.463288
## iter 310 value 706.631699
## iter 320 value 696.501937
## iter 330 value 694.569701
## iter 340 value 692.815238
## iter 350 value 688.003245
## iter 360 value 686.455761
## iter 370 value 686.348245
## iter 380 value 686.025445
## iter 390 value 684.892606
## iter 400 value 680.410171
## iter 410 value 660.073055
## iter 420 value 641.227943
## iter 430 value 639.112830
## iter 440 value 638.844849
## iter 450 value 635.054568
## iter 460 value 627.046984
## iter 470 value 621.586960
## iter 480 value 615.410465
## iter 490 value 612.881790
## iter 500 value 611.632346
## final value 611.632346
## stopped after 500 iterations
## # weights: 121
## initial value 1411255.943615
## iter 10 value 1489.192374
## iter 20 value 904.662110
## iter 30 value 682.182114
## iter 40 value 595.949818
## iter 50 value 530.822908
## iter 60 value 434.284974
## iter 70 value 402.604829
## iter 80 value 380.086795
## iter 90 value 362.611397
## iter 100 value 350.866009
## iter 110 value 333.697895
## iter 120 value 313.809907
## iter 130 value 302.175383
## iter 140 value 293.679026
## iter 150 value 287.247597
## iter 160 value 282.130826
## iter 170 value 277.043860
## iter 180 value 273.442509
## iter 190 value 269.121053
## iter 200 value 265.354960
## iter 210 value 259.979844
## iter 220 value 254.864155
## iter 230 value 249.163637
## iter 240 value 243.838952
## iter 250 value 240.947723
## iter 260 value 240.096199
## iter 270 value 238.193440
## iter 280 value 236.023634
## iter 290 value 234.551347
## iter 300 value 232.120841
## iter 310 value 226.717081
## iter 320 value 218.164881
## iter 330 value 214.066433
## iter 340 value 212.773693
## iter 350 value 212.036970
## iter 360 value 211.323280
## iter 370 value 210.173596
## iter 380 value 209.234614
## iter 390 value 208.405040
## iter 400 value 207.482467
## iter 410 value 206.610139
## iter 420 value 205.917796
## iter 430 value 205.262412
## iter 440 value 204.325445
## iter 450 value 203.393154
## iter 460 value 202.741896
## iter 470 value 202.104181
## iter 480 value 201.885197
## iter 490 value 201.567738
## iter 500 value 201.500105
## final value 201.500105
## stopped after 500 iterations
## # weights: 181
## initial value 1434844.637540
## iter 10 value 1184.427642
## iter 20 value 728.082910
## iter 30 value 584.601597
## iter 40 value 491.174552
## iter 50 value 379.578607
## iter 60 value 316.269424
## iter 70 value 290.097992
## iter 80 value 261.800176
## iter 90 value 236.470335
## iter 100 value 217.895509
## iter 110 value 202.447591
## iter 120 value 190.090521
## iter 130 value 180.668630
## iter 140 value 171.543709
## iter 150 value 163.590712
## iter 160 value 155.321165
## iter 170 value 147.927595
## iter 180 value 143.257079
## iter 190 value 136.846862
## iter 200 value 128.905755
## iter 210 value 122.553276
## iter 220 value 117.719034
## iter 230 value 114.749678
## iter 240 value 112.599718
## iter 250 value 110.594173
## iter 260 value 108.804690
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## iter 300 value 101.232549
## iter 310 value 100.160108
## iter 320 value 98.861558
## iter 330 value 97.954943
## iter 340 value 97.395861
## iter 350 value 96.343697
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## iter 380 value 93.795050
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## iter 400 value 93.025029
## iter 410 value 92.777496
## iter 420 value 92.491373
## iter 430 value 92.207522
## iter 440 value 91.758916
## iter 450 value 91.199482
## iter 460 value 90.486687
## iter 470 value 89.854643
## iter 480 value 89.213082
## iter 490 value 88.360184
## iter 500 value 87.870045
## final value 87.870045
## stopped after 500 iterations
## # weights: 241
## initial value 1382697.243098
## iter 10 value 1493.267698
## iter 20 value 710.446704
## iter 30 value 522.101126
## iter 40 value 412.268971
## iter 50 value 307.041334
## iter 60 value 245.734747
## iter 70 value 195.892458
## iter 80 value 171.936008
## iter 90 value 150.794560
## iter 100 value 129.363700
## iter 110 value 114.482431
## iter 120 value 105.113263
## iter 130 value 97.599127
## iter 140 value 89.554702
## iter 150 value 82.529855
## iter 160 value 76.982194
## iter 170 value 72.438439
## iter 180 value 67.404799
## iter 190 value 62.159889
## iter 200 value 57.526342
## iter 210 value 52.566246
## iter 220 value 48.292203
## iter 230 value 45.053868
## iter 240 value 43.139785
## iter 250 value 41.246862
## iter 260 value 39.656709
## iter 270 value 38.424544
## iter 280 value 37.678135
## iter 290 value 37.163558
## iter 300 value 36.699897
## iter 310 value 35.984027
## iter 320 value 35.219021
## iter 330 value 34.563968
## iter 340 value 33.658823
## iter 350 value 33.229745
## iter 360 value 32.886542
## iter 370 value 32.588148
## iter 380 value 32.322135
## iter 390 value 32.139654
## iter 400 value 32.017677
## iter 410 value 31.884429
## iter 420 value 31.735473
## iter 430 value 31.592573
## iter 440 value 31.398102
## iter 450 value 31.171639
## iter 460 value 31.000879
## iter 470 value 30.802598
## iter 480 value 30.682696
## iter 490 value 30.630241
## iter 500 value 30.622676
## final value 30.622676
## stopped after 500 iterations
## # weights: 25
## initial value 1409918.068392
## iter 10 value 16905.025602
## iter 20 value 15330.283983
## iter 30 value 12017.812397
## iter 40 value 9351.245414
## iter 50 value 9160.275017
## iter 60 value 8264.798017
## iter 70 value 5493.784567
## iter 80 value 4263.450085
## iter 90 value 3455.930936
## iter 100 value 3110.788606
## iter 110 value 2439.891365
## iter 120 value 1543.696445
## iter 130 value 1334.505194
## iter 140 value 1306.235537
## iter 150 value 1189.961690
## iter 160 value 1097.234368
## iter 170 value 1069.640702
## iter 180 value 1048.418999
## iter 190 value 1045.881461
## iter 200 value 1044.436021
## iter 210 value 1042.579737
## iter 220 value 1039.292606
## iter 230 value 1039.045307
## iter 240 value 1039.038680
## iter 250 value 1039.034621
## iter 260 value 1039.007116
## iter 270 value 1038.892412
## iter 280 value 1032.219834
## iter 290 value 1028.889223
## iter 300 value 981.947968
## iter 310 value 950.125371
## iter 320 value 942.638197
## iter 330 value 922.043655
## iter 340 value 856.699774
## iter 350 value 838.178891
## iter 360 value 835.730654
## iter 370 value 835.399445
## iter 380 value 835.377402
## iter 390 value 833.583750
## iter 400 value 830.919136
## iter 410 value 830.567582
## iter 420 value 830.546002
## iter 430 value 830.521693
## iter 440 value 830.507614
## final value 830.506138
## converged
## # weights: 61
## initial value 1408705.017691
## iter 10 value 42025.741640
## iter 20 value 18670.554170
## iter 30 value 8602.447370
## iter 40 value 5177.466722
## iter 50 value 3277.005152
## iter 60 value 1748.064409
## iter 70 value 1229.721630
## iter 80 value 1180.004060
## iter 90 value 1124.657431
## iter 100 value 1050.231514
## iter 110 value 1011.624649
## iter 120 value 991.110461
## iter 130 value 971.244701
## iter 140 value 969.923877
## iter 150 value 967.619506
## iter 160 value 960.798756
## iter 170 value 903.852460
## iter 180 value 829.554698
## iter 190 value 773.912048
## iter 200 value 745.529011
## iter 210 value 740.994945
## iter 220 value 734.454413
## iter 230 value 715.310730
## iter 240 value 700.505797
## iter 250 value 700.361044
## iter 260 value 697.756764
## iter 270 value 694.266614
## iter 280 value 643.096759
## iter 290 value 626.870769
## iter 300 value 621.766025
## iter 310 value 618.279765
## iter 320 value 615.252026
## iter 330 value 614.298803
## iter 340 value 611.049181
## iter 350 value 608.613538
## iter 360 value 606.915875
## iter 370 value 606.211874
## iter 380 value 604.504552
## iter 390 value 602.339734
## iter 400 value 601.401862
## iter 410 value 601.305742
## iter 420 value 601.274790
## iter 430 value 601.263726
## iter 440 value 601.258588
## iter 450 value 601.258197
## iter 460 value 601.256424
## iter 470 value 601.255349
## iter 480 value 601.252471
## final value 601.251998
## converged
## # weights: 121
## initial value 1472655.832292
## iter 10 value 1956.723679
## iter 20 value 967.530446
## iter 30 value 722.712671
## iter 40 value 606.824269
## iter 50 value 538.172570
## iter 60 value 492.956037
## iter 70 value 463.905162
## iter 80 value 445.866921
## iter 90 value 424.805603
## iter 100 value 404.385967
## iter 110 value 393.156150
## iter 120 value 383.676734
## iter 130 value 375.305907
## iter 140 value 348.045402
## iter 150 value 334.103718
## iter 160 value 321.654543
## iter 170 value 314.269519
## iter 180 value 309.615040
## iter 190 value 306.760189
## iter 200 value 304.609987
## iter 210 value 298.944764
## iter 220 value 291.634758
## iter 230 value 286.634740
## iter 240 value 283.484222
## iter 250 value 282.695660
## iter 260 value 282.382013
## iter 270 value 281.284241
## iter 280 value 279.302152
## iter 290 value 275.753761
## iter 300 value 272.669915
## iter 310 value 267.508058
## iter 320 value 262.474602
## iter 330 value 259.271182
## iter 340 value 257.264964
## iter 350 value 255.115456
## iter 360 value 253.426470
## iter 370 value 252.821986
## iter 380 value 251.673142
## iter 390 value 251.007288
## iter 400 value 250.590097
## iter 410 value 250.518220
## iter 420 value 250.501716
## iter 430 value 250.491816
## iter 440 value 250.482799
## iter 450 value 250.477572
## iter 460 value 250.474448
## iter 470 value 250.473641
## iter 480 value 250.473350
## final value 250.473239
## converged
## # weights: 181
## initial value 1383724.947353
## iter 10 value 1141.441234
## iter 20 value 687.837505
## iter 30 value 560.186222
## iter 40 value 468.965350
## iter 50 value 390.780950
## iter 60 value 344.855503
## iter 70 value 317.745115
## iter 80 value 296.916820
## iter 90 value 267.648670
## iter 100 value 243.713436
## iter 110 value 229.074624
## iter 120 value 218.386094
## iter 130 value 204.742126
## iter 140 value 191.763859
## iter 150 value 180.676860
## iter 160 value 170.006734
## iter 170 value 160.574999
## iter 180 value 151.451518
## iter 190 value 144.772767
## iter 200 value 138.301370
## iter 210 value 135.517179
## iter 220 value 131.485237
## iter 230 value 127.375693
## iter 240 value 123.559587
## iter 250 value 120.663533
## iter 260 value 118.902348
## iter 270 value 116.919639
## iter 280 value 115.526535
## iter 290 value 114.504503
## iter 300 value 112.747905
## iter 310 value 111.441066
## iter 320 value 110.312505
## iter 330 value 109.682260
## iter 340 value 109.266066
## iter 350 value 108.891778
## iter 360 value 108.589258
## iter 370 value 108.429458
## iter 380 value 108.350093
## iter 390 value 108.262623
## iter 400 value 108.032119
## iter 410 value 107.837030
## iter 420 value 107.478651
## iter 430 value 107.177203
## iter 440 value 106.949142
## iter 450 value 106.592870
## iter 460 value 106.036549
## iter 470 value 105.528440
## iter 480 value 105.190372
## iter 490 value 105.032831
## iter 500 value 104.872168
## final value 104.872168
## stopped after 500 iterations
## # weights: 241
## initial value 1394784.461153
## iter 10 value 1254.868237
## iter 20 value 743.546752
## iter 30 value 579.260979
## iter 40 value 491.045670
## iter 50 value 399.669947
## iter 60 value 339.592065
## iter 70 value 304.862690
## iter 80 value 278.848958
## iter 90 value 244.516066
## iter 100 value 224.609553
## iter 110 value 203.230965
## iter 120 value 186.101552
## iter 130 value 170.314600
## iter 140 value 159.154036
## iter 150 value 151.384445
## iter 160 value 144.478857
## iter 170 value 137.649443
## iter 180 value 130.706335
## iter 190 value 123.681111
## iter 200 value 115.600829
## iter 210 value 107.295331
## iter 220 value 99.042349
## iter 230 value 90.505848
## iter 240 value 83.123374
## iter 250 value 76.939334
## iter 260 value 73.020836
## iter 270 value 69.414517
## iter 280 value 67.016704
## iter 290 value 65.744524
## iter 300 value 64.576142
## iter 310 value 63.254679
## iter 320 value 62.058139
## iter 330 value 60.765607
## iter 340 value 59.616480
## iter 350 value 58.558523
## iter 360 value 57.898782
## iter 370 value 57.199478
## iter 380 value 56.354760
## iter 390 value 55.762647
## iter 400 value 55.118274
## iter 410 value 54.397593
## iter 420 value 53.909854
## iter 430 value 53.472602
## iter 440 value 53.066389
## iter 450 value 52.730105
## iter 460 value 52.472489
## iter 470 value 52.235832
## iter 480 value 52.024723
## iter 490 value 51.930110
## iter 500 value 51.897325
## final value 51.897325
## stopped after 500 iterations
## # weights: 25
## initial value 1407645.059689
## iter 10 value 17200.353710
## iter 20 value 13395.357940
## iter 30 value 10851.070805
## iter 40 value 8308.190995
## iter 50 value 4183.084482
## iter 60 value 2845.354584
## iter 70 value 2009.425469
## iter 80 value 1598.702813
## iter 90 value 1355.288664
## iter 100 value 1290.089562
## iter 110 value 1285.166909
## iter 120 value 1283.278964
## iter 130 value 1280.074542
## final value 1280.057176
## converged
## # weights: 61
## initial value 1397989.962445
## iter 10 value 3819.153986
## iter 20 value 2167.693736
## iter 30 value 1797.264819
## iter 40 value 1472.521922
## iter 50 value 1287.770839
## iter 60 value 1082.473093
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## iter 180 value 778.888381
## iter 190 value 778.670342
## iter 200 value 778.668461
## final value 778.668447
## converged
## # weights: 121
## initial value 1358737.149062
## iter 10 value 2855.760148
## iter 20 value 1752.350888
## iter 30 value 1452.438511
## iter 40 value 1243.620664
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## iter 500 value 533.176003
## final value 533.176003
## stopped after 500 iterations
## # weights: 181
## initial value 1370360.487961
## iter 10 value 1207.305296
## iter 20 value 813.112738
## iter 30 value 701.098764
## iter 40 value 618.689514
## iter 50 value 575.088618
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## final value 330.333451
## stopped after 500 iterations
## # weights: 241
## initial value 1394535.440087
## iter 10 value 1652.455110
## iter 20 value 822.514959
## iter 30 value 699.808751
## iter 40 value 594.469850
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## iter 470 value 319.403449
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## iter 500 value 319.293845
## final value 319.293845
## stopped after 500 iterations
## # weights: 25
## initial value 1393612.292566
## iter 10 value 6291.180642
## iter 20 value 6265.542738
## iter 20 value 6265.542688
## iter 20 value 6265.542688
## final value 6265.542688
## converged
## # weights: 61
## initial value 1440695.729584
## iter 10 value 62290.459830
## iter 20 value 4751.281675
## iter 30 value 3775.951060
## iter 40 value 3030.171394
## iter 50 value 1510.024531
## iter 60 value 1357.852890
## iter 70 value 1259.185632
## iter 80 value 1073.131229
## iter 90 value 988.031675
## iter 100 value 953.456285
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## iter 240 value 713.736118
## iter 250 value 711.474880
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## iter 270 value 704.791343
## iter 280 value 700.433575
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## iter 320 value 694.280494
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## iter 470 value 627.166726
## iter 480 value 627.104873
## iter 490 value 626.487275
## iter 500 value 622.075383
## final value 622.075383
## stopped after 500 iterations
## # weights: 121
## initial value 1390036.315580
## iter 10 value 2272.500271
## iter 20 value 938.577558
## iter 30 value 764.399625
## iter 40 value 642.698333
## iter 50 value 581.513296
## iter 60 value 525.736839
## iter 70 value 492.709877
## iter 80 value 470.359903
## iter 90 value 449.420348
## iter 100 value 424.244315
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## iter 170 value 324.992645
## iter 180 value 317.912289
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## iter 200 value 310.151792
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## iter 490 value 280.339714
## iter 500 value 280.281032
## final value 280.281032
## stopped after 500 iterations
## # weights: 181
## initial value 1406500.058150
## iter 10 value 1205.902608
## iter 20 value 808.220355
## iter 30 value 650.812361
## iter 40 value 529.300351
## iter 50 value 441.835262
## iter 60 value 382.485628
## iter 70 value 316.382992
## iter 80 value 280.632855
## iter 90 value 260.914881
## iter 100 value 239.610672
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## final value 99.567269
## stopped after 500 iterations
## # weights: 241
## initial value 1425732.097986
## iter 10 value 1375.640186
## iter 20 value 821.895031
## iter 30 value 674.939125
## iter 40 value 502.232512
## iter 50 value 429.798325
## iter 60 value 358.629293
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## iter 80 value 271.940674
## iter 90 value 246.675038
## iter 100 value 224.864809
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## iter 320 value 70.152405
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## iter 400 value 48.655191
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## iter 470 value 42.158281
## iter 480 value 41.509030
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## iter 500 value 41.096494
## final value 41.096494
## stopped after 500 iterations
## # weights: 25
## initial value 1364156.217056
## iter 10 value 18269.221518
## iter 20 value 13475.061511
## iter 30 value 9629.319203
## iter 40 value 6671.501158
## iter 50 value 5299.293510
## iter 60 value 3166.346011
## iter 70 value 1733.319600
## iter 80 value 1360.425481
## iter 90 value 1353.776046
## iter 100 value 1345.354833
## iter 110 value 1344.569411
## iter 120 value 1339.296610
## iter 130 value 1335.950002
## iter 140 value 1334.570254
## iter 150 value 1333.703517
## iter 160 value 1333.682468
## iter 170 value 1333.074462
## iter 180 value 1332.484928
## iter 190 value 1332.053172
## iter 200 value 1331.840710
## final value 1331.840250
## converged
## # weights: 61
## initial value 1414574.639448
## iter 10 value 74404.608292
## iter 20 value 31781.216023
## iter 30 value 18390.347661
## iter 40 value 8885.452614
## iter 50 value 2235.951137
## iter 60 value 1591.800972
## iter 70 value 1390.031093
## iter 80 value 1166.507551
## iter 90 value 1091.906081
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## iter 170 value 832.091069
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## iter 210 value 761.197537
## iter 220 value 755.784054
## iter 230 value 754.581941
## iter 240 value 746.820805
## iter 250 value 709.935781
## iter 260 value 690.454444
## iter 270 value 676.409301
## iter 280 value 663.448097
## iter 290 value 660.128353
## iter 300 value 658.747201
## iter 310 value 656.792685
## iter 320 value 655.258559
## iter 330 value 655.162165
## iter 340 value 654.492991
## iter 350 value 653.504942
## iter 360 value 653.005673
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## iter 400 value 650.908469
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## iter 470 value 649.297667
## iter 480 value 648.892847
## iter 490 value 648.736538
## iter 500 value 648.387214
## final value 648.387214
## stopped after 500 iterations
## # weights: 121
## initial value 1398787.227659
## iter 10 value 1131.808015
## iter 20 value 813.078320
## iter 30 value 655.556487
## iter 40 value 566.758219
## iter 50 value 519.180216
## iter 60 value 467.921886
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## iter 300 value 239.048754
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## iter 480 value 219.264910
## iter 490 value 219.243279
## iter 500 value 219.133315
## final value 219.133315
## stopped after 500 iterations
## # weights: 181
## initial value 1346720.724982
## iter 10 value 1022.198683
## iter 20 value 779.803239
## iter 30 value 663.792973
## iter 40 value 543.544539
## iter 50 value 458.727050
## iter 60 value 405.704627
## iter 70 value 357.277567
## iter 80 value 315.458426
## iter 90 value 271.449028
## iter 100 value 245.086950
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## iter 300 value 119.171812
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## iter 320 value 118.087634
## iter 330 value 117.762066
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## iter 400 value 113.009754
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## iter 470 value 110.651755
## iter 480 value 110.069217
## iter 490 value 109.462269
## iter 500 value 108.348943
## final value 108.348943
## stopped after 500 iterations
## # weights: 241
## initial value 1375342.621822
## iter 10 value 1241.698748
## iter 20 value 823.326228
## iter 30 value 597.601678
## iter 40 value 494.272467
## iter 50 value 410.415240
## iter 60 value 356.093394
## iter 70 value 315.977675
## iter 80 value 283.437295
## iter 90 value 257.066348
## iter 100 value 237.993795
## iter 110 value 219.203280
## iter 120 value 199.822760
## iter 130 value 178.767423
## iter 140 value 161.751379
## iter 150 value 150.272940
## iter 160 value 137.701897
## iter 170 value 126.887380
## iter 180 value 115.209999
## iter 190 value 103.912457
## iter 200 value 96.021856
## iter 210 value 91.034586
## iter 220 value 85.861334
## iter 230 value 80.024086
## iter 240 value 74.881097
## iter 250 value 70.780686
## iter 260 value 67.533640
## iter 270 value 62.111864
## iter 280 value 57.586626
## iter 290 value 53.866762
## iter 300 value 51.249059
## iter 310 value 49.513083
## iter 320 value 47.854755
## iter 330 value 46.497514
## iter 340 value 45.404367
## iter 350 value 44.420428
## iter 360 value 43.791711
## iter 370 value 43.273921
## iter 380 value 42.655659
## iter 390 value 41.534482
## iter 400 value 40.583315
## iter 410 value 40.035747
## iter 420 value 39.627201
## iter 430 value 39.222367
## iter 440 value 38.827840
## iter 450 value 38.437411
## iter 460 value 37.984439
## iter 470 value 37.580332
## iter 480 value 37.204985
## iter 490 value 37.071207
## iter 500 value 37.027860
## final value 37.027860
## stopped after 500 iterations
## # weights: 25
## initial value 1394525.922485
## iter 10 value 96563.917233
## iter 20 value 16866.545419
## iter 30 value 15327.736070
## iter 40 value 15094.156086
## iter 50 value 15066.776092
## iter 60 value 13590.662701
## iter 70 value 10160.845966
## iter 80 value 7228.372587
## iter 90 value 4488.432552
## iter 100 value 3759.238182
## iter 110 value 3570.944810
## iter 120 value 3406.374325
## iter 130 value 2050.796356
## iter 140 value 1530.585269
## iter 150 value 1355.674639
## iter 160 value 1260.883588
## iter 170 value 1260.759252
## iter 180 value 1256.859995
## iter 190 value 1256.412601
## iter 200 value 1253.203674
## iter 210 value 1252.406323
## iter 220 value 1249.855451
## iter 230 value 1247.603204
## iter 240 value 1245.999381
## iter 250 value 1245.548232
## iter 260 value 1245.508608
## iter 270 value 1245.391679
## iter 280 value 1245.211120
## iter 290 value 1244.630233
## iter 300 value 1243.711651
## iter 310 value 1243.042698
## iter 320 value 1242.909794
## iter 330 value 1242.704091
## iter 340 value 1242.642847
## iter 340 value 1242.642841
## iter 350 value 1242.642339
## iter 350 value 1242.642339
## iter 360 value 1242.642132
## iter 360 value 1242.642132
## final value 1242.642110
## converged
## # weights: 61
## initial value 1382301.831569
## iter 10 value 197516.743279
## iter 20 value 9615.255518
## iter 30 value 4661.045760
## iter 40 value 3345.939011
## iter 50 value 1951.741228
## iter 60 value 1500.268711
## iter 70 value 1371.696350
## iter 80 value 1150.797944
## iter 90 value 986.139675
## iter 100 value 855.493323
## iter 110 value 821.007130
## iter 120 value 793.399815
## iter 130 value 778.960976
## iter 140 value 776.843728
## iter 150 value 771.852676
## iter 160 value 750.841811
## iter 170 value 712.700904
## iter 180 value 706.428963
## iter 190 value 704.437838
## iter 200 value 701.133008
## iter 210 value 699.829575
## iter 220 value 698.020215
## iter 230 value 696.706057
## iter 240 value 696.434125
## iter 250 value 695.884491
## iter 260 value 695.749930
## iter 270 value 695.623088
## iter 280 value 695.433360
## iter 290 value 694.476620
## iter 300 value 693.551631
## iter 310 value 692.952079
## iter 320 value 692.676044
## iter 330 value 692.399137
## iter 340 value 691.767141
## iter 350 value 690.034315
## iter 360 value 689.026419
## iter 370 value 682.911563
## iter 380 value 681.772056
## iter 390 value 670.960772
## iter 400 value 647.193142
## iter 410 value 638.152518
## iter 420 value 628.006591
## iter 430 value 619.427779
## iter 440 value 617.520796
## iter 450 value 616.033071
## iter 460 value 614.385915
## iter 470 value 611.570209
## iter 480 value 606.193003
## iter 490 value 603.801266
## iter 500 value 603.498493
## final value 603.498493
## stopped after 500 iterations
## # weights: 121
## initial value 1409867.356317
## iter 10 value 1684.962673
## iter 20 value 856.348870
## iter 30 value 660.267640
## iter 40 value 605.335315
## iter 50 value 524.784767
## iter 60 value 464.677985
## iter 70 value 434.910607
## iter 80 value 409.793507
## iter 90 value 396.447514
## iter 100 value 386.022818
## iter 110 value 377.117836
## iter 120 value 360.524338
## iter 130 value 349.848787
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## iter 480 value 276.140545
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## iter 500 value 276.027543
## final value 276.027543
## stopped after 500 iterations
## # weights: 181
## initial value 1374732.297412
## iter 10 value 993.172104
## iter 20 value 797.767413
## iter 30 value 631.609446
## iter 40 value 514.986434
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## iter 480 value 113.995292
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## iter 500 value 110.346004
## final value 110.346004
## stopped after 500 iterations
## # weights: 241
## initial value 1420191.698180
## iter 10 value 2056.780777
## iter 20 value 893.629456
## iter 30 value 684.309794
## iter 40 value 535.585418
## iter 50 value 373.828552
## iter 60 value 278.565472
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## iter 80 value 204.643094
## iter 90 value 168.208130
## iter 100 value 148.365329
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## iter 460 value 42.175431
## iter 470 value 40.782103
## iter 480 value 39.952879
## iter 490 value 39.563792
## iter 500 value 39.429882
## final value 39.429882
## stopped after 500 iterations
## # weights: 25
## initial value 1398875.046314
## iter 10 value 8867.948014
## iter 20 value 5993.622683
## iter 30 value 5646.722816
## iter 40 value 5435.909169
## iter 50 value 5362.765514
## iter 60 value 4536.142053
## iter 70 value 4495.222704
## iter 80 value 4485.764331
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## iter 100 value 4342.511875
## iter 110 value 3687.301459
## iter 120 value 3045.585932
## iter 130 value 1918.852159
## iter 140 value 1390.904526
## iter 150 value 1317.883261
## iter 160 value 1309.243243
## iter 170 value 1289.933442
## iter 180 value 1257.096041
## iter 190 value 1226.675726
## iter 200 value 1219.077462
## iter 210 value 1173.592771
## iter 220 value 1160.681757
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## iter 320 value 942.779352
## iter 330 value 940.516515
## iter 340 value 938.517010
## iter 350 value 938.298298
## final value 938.277994
## converged
## # weights: 61
## initial value 1383047.427778
## iter 10 value 16097.711861
## iter 20 value 13996.074933
## iter 30 value 11464.903557
## iter 40 value 6056.782398
## iter 50 value 2665.760001
## iter 60 value 2086.981202
## iter 70 value 1512.812962
## iter 80 value 1188.484216
## iter 90 value 1056.784378
## iter 100 value 999.337705
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## iter 120 value 926.298884
## iter 130 value 906.313212
## iter 140 value 887.000545
## iter 150 value 853.462135
## iter 160 value 814.084344
## iter 170 value 800.142836
## iter 180 value 796.685038
## iter 190 value 785.673658
## iter 200 value 744.547448
## iter 210 value 719.527059
## iter 220 value 715.829710
## iter 230 value 712.215329
## iter 240 value 706.829763
## iter 250 value 699.825457
## iter 260 value 695.920420
## iter 270 value 692.635028
## iter 280 value 691.513080
## iter 290 value 690.769966
## iter 300 value 690.743778
## iter 310 value 690.695572
## iter 320 value 690.403141
## iter 330 value 689.625702
## iter 340 value 689.134675
## iter 350 value 688.897080
## iter 360 value 688.788519
## iter 370 value 688.495166
## iter 380 value 687.541361
## iter 390 value 686.519352
## iter 400 value 685.457728
## iter 410 value 665.693834
## iter 420 value 658.208763
## iter 430 value 657.293307
## iter 440 value 654.923702
## iter 450 value 653.686242
## iter 460 value 652.987449
## iter 470 value 652.391045
## iter 480 value 652.051909
## iter 490 value 648.666593
## iter 500 value 641.324877
## final value 641.324877
## stopped after 500 iterations
## # weights: 121
## initial value 1370582.855442
## iter 10 value 3634.042647
## iter 20 value 1789.725686
## iter 30 value 1004.140210
## iter 40 value 747.558390
## iter 50 value 626.230697
## iter 60 value 578.153462
## iter 70 value 541.447040
## iter 80 value 512.601448
## iter 90 value 492.060724
## iter 100 value 475.787008
## iter 110 value 452.065622
## iter 120 value 433.269551
## iter 130 value 413.524123
## iter 140 value 377.496235
## iter 150 value 356.806908
## iter 160 value 346.207773
## iter 170 value 337.465667
## iter 180 value 332.908414
## iter 190 value 331.400341
## iter 200 value 330.624149
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## iter 220 value 328.600686
## iter 230 value 327.315347
## iter 240 value 324.452876
## iter 250 value 323.066543
## iter 260 value 322.756085
## iter 270 value 322.642127
## iter 280 value 322.602356
## iter 290 value 322.591816
## iter 300 value 322.562904
## iter 310 value 322.531566
## iter 320 value 322.528408
## final value 322.528242
## converged
## # weights: 181
## initial value 1434633.488974
## iter 10 value 1211.861663
## iter 20 value 741.622761
## iter 30 value 617.798101
## iter 40 value 504.417532
## iter 50 value 412.338094
## iter 60 value 371.324556
## iter 70 value 340.397102
## iter 80 value 302.248976
## iter 90 value 272.152936
## iter 100 value 249.972009
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## iter 130 value 211.467481
## iter 140 value 203.816955
## iter 150 value 192.621959
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## iter 190 value 144.485194
## iter 200 value 131.606989
## iter 210 value 125.689084
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## iter 230 value 120.284676
## iter 240 value 117.859599
## iter 250 value 116.095321
## iter 260 value 114.349994
## iter 270 value 112.386997
## iter 280 value 110.593585
## iter 290 value 109.772050
## iter 300 value 109.073959
## iter 310 value 108.544328
## iter 320 value 108.030098
## iter 330 value 107.254260
## iter 340 value 106.195342
## iter 350 value 104.697772
## iter 360 value 103.193881
## iter 370 value 102.654090
## iter 380 value 102.439906
## iter 390 value 101.963333
## iter 400 value 101.388648
## iter 410 value 100.874143
## iter 420 value 100.310271
## iter 430 value 99.585308
## iter 440 value 98.913255
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## iter 460 value 97.648008
## iter 470 value 97.205228
## iter 480 value 96.630771
## iter 490 value 95.981050
## iter 500 value 95.148898
## final value 95.148898
## stopped after 500 iterations
## # weights: 241
## initial value 1380919.758380
## iter 10 value 1185.503739
## iter 20 value 790.683169
## iter 30 value 627.034190
## iter 40 value 514.864460
## iter 50 value 406.597354
## iter 60 value 358.938303
## iter 70 value 302.339199
## iter 80 value 271.161883
## iter 90 value 246.617239
## iter 100 value 221.158591
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## iter 230 value 98.221466
## iter 240 value 95.298610
## iter 250 value 93.506571
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## iter 270 value 90.204602
## iter 280 value 87.749170
## iter 290 value 85.845178
## iter 300 value 84.335200
## iter 310 value 83.127670
## iter 320 value 82.373073
## iter 330 value 81.601811
## iter 340 value 80.734315
## iter 350 value 80.145589
## iter 360 value 79.483337
## iter 370 value 78.846941
## iter 380 value 78.131066
## iter 390 value 77.590630
## iter 400 value 76.953585
## iter 410 value 76.047199
## iter 420 value 74.902899
## iter 430 value 74.321353
## iter 440 value 73.743920
## iter 450 value 73.211313
## iter 460 value 72.724950
## iter 470 value 72.243749
## iter 480 value 71.753237
## iter 490 value 71.584926
## iter 500 value 71.527097
## final value 71.527097
## stopped after 500 iterations
## # weights: 25
## initial value 1381292.049123
## iter 10 value 20287.215687
## iter 20 value 12854.246572
## iter 30 value 9331.123678
## iter 40 value 7324.673398
## iter 50 value 6394.784077
## iter 60 value 6078.828353
## iter 70 value 5451.364611
## iter 80 value 4879.160754
## iter 90 value 4152.663564
## iter 100 value 2245.441698
## iter 110 value 1652.384291
## iter 120 value 1406.330990
## iter 130 value 1383.511603
## iter 140 value 1360.773843
## iter 150 value 1344.616990
## iter 160 value 1342.205575
## iter 170 value 1342.031844
## final value 1342.031267
## converged
## # weights: 61
## initial value 1419396.305294
## iter 10 value 184959.023348
## iter 20 value 11318.475570
## iter 30 value 10334.129018
## iter 40 value 9301.190262
## iter 50 value 8234.280747
## iter 60 value 7016.784769
## iter 70 value 6059.059114
## iter 80 value 4430.428399
## iter 90 value 3102.328648
## iter 100 value 2116.677718
## iter 110 value 1856.153645
## iter 120 value 1654.354334
## iter 130 value 1618.838642
## iter 140 value 1598.662422
## iter 150 value 1502.227035
## iter 160 value 1341.668973
## iter 170 value 1261.544451
## iter 180 value 1179.624678
## iter 190 value 1118.587334
## iter 200 value 1079.348832
## iter 210 value 1041.432806
## iter 220 value 1001.745157
## iter 230 value 961.081803
## iter 240 value 941.215148
## iter 250 value 933.249508
## iter 260 value 930.191127
## iter 270 value 927.092230
## iter 280 value 921.600491
## iter 290 value 918.671299
## iter 300 value 917.111812
## iter 310 value 915.623310
## iter 320 value 915.547885
## final value 915.547866
## converged
## # weights: 121
## initial value 1429579.870413
## iter 10 value 2676.843973
## iter 20 value 1328.866594
## iter 30 value 1136.312087
## iter 40 value 1080.732667
## iter 50 value 1051.095478
## iter 60 value 1036.318257
## iter 70 value 1013.954950
## iter 80 value 987.822197
## iter 90 value 942.212725
## iter 100 value 905.205979
## iter 110 value 853.472609
## iter 120 value 804.975814
## iter 130 value 772.319125
## iter 140 value 727.066991
## iter 150 value 694.309307
## iter 160 value 678.135473
## iter 170 value 670.144468
## iter 180 value 662.467012
## iter 190 value 653.599897
## iter 200 value 644.891181
## iter 210 value 640.383760
## iter 220 value 634.002636
## iter 230 value 629.109090
## iter 240 value 622.475200
## iter 250 value 617.628729
## iter 260 value 614.018865
## iter 270 value 605.798801
## iter 280 value 594.718992
## iter 290 value 570.303635
## iter 300 value 541.318117
## iter 310 value 525.691143
## iter 320 value 522.553924
## iter 330 value 518.840760
## iter 340 value 515.276227
## iter 350 value 512.314690
## iter 360 value 508.776298
## iter 370 value 505.151256
## iter 380 value 502.042305
## iter 390 value 498.143774
## iter 400 value 495.528422
## iter 410 value 492.741194
## iter 420 value 479.613655
## iter 430 value 471.097821
## iter 440 value 467.872219
## iter 450 value 466.112631
## iter 460 value 464.786943
## iter 470 value 463.906956
## iter 480 value 463.788169
## iter 490 value 463.727789
## iter 500 value 463.718523
## final value 463.718523
## stopped after 500 iterations
## # weights: 181
## initial value 1350758.806442
## iter 10 value 1305.656452
## iter 20 value 857.144756
## iter 30 value 725.492586
## iter 40 value 600.979729
## iter 50 value 547.813998
## iter 60 value 521.245762
## iter 70 value 483.491460
## iter 80 value 459.643289
## iter 90 value 444.644242
## iter 100 value 431.418359
## iter 110 value 416.471585
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## iter 130 value 401.003529
## iter 140 value 397.503648
## iter 150 value 393.405531
## iter 160 value 390.506549
## iter 170 value 386.888534
## iter 180 value 379.886769
## iter 190 value 375.561956
## iter 200 value 373.263663
## iter 210 value 371.718795
## iter 220 value 368.687517
## iter 230 value 363.769758
## iter 240 value 361.193453
## iter 250 value 358.212537
## iter 260 value 356.559336
## iter 270 value 355.405214
## iter 280 value 354.005756
## iter 290 value 353.073280
## iter 300 value 352.014675
## iter 310 value 351.156512
## iter 320 value 350.491249
## iter 330 value 349.873665
## iter 340 value 349.357534
## iter 350 value 348.889836
## iter 360 value 347.894064
## iter 370 value 347.102972
## iter 380 value 346.842744
## iter 390 value 346.349524
## iter 400 value 345.963008
## iter 410 value 345.827065
## iter 420 value 345.743161
## iter 430 value 345.705685
## iter 440 value 345.682398
## iter 450 value 345.661600
## iter 460 value 345.632443
## iter 470 value 345.578164
## iter 480 value 345.516618
## iter 490 value 345.351348
## iter 500 value 345.272303
## final value 345.272303
## stopped after 500 iterations
## # weights: 241
## initial value 1362499.251470
## iter 10 value 1272.672194
## iter 20 value 883.943841
## iter 30 value 743.769012
## iter 40 value 629.600859
## iter 50 value 547.787256
## iter 60 value 498.847486
## iter 70 value 475.077643
## iter 80 value 454.458265
## iter 90 value 441.592704
## iter 100 value 430.776356
## iter 110 value 424.534253
## iter 120 value 419.569638
## iter 130 value 413.936907
## iter 140 value 407.280641
## iter 150 value 400.826140
## iter 160 value 396.181335
## iter 170 value 392.125717
## iter 180 value 385.320780
## iter 190 value 380.372983
## iter 200 value 376.124581
## iter 210 value 372.841356
## iter 220 value 370.166136
## iter 230 value 366.412347
## iter 240 value 363.349710
## iter 250 value 361.329537
## iter 260 value 359.127974
## iter 270 value 357.227443
## iter 280 value 355.681249
## iter 290 value 354.020479
## iter 300 value 351.854725
## iter 310 value 349.690689
## iter 320 value 347.262095
## iter 330 value 344.203866
## iter 340 value 341.311006
## iter 350 value 339.219991
## iter 360 value 338.020488
## iter 370 value 336.930317
## iter 380 value 336.076126
## iter 390 value 335.278900
## iter 400 value 334.573043
## iter 410 value 333.811137
## iter 420 value 333.005316
## iter 430 value 332.181636
## iter 440 value 331.468455
## iter 450 value 330.429249
## iter 460 value 329.360251
## iter 470 value 328.382914
## iter 480 value 327.561672
## iter 490 value 327.198614
## iter 500 value 326.698237
## final value 326.698237
## stopped after 500 iterations
## # weights: 25
## initial value 1352804.101603
## iter 10 value 7275.747305
## iter 20 value 5671.697933
## iter 30 value 4972.380138
## iter 40 value 3179.032765
## iter 50 value 1704.377016
## iter 60 value 1450.697286
## iter 70 value 1432.154930
## iter 80 value 1354.504384
## iter 90 value 1336.069149
## iter 100 value 1329.949531
## iter 110 value 1327.451469
## iter 120 value 1327.351137
## iter 130 value 1325.226789
## iter 140 value 1323.396121
## iter 150 value 1322.155002
## iter 160 value 1321.358865
## iter 170 value 1321.326107
## final value 1321.326029
## converged
## # weights: 61
## initial value 1381519.965639
## iter 10 value 156745.089010
## iter 20 value 19165.135566
## iter 30 value 4707.429300
## iter 40 value 2910.576358
## iter 50 value 2507.757296
## iter 60 value 2442.203789
## iter 70 value 2400.313853
## iter 80 value 2392.664782
## iter 90 value 2380.635613
## iter 100 value 2325.955800
## iter 110 value 2199.569931
## iter 120 value 1817.425136
## iter 130 value 1391.646552
## iter 140 value 1254.540213
## iter 150 value 1219.271583
## iter 160 value 1198.840266
## iter 170 value 1174.080006
## iter 180 value 1172.475059
## iter 190 value 1171.034220
## iter 200 value 1170.555707
## iter 210 value 1170.345513
## iter 220 value 1170.212457
## final value 1170.212293
## converged
## # weights: 121
## initial value 1413421.109585
## iter 10 value 1164.292169
## iter 20 value 917.817139
## iter 30 value 794.873953
## iter 40 value 704.864810
## iter 50 value 645.498750
## iter 60 value 599.702107
## iter 70 value 534.997492
## iter 80 value 502.120381
## iter 90 value 474.354445
## iter 100 value 456.554333
## iter 110 value 429.374554
## iter 120 value 393.297491
## iter 130 value 370.217467
## iter 140 value 351.341173
## iter 150 value 339.785544
## iter 160 value 331.455091
## iter 170 value 321.697691
## iter 180 value 310.101631
## iter 190 value 297.704618
## iter 200 value 288.833757
## iter 210 value 279.004290
## iter 220 value 271.420859
## iter 230 value 266.658103
## iter 240 value 261.757831
## iter 250 value 258.717112
## iter 260 value 257.455015
## iter 270 value 256.695570
## iter 280 value 254.898675
## iter 290 value 253.070971
## iter 300 value 251.184672
## iter 310 value 249.060118
## iter 320 value 246.855712
## iter 330 value 244.456969
## iter 340 value 241.698852
## iter 350 value 239.301865
## iter 360 value 236.819552
## iter 370 value 235.800689
## iter 380 value 235.176343
## iter 390 value 234.545566
## iter 400 value 234.206551
## iter 410 value 233.947747
## iter 420 value 233.490559
## iter 430 value 232.665740
## iter 440 value 231.071099
## iter 450 value 229.779929
## iter 460 value 226.675373
## iter 470 value 225.458197
## iter 480 value 224.716210
## iter 490 value 224.045071
## iter 500 value 223.982740
## final value 223.982740
## stopped after 500 iterations
## # weights: 181
## initial value 1401015.855766
## iter 10 value 1199.275633
## iter 20 value 835.197898
## iter 30 value 667.544267
## iter 40 value 529.879974
## iter 50 value 442.998801
## iter 60 value 394.055870
## iter 70 value 339.969879
## iter 80 value 309.182838
## iter 90 value 282.462925
## iter 100 value 261.871797
## iter 110 value 246.757338
## iter 120 value 233.521851
## iter 130 value 225.709182
## iter 140 value 216.937743
## iter 150 value 207.225850
## iter 160 value 196.613876
## iter 170 value 189.384304
## iter 180 value 182.776166
## iter 190 value 174.835674
## iter 200 value 169.414834
## iter 210 value 165.400080
## iter 220 value 162.083955
## iter 230 value 159.058170
## iter 240 value 155.684739
## iter 250 value 150.590213
## iter 260 value 147.163452
## iter 270 value 144.798286
## iter 280 value 141.196879
## iter 290 value 139.373404
## iter 300 value 137.832535
## iter 310 value 136.166298
## iter 320 value 134.235930
## iter 330 value 131.245240
## iter 340 value 124.752549
## iter 350 value 120.818687
## iter 360 value 117.970698
## iter 370 value 116.948100
## iter 380 value 116.534072
## iter 390 value 115.951792
## iter 400 value 115.089880
## iter 410 value 114.442252
## iter 420 value 114.124754
## iter 430 value 113.681684
## iter 440 value 113.234187
## iter 450 value 112.707178
## iter 460 value 112.293287
## iter 470 value 111.870424
## iter 480 value 110.661587
## iter 490 value 107.888475
## iter 500 value 105.202963
## final value 105.202963
## stopped after 500 iterations
## # weights: 241
## initial value 1347790.223486
## iter 10 value 1274.694409
## iter 20 value 808.576311
## iter 30 value 648.638270
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## iter 470 value 41.190365
## iter 480 value 40.540935
## iter 490 value 40.315329
## iter 500 value 40.266483
## final value 40.266483
## stopped after 500 iterations
## # weights: 25
## initial value 1401025.334235
## iter 10 value 7284.059451
## iter 20 value 5587.156652
## iter 30 value 5319.201372
## iter 40 value 5255.107886
## iter 50 value 5188.325682
## iter 60 value 5143.539109
## iter 70 value 5070.790555
## iter 80 value 5029.732484
## iter 90 value 3813.249512
## iter 100 value 2371.969911
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## iter 120 value 1413.086943
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## iter 220 value 1322.422833
## iter 230 value 1321.750752
## iter 240 value 1321.213118
## iter 250 value 1320.859134
## final value 1320.858722
## converged
## # weights: 61
## initial value 1404180.067285
## iter 10 value 22671.133453
## iter 20 value 5360.307533
## iter 30 value 4207.555920
## iter 40 value 3919.162721
## iter 50 value 3813.449596
## iter 60 value 3780.048255
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## iter 80 value 3625.125671
## iter 90 value 3613.359982
## iter 100 value 3596.773197
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## iter 120 value 3593.030214
## iter 130 value 3592.574417
## iter 140 value 3592.193419
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## iter 160 value 3584.427380
## iter 170 value 3583.559293
## iter 180 value 3583.116588
## iter 190 value 3582.987442
## iter 200 value 3582.777231
## iter 210 value 3576.875601
## iter 220 value 3518.912091
## iter 230 value 3404.034133
## iter 240 value 3212.200627
## iter 250 value 2841.745081
## iter 260 value 2764.397597
## iter 270 value 2696.080490
## iter 280 value 2647.740139
## iter 290 value 2638.625154
## iter 300 value 2620.145531
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## iter 320 value 2585.700893
## iter 330 value 2585.623124
## iter 340 value 2570.545051
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## iter 470 value 2285.238267
## iter 480 value 2284.033843
## iter 490 value 2283.416994
## iter 500 value 2283.045388
## final value 2283.045388
## stopped after 500 iterations
## # weights: 121
## initial value 1370780.538871
## iter 10 value 1735.960221
## iter 20 value 1076.013267
## iter 30 value 801.138301
## iter 40 value 661.683896
## iter 50 value 588.912375
## iter 60 value 547.488980
## iter 70 value 516.016607
## iter 80 value 492.769867
## iter 90 value 477.034863
## iter 100 value 454.225233
## iter 110 value 430.739833
## iter 120 value 408.415240
## iter 130 value 385.521385
## iter 140 value 370.325777
## iter 150 value 363.577648
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## iter 170 value 351.908763
## iter 180 value 342.439786
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## iter 200 value 327.709196
## iter 210 value 324.751174
## iter 220 value 319.408574
## iter 230 value 315.372046
## iter 240 value 311.279525
## iter 250 value 310.105779
## iter 260 value 309.663263
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## iter 280 value 308.165743
## iter 290 value 307.234542
## iter 300 value 306.471767
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## iter 470 value 291.931915
## iter 480 value 291.504126
## iter 490 value 291.336734
## iter 500 value 291.332069
## final value 291.332069
## stopped after 500 iterations
## # weights: 181
## initial value 1396667.071372
## iter 10 value 1366.611597
## iter 20 value 859.571418
## iter 30 value 668.131074
## iter 40 value 549.455972
## iter 50 value 478.297412
## iter 60 value 434.499136
## iter 70 value 374.530645
## iter 80 value 320.307281
## iter 90 value 282.719803
## iter 100 value 256.786556
## iter 110 value 235.016765
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## iter 140 value 176.038959
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## iter 160 value 137.730718
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## iter 460 value 82.707169
## iter 470 value 82.218878
## iter 480 value 81.278060
## iter 490 value 79.015409
## iter 500 value 77.319640
## final value 77.319640
## stopped after 500 iterations
## # weights: 241
## initial value 1352819.001937
## iter 10 value 1261.009641
## iter 20 value 837.738397
## iter 30 value 653.867019
## iter 40 value 511.778327
## iter 50 value 391.650299
## iter 60 value 326.723595
## iter 70 value 302.777406
## iter 80 value 256.025471
## iter 90 value 212.113029
## iter 100 value 183.820656
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## iter 180 value 73.617740
## iter 190 value 68.447576
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## iter 480 value 25.759427
## iter 490 value 25.720686
## iter 500 value 25.698199
## final value 25.698199
## stopped after 500 iterations
## # weights: 25
## initial value 1371501.389672
## iter 10 value 15815.073506
## iter 20 value 14614.011160
## iter 30 value 10194.045091
## iter 40 value 6571.548063
## iter 50 value 5241.104461
## iter 60 value 1911.651971
## iter 70 value 1553.706446
## iter 80 value 1390.593124
## iter 90 value 1361.868945
## iter 100 value 1356.842422
## iter 110 value 1336.677195
## iter 120 value 1330.784663
## iter 130 value 1328.491950
## iter 140 value 1326.210357
## iter 150 value 1326.158931
## iter 160 value 1325.436196
## iter 170 value 1324.582233
## iter 180 value 1313.507536
## iter 190 value 1298.386388
## iter 200 value 1292.526271
## iter 210 value 1264.965888
## iter 220 value 1204.078173
## iter 230 value 1184.048488
## iter 240 value 1148.655452
## iter 250 value 1147.589780
## iter 260 value 1141.319227
## iter 270 value 1136.746382
## iter 280 value 1134.763864
## iter 290 value 1134.671998
## iter 300 value 1134.584800
## iter 310 value 1134.480873
## iter 320 value 1134.313015
## iter 330 value 1134.072244
## iter 340 value 1134.027020
## final value 1134.026804
## converged
## # weights: 61
## initial value 1367512.773204
## iter 10 value 5604.994907
## iter 20 value 3359.931736
## iter 30 value 3081.183087
## iter 40 value 2863.801073
## iter 50 value 2758.543282
## iter 60 value 2497.887377
## iter 70 value 2065.485635
## iter 80 value 1421.367137
## iter 90 value 1038.484875
## iter 100 value 958.716354
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## iter 130 value 933.403260
## iter 140 value 921.810942
## iter 150 value 894.625385
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## iter 180 value 874.017676
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## iter 200 value 871.011545
## iter 210 value 868.181329
## iter 220 value 867.734755
## iter 230 value 866.437055
## iter 240 value 866.073146
## iter 250 value 865.692923
## iter 260 value 864.772413
## iter 270 value 864.486307
## iter 280 value 864.333982
## iter 290 value 860.877652
## iter 300 value 854.254309
## iter 310 value 823.710856
## iter 320 value 812.120582
## iter 330 value 811.403515
## iter 340 value 809.168905
## iter 350 value 809.010864
## iter 360 value 808.952847
## iter 370 value 808.866586
## iter 380 value 808.638352
## iter 390 value 808.629291
## iter 400 value 807.641652
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## iter 420 value 714.636228
## iter 430 value 700.373567
## iter 440 value 696.801482
## iter 450 value 695.835918
## iter 460 value 695.694068
## iter 470 value 695.610381
## iter 480 value 695.505235
## iter 490 value 695.485199
## iter 500 value 695.469437
## final value 695.469437
## stopped after 500 iterations
## # weights: 121
## initial value 1400728.413880
## iter 10 value 1363.585280
## iter 20 value 873.601261
## iter 30 value 767.053150
## iter 40 value 627.101185
## iter 50 value 540.107163
## iter 60 value 471.949974
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## iter 80 value 417.899453
## iter 90 value 402.783649
## iter 100 value 389.996237
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## iter 120 value 367.786890
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## iter 150 value 328.572736
## iter 160 value 317.878277
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## iter 200 value 293.579884
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## iter 220 value 289.055955
## iter 230 value 285.856664
## iter 240 value 282.098029
## iter 250 value 280.797279
## iter 260 value 280.153352
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## iter 280 value 278.289290
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## iter 300 value 271.557460
## iter 310 value 266.867384
## iter 320 value 263.522622
## iter 330 value 261.175500
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## iter 390 value 245.418617
## iter 400 value 244.457939
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## iter 460 value 243.237898
## iter 470 value 243.186002
## iter 480 value 243.116300
## iter 490 value 243.090502
## iter 500 value 243.090008
## final value 243.090008
## stopped after 500 iterations
## # weights: 181
## initial value 1355913.141531
## iter 10 value 1213.449689
## iter 20 value 881.802980
## iter 30 value 689.143307
## iter 40 value 537.528342
## iter 50 value 398.892445
## iter 60 value 362.634637
## iter 70 value 303.551921
## iter 80 value 269.765047
## iter 90 value 242.699828
## iter 100 value 225.115190
## iter 110 value 216.593478
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## iter 150 value 170.047450
## iter 160 value 160.068666
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## iter 200 value 142.119570
## iter 210 value 139.834791
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## iter 350 value 121.529965
## iter 360 value 121.360051
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## iter 380 value 121.264644
## iter 390 value 121.223714
## iter 400 value 121.180452
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## iter 420 value 120.992501
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## iter 440 value 120.482895
## iter 450 value 120.057076
## iter 460 value 119.631656
## iter 470 value 119.173482
## iter 480 value 118.585147
## iter 490 value 117.904547
## iter 500 value 117.324056
## final value 117.324056
## stopped after 500 iterations
## # weights: 241
## initial value 1437169.761367
## iter 10 value 1016.738092
## iter 20 value 777.575334
## iter 30 value 655.091731
## iter 40 value 531.925574
## iter 50 value 406.624093
## iter 60 value 331.028081
## iter 70 value 273.735397
## iter 80 value 237.175688
## iter 90 value 213.011085
## iter 100 value 194.870318
## iter 110 value 182.300884
## iter 120 value 166.518027
## iter 130 value 151.100913
## iter 140 value 141.958977
## iter 150 value 134.737956
## iter 160 value 128.700533
## iter 170 value 124.377930
## iter 180 value 118.694785
## iter 190 value 110.151650
## iter 200 value 102.621544
## iter 210 value 95.453264
## iter 220 value 88.075954
## iter 230 value 80.601564
## iter 240 value 75.418833
## iter 250 value 72.017731
## iter 260 value 69.893594
## iter 270 value 67.735721
## iter 280 value 65.527623
## iter 290 value 63.328681
## iter 300 value 61.433226
## iter 310 value 59.520209
## iter 320 value 57.862201
## iter 330 value 55.571115
## iter 340 value 53.857480
## iter 350 value 52.706519
## iter 360 value 51.077815
## iter 370 value 49.494233
## iter 380 value 48.583871
## iter 390 value 47.797846
## iter 400 value 47.035803
## iter 410 value 46.206622
## iter 420 value 45.170743
## iter 430 value 44.109184
## iter 440 value 42.567203
## iter 450 value 41.210057
## iter 460 value 40.384792
## iter 470 value 39.831887
## iter 480 value 39.287501
## iter 490 value 38.954009
## iter 500 value 38.858889
## final value 38.858889
## stopped after 500 iterations
## # weights: 25
## initial value 1377807.128967
## iter 10 value 6582.290470
## iter 20 value 5508.642516
## iter 30 value 5080.982529
## iter 40 value 4740.683518
## iter 50 value 4227.903126
## iter 60 value 2087.065988
## iter 70 value 1428.445220
## iter 80 value 1310.069237
## iter 90 value 1295.675950
## iter 100 value 1273.716215
## iter 110 value 1224.686210
## iter 120 value 1203.038553
## iter 130 value 1198.956890
## iter 140 value 1198.849509
## iter 150 value 1198.152293
## iter 160 value 1195.113686
## iter 170 value 1192.961112
## iter 180 value 1192.776110
## iter 190 value 1191.383366
## iter 200 value 1177.103352
## iter 210 value 1176.432713
## iter 220 value 1158.589045
## iter 230 value 982.354673
## iter 240 value 944.698121
## iter 250 value 939.000986
## iter 260 value 938.938787
## iter 270 value 938.239078
## iter 280 value 935.731395
## iter 290 value 934.662017
## iter 300 value 934.483520
## iter 310 value 934.477706
## iter 320 value 934.472090
## iter 330 value 934.386992
## iter 340 value 934.005259
## iter 350 value 929.597745
## iter 360 value 929.120999
## iter 370 value 928.666392
## iter 380 value 927.962362
## iter 390 value 927.580306
## iter 400 value 927.409867
## iter 410 value 927.392970
## iter 420 value 927.391246
## iter 430 value 927.380780
## iter 440 value 927.377445
## final value 927.376360
## converged
## # weights: 61
## initial value 1394416.286883
## iter 10 value 17220.208746
## iter 20 value 7747.878990
## iter 30 value 5051.578783
## iter 40 value 3121.547604
## iter 50 value 1768.449111
## iter 60 value 1192.214031
## iter 70 value 1080.187373
## iter 80 value 1025.264518
## iter 90 value 1010.585021
## iter 100 value 989.691703
## iter 110 value 954.378623
## iter 120 value 896.064139
## iter 130 value 854.689155
## iter 140 value 798.152878
## iter 150 value 773.398477
## iter 160 value 756.428576
## iter 170 value 740.809695
## iter 180 value 732.101064
## iter 190 value 716.393906
## iter 200 value 701.659813
## iter 210 value 689.749053
## iter 220 value 684.868642
## iter 230 value 679.235501
## iter 240 value 664.838744
## iter 250 value 652.804734
## iter 260 value 649.168417
## iter 270 value 643.088603
## iter 280 value 636.531358
## iter 290 value 632.961249
## iter 300 value 631.572475
## iter 310 value 631.111005
## iter 320 value 630.906092
## iter 330 value 630.757792
## iter 340 value 630.754553
## iter 350 value 630.748148
## iter 360 value 630.732007
## iter 370 value 630.700259
## iter 380 value 628.687600
## iter 390 value 628.315681
## iter 400 value 628.162033
## iter 410 value 628.121174
## iter 420 value 628.104709
## final value 628.098970
## converged
## # weights: 121
## initial value 1390362.510508
## iter 10 value 2649.200572
## iter 20 value 1207.976790
## iter 30 value 920.473514
## iter 40 value 804.393514
## iter 50 value 700.980690
## iter 60 value 630.617975
## iter 70 value 589.459419
## iter 80 value 557.543020
## iter 90 value 533.539162
## iter 100 value 489.609764
## iter 110 value 454.142782
## iter 120 value 408.632791
## iter 130 value 377.962137
## iter 140 value 350.570100
## iter 150 value 335.162560
## iter 160 value 326.483062
## iter 170 value 317.930706
## iter 180 value 314.825175
## iter 190 value 312.351039
## iter 200 value 310.279597
## iter 210 value 308.181375
## iter 220 value 306.186959
## iter 230 value 303.979851
## iter 240 value 301.598040
## iter 250 value 300.482051
## iter 260 value 299.971541
## iter 270 value 299.110014
## iter 280 value 298.392082
## iter 290 value 297.204832
## iter 300 value 296.038760
## iter 310 value 293.784063
## iter 320 value 292.397213
## iter 330 value 290.829776
## iter 340 value 290.206335
## iter 350 value 289.532676
## iter 360 value 288.629698
## iter 370 value 287.441981
## iter 380 value 287.266482
## iter 390 value 287.212570
## iter 400 value 287.195338
## iter 410 value 287.189516
## iter 420 value 287.187231
## iter 430 value 287.186553
## iter 440 value 287.186145
## iter 450 value 287.186009
## final value 287.186004
## converged
## # weights: 181
## initial value 1383869.672376
## iter 10 value 1092.333771
## iter 20 value 807.214112
## iter 30 value 685.324766
## iter 40 value 551.811317
## iter 50 value 463.026045
## iter 60 value 410.785969
## iter 70 value 375.091130
## iter 80 value 336.584303
## iter 90 value 306.276299
## iter 100 value 289.240208
## iter 110 value 272.296663
## iter 120 value 255.567922
## iter 130 value 241.656171
## iter 140 value 229.909904
## iter 150 value 217.290821
## iter 160 value 203.287707
## iter 170 value 195.347011
## iter 180 value 184.334690
## iter 190 value 179.597415
## iter 200 value 176.856171
## iter 210 value 173.100870
## iter 220 value 169.624765
## iter 230 value 167.029093
## iter 240 value 164.379752
## iter 250 value 162.024558
## iter 260 value 160.343251
## iter 270 value 158.950562
## iter 280 value 155.580142
## iter 290 value 152.841511
## iter 300 value 149.943401
## iter 310 value 148.495006
## iter 320 value 146.686591
## iter 330 value 145.696641
## iter 340 value 145.109605
## iter 350 value 144.381624
## iter 360 value 143.251849
## iter 370 value 142.611658
## iter 380 value 142.343252
## iter 390 value 142.024499
## iter 400 value 141.412376
## iter 410 value 140.834330
## iter 420 value 139.526064
## iter 430 value 137.521205
## iter 440 value 135.522022
## iter 450 value 134.376111
## iter 460 value 133.663173
## iter 470 value 132.914036
## iter 480 value 132.350281
## iter 490 value 131.994557
## iter 500 value 131.842429
## final value 131.842429
## stopped after 500 iterations
## # weights: 241
## initial value 1291557.154878
## iter 10 value 1822.996062
## iter 20 value 863.117476
## iter 30 value 664.052866
## iter 40 value 538.442347
## iter 50 value 429.187327
## iter 60 value 324.827280
## iter 70 value 279.942713
## iter 80 value 254.797148
## iter 90 value 229.327432
## iter 100 value 204.395092
## iter 110 value 182.691786
## iter 120 value 163.120875
## iter 130 value 146.356131
## iter 140 value 131.913924
## iter 150 value 123.748953
## iter 160 value 117.828552
## iter 170 value 112.404097
## iter 180 value 105.989116
## iter 190 value 100.002400
## iter 200 value 95.423052
## iter 210 value 90.230996
## iter 220 value 84.567531
## iter 230 value 79.877048
## iter 240 value 75.321164
## iter 250 value 72.081778
## iter 260 value 69.361703
## iter 270 value 67.200481
## iter 280 value 65.736415
## iter 290 value 64.083852
## iter 300 value 62.934060
## iter 310 value 61.815333
## iter 320 value 60.604699
## iter 330 value 59.763782
## iter 340 value 59.166337
## iter 350 value 58.712735
## iter 360 value 58.271996
## iter 370 value 57.882915
## iter 380 value 57.504080
## iter 390 value 56.914200
## iter 400 value 56.365150
## iter 410 value 55.719298
## iter 420 value 54.949924
## iter 430 value 54.169159
## iter 440 value 53.568176
## iter 450 value 53.014802
## iter 460 value 52.590100
## iter 470 value 52.216198
## iter 480 value 51.856371
## iter 490 value 51.747844
## iter 500 value 51.714740
## final value 51.714740
## stopped after 500 iterations
## # weights: 25
## initial value 1372580.110498
## iter 10 value 7796.715498
## iter 20 value 6125.311582
## iter 30 value 4736.369899
## iter 40 value 2616.454685
## iter 50 value 1828.938500
## iter 60 value 1582.638888
## iter 70 value 1334.507169
## iter 80 value 1257.647568
## iter 90 value 1226.586263
## iter 100 value 1220.954055
## iter 110 value 1191.297730
## iter 120 value 1174.844944
## iter 130 value 1174.304547
## final value 1174.304322
## converged
## # weights: 61
## initial value 1409266.100448
## iter 10 value 11254.188241
## iter 20 value 8712.066875
## iter 30 value 7085.147988
## iter 40 value 5420.681639
## iter 50 value 3944.948525
## iter 60 value 3170.526045
## iter 70 value 2817.016709
## iter 80 value 2523.301074
## iter 90 value 2367.408547
## iter 100 value 2208.834604
## iter 110 value 1949.233258
## iter 120 value 1720.447614
## iter 130 value 1626.985772
## iter 140 value 1602.582657
## iter 150 value 1495.630571
## iter 160 value 1332.985045
## iter 170 value 1242.735519
## iter 180 value 1197.682314
## iter 190 value 1141.925591
## iter 200 value 1121.117100
## iter 210 value 1099.906399
## iter 220 value 1079.352164
## iter 230 value 1060.145911
## iter 240 value 1041.534335
## iter 250 value 1023.403488
## iter 260 value 1008.616498
## iter 270 value 984.418456
## iter 280 value 965.852370
## iter 290 value 954.086057
## iter 300 value 947.008987
## iter 310 value 932.660972
## iter 320 value 912.851924
## iter 330 value 902.507394
## iter 340 value 895.708933
## iter 350 value 893.920694
## iter 360 value 893.447271
## iter 370 value 893.362106
## iter 380 value 893.351854
## iter 390 value 893.323349
## iter 400 value 893.279525
## iter 410 value 893.264447
## iter 420 value 893.261154
## final value 893.261045
## converged
## # weights: 121
## initial value 1348772.984296
## iter 10 value 2285.383073
## iter 20 value 1072.096445
## iter 30 value 894.562944
## iter 40 value 845.481911
## iter 50 value 781.020663
## iter 60 value 744.103312
## iter 70 value 713.874642
## iter 80 value 686.751157
## iter 90 value 660.156664
## iter 100 value 638.490625
## iter 110 value 622.900259
## iter 120 value 605.976933
## iter 130 value 588.820740
## iter 140 value 578.795191
## iter 150 value 569.857569
## iter 160 value 564.268886
## iter 170 value 561.307149
## iter 180 value 559.436292
## iter 190 value 554.821548
## iter 200 value 551.366416
## iter 210 value 549.017685
## iter 220 value 547.231149
## iter 230 value 545.479393
## iter 240 value 544.491721
## iter 250 value 544.016511
## iter 260 value 543.686231
## iter 270 value 542.913798
## iter 280 value 542.424311
## iter 290 value 541.700294
## iter 300 value 540.343089
## iter 310 value 539.877669
## iter 320 value 539.823601
## iter 330 value 539.814267
## iter 340 value 539.813678
## iter 340 value 539.813673
## iter 340 value 539.813673
## final value 539.813673
## converged
## # weights: 181
## initial value 1430872.257193
## iter 10 value 1304.712967
## iter 20 value 953.468449
## iter 30 value 802.304534
## iter 40 value 687.093412
## iter 50 value 589.366754
## iter 60 value 539.206056
## iter 70 value 509.363586
## iter 80 value 486.141922
## iter 90 value 470.655134
## iter 100 value 457.679315
## iter 110 value 448.857180
## iter 120 value 441.409939
## iter 130 value 434.581021
## iter 140 value 426.457880
## iter 150 value 417.272802
## iter 160 value 408.983417
## iter 170 value 403.403516
## iter 180 value 399.642424
## iter 190 value 396.840433
## iter 200 value 395.331645
## iter 210 value 393.834610
## iter 220 value 390.804489
## iter 230 value 386.808378
## iter 240 value 383.337005
## iter 250 value 381.123074
## iter 260 value 378.858517
## iter 270 value 376.111459
## iter 280 value 373.821366
## iter 290 value 372.271325
## iter 300 value 370.657367
## iter 310 value 369.563837
## iter 320 value 369.049950
## iter 330 value 368.896952
## iter 340 value 368.783105
## iter 350 value 368.670589
## iter 360 value 368.594260
## iter 370 value 368.569858
## iter 380 value 368.539278
## iter 390 value 368.491377
## iter 400 value 368.462061
## iter 410 value 368.451379
## iter 420 value 368.443969
## iter 430 value 368.440427
## iter 440 value 368.438721
## iter 450 value 368.438123
## iter 460 value 368.437919
## final value 368.437889
## converged
## # weights: 241
## initial value 1447581.346562
## iter 10 value 2959.288466
## iter 20 value 1438.771771
## iter 30 value 1026.502343
## iter 40 value 832.320200
## iter 50 value 755.250862
## iter 60 value 685.812617
## iter 70 value 632.508042
## iter 80 value 605.363994
## iter 90 value 587.883698
## iter 100 value 577.026763
## iter 110 value 564.540374
## iter 120 value 551.902322
## iter 130 value 541.885847
## iter 140 value 532.632171
## iter 150 value 518.714511
## iter 160 value 508.524854
## iter 170 value 493.328361
## iter 180 value 481.304159
## iter 190 value 469.840988
## iter 200 value 461.771436
## iter 210 value 454.818338
## iter 220 value 447.658954
## iter 230 value 437.782682
## iter 240 value 430.906869
## iter 250 value 426.078625
## iter 260 value 421.511465
## iter 270 value 416.482177
## iter 280 value 411.402443
## iter 290 value 407.458398
## iter 300 value 403.702439
## iter 310 value 400.778424
## iter 320 value 397.865260
## iter 330 value 394.358452
## iter 340 value 391.684744
## iter 350 value 388.754587
## iter 360 value 387.526570
## iter 370 value 385.549989
## iter 380 value 383.890309
## iter 390 value 383.237766
## iter 400 value 382.697901
## iter 410 value 381.866439
## iter 420 value 380.997695
## iter 430 value 380.156723
## iter 440 value 379.293233
## iter 450 value 378.555413
## iter 460 value 377.942716
## iter 470 value 376.967300
## iter 480 value 376.130196
## iter 490 value 375.575712
## iter 500 value 375.133576
## final value 375.133576
## stopped after 500 iterations
## # weights: 25
## initial value 1399249.548941
## iter 10 value 7003.306263
## iter 20 value 5131.185983
## iter 30 value 2857.338188
## iter 40 value 1762.670837
## iter 50 value 1286.671748
## iter 60 value 1246.243551
## iter 70 value 1225.658074
## iter 80 value 1158.227390
## iter 90 value 1143.215915
## iter 100 value 1135.833051
## iter 110 value 1134.240530
## iter 120 value 1133.685525
## iter 130 value 1132.255449
## iter 140 value 1130.797270
## iter 150 value 1129.767567
## iter 160 value 1129.447015
## iter 170 value 1129.435259
## iter 180 value 1129.239655
## iter 190 value 1128.799157
## iter 200 value 1128.619499
## final value 1128.583892
## converged
## # weights: 61
## initial value 1370760.779359
## iter 10 value 19907.146053
## iter 20 value 3239.762629
## iter 30 value 3102.842849
## iter 40 value 3013.132086
## iter 50 value 2948.650387
## iter 60 value 2893.249232
## iter 70 value 2819.007379
## iter 80 value 2782.237329
## iter 90 value 2762.406226
## iter 100 value 2758.731294
## iter 110 value 2755.521585
## iter 120 value 2751.978833
## iter 130 value 2735.855152
## iter 140 value 2733.920928
## iter 150 value 2733.262579
## iter 160 value 2732.679618
## iter 170 value 2731.842361
## iter 180 value 2731.573955
## iter 190 value 2729.314204
## iter 200 value 2713.701059
## iter 210 value 2706.317183
## iter 220 value 2691.657400
## iter 230 value 2689.880493
## iter 240 value 2678.279249
## iter 250 value 2667.300336
## iter 260 value 2666.123031
## iter 270 value 2640.826370
## iter 280 value 2638.156135
## iter 290 value 2628.133612
## iter 300 value 2403.963658
## iter 310 value 2044.160707
## iter 320 value 1475.744751
## iter 330 value 1272.201363
## iter 340 value 1206.136339
## iter 350 value 1194.682270
## iter 360 value 1190.650892
## iter 370 value 1189.620567
## iter 380 value 1189.118859
## iter 390 value 1187.988302
## iter 400 value 1186.085736
## iter 410 value 1186.062944
## final value 1186.062177
## converged
## # weights: 121
## initial value 1351488.804482
## iter 10 value 1274.506092
## iter 20 value 900.715802
## iter 30 value 757.656657
## iter 40 value 644.489324
## iter 50 value 564.932621
## iter 60 value 520.276777
## iter 70 value 471.698120
## iter 80 value 442.779524
## iter 90 value 422.462933
## iter 100 value 408.357824
## iter 110 value 395.506349
## iter 120 value 384.665575
## iter 130 value 376.388211
## iter 140 value 368.343874
## iter 150 value 361.125107
## iter 160 value 354.937938
## iter 170 value 350.923256
## iter 180 value 344.337857
## iter 190 value 337.660117
## iter 200 value 333.167933
## iter 210 value 330.114755
## iter 220 value 327.808591
## iter 230 value 324.523861
## iter 240 value 322.006802
## iter 250 value 320.853066
## iter 260 value 320.224749
## iter 270 value 317.176238
## iter 280 value 313.440666
## iter 290 value 310.918030
## iter 300 value 309.519708
## iter 310 value 307.794084
## iter 320 value 303.841030
## iter 330 value 300.399886
## iter 340 value 299.564688
## iter 350 value 298.166221
## iter 360 value 297.639823
## iter 370 value 297.337774
## iter 380 value 295.457185
## iter 390 value 293.231000
## iter 400 value 292.067611
## iter 410 value 290.788416
## iter 420 value 287.644827
## iter 430 value 285.054687
## iter 440 value 283.389651
## iter 450 value 281.085013
## iter 460 value 278.140069
## iter 470 value 276.125443
## iter 480 value 274.818999
## iter 490 value 273.786823
## iter 500 value 273.615208
## final value 273.615208
## stopped after 500 iterations
## # weights: 181
## initial value 1415817.314266
## iter 10 value 1153.746106
## iter 20 value 832.706463
## iter 30 value 736.038801
## iter 40 value 615.890780
## iter 50 value 494.425847
## iter 60 value 430.101505
## iter 70 value 376.750882
## iter 80 value 328.624886
## iter 90 value 303.685305
## iter 100 value 278.555990
## iter 110 value 251.284476
## iter 120 value 237.195157
## iter 130 value 224.938165
## iter 140 value 215.014507
## iter 150 value 208.263521
## iter 160 value 202.777010
## iter 170 value 198.386358
## iter 180 value 193.232424
## iter 190 value 189.899769
## iter 200 value 186.888340
## iter 210 value 182.727949
## iter 220 value 180.164476
## iter 230 value 176.324788
## iter 240 value 174.429170
## iter 250 value 173.034823
## iter 260 value 172.218962
## iter 270 value 171.557560
## iter 280 value 171.038350
## iter 290 value 170.584040
## iter 300 value 170.214840
## iter 310 value 169.397091
## iter 320 value 168.188245
## iter 330 value 166.969432
## iter 340 value 165.835678
## iter 350 value 164.841096
## iter 360 value 163.915017
## iter 370 value 163.153419
## iter 380 value 162.791262
## iter 390 value 162.174939
## iter 400 value 161.237640
## iter 410 value 160.027661
## iter 420 value 158.675966
## iter 430 value 157.343618
## iter 440 value 155.833902
## iter 450 value 154.797980
## iter 460 value 153.169628
## iter 470 value 150.788225
## iter 480 value 148.332568
## iter 490 value 145.043753
## iter 500 value 142.875852
## final value 142.875852
## stopped after 500 iterations
## # weights: 241
## initial value 1369819.313009
## iter 10 value 1539.309919
## iter 20 value 838.402909
## iter 30 value 728.270365
## iter 40 value 562.696980
## iter 50 value 438.775254
## iter 60 value 372.495928
## iter 70 value 320.728187
## iter 80 value 289.415911
## iter 90 value 248.421982
## iter 100 value 220.815497
## iter 110 value 204.095880
## iter 120 value 186.211479
## iter 130 value 171.728556
## iter 140 value 160.688435
## iter 150 value 153.035179
## iter 160 value 146.617843
## iter 170 value 138.845792
## iter 180 value 133.165401
## iter 190 value 129.035622
## iter 200 value 124.474376
## iter 210 value 119.018202
## iter 220 value 112.296760
## iter 230 value 107.162626
## iter 240 value 103.136083
## iter 250 value 97.478991
## iter 260 value 92.199305
## iter 270 value 88.770772
## iter 280 value 87.430672
## iter 290 value 86.579492
## iter 300 value 86.023614
## iter 310 value 84.610445
## iter 320 value 83.296348
## iter 330 value 82.022265
## iter 340 value 80.599078
## iter 350 value 79.400415
## iter 360 value 78.445209
## iter 370 value 77.183573
## iter 380 value 75.491217
## iter 390 value 72.918922
## iter 400 value 70.337756
## iter 410 value 68.710865
## iter 420 value 66.708595
## iter 430 value 64.351618
## iter 440 value 60.124377
## iter 450 value 55.873978
## iter 460 value 53.803475
## iter 470 value 52.027364
## iter 480 value 50.823363
## iter 490 value 50.311411
## iter 500 value 50.163579
## final value 50.163579
## stopped after 500 iterations
## # weights: 25
## initial value 1400993.701429
## iter 10 value 7286.924569
## iter 20 value 6169.348781
## iter 30 value 3574.186681
## iter 40 value 2555.829148
## iter 50 value 1789.560102
## iter 60 value 1355.869399
## iter 70 value 1169.551700
## iter 80 value 1134.231447
## iter 90 value 1132.491137
## iter 100 value 1114.476538
## iter 110 value 1075.692237
## iter 120 value 1062.059411
## iter 130 value 1047.381628
## iter 140 value 1044.352079
## iter 150 value 1031.830323
## iter 160 value 1001.355214
## iter 170 value 997.196055
## iter 180 value 996.462403
## iter 190 value 996.421291
## iter 200 value 996.269299
## iter 210 value 995.353467
## iter 220 value 994.511071
## iter 230 value 993.976228
## iter 240 value 993.958154
## iter 250 value 993.954769
## iter 250 value 993.954768
## final value 993.954725
## converged
## # weights: 61
## initial value 1421367.163171
## iter 10 value 3801.275557
## iter 20 value 1833.895676
## iter 30 value 1551.830802
## iter 40 value 1298.713281
## iter 50 value 1195.702349
## iter 60 value 1026.301921
## iter 70 value 880.421993
## iter 80 value 809.693984
## iter 90 value 774.331597
## iter 100 value 739.962961
## iter 110 value 723.903418
## iter 120 value 713.176692
## iter 130 value 703.837327
## iter 140 value 694.597519
## iter 150 value 693.679250
## iter 160 value 692.263834
## iter 170 value 689.627343
## iter 180 value 685.489837
## iter 190 value 678.060038
## iter 200 value 676.052089
## iter 210 value 674.837394
## iter 220 value 672.776495
## iter 230 value 668.218115
## iter 240 value 666.645731
## iter 250 value 664.352842
## iter 260 value 659.053745
## iter 270 value 657.898756
## iter 280 value 657.781801
## iter 290 value 657.545448
## iter 300 value 657.240692
## iter 310 value 656.479659
## iter 320 value 655.917766
## iter 330 value 655.499990
## iter 340 value 654.925337
## iter 350 value 654.405208
## iter 360 value 654.165909
## iter 370 value 653.894195
## iter 380 value 653.614006
## iter 390 value 653.497274
## iter 400 value 653.494752
## iter 410 value 653.486358
## iter 420 value 653.474719
## iter 430 value 653.388216
## iter 440 value 653.232419
## iter 450 value 653.127303
## iter 460 value 653.034567
## iter 470 value 652.971672
## iter 480 value 652.885445
## iter 490 value 652.815117
## iter 500 value 652.755476
## final value 652.755476
## stopped after 500 iterations
## # weights: 121
## initial value 1415602.608750
## iter 10 value 1131.724780
## iter 20 value 854.076189
## iter 30 value 683.484522
## iter 40 value 602.559641
## iter 50 value 529.918591
## iter 60 value 481.979000
## iter 70 value 443.203096
## iter 80 value 416.858570
## iter 90 value 390.489370
## iter 100 value 367.867105
## iter 110 value 342.731487
## iter 120 value 328.864006
## iter 130 value 319.099743
## iter 140 value 309.522168
## iter 150 value 302.895755
## iter 160 value 297.557113
## iter 170 value 293.862931
## iter 180 value 292.114980
## iter 190 value 290.639564
## iter 200 value 288.482929
## iter 210 value 287.366481
## iter 220 value 286.335792
## iter 230 value 285.218083
## iter 240 value 284.067250
## iter 250 value 283.658063
## iter 260 value 283.449817
## iter 270 value 283.158512
## iter 280 value 282.432274
## iter 290 value 281.315564
## iter 300 value 279.712388
## iter 310 value 278.141529
## iter 320 value 277.328944
## iter 330 value 276.888538
## iter 340 value 276.763233
## iter 350 value 276.501600
## iter 360 value 275.423381
## iter 370 value 273.058743
## iter 380 value 272.531471
## iter 390 value 272.160524
## iter 400 value 272.091444
## iter 410 value 272.064283
## iter 420 value 272.051825
## iter 430 value 272.049844
## iter 440 value 272.036350
## iter 450 value 272.026493
## iter 460 value 272.008482
## iter 470 value 272.000882
## iter 480 value 268.993504
## iter 490 value 268.639132
## iter 500 value 268.624311
## final value 268.624311
## stopped after 500 iterations
## # weights: 181
## initial value 1355871.586909
## iter 10 value 1184.136649
## iter 20 value 802.268861
## iter 30 value 678.437420
## iter 40 value 552.443959
## iter 50 value 441.127757
## iter 60 value 383.976764
## iter 70 value 346.153647
## iter 80 value 308.843206
## iter 90 value 289.959258
## iter 100 value 270.953095
## iter 110 value 243.014335
## iter 120 value 220.509667
## iter 130 value 202.647417
## iter 140 value 191.622385
## iter 150 value 184.115241
## iter 160 value 175.728647
## iter 170 value 165.887444
## iter 180 value 156.588117
## iter 190 value 151.471111
## iter 200 value 146.200880
## iter 210 value 142.010271
## iter 220 value 137.879208
## iter 230 value 134.621731
## iter 240 value 132.038651
## iter 250 value 128.993549
## iter 260 value 124.260771
## iter 270 value 119.745292
## iter 280 value 116.775291
## iter 290 value 114.378241
## iter 300 value 111.513364
## iter 310 value 108.936192
## iter 320 value 106.044773
## iter 330 value 103.181736
## iter 340 value 99.863907
## iter 350 value 98.698850
## iter 360 value 98.220772
## iter 370 value 98.034159
## iter 380 value 97.933845
## iter 390 value 97.821874
## iter 400 value 97.626473
## iter 410 value 97.337563
## iter 420 value 97.014430
## iter 430 value 96.560019
## iter 440 value 96.043657
## iter 450 value 94.360175
## iter 460 value 92.536858
## iter 470 value 92.194955
## iter 480 value 91.892035
## iter 490 value 91.394640
## iter 500 value 90.276380
## final value 90.276380
## stopped after 500 iterations
## # weights: 241
## initial value 1372258.678841
## iter 10 value 1149.771341
## iter 20 value 785.212625
## iter 30 value 646.586130
## iter 40 value 503.263755
## iter 50 value 401.802374
## iter 60 value 353.768992
## iter 70 value 298.502214
## iter 80 value 247.066554
## iter 90 value 206.890318
## iter 100 value 178.046761
## iter 110 value 162.193620
## iter 120 value 150.730782
## iter 130 value 141.380209
## iter 140 value 134.133471
## iter 150 value 126.608314
## iter 160 value 118.867486
## iter 170 value 110.990553
## iter 180 value 103.031639
## iter 190 value 97.285074
## iter 200 value 90.562218
## iter 210 value 82.410562
## iter 220 value 75.531322
## iter 230 value 68.922569
## iter 240 value 63.826676
## iter 250 value 60.268014
## iter 260 value 57.228857
## iter 270 value 54.574929
## iter 280 value 51.014055
## iter 290 value 46.871660
## iter 300 value 44.390258
## iter 310 value 41.580841
## iter 320 value 39.563706
## iter 330 value 38.035230
## iter 340 value 36.321815
## iter 350 value 34.140028
## iter 360 value 31.817678
## iter 370 value 30.168141
## iter 380 value 28.995741
## iter 390 value 27.598648
## iter 400 value 25.796509
## iter 410 value 24.812816
## iter 420 value 24.331756
## iter 430 value 24.062449
## iter 440 value 23.914152
## iter 450 value 23.757570
## iter 460 value 23.583903
## iter 470 value 23.434727
## iter 480 value 23.296581
## iter 490 value 23.234394
## iter 500 value 23.220561
## final value 23.220561
## stopped after 500 iterations
## # weights: 25
## initial value 1369324.544997
## iter 10 value 7025.615488
## iter 20 value 4680.973133
## iter 30 value 3838.834364
## iter 40 value 2469.322735
## iter 50 value 1905.230958
## iter 60 value 1505.307199
## iter 70 value 1451.852756
## iter 80 value 1423.629224
## iter 90 value 1413.427977
## iter 100 value 1411.047402
## iter 110 value 1404.883621
## iter 120 value 1402.105592
## iter 130 value 1400.522710
## iter 140 value 1399.809101
## iter 150 value 1399.776171
## iter 160 value 1399.237090
## iter 170 value 1399.085336
## final value 1399.053295
## converged
## # weights: 61
## initial value 1403025.232844
## iter 10 value 4726.215708
## iter 20 value 2672.392370
## iter 30 value 1982.050461
## iter 40 value 1600.025290
## iter 50 value 1129.167559
## iter 60 value 919.669549
## iter 70 value 837.774313
## iter 80 value 785.820942
## iter 90 value 757.241050
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## iter 480 value 582.811397
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## iter 500 value 582.707143
## final value 582.707143
## stopped after 500 iterations
## # weights: 121
## initial value 1320129.737371
## iter 10 value 1624.506124
## iter 20 value 917.282825
## iter 30 value 789.229924
## iter 40 value 680.479243
## iter 50 value 625.714741
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## iter 470 value 244.492872
## iter 480 value 244.469083
## iter 490 value 244.439086
## iter 500 value 244.282685
## final value 244.282685
## stopped after 500 iterations
## # weights: 181
## initial value 1397857.760328
## iter 10 value 1389.843572
## iter 20 value 825.989549
## iter 30 value 666.513984
## iter 40 value 539.944056
## iter 50 value 439.599134
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## iter 100 value 251.139835
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## iter 400 value 128.515748
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## iter 470 value 124.932161
## iter 480 value 123.964446
## iter 490 value 122.935869
## iter 500 value 122.191363
## final value 122.191363
## stopped after 500 iterations
## # weights: 241
## initial value 1359008.769885
## iter 10 value 1462.510466
## iter 20 value 723.271013
## iter 30 value 566.481990
## iter 40 value 442.789109
## iter 50 value 358.333365
## iter 60 value 304.736522
## iter 70 value 252.980201
## iter 80 value 204.397746
## iter 90 value 170.681692
## iter 100 value 135.710306
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## iter 130 value 93.986501
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## iter 330 value 33.550049
## iter 340 value 33.007241
## iter 350 value 32.296169
## iter 360 value 31.666588
## iter 370 value 31.090456
## iter 380 value 30.388627
## iter 390 value 29.780454
## iter 400 value 29.227964
## iter 410 value 28.610060
## iter 420 value 28.009516
## iter 430 value 27.500394
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## iter 450 value 26.677994
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## iter 470 value 25.918661
## iter 480 value 25.656754
## iter 490 value 25.521249
## iter 500 value 25.482336
## final value 25.482336
## stopped after 500 iterations
## # weights: 25
## initial value 1417650.253061
## iter 10 value 4084.053345
## iter 20 value 2715.588637
## iter 30 value 2138.728630
## iter 40 value 1189.330704
## iter 50 value 1009.620663
## iter 60 value 962.373447
## iter 70 value 954.767990
## iter 80 value 950.896096
## iter 90 value 936.624246
## iter 100 value 925.907521
## iter 110 value 920.523740
## iter 120 value 920.081526
## iter 130 value 919.962997
## iter 140 value 918.562620
## iter 150 value 918.020102
## iter 160 value 917.764499
## iter 170 value 917.746802
## iter 180 value 917.734281
## iter 190 value 917.697416
## iter 200 value 917.694667
## final value 917.693065
## converged
## # weights: 61
## initial value 1401539.000039
## iter 10 value 3098.745033
## iter 20 value 1322.295492
## iter 30 value 1086.346817
## iter 40 value 929.204248
## iter 50 value 838.768014
## iter 60 value 755.052310
## iter 70 value 695.218883
## iter 80 value 670.491005
## iter 90 value 654.245207
## iter 100 value 646.392225
## iter 110 value 640.878637
## iter 120 value 637.579079
## iter 130 value 635.349704
## iter 140 value 633.898970
## iter 150 value 631.327864
## iter 160 value 626.956828
## iter 170 value 619.654651
## iter 180 value 613.850636
## iter 190 value 608.093214
## iter 200 value 603.988686
## iter 210 value 599.942005
## iter 220 value 598.686590
## iter 230 value 598.386494
## iter 240 value 598.086972
## iter 250 value 597.763038
## iter 260 value 597.659772
## iter 270 value 596.713514
## iter 280 value 593.822092
## iter 290 value 590.576494
## iter 300 value 589.744958
## iter 310 value 589.326799
## iter 320 value 588.765574
## iter 330 value 588.619048
## iter 340 value 588.586951
## iter 350 value 588.585413
## final value 588.585377
## converged
## # weights: 121
## initial value 1387618.133988
## iter 10 value 2755.396783
## iter 20 value 1898.382193
## iter 30 value 1152.063702
## iter 40 value 898.125173
## iter 50 value 771.834498
## iter 60 value 691.650032
## iter 70 value 633.233149
## iter 80 value 580.548834
## iter 90 value 547.099971
## iter 100 value 519.919839
## iter 110 value 507.866666
## iter 120 value 494.710788
## iter 130 value 472.207826
## iter 140 value 454.671771
## iter 150 value 430.670394
## iter 160 value 398.197496
## iter 170 value 380.150740
## iter 180 value 371.362686
## iter 190 value 364.037217
## iter 200 value 359.261556
## iter 210 value 354.503980
## iter 220 value 350.648150
## iter 230 value 344.865215
## iter 240 value 331.771181
## iter 250 value 325.367721
## iter 260 value 323.240389
## iter 270 value 318.058411
## iter 280 value 313.153201
## iter 290 value 307.073215
## iter 300 value 302.646817
## iter 310 value 300.486933
## iter 320 value 299.572486
## iter 330 value 298.624885
## iter 340 value 297.010336
## iter 350 value 296.514963
## iter 360 value 296.143007
## iter 370 value 295.967168
## iter 380 value 295.900194
## iter 390 value 295.885235
## iter 400 value 295.881128
## iter 410 value 295.879786
## iter 420 value 295.879329
## final value 295.879285
## converged
## # weights: 181
## initial value 1353109.857907
## iter 10 value 1165.416312
## iter 20 value 843.752321
## iter 30 value 655.253237
## iter 40 value 536.410116
## iter 50 value 457.419045
## iter 60 value 404.874448
## iter 70 value 352.908524
## iter 80 value 309.105905
## iter 90 value 285.359892
## iter 100 value 269.052267
## iter 110 value 250.935847
## iter 120 value 234.362230
## iter 130 value 222.713092
## iter 140 value 212.620639
## iter 150 value 206.229230
## iter 160 value 198.722980
## iter 170 value 192.233115
## iter 180 value 188.060990
## iter 190 value 184.665361
## iter 200 value 181.934337
## iter 210 value 178.841313
## iter 220 value 177.155107
## iter 230 value 175.699525
## iter 240 value 173.471433
## iter 250 value 171.385441
## iter 260 value 169.651503
## iter 270 value 166.874975
## iter 280 value 164.673550
## iter 290 value 162.791373
## iter 300 value 161.190808
## iter 310 value 159.902460
## iter 320 value 158.452868
## iter 330 value 157.531684
## iter 340 value 156.717297
## iter 350 value 155.515158
## iter 360 value 153.948118
## iter 370 value 153.479566
## iter 380 value 153.347850
## iter 390 value 153.064633
## iter 400 value 152.508160
## iter 410 value 151.896849
## iter 420 value 150.940807
## iter 430 value 149.456858
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## iter 450 value 146.017017
## iter 460 value 143.766589
## iter 470 value 141.991566
## iter 480 value 140.570218
## iter 490 value 139.752987
## iter 500 value 139.168733
## final value 139.168733
## stopped after 500 iterations
## # weights: 241
## initial value 1405068.224245
## iter 10 value 1596.038062
## iter 20 value 795.607971
## iter 30 value 623.269353
## iter 40 value 509.679559
## iter 50 value 436.220921
## iter 60 value 383.891636
## iter 70 value 338.833303
## iter 80 value 305.866338
## iter 90 value 279.324549
## iter 100 value 246.850977
## iter 110 value 222.836651
## iter 120 value 197.523648
## iter 130 value 176.006235
## iter 140 value 161.946539
## iter 150 value 149.497127
## iter 160 value 137.447945
## iter 170 value 130.171465
## iter 180 value 123.922143
## iter 190 value 118.306383
## iter 200 value 113.787738
## iter 210 value 110.244247
## iter 220 value 107.646561
## iter 230 value 104.692502
## iter 240 value 102.580985
## iter 250 value 101.111849
## iter 260 value 99.834491
## iter 270 value 98.607042
## iter 280 value 97.113060
## iter 290 value 95.751318
## iter 300 value 94.497124
## iter 310 value 92.636988
## iter 320 value 90.879702
## iter 330 value 89.326452
## iter 340 value 87.691596
## iter 350 value 85.540872
## iter 360 value 84.316849
## iter 370 value 83.180181
## iter 380 value 82.223929
## iter 390 value 81.484933
## iter 400 value 80.743547
## iter 410 value 79.916969
## iter 420 value 79.257957
## iter 430 value 78.699635
## iter 440 value 78.222871
## iter 450 value 77.832244
## iter 460 value 77.519520
## iter 470 value 77.269783
## iter 480 value 76.985714
## iter 490 value 76.910455
## iter 500 value 76.875690
## final value 76.875690
## stopped after 500 iterations
## # weights: 25
## initial value 1400164.074754
## iter 10 value 6642.901198
## iter 20 value 6387.370291
## iter 30 value 6044.695178
## iter 40 value 5062.146424
## iter 50 value 4059.457495
## iter 60 value 2001.222288
## iter 70 value 1555.128719
## iter 80 value 1465.293944
## iter 90 value 1398.517813
## iter 100 value 1310.299327
## iter 110 value 1241.127429
## iter 120 value 1215.797922
## iter 130 value 1204.421555
## iter 140 value 1201.592970
## iter 150 value 1191.265908
## iter 160 value 1190.345961
## final value 1190.338876
## converged
## # weights: 61
## initial value 1385280.374491
## iter 10 value 2582.024502
## iter 20 value 1971.944531
## iter 30 value 1450.112898
## iter 40 value 1146.645064
## iter 50 value 1056.306893
## iter 60 value 988.428688
## iter 70 value 947.330373
## iter 80 value 923.817251
## iter 90 value 902.099049
## iter 100 value 857.755764
## iter 110 value 843.351629
## iter 120 value 833.606703
## iter 130 value 826.727717
## iter 140 value 816.722511
## iter 150 value 806.684808
## iter 160 value 796.042540
## iter 170 value 789.793169
## iter 180 value 788.082572
## iter 190 value 787.325292
## iter 200 value 787.182430
## final value 787.180783
## converged
## # weights: 121
## initial value 1417214.318840
## iter 10 value 5868.205115
## iter 20 value 2619.929992
## iter 30 value 2083.122921
## iter 40 value 1877.312490
## iter 50 value 1646.096891
## iter 60 value 1500.504506
## iter 70 value 1352.908004
## iter 80 value 1245.187137
## iter 90 value 1148.551396
## iter 100 value 1074.059166
## iter 110 value 995.988131
## iter 120 value 941.215089
## iter 130 value 896.848901
## iter 140 value 852.545749
## iter 150 value 798.526730
## iter 160 value 772.647326
## iter 170 value 731.094855
## iter 180 value 692.493018
## iter 190 value 660.076309
## iter 200 value 641.638607
## iter 210 value 631.134769
## iter 220 value 624.724158
## iter 230 value 599.604951
## iter 240 value 585.361793
## iter 250 value 580.361903
## iter 260 value 577.447794
## iter 270 value 565.443942
## iter 280 value 554.309796
## iter 290 value 540.323672
## iter 300 value 529.512559
## iter 310 value 522.124497
## iter 320 value 518.501014
## iter 330 value 515.559415
## iter 340 value 514.249077
## iter 350 value 513.233101
## iter 360 value 512.873349
## iter 370 value 512.407446
## iter 380 value 511.940658
## iter 390 value 511.812916
## iter 400 value 511.799308
## iter 410 value 511.794300
## iter 420 value 511.793443
## final value 511.793426
## converged
## # weights: 181
## initial value 1389331.108552
## iter 10 value 1341.599548
## iter 20 value 943.350027
## iter 30 value 789.295542
## iter 40 value 681.720649
## iter 50 value 627.641656
## iter 60 value 598.407514
## iter 70 value 566.438870
## iter 80 value 540.268529
## iter 90 value 513.754058
## iter 100 value 491.992306
## iter 110 value 480.722220
## iter 120 value 472.846814
## iter 130 value 467.173552
## iter 140 value 461.613403
## iter 150 value 455.879042
## iter 160 value 451.865006
## iter 170 value 447.365709
## iter 180 value 443.875230
## iter 190 value 441.820392
## iter 200 value 439.510564
## iter 210 value 436.956524
## iter 220 value 435.512898
## iter 230 value 434.130545
## iter 240 value 432.786256
## iter 250 value 431.379109
## iter 260 value 428.978688
## iter 270 value 427.376248
## iter 280 value 426.362326
## iter 290 value 425.587317
## iter 300 value 425.208193
## iter 310 value 424.779498
## iter 320 value 424.430272
## iter 330 value 424.179110
## iter 340 value 423.908331
## iter 350 value 421.839731
## iter 360 value 419.343731
## iter 370 value 418.842831
## iter 380 value 418.526914
## iter 390 value 418.021076
## iter 400 value 417.566077
## iter 410 value 417.115466
## iter 420 value 415.970516
## iter 430 value 415.221372
## iter 440 value 414.807455
## iter 450 value 414.640562
## iter 460 value 414.557247
## iter 470 value 414.519031
## iter 480 value 414.490407
## iter 490 value 414.477749
## iter 500 value 414.450538
## final value 414.450538
## stopped after 500 iterations
## # weights: 241
## initial value 1404155.475257
## iter 10 value 1313.398000
## iter 20 value 863.435281
## iter 30 value 719.114376
## iter 40 value 625.043082
## iter 50 value 565.166599
## iter 60 value 506.210547
## iter 70 value 475.998538
## iter 80 value 455.297863
## iter 90 value 439.189093
## iter 100 value 428.083829
## iter 110 value 420.308268
## iter 120 value 413.383190
## iter 130 value 407.542205
## iter 140 value 403.819131
## iter 150 value 400.788510
## iter 160 value 397.410619
## iter 170 value 393.600037
## iter 180 value 388.757840
## iter 190 value 385.053695
## iter 200 value 381.660399
## iter 210 value 379.328134
## iter 220 value 378.092508
## iter 230 value 376.705707
## iter 240 value 374.414701
## iter 250 value 371.757390
## iter 260 value 369.178311
## iter 270 value 367.663244
## iter 280 value 365.786771
## iter 290 value 363.741240
## iter 300 value 361.548677
## iter 310 value 359.573270
## iter 320 value 358.365601
## iter 330 value 357.233943
## iter 340 value 356.006934
## iter 350 value 354.930555
## iter 360 value 353.432631
## iter 370 value 351.723026
## iter 380 value 350.273249
## iter 390 value 348.867074
## iter 400 value 347.817545
## iter 410 value 345.120932
## iter 420 value 342.102345
## iter 430 value 339.704351
## iter 440 value 338.141523
## iter 450 value 336.761145
## iter 460 value 335.549676
## iter 470 value 334.602891
## iter 480 value 334.134254
## iter 490 value 333.984825
## iter 500 value 333.778736
## final value 333.778736
## stopped after 500 iterations
## # weights: 25
## initial value 1416989.041823
## iter 10 value 2960.777171
## iter 20 value 1719.661239
## iter 30 value 1410.996786
## iter 40 value 1249.601392
## iter 50 value 1185.450602
## iter 60 value 1174.047284
## iter 70 value 1160.482656
## iter 80 value 1133.990520
## iter 90 value 1042.532070
## iter 100 value 963.746576
## iter 110 value 946.125407
## iter 120 value 939.834232
## iter 130 value 916.539892
## iter 140 value 916.169692
## iter 150 value 915.643496
## iter 160 value 915.563343
## iter 170 value 915.561790
## iter 180 value 915.548558
## iter 190 value 915.479505
## iter 200 value 915.465998
## final value 915.465940
## converged
## # weights: 61
## initial value 1376621.149496
## iter 10 value 16065.394452
## iter 20 value 13781.148611
## iter 30 value 7675.871127
## iter 40 value 4916.573484
## iter 50 value 4012.861697
## iter 60 value 3534.644192
## iter 70 value 3392.922666
## iter 80 value 3372.988033
## iter 90 value 3281.374845
## iter 100 value 2928.796444
## iter 110 value 2719.223246
## iter 120 value 2589.669803
## iter 130 value 2443.890697
## iter 140 value 2111.945079
## iter 150 value 1636.519692
## iter 160 value 1445.032852
## iter 170 value 1338.128519
## iter 180 value 1320.020574
## iter 190 value 1291.274325
## iter 200 value 1232.413561
## iter 210 value 1029.095621
## iter 220 value 971.659463
## iter 230 value 950.468630
## iter 240 value 933.611948
## iter 250 value 914.779687
## iter 260 value 900.079159
## iter 270 value 892.927943
## iter 280 value 888.280447
## iter 290 value 885.154634
## iter 300 value 884.374327
## iter 310 value 883.577239
## iter 320 value 883.563140
## iter 330 value 883.461320
## iter 340 value 882.870279
## iter 350 value 882.840623
## iter 360 value 882.699917
## iter 370 value 881.846974
## iter 380 value 881.669647
## iter 390 value 878.787846
## iter 400 value 877.665008
## iter 410 value 876.869440
## iter 420 value 876.093261
## iter 430 value 875.652578
## iter 440 value 875.408594
## iter 450 value 875.267781
## iter 460 value 874.215600
## iter 470 value 874.067983
## iter 480 value 873.997632
## iter 490 value 873.828343
## iter 500 value 873.827342
## final value 873.827342
## stopped after 500 iterations
## # weights: 121
## initial value 1447298.929750
## iter 10 value 5181.929340
## iter 20 value 1834.352568
## iter 30 value 1225.653568
## iter 40 value 1004.525538
## iter 50 value 780.880576
## iter 60 value 671.151369
## iter 70 value 618.964004
## iter 80 value 585.118213
## iter 90 value 567.470666
## iter 100 value 546.258135
## iter 110 value 521.214113
## iter 120 value 498.475328
## iter 130 value 488.175707
## iter 140 value 484.194106
## iter 150 value 477.805818
## iter 160 value 472.216789
## iter 170 value 466.072367
## iter 180 value 460.034552
## iter 190 value 457.442810
## iter 200 value 454.922184
## iter 210 value 453.047270
## iter 220 value 451.910727
## iter 230 value 451.326885
## iter 240 value 451.028485
## iter 250 value 450.966261
## iter 260 value 450.913724
## iter 270 value 450.797581
## iter 280 value 450.592101
## iter 290 value 450.024132
## iter 300 value 449.604921
## iter 310 value 449.253676
## iter 320 value 447.676271
## iter 330 value 445.942952
## iter 340 value 444.375442
## iter 350 value 443.058276
## iter 360 value 442.127030
## iter 370 value 441.366972
## iter 380 value 440.866502
## iter 390 value 440.656359
## iter 400 value 440.230960
## iter 410 value 439.640559
## iter 420 value 437.857674
## iter 430 value 435.193435
## iter 440 value 431.927080
## iter 450 value 427.592689
## iter 460 value 423.121874
## iter 470 value 418.625128
## iter 480 value 415.019988
## iter 490 value 413.482492
## iter 500 value 413.303608
## final value 413.303608
## stopped after 500 iterations
## # weights: 181
## initial value 1401242.562861
## iter 10 value 1038.030963
## iter 20 value 806.540386
## iter 30 value 653.029115
## iter 40 value 528.121089
## iter 50 value 430.921571
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## iter 500 value 81.437220
## final value 81.437220
## stopped after 500 iterations
## # weights: 241
## initial value 1383509.087273
## iter 10 value 1553.884628
## iter 20 value 808.961596
## iter 30 value 616.847233
## iter 40 value 519.128414
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## iter 470 value 30.910393
## iter 480 value 29.783656
## iter 490 value 29.194707
## iter 500 value 28.986760
## final value 28.986760
## stopped after 500 iterations
## # weights: 25
## initial value 1402566.076914
## iter 10 value 7261.654855
## iter 20 value 5207.359198
## iter 30 value 4439.362328
## iter 40 value 3183.006812
## iter 50 value 1828.477348
## iter 60 value 1565.969596
## iter 70 value 1529.718432
## iter 80 value 1391.768882
## iter 90 value 1355.511895
## iter 100 value 1325.174853
## iter 110 value 1316.033133
## iter 120 value 1308.370581
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## iter 140 value 1279.975529
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## iter 160 value 1270.606752
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## iter 270 value 1249.608336
## iter 280 value 1249.121694
## iter 290 value 1248.588230
## iter 300 value 1248.293616
## final value 1248.293523
## converged
## # weights: 61
## initial value 1394754.676853
## iter 10 value 6287.953677
## iter 20 value 5877.508740
## iter 30 value 5662.835856
## iter 40 value 5368.665359
## iter 50 value 4330.848559
## iter 60 value 3492.747608
## iter 70 value 3074.246026
## iter 80 value 2477.500931
## iter 90 value 1623.101047
## iter 100 value 1354.410301
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## iter 130 value 1280.881685
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## iter 180 value 1256.784800
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## iter 200 value 1174.028229
## iter 210 value 1106.393216
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## iter 240 value 1049.251113
## iter 250 value 1045.089319
## iter 260 value 1040.075482
## iter 270 value 1037.394133
## iter 280 value 1037.035009
## iter 290 value 1036.740568
## iter 300 value 1036.723106
## iter 310 value 1036.643967
## iter 320 value 1036.190309
## iter 330 value 1034.869975
## iter 340 value 1033.822092
## iter 350 value 1033.182624
## iter 360 value 1032.677233
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## iter 390 value 1031.268442
## iter 400 value 1030.967446
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## iter 470 value 1027.744675
## iter 480 value 1025.562395
## iter 490 value 1025.323638
## iter 500 value 1025.279347
## final value 1025.279347
## stopped after 500 iterations
## # weights: 121
## initial value 1367475.617787
## iter 10 value 1654.361031
## iter 20 value 1102.767328
## iter 30 value 820.586113
## iter 40 value 677.090345
## iter 50 value 596.180584
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## iter 100 value 401.034558
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## iter 200 value 313.192234
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## iter 300 value 295.381860
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## iter 340 value 288.789914
## iter 350 value 285.932165
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## iter 470 value 276.956517
## iter 480 value 276.531282
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## iter 500 value 276.042737
## final value 276.042737
## stopped after 500 iterations
## # weights: 181
## initial value 1417204.418384
## iter 10 value 1140.637693
## iter 20 value 818.843893
## iter 30 value 701.413525
## iter 40 value 603.209337
## iter 50 value 474.589439
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## iter 70 value 362.221246
## iter 80 value 315.518473
## iter 90 value 282.826919
## iter 100 value 251.967307
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## iter 470 value 87.643214
## iter 480 value 86.933899
## iter 490 value 86.352338
## iter 500 value 85.858064
## final value 85.858064
## stopped after 500 iterations
## # weights: 241
## initial value 1393322.400042
## iter 10 value 1205.207627
## iter 20 value 851.168503
## iter 30 value 674.000796
## iter 40 value 518.316814
## iter 50 value 415.685489
## iter 60 value 330.687844
## iter 70 value 290.780672
## iter 80 value 267.215926
## iter 90 value 241.289060
## iter 100 value 213.003486
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## iter 310 value 31.843356
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## iter 330 value 27.363368
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## final value 13.599348
## stopped after 500 iterations
## # weights: 25
## initial value 1431258.850509
## iter 10 value 6440.114401
## iter 20 value 5125.540306
## iter 30 value 2206.780975
## iter 40 value 1387.680679
## iter 50 value 1138.726850
## iter 60 value 1135.377904
## iter 70 value 1122.514121
## iter 80 value 1084.884544
## iter 90 value 1070.345124
## iter 100 value 1065.086750
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## iter 120 value 1061.057067
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## iter 140 value 1051.227633
## iter 150 value 1043.432472
## iter 160 value 1039.850547
## iter 170 value 1039.369968
## iter 180 value 1034.081873
## iter 190 value 950.556535
## iter 200 value 929.607193
## iter 210 value 924.650914
## iter 220 value 923.624803
## iter 230 value 921.494632
## iter 240 value 918.998389
## iter 250 value 917.927299
## iter 260 value 917.547007
## iter 270 value 917.231732
## iter 280 value 916.473825
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## iter 300 value 916.128720
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## iter 320 value 916.124717
## iter 330 value 916.124147
## iter 340 value 916.123874
## iter 350 value 916.121167
## iter 360 value 916.114286
## final value 916.109024
## converged
## # weights: 61
## initial value 1362357.532824
## iter 10 value 14376.744770
## iter 20 value 3935.477363
## iter 30 value 2435.497625
## iter 40 value 2169.313473
## iter 50 value 2141.868996
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## iter 70 value 2117.329260
## iter 80 value 2094.723944
## iter 90 value 2087.620364
## iter 100 value 2084.261977
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## iter 180 value 1386.444880
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## iter 200 value 1199.249570
## iter 210 value 1174.207627
## iter 220 value 1165.800405
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## iter 240 value 1102.519203
## iter 250 value 1083.551168
## iter 260 value 1079.112846
## iter 270 value 1078.277719
## iter 280 value 1076.609711
## iter 290 value 1072.435987
## iter 300 value 1068.482678
## iter 310 value 1067.044306
## iter 320 value 1065.825907
## iter 330 value 1065.279357
## iter 340 value 1065.152371
## iter 350 value 1064.975296
## iter 360 value 1064.878213
## iter 370 value 1064.833656
## iter 380 value 1064.828297
## iter 390 value 1064.663800
## iter 400 value 1064.237691
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## iter 420 value 1052.219491
## iter 430 value 1008.067288
## iter 440 value 998.416930
## iter 450 value 995.142617
## iter 460 value 992.602135
## iter 470 value 991.414141
## iter 480 value 986.600661
## iter 490 value 981.079596
## iter 500 value 976.518808
## final value 976.518808
## stopped after 500 iterations
## # weights: 121
## initial value 1400052.949230
## iter 10 value 1357.363423
## iter 20 value 955.856413
## iter 30 value 785.715592
## iter 40 value 668.588330
## iter 50 value 623.568769
## iter 60 value 561.960622
## iter 70 value 520.059325
## iter 80 value 484.945296
## iter 90 value 454.749531
## iter 100 value 436.161761
## iter 110 value 424.895547
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## iter 140 value 356.382230
## iter 150 value 329.474240
## iter 160 value 318.040086
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## iter 250 value 285.703086
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## iter 270 value 285.113023
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## iter 300 value 283.764449
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## iter 320 value 283.070114
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## iter 340 value 280.336275
## iter 350 value 277.665658
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## iter 370 value 271.824590
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## iter 390 value 268.584779
## iter 400 value 268.039697
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## iter 480 value 267.307100
## iter 490 value 267.300631
## iter 490 value 267.300629
## iter 500 value 267.300466
## final value 267.300466
## stopped after 500 iterations
## # weights: 181
## initial value 1397145.145736
## iter 10 value 1106.518677
## iter 20 value 801.286081
## iter 30 value 622.146232
## iter 40 value 501.421257
## iter 50 value 382.790778
## iter 60 value 317.552276
## iter 70 value 283.276984
## iter 80 value 259.356579
## iter 90 value 235.093325
## iter 100 value 218.771039
## iter 110 value 207.968776
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## iter 300 value 110.626954
## iter 310 value 108.482130
## iter 320 value 106.563093
## iter 330 value 104.808079
## iter 340 value 103.070754
## iter 350 value 101.755176
## iter 360 value 99.535877
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## iter 390 value 98.110874
## iter 400 value 97.622415
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## iter 470 value 93.575642
## iter 480 value 93.081901
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## iter 500 value 92.185773
## final value 92.185773
## stopped after 500 iterations
## # weights: 241
## initial value 1458997.651564
## iter 10 value 1727.923925
## iter 20 value 885.671062
## iter 30 value 638.751078
## iter 40 value 459.473165
## iter 50 value 366.408861
## iter 60 value 315.452950
## iter 70 value 268.751903
## iter 80 value 220.458614
## iter 90 value 193.637491
## iter 100 value 171.898195
## iter 110 value 146.951051
## iter 120 value 121.939895
## iter 130 value 104.156560
## iter 140 value 96.918556
## iter 150 value 90.991373
## iter 160 value 85.777655
## iter 170 value 80.961729
## iter 180 value 74.709232
## iter 190 value 68.881175
## iter 200 value 63.180418
## iter 210 value 58.952547
## iter 220 value 54.182209
## iter 230 value 50.550535
## iter 240 value 47.186513
## iter 250 value 44.902050
## iter 260 value 43.049701
## iter 270 value 41.484372
## iter 280 value 40.504717
## iter 290 value 39.405958
## iter 300 value 38.596205
## iter 310 value 37.998230
## iter 320 value 37.171571
## iter 330 value 36.201169
## iter 340 value 35.341783
## iter 350 value 34.719914
## iter 360 value 34.033353
## iter 370 value 33.405781
## iter 380 value 32.796076
## iter 390 value 32.294109
## iter 400 value 31.923664
## iter 410 value 31.486506
## iter 420 value 31.153569
## iter 430 value 30.836815
## iter 440 value 30.511071
## iter 450 value 30.063056
## iter 460 value 29.753299
## iter 470 value 29.504078
## iter 480 value 29.340961
## iter 490 value 29.294698
## iter 500 value 29.284943
## final value 29.284943
## stopped after 500 iterations
## # weights: 25
## initial value 1383411.365924
## iter 10 value 13176.155167
## iter 20 value 6648.324684
## iter 30 value 4106.272620
## iter 40 value 3195.037278
## iter 50 value 2056.173808
## iter 60 value 1739.309139
## iter 70 value 1471.075933
## iter 80 value 1335.619102
## iter 90 value 1282.273418
## iter 100 value 1263.080329
## iter 110 value 1258.251806
## iter 120 value 1252.782064
## iter 130 value 1241.376627
## iter 140 value 1236.706357
## iter 150 value 1235.432813
## final value 1235.417412
## converged
## # weights: 61
## initial value 1409907.368082
## iter 10 value 5775.572850
## iter 20 value 2847.962920
## iter 30 value 1740.396940
## iter 40 value 1440.710364
## iter 50 value 1131.801176
## iter 60 value 963.259419
## iter 70 value 885.083859
## iter 80 value 856.386152
## iter 90 value 831.698069
## iter 100 value 817.241191
## iter 110 value 808.815931
## iter 120 value 805.332031
## iter 130 value 800.735182
## iter 140 value 800.519710
## iter 150 value 799.413169
## iter 160 value 795.631023
## iter 170 value 784.554314
## iter 180 value 779.479845
## iter 190 value 776.965150
## iter 200 value 766.477345
## iter 210 value 764.825680
## iter 220 value 764.354804
## iter 230 value 764.105223
## iter 240 value 763.920431
## iter 250 value 763.871016
## iter 260 value 763.859246
## iter 270 value 763.858018
## iter 280 value 763.853334
## iter 290 value 763.848144
## iter 300 value 763.831635
## iter 310 value 763.820810
## iter 320 value 763.819505
## final value 763.819201
## converged
## # weights: 121
## initial value 1405209.978527
## iter 10 value 1510.705932
## iter 20 value 950.789957
## iter 30 value 809.656908
## iter 40 value 687.839578
## iter 50 value 624.310844
## iter 60 value 577.615530
## iter 70 value 535.978895
## iter 80 value 503.644801
## iter 90 value 469.809418
## iter 100 value 440.728971
## iter 110 value 418.103138
## iter 120 value 391.870067
## iter 130 value 369.939068
## iter 140 value 359.231562
## iter 150 value 344.750887
## iter 160 value 335.470052
## iter 170 value 326.447928
## iter 180 value 318.025775
## iter 190 value 312.195093
## iter 200 value 308.205248
## iter 210 value 304.070574
## iter 220 value 298.899548
## iter 230 value 294.497851
## iter 240 value 291.544936
## iter 250 value 290.763028
## iter 260 value 290.187337
## iter 270 value 289.413231
## iter 280 value 288.532550
## iter 290 value 287.711441
## iter 300 value 286.813426
## iter 310 value 285.474588
## iter 320 value 283.981647
## iter 330 value 282.670745
## iter 340 value 282.195010
## iter 350 value 281.803236
## iter 360 value 281.605857
## iter 370 value 281.423067
## iter 380 value 281.297488
## iter 390 value 281.004092
## iter 400 value 280.788142
## iter 410 value 280.750687
## iter 420 value 280.743392
## iter 430 value 280.741697
## iter 440 value 280.740229
## iter 450 value 280.737966
## iter 460 value 280.736116
## iter 470 value 280.734445
## iter 480 value 280.733747
## final value 280.733595
## converged
## # weights: 181
## initial value 1375284.467041
## iter 10 value 1211.724656
## iter 20 value 837.785872
## iter 30 value 699.019977
## iter 40 value 564.835891
## iter 50 value 454.688859
## iter 60 value 399.106571
## iter 70 value 369.897105
## iter 80 value 332.726600
## iter 90 value 300.070670
## iter 100 value 267.322555
## iter 110 value 243.022765
## iter 120 value 226.841315
## iter 130 value 214.336317
## iter 140 value 201.542994
## iter 150 value 191.912391
## iter 160 value 184.018398
## iter 170 value 176.487274
## iter 180 value 170.297201
## iter 190 value 165.933405
## iter 200 value 160.748919
## iter 210 value 155.553175
## iter 220 value 152.445399
## iter 230 value 149.347842
## iter 240 value 145.941835
## iter 250 value 142.786840
## iter 260 value 140.561148
## iter 270 value 138.901891
## iter 280 value 137.292901
## iter 290 value 135.934226
## iter 300 value 134.224634
## iter 310 value 132.962902
## iter 320 value 131.875649
## iter 330 value 131.254310
## iter 340 value 130.641487
## iter 350 value 130.253037
## iter 360 value 129.744733
## iter 370 value 129.297012
## iter 380 value 129.158130
## iter 390 value 128.852754
## iter 400 value 128.328289
## iter 410 value 127.902182
## iter 420 value 127.340792
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## iter 470 value 123.658471
## iter 480 value 121.787651
## iter 490 value 119.330958
## iter 500 value 117.605425
## final value 117.605425
## stopped after 500 iterations
## # weights: 241
## initial value 1406530.193196
## iter 10 value 1440.773881
## iter 20 value 792.375361
## iter 30 value 599.318976
## iter 40 value 499.608983
## iter 50 value 426.993878
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## iter 190 value 111.927140
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## iter 230 value 92.200563
## iter 240 value 87.460630
## iter 250 value 83.660051
## iter 260 value 79.432562
## iter 270 value 75.442339
## iter 280 value 72.116507
## iter 290 value 68.191886
## iter 300 value 65.755646
## iter 310 value 62.815939
## iter 320 value 60.222220
## iter 330 value 58.682930
## iter 340 value 57.511061
## iter 350 value 55.887636
## iter 360 value 54.131364
## iter 370 value 52.757081
## iter 380 value 51.639895
## iter 390 value 50.352379
## iter 400 value 49.315649
## iter 410 value 48.517888
## iter 420 value 47.913504
## iter 430 value 47.202204
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## iter 450 value 45.970744
## iter 460 value 45.549546
## iter 470 value 45.053806
## iter 480 value 44.620932
## iter 490 value 44.428495
## iter 500 value 44.335401
## final value 44.335401
## stopped after 500 iterations
## # weights: 25
## initial value 1424522.631132
## iter 10 value 4905.190511
## iter 20 value 3076.583215
## iter 30 value 2550.154399
## iter 40 value 1725.175454
## iter 50 value 1491.399008
## iter 60 value 1429.319279
## iter 70 value 1384.057690
## iter 80 value 1382.773636
## iter 90 value 1378.309047
## iter 100 value 1329.438196
## iter 110 value 1260.308757
## iter 120 value 1217.582772
## iter 130 value 1212.811501
## iter 140 value 1207.028625
## iter 150 value 1199.648691
## iter 160 value 1199.434787
## final value 1199.434422
## converged
## # weights: 61
## initial value 1388301.354076
## iter 10 value 5320.452093
## iter 20 value 2448.211578
## iter 30 value 2000.974553
## iter 40 value 1565.611060
## iter 50 value 1361.343576
## iter 60 value 1231.082778
## iter 70 value 1156.006346
## iter 80 value 1089.622504
## iter 90 value 1049.173921
## iter 100 value 1005.401455
## iter 110 value 983.573063
## iter 120 value 969.183075
## iter 130 value 959.659044
## iter 140 value 954.007739
## iter 150 value 942.996096
## iter 160 value 926.800049
## iter 170 value 914.607880
## iter 180 value 895.078842
## iter 190 value 878.949672
## iter 200 value 866.782065
## iter 210 value 862.762837
## iter 220 value 861.429588
## iter 230 value 860.400088
## iter 240 value 859.445822
## iter 250 value 859.177207
## iter 260 value 859.105534
## iter 270 value 859.002501
## iter 280 value 858.964111
## iter 290 value 858.952301
## final value 858.951682
## converged
## # weights: 121
## initial value 1387701.653621
## iter 10 value 5845.724574
## iter 20 value 3272.720371
## iter 30 value 2536.862835
## iter 40 value 2114.561955
## iter 50 value 1968.945949
## iter 60 value 1793.822625
## iter 70 value 1510.731479
## iter 80 value 1334.544657
## iter 90 value 1229.716476
## iter 100 value 1159.435829
## iter 110 value 1071.543440
## iter 120 value 1014.834917
## iter 130 value 958.333550
## iter 140 value 915.167466
## iter 150 value 882.043632
## iter 160 value 848.262076
## iter 170 value 814.289591
## iter 180 value 790.376299
## iter 190 value 763.222557
## iter 200 value 750.930020
## iter 210 value 741.509222
## iter 220 value 731.887432
## iter 230 value 721.485616
## iter 240 value 709.111824
## iter 250 value 702.382650
## iter 260 value 694.987050
## iter 270 value 688.569509
## iter 280 value 673.999878
## iter 290 value 658.561824
## iter 300 value 635.372869
## iter 310 value 608.247091
## iter 320 value 577.398462
## iter 330 value 564.962186
## iter 340 value 553.603395
## iter 350 value 543.557845
## iter 360 value 536.285542
## iter 370 value 530.921784
## iter 380 value 527.883488
## iter 390 value 526.205167
## iter 400 value 525.026743
## iter 410 value 524.738317
## iter 420 value 524.627068
## iter 430 value 524.597295
## iter 440 value 524.590146
## iter 450 value 524.588822
## iter 460 value 524.588076
## iter 470 value 524.587938
## final value 524.587916
## converged
## # weights: 181
## initial value 1304402.492249
## iter 10 value 1215.159830
## iter 20 value 911.743994
## iter 30 value 761.925857
## iter 40 value 651.636236
## iter 50 value 576.107710
## iter 60 value 525.692038
## iter 70 value 489.034086
## iter 80 value 461.158956
## iter 90 value 444.210661
## iter 100 value 432.651721
## iter 110 value 424.118038
## iter 120 value 417.578814
## iter 130 value 412.100245
## iter 140 value 406.026653
## iter 150 value 400.602751
## iter 160 value 396.806763
## iter 170 value 394.042741
## iter 180 value 392.416705
## iter 190 value 390.700454
## iter 200 value 382.585334
## iter 210 value 373.869584
## iter 220 value 370.041569
## iter 230 value 367.255461
## iter 240 value 362.657555
## iter 250 value 359.811037
## iter 260 value 357.921117
## iter 270 value 356.201324
## iter 280 value 354.583303
## iter 290 value 353.377330
## iter 300 value 352.316496
## iter 310 value 351.632112
## iter 320 value 351.333038
## iter 330 value 351.080588
## iter 340 value 350.847396
## iter 350 value 350.715172
## iter 360 value 350.598823
## iter 370 value 350.492111
## iter 380 value 350.446133
## iter 390 value 350.332394
## iter 400 value 350.221725
## iter 410 value 350.099649
## iter 420 value 349.902817
## iter 430 value 349.662171
## iter 440 value 349.591455
## iter 450 value 349.566049
## iter 460 value 349.555760
## iter 470 value 349.553553
## final value 349.553344
## converged
## # weights: 241
## initial value 1494796.848723
## iter 10 value 1793.794661
## iter 20 value 1026.296705
## iter 30 value 782.092947
## iter 40 value 635.817780
## iter 50 value 556.461285
## iter 60 value 523.980519
## iter 70 value 496.592607
## iter 80 value 474.720981
## iter 90 value 456.772187
## iter 100 value 444.305659
## iter 110 value 433.447878
## iter 120 value 425.797671
## iter 130 value 420.977147
## iter 140 value 418.684898
## iter 150 value 416.639335
## iter 160 value 414.744911
## iter 170 value 412.111436
## iter 180 value 408.342219
## iter 190 value 403.247575
## iter 200 value 397.876191
## iter 210 value 392.967486
## iter 220 value 388.253617
## iter 230 value 384.046870
## iter 240 value 380.049275
## iter 250 value 377.053067
## iter 260 value 374.006070
## iter 270 value 371.744175
## iter 280 value 369.936403
## iter 290 value 368.227877
## iter 300 value 366.004897
## iter 310 value 364.053444
## iter 320 value 362.403936
## iter 330 value 360.425414
## iter 340 value 356.852204
## iter 350 value 353.565506
## iter 360 value 351.214620
## iter 370 value 349.403509
## iter 380 value 346.459825
## iter 390 value 342.495363
## iter 400 value 340.669583
## iter 410 value 339.280219
## iter 420 value 337.884201
## iter 430 value 337.120139
## iter 440 value 336.619893
## iter 450 value 336.265756
## iter 460 value 336.010782
## iter 470 value 335.864655
## iter 480 value 335.732985
## iter 490 value 335.667572
## iter 500 value 335.559510
## final value 335.559510
## stopped after 500 iterations
## # weights: 25
## initial value 1536496.891690
## iter 10 value 21717.180299
## iter 20 value 16517.954314
## iter 30 value 14853.986863
## iter 40 value 9493.319339
## iter 50 value 3352.471783
## iter 60 value 2546.656437
## iter 70 value 2362.381514
## iter 80 value 2144.918770
## iter 90 value 1772.315155
## iter 100 value 1727.300659
## iter 110 value 1603.895751
## iter 120 value 1529.043363
## iter 130 value 1517.249470
## iter 140 value 1517.047736
## iter 140 value 1517.047735
## final value 1517.047735
## converged##################################
# Reporting the apparent results
# for the NN model
##################################
NN_DALEX <- DALEX::explain(NN_Tune,
data = MD.Model.Predictors,
y = MD$LIFEXP,
verbose = FALSE,
label = "NN")
(NN_DALEX_Performance <- model_performance(NN_DALEX))## Measures for: regression
## mse : 3.792286
## rmse : 1.947379
## r2 : 0.9387186
## mad : 1.118942
##
## Residuals:
## 0% 10% 20% 30% 40% 50% 60%
## -6.7019930 -2.2284111 -1.3596353 -0.7496302 -0.2984808 0.0323945 0.3834313
## 70% 80% 90% 100%
## 0.8343248 1.5340057 2.1809442 6.4827220(NN_DALEX_Diagnostics <- model_diagnostics(NN_DALEX))## GENDER CONTIN INFMOR PERCAP
## Male :139 Africa :89 Min. :0.3365 Min. :-1.4775
## Female:153 Asia :73 1st Qu.:1.7047 1st Qu.: 0.6183
## Europe :62 Median :2.6100 Median : 1.7960
## North America:31 Mean :2.5569 Mean : 1.7571
## Oceania :18 3rd Qu.:3.5025 3rd Qu.: 2.7938
## South America:19 Max. :4.4864 Max. : 4.7293
## CLTECH NCOMOR y y_hat
## Min. : 0.00 Min. :1.866 Min. :52.84 Min. :56.39
## 1st Qu.: 24.35 1st Qu.:3.944 1st Qu.:66.93 1st Qu.:66.84
## Median : 82.80 Median :4.717 Median :73.53 Median :73.71
## Mean : 64.60 Mean :4.652 Mean :72.47 Mean :72.46
## 3rd Qu.:100.00 3rd Qu.:5.347 3rd Qu.:78.54 3rd Qu.:78.31
## Max. :100.00 Max. :7.959 Max. :87.45 Max. :85.01
## residuals abs_residuals label ids
## Min. :-6.7020 Min. :0.006932 Length:292 Min. : 1.00
## 1st Qu.:-0.9466 1st Qu.:0.423443 Class :character 1st Qu.: 73.75
## Median : 0.0324 Median :1.118942 Mode :character Median :146.50
## Mean : 0.0151 Mean :1.437687 Mean :146.50
## 3rd Qu.: 1.2088 3rd Qu.:2.042302 3rd Qu.:219.25
## Max. : 6.4827 Max. :6.701993 Max. :292.00plot(NN_DALEX_Diagnostics,
variable = "y",
yvariable = "y_hat") +
geom_point(size=3) +
scale_x_continuous("Observed LIFEXP") +
scale_y_continuous("Predicted LIFEXP") +
geom_abline(slope = 1) +
ggtitle("RF: Observed and Predicted LIFEXP")
(NN_DALEX_VariableImportance <- model_parts(NN_DALEX,
loss_function = loss_root_mean_square,
B = 200,
N = NULL))## variable mean_dropout_loss label
## 1 _full_model_ 1.947379 NN
## 2 PERCAP 2.332572 NN
## 3 GENDER 2.512774 NN
## 4 CONTIN 3.072153 NN
## 5 CLTECH 3.103069 NN
## 6 NCOMOR 3.675613 NN
## 7 INFMOR 7.701794 NN
## 8 _baseline_ 10.925591 NNplot(NN_DALEX_VariableImportance)
##################################
# Reporting the cross-validation results
# for the NN model
##################################
NN_Tune## Neural Network
##
## 292 samples
## 6 predictor
##
## Pre-processing: centered (4), scaled (4), ignore (2)
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 264, 263, 263, 262, 263, 264, ...
## Resampling results across tuning parameters:
##
## size decay RMSE Rsquared MAE
## 2 0e+00 2.913158 0.8500380 2.299048
## 2 1e-05 2.542092 0.8983528 2.007270
## 2 1e-04 2.246056 0.9195811 1.696021
## 2 1e-03 2.210859 0.9230509 1.673741
## 2 1e-01 2.060613 0.9326348 1.519616
## 5 0e+00 5.259886 0.8372621 2.275294
## 5 1e-05 5.270385 0.8300151 2.667797
## 5 1e-04 2.577673 0.8929534 1.711195
## 5 1e-03 2.171704 0.9258110 1.639417
## 5 1e-01 2.140877 0.9270724 1.619764
## 10 0e+00 4.090892 0.8342633 2.272149
## 10 1e-05 3.994206 0.7975050 2.411226
## 10 1e-04 2.947823 0.8735867 2.190190
## 10 1e-03 3.059333 0.8585939 2.084144
## 10 1e-01 2.708700 0.8913386 1.963156
## 15 0e+00 4.182909 0.7642496 2.918018
## 15 1e-05 5.843658 0.7293836 3.156435
## 15 1e-04 3.793863 0.7964659 2.651672
## 15 1e-03 3.559971 0.8145775 2.701506
## 15 1e-01 2.760529 0.8802960 2.066406
## 20 0e+00 4.804438 0.7226852 3.660531
## 20 1e-05 4.162649 0.7793587 3.109769
## 20 1e-04 4.963562 0.6832818 3.595233
## 20 1e-03 5.225087 0.6796311 3.719564
## 20 1e-01 2.961232 0.8731716 2.220592
##
## RMSE was used to select the optimal model using the smallest value.
## The final values used for the model were size = 2 and decay = 0.1.NN_Tune$finalModel## a 10-2-1 network with 25 weights
## inputs: GENDERFemale CONTINAsia CONTINEurope CONTINNorth America CONTINOceania CONTINSouth America INFMOR PERCAP CLTECH NCOMOR
## output(s): .outcome
## options were - linear output units decay=0.1(NN_Tune_RMSE <- NN_Tune$results[NN_Tune$results$size==NN_Tune$bestTune$size &
NN_Tune$results$decay==NN_Tune$bestTune$decay,
c("RMSE")])## [1] 2.060613(NN_Tune_Rsquared <- NN_Tune$results[NN_Tune$results$size==NN_Tune$bestTune$size &
NN_Tune$results$decay==NN_Tune$bestTune$decay,
c("Rsquared")])## [1] 0.9326348(NN_Tune_MAE <- NN_Tune$results[NN_Tune$results$size==NN_Tune$bestTune$size &
NN_Tune$results$decay==NN_Tune$bestTune$decay,
c("MAE")])## [1] 1.5196161.3.6.5 Partial Least Squares Regression
[A] The partial least squares regression model from the pls package was implemented through the caret package.
[B] The model contains 1 hyperparameter:
[B.1] ncomp = number of components made to vary across a range of values equal to 1 to 5 with intervals of 1
[C] The 10-fold cross-validated model performance of the optimal model is summarized as follows:
[C.1] Optimal model used ncomp=5 which demonstrated the lowest root mean square error
[C.2] Root Mean Square Error = 2.4632
[C.3] R-Squared = 0.9087
[D] The apparent model performance of the optimal model is summarized as follows:
[D.1] Root Mean Square Error = 2.4242
[D.2] R-Squared = 0.9050
[E] The top-performing predictors in the model ranked using the root mean square error loss after permutations are as follows:
[E.1] INFMOR (numeric) = 7.84
[E.2] GENDER (factor) = 3.25
[E.3] CLTECH (numeric) = 3.16
[E.4] CONTIN (factor) = 3.07
[E.5] NCOMOR (numeric) = 2.97
[E.6] PERCAP (numeric) = 2.43
Code Chunk | Output
##################################
# Defining the model hyperparameter values
# for the PLS model
##################################
PLS_Grid = expand.grid(ncomp = 1:5)
##################################
# Running the PLS model
# by setting the caret method to 'pls'
##################################
set.seed(12345678)
PLS_Tune <- train(x = MD.Model.Predictors,
y = MD$LIFEXP,
method = "pls",
tuneGrid = PLS_Grid,
trControl = KFold_Control)
##################################
# Reporting the apparent results
# for the PLS model
##################################
PLS_DALEX <- DALEX::explain(PLS_Tune,
data = MD.Model.Predictors,
y = MD$LIFEXP,
verbose = FALSE,
label = "PLS")
(PLS_DALEX_Performance <- model_performance(PLS_DALEX))## Measures for: regression
## mse : 5.876756
## rmse : 2.424202
## r2 : 0.9050347
## mad : 1.524704
##
## Residuals:
## 0% 10% 20% 30% 40% 50% 60%
## -7.8171653 -3.1634756 -1.8497887 -1.1383462 -0.5658555 0.2131137 0.6858102
## 70% 80% 90% 100%
## 1.2703500 1.8554207 2.8334884 6.6842346(PLS_DALEX_Diagnostics <- model_diagnostics(PLS_DALEX))## GENDER CONTIN INFMOR PERCAP
## Male :139 Africa :89 Min. :0.3365 Min. :-1.4775
## Female:153 Asia :73 1st Qu.:1.7047 1st Qu.: 0.6183
## Europe :62 Median :2.6100 Median : 1.7960
## North America:31 Mean :2.5569 Mean : 1.7571
## Oceania :18 3rd Qu.:3.5025 3rd Qu.: 2.7938
## South America:19 Max. :4.4864 Max. : 4.7293
## CLTECH NCOMOR y y_hat
## Min. : 0.00 Min. :1.866 Min. :52.84 Min. :57.87
## 1st Qu.: 24.35 1st Qu.:3.944 1st Qu.:66.93 1st Qu.:66.38
## Median : 82.80 Median :4.717 Median :73.53 Median :73.39
## Mean : 64.60 Mean :4.652 Mean :72.47 Mean :72.47
## 3rd Qu.:100.00 3rd Qu.:5.347 3rd Qu.:78.54 3rd Qu.:78.18
## Max. :100.00 Max. :7.959 Max. :87.45 Max. :88.65
## residuals abs_residuals label ids
## Min. :-7.8172 Min. :0.01345 Length:292 Min. : 1.00
## 1st Qu.:-1.3989 1st Qu.:0.74951 Class :character 1st Qu.: 73.75
## Median : 0.2131 Median :1.52470 Mode :character Median :146.50
## Mean : 0.0000 Mean :1.89719 Mean :146.50
## 3rd Qu.: 1.5808 3rd Qu.:2.60138 3rd Qu.:219.25
## Max. : 6.6842 Max. :7.81717 Max. :292.00plot(PLS_DALEX_Diagnostics,
variable = "y",
yvariable = "y_hat") +
geom_point(size=3) +
scale_x_continuous("Observed LIFEXP") +
scale_y_continuous("Predicted LIFEXP") +
geom_abline(slope = 1) +
ggtitle("RF: Observed and Predicted LIFEXP")
(PLS_DALEX_VariableImportance <- model_parts(PLS_DALEX,
loss_function = loss_root_mean_square,
B = 200,
N = NULL))## variable mean_dropout_loss label
## 1 _full_model_ 2.424202 PLS
## 2 PERCAP 2.426869 PLS
## 3 NCOMOR 2.967139 PLS
## 4 CONTIN 3.066523 PLS
## 5 CLTECH 3.165265 PLS
## 6 GENDER 3.250488 PLS
## 7 INFMOR 7.849344 PLS
## 8 _baseline_ 10.860376 PLSplot(PLS_DALEX_VariableImportance)
##################################
# Reporting the cross-validation results
# for the PLS model
##################################
PLS_Tune## Partial Least Squares
##
## 292 samples
## 6 predictor
##
## No pre-processing
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 264, 263, 263, 262, 263, 264, ...
## Resampling results across tuning parameters:
##
## ncomp RMSE Rsquared MAE
## 1 4.997627 0.6039633 4.073632
## 2 3.251276 0.8416354 2.478243
## 3 2.705139 0.8916091 2.067882
## 4 2.572417 0.9014321 2.006329
## 5 2.463222 0.9087387 1.954148
##
## RMSE was used to select the optimal model using the smallest value.
## The final value used for the model was ncomp = 5.PLS_Tune$finalModel## Partial least squares regression, fitted with the orthogonal scores algorithm.
## Call:
## plsr(formula = .outcome ~ ., ncomp = ncomp, data = dat, method = "oscorespls")(PLS_Tune_RMSE <- PLS_Tune$results[PLS_Tune$results$ncomp==PLS_Tune$bestTune$ncomp,
c("RMSE")])## [1] 2.463222(PLS_Tune_Rsquared <- PLS_Tune$results[PLS_Tune$results$ncomp==PLS_Tune$bestTune$ncomp,
c("Rsquared")])## [1] 0.9087387(PLS_Tune_MAE <- PLS_Tune$results[PLS_Tune$results$ncomp==PLS_Tune$bestTune$ncomp,
c("MAE")])## [1] 1.9541481.3.6.6 Cubist Regression
[A] The cubist regression model from the Cubist package was implemented through the caret package.
[B] The model contains 2 hyperparameters:
[B.1] committees = number of committees made to vary across a range of values equal to 10 to 50 with intervals of 10
[B.2] neighbors = number of neighbors made to vary across a range of values equal to 0 to 9 with intervals of 3
[C] The 10-fold cross-validated model performance of the optimal model is summarized as follows:
[C.1] Optimal model used committees=50 and neighbors=0 which demonstrated the lowest root mean square error
[C.2] Root Mean Square Error = 2.0967
[C.3] R-Squared = 0.9316
[D] The apparent model performance of the optimal model is summarized as follows:
[D.1] Root Mean Square Error = 1.9126
[D.2] R-Squared = 0.9408
[E] The top-performing predictors in the model ranked using the root mean square error loss after permutations are as follows:
[E.1] INFMOR (numeric) = 7.73
[E.2] NCOMOR (numeric) = 3.75
[E.3] CONTIN (factor) = 2.36
[E.4] GENDER (factor) = 2.29
[E.5] PERCAP (numeric) = 2.09
[E.6] CLTECH (numeric) = 1.92
Code Chunk | Output
##################################
# Defining the model hyperparameter values
# for the CUBIST model
##################################
CUBIST_Grid = expand.grid(committees = c(10, 20, 30, 40, 50),
neighbors = c(0, 3, 6, 9))
##################################
# Running the CUBIST model
# by setting the caret method to 'cubist'
##################################
set.seed(12345678)
CUBIST_Tune <- train(x = MD.Model.Predictors,
y = MD$LIFEXP,
method = "cubist",
tuneGrid = CUBIST_Grid,
trControl = KFold_Control)
##################################
# Reporting the apparent results
# for the CUBIST model
##################################
CUBIST_DALEX <- DALEX::explain(CUBIST_Tune,
data = MD.Model.Predictors,
y = MD$LIFEXP,
verbose = FALSE,
label = "CUBIST")
(CUBIST_DALEX_Performance <- model_performance(CUBIST_DALEX))## Measures for: regression
## mse : 3.658042
## rmse : 1.912601
## r2 : 0.9408879
## mad : 1.05533
##
## Residuals:
## 0% 10% 20% 30% 40% 50%
## -6.90777771 -2.15534625 -1.41800017 -0.72510388 -0.33359399 0.01040643
## 60% 70% 80% 90% 100%
## 0.32413965 0.72477280 1.37141615 2.49934372 6.82557834(CUBIST_DALEX_Diagnostics <- model_diagnostics(CUBIST_DALEX))## GENDER CONTIN INFMOR PERCAP
## Male :139 Africa :89 Min. :0.3365 Min. :-1.4775
## Female:153 Asia :73 1st Qu.:1.7047 1st Qu.: 0.6183
## Europe :62 Median :2.6100 Median : 1.7960
## North America:31 Mean :2.5569 Mean : 1.7571
## Oceania :18 3rd Qu.:3.5025 3rd Qu.: 2.7938
## South America:19 Max. :4.4864 Max. : 4.7293
## CLTECH NCOMOR y y_hat
## Min. : 0.00 Min. :1.866 Min. :52.84 Min. :55.50
## 1st Qu.: 24.35 1st Qu.:3.944 1st Qu.:66.93 1st Qu.:66.51
## Median : 82.80 Median :4.717 Median :73.53 Median :73.61
## Mean : 64.60 Mean :4.652 Mean :72.47 Mean :72.40
## 3rd Qu.:100.00 3rd Qu.:5.347 3rd Qu.:78.54 3rd Qu.:78.06
## Max. :100.00 Max. :7.959 Max. :87.45 Max. :86.25
## residuals abs_residuals label ids
## Min. :-6.90778 Min. :0.003691 Length:292 Min. : 1.00
## 1st Qu.:-1.06822 1st Qu.:0.447052 Class :character 1st Qu.: 73.75
## Median : 0.01041 Median :1.055330 Mode :character Median :146.50
## Mean : 0.07148 Mean :1.409394 Mean :146.50
## 3rd Qu.: 1.00547 3rd Qu.:2.063695 3rd Qu.:219.25
## Max. : 6.82558 Max. :6.907778 Max. :292.00plot(CUBIST_DALEX_Diagnostics,
variable = "y",
yvariable = "y_hat") +
geom_point(size=3) +
scale_x_continuous("Observed LIFEXP") +
scale_y_continuous("Predicted LIFEXP") +
geom_abline(slope = 1) +
ggtitle("RF: Observed and Predicted LIFEXP")
(CUBIST_DALEX_VariableImportance <- model_parts(CUBIST_DALEX,
loss_function = loss_root_mean_square,
B = 200,
N = NULL))## variable mean_dropout_loss label
## 1 _full_model_ 1.912601 CUBIST
## 2 CLTECH 1.921536 CUBIST
## 3 PERCAP 2.093838 CUBIST
## 4 GENDER 2.299057 CUBIST
## 5 CONTIN 2.365873 CUBIST
## 6 NCOMOR 3.758193 CUBIST
## 7 INFMOR 7.730290 CUBIST
## 8 _baseline_ 10.979674 CUBISTplot(CUBIST_DALEX_VariableImportance)
##################################
# Reporting the cross-validation results
# for the CUBIST model
##################################
CUBIST_Tune## Cubist
##
## 292 samples
## 6 predictor
##
## No pre-processing
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 264, 263, 263, 262, 263, 264, ...
## Resampling results across tuning parameters:
##
## committees neighbors RMSE Rsquared MAE
## 10 0 2.143307 0.9284329 1.597946
## 10 3 2.161575 0.9264713 1.628394
## 10 6 2.135997 0.9293777 1.600862
## 10 9 2.120955 0.9307087 1.593085
## 20 0 2.118571 0.9293801 1.589678
## 20 3 2.171113 0.9255303 1.629055
## 20 6 2.138896 0.9287024 1.597065
## 20 9 2.118487 0.9303570 1.588802
## 30 0 2.104752 0.9307090 1.577201
## 30 3 2.171787 0.9257896 1.634946
## 30 6 2.135237 0.9293559 1.598399
## 30 9 2.113010 0.9311860 1.587534
## 40 0 2.099914 0.9312085 1.569779
## 40 3 2.164063 0.9263082 1.632470
## 40 6 2.126530 0.9299765 1.592498
## 40 9 2.104461 0.9318479 1.580217
## 50 0 2.096744 0.9316609 1.568414
## 50 3 2.158509 0.9267267 1.629668
## 50 6 2.120160 0.9304991 1.588673
## 50 9 2.097972 0.9324203 1.575005
##
## RMSE was used to select the optimal model using the smallest value.
## The final values used for the model were committees = 50 and neighbors = 0.CUBIST_Tune$finalModel##
## Call:
## cubist.default(x = x, y = y, committees = param$committees)
##
## Number of samples: 292
## Number of predictors: 6
##
## Number of committees: 50
## Number of rules per committee: 6, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2 ...(CUBIST_Tune_RMSE <- CUBIST_Tune$results[CUBIST_Tune$results$committees==CUBIST_Tune$bestTune$committees &
CUBIST_Tune$results$neighbors==CUBIST_Tune$bestTune$neighbors,
c("RMSE")])## [1] 2.096744(CUBIST_Tune_Rsquared <- CUBIST_Tune$results[CUBIST_Tune$results$committees==CUBIST_Tune$bestTune$committees &
CUBIST_Tune$results$neighbors==CUBIST_Tune$bestTune$neighbors,
c("Rsquared")])## [1] 0.9316609(CUBIST_Tune_MAE <- CUBIST_Tune$results[CUBIST_Tune$results$committees==CUBIST_Tune$bestTune$committees &
CUBIST_Tune$results$neighbors==CUBIST_Tune$bestTune$neighbors,
c("MAE")])## [1] 1.5684141.3.7 Model Performance Validation
[A] Apparent performance ranked from best to worst among the candidate optimal models are provided as follows:
[A.1] RF: Random Forest (randomForest package)
[A.1.1] Root Mean Square Error = 0.9698
[A.1.2] R-Squared = 0.9848
[A.2] GBM: Stochastic Gradient Boosting (gbm package)
[A.2.1] Root Mean Square Error = 1.7044
[A.2.2] R-Squared = 0.9530
[A.3] CUB: Cubist (Cubist package)
[A.3.1] Root Mean Square Error = 1.9126
[A.3.2] R-Squared = 0.9408
[A.4] NN: Neural Network (nnet package)
[A.4.1] Root Mean Square Error = 1.9473
[A.4.2] R-Squared = 0.9387
[A.5] LR: Linear Regression (stats package)
[A.5.1] Root Mean Square Error = 2.3622
[A.5.2] R-Squared = 0.9098
[A.6] PLS: Partial Least Squares (pls package)
[A.6.1] Root Mean Square Error = 2.4242
[A.6.2] R-Squared = 0.9050
[B] 10-fold cross-validated performance ranked from best to worst among the candidate optimal models are provided as follows:
[B.1] NN: Neural Network (nnet package)
[B.1.1] Root Mean Square Error = 2.0701
[B.1.2] R-Squared = 0.9321
[B.2] CUB: Cubist (Cubist package)
[B.2.1] Root Mean Square Error = 2.0967
[B.2.2] R-Squared = 0.9316
[B.3] GBM: Stochastic Gradient Boosting (gbm package)
[B.3.1] Root Mean Square Error = 2.0978
[B.3.2] R-Squared = 0.9288
[B.4] RF: Random Forest (randomForest package)
[B.4.1] Root Mean Square Error = 2.2390
[B.4.2] R-Squared = 0.9201
[B.5] LR: Linear Regression (stats package)
[B.5.1] Root Mean Square Error = 2.4078
[B.5.2] R-Squared = 0.9116
[B.6] PLS: Partial Least Squares (pls package)
[B.6.1] Root Mean Square Error = 2.4632
[B.6.2] R-Squared = 0.9087
[C] Externally-validated performance ranked from best to worst among the candidate optimal models are provided as follows:
[C.1] GBM: Stochastic Gradient Boosting (gbm package)
[C.1.1] Root Mean Square Error = 2.1007
[C.1.2] R-Squared = 0.9171
[C.2] CUB: Cubist (Cubist package)
[C.2.1] Root Mean Square Error = 2.2261
[C.2.2] R-Squared = 0.9069
[C.3] NN: Neural Network (nnet package)
[C.3.1] Root Mean Square Error = 2.3021
[C.3.2] R-Squared = 0.9005
[C.4] RF: Random Forest (randomForest package)
[C.4.1] Root Mean Square Error = 2.5012
[C.4.2] R-Squared = 0.8825
[C.5] LR: Linear Regression (stats package)
[C.5.1] Root Mean Square Error = 2.5330
[C.5.2] R-Squared = 0.8795
[C.6] PLS: Partial Least Squares (pls package)
[C.6.1] Root Mean Square Error = 2.6870
[C.6.2] R-Squared = 0.8644
[D] The formulated models below demonstrated consistently good performance from both internal and external validation.
[D.1] GBM: Stochastic Gradient Boosting (gbm package)
[D.2] CUB: Cubist (Cubist package)
[D.3] NN: Neural Network (nnet package)
[E] The formulated model below showed signs of overfitting due to the huge difference in performance as observed between internal and external validation.
[E.1] RF: Random Forest (randomForest package)
[F] The formulated models below showed consistently worse performance from both internal and external validation.
[F.1] LR: Linear Regression (stats package)
[F.2] PLS: Partial Least Squares (pls package)
Code Chunk | Output
##################################
# Evaluating the models
# on the model test data
##################################
##################################
# Formulating the DALEX object
# for the Best LR model
# as applied to the model test data
##################################
LR_DALEX <- DALEX::explain(LR_Tune,
data = MT.Model.Predictors,
y = MT$LIFEXP,
verbose = FALSE,
label = "LR")
(LR_DALEX_Performance <- model_performance(LR_DALEX))## Measures for: regression
## mse : 6.416206
## rmse : 2.533023
## r2 : 0.8795864
## mad : 1.666297
##
## Residuals:
## 0% 10% 20% 30% 40% 50%
## -6.68650492 -2.17549229 -1.55804047 -1.21751513 -0.94885907 0.07197521
## 60% 70% 80% 90% 100%
## 0.69695404 1.75887103 2.14536061 3.43457974 6.50245312(LR_DALEX_Diagnostics <- model_diagnostics(LR_DALEX))## GENDER CONTIN INFMOR PERCAP
## Male :43 Africa :17 Min. :0.6419 Min. :-1.4775
## Female:29 Asia :21 1st Qu.:1.7090 1st Qu.: 0.7926
## Europe :16 Median :2.6602 Median : 1.7266
## North America:11 Mean :2.5559 Mean : 1.7439
## Oceania : 2 3rd Qu.:3.4135 3rd Qu.: 2.8314
## South America: 5 Max. :4.3858 Max. : 4.4466
## CLTECH NCOMOR y y_hat
## Min. : 0.20 Min. :2.511 Min. :51.20 Min. :56.74
## 1st Qu.: 49.02 1st Qu.:3.887 1st Qu.:67.42 1st Qu.:66.94
## Median : 90.20 Median :4.742 Median :73.51 Median :73.34
## Mean : 72.08 Mean :4.752 Mean :72.61 Mean :72.41
## 3rd Qu.:100.00 3rd Qu.:5.631 3rd Qu.:78.47 3rd Qu.:77.82
## Max. :100.00 Max. :7.406 Max. :86.20 Max. :84.86
## residuals abs_residuals label ids
## Min. :-6.68651 Min. :0.04811 Length:72 Min. : 1.00
## 1st Qu.:-1.39437 1st Qu.:1.00127 Class :character 1st Qu.:18.75
## Median : 0.07197 Median :1.66630 Mode :character Median :36.50
## Mean : 0.19567 Mean :2.02378 Mean :36.50
## 3rd Qu.: 1.95312 3rd Qu.:2.78440 3rd Qu.:54.25
## Max. : 6.50245 Max. :6.68651 Max. :72.00plot(LR_DALEX_Diagnostics,
variable = "y",
yvariable = "y_hat") +
geom_point(size=3) +
scale_x_continuous("Observed LIFEXP") +
scale_y_continuous("Predicted LIFEXP") +
geom_abline(slope = 1) +
ggtitle("LR: Observed and Predicted LIFEXP")
##################################
# Formulating the DALEX object
# for the Best GBM model
# as applied to the model test data
##################################
GBM_DALEX <- DALEX::explain(GBM_Tune,
data = MT.Model.Predictors,
y = MT$LIFEXP,
verbose = FALSE,
label = "GBM")
(GBM_DALEX_Performance <- model_performance(GBM_DALEX))## Measures for: regression
## mse : 4.413299
## rmse : 2.100785
## r2 : 0.9171752
## mad : 1.322106
##
## Residuals:
## 0% 10% 20% 30% 40% 50% 60%
## -4.3962340 -2.2405259 -1.4211432 -1.0660296 -0.8370857 -0.1835420 0.3528943
## 70% 80% 90% 100%
## 1.1053654 1.6763495 3.0065703 5.6991483(GBM_DALEX_Diagnostics <- model_diagnostics(GBM_DALEX))## GENDER CONTIN INFMOR PERCAP
## Male :43 Africa :17 Min. :0.6419 Min. :-1.4775
## Female:29 Asia :21 1st Qu.:1.7090 1st Qu.: 0.7926
## Europe :16 Median :2.6602 Median : 1.7266
## North America:11 Mean :2.5559 Mean : 1.7439
## Oceania : 2 3rd Qu.:3.4135 3rd Qu.: 2.8314
## South America: 5 Max. :4.3858 Max. : 4.4466
## CLTECH NCOMOR y y_hat
## Min. : 0.20 Min. :2.511 Min. :51.20 Min. :53.19
## 1st Qu.: 49.02 1st Qu.:3.887 1st Qu.:67.42 1st Qu.:68.35
## Median : 90.20 Median :4.742 Median :73.51 Median :73.10
## Mean : 72.08 Mean :4.752 Mean :72.61 Mean :72.47
## 3rd Qu.:100.00 3rd Qu.:5.631 3rd Qu.:78.47 3rd Qu.:78.40
## Max. :100.00 Max. :7.406 Max. :86.20 Max. :84.89
## residuals abs_residuals label ids
## Min. :-4.3962 Min. :0.04636 Length:72 Min. : 1.00
## 1st Qu.:-1.1836 1st Qu.:0.78949 Class :character 1st Qu.:18.75
## Median :-0.1835 Median :1.32211 Mode :character Median :36.50
## Mean : 0.1348 Mean :1.65758 Mean :36.50
## 3rd Qu.: 1.3846 3rd Qu.:2.30067 3rd Qu.:54.25
## Max. : 5.6991 Max. :5.69915 Max. :72.00plot(GBM_DALEX_Diagnostics,
variable = "y",
yvariable = "y_hat") +
geom_point(size=3) +
scale_x_continuous("Observed LIFEXP") +
scale_y_continuous("Predicted LIFEXP") +
geom_abline(slope = 1) +
ggtitle("GBM: Observed and Predicted LIFEXP")
##################################
# Formulating the DALEX object
# for the Best RF model
# as applied to the model test data
##################################
RF_DALEX <- DALEX::explain(RF_Tune,
data = MT.Model.Predictors,
y = MT$LIFEXP,
verbose = FALSE,
label = "RF")
(RF_DALEX_Performance <- model_performance(RF_DALEX))## Measures for: regression
## mse : 6.25631
## rmse : 2.501262
## r2 : 0.8825872
## mad : 1.594092
##
## Residuals:
## 0% 10% 20% 30% 40% 50% 60%
## -7.2080243 -3.0176466 -2.1047296 -1.2247647 -0.2712163 0.1992440 0.7128073
## 70% 80% 90% 100%
## 1.1067986 1.7677385 3.1744799 7.4973476(RF_DALEX_Diagnostics <- model_diagnostics(RF_DALEX))## GENDER CONTIN INFMOR PERCAP
## Male :43 Africa :17 Min. :0.6419 Min. :-1.4775
## Female:29 Asia :21 1st Qu.:1.7090 1st Qu.: 0.7926
## Europe :16 Median :2.6602 Median : 1.7266
## North America:11 Mean :2.5559 Mean : 1.7439
## Oceania : 2 3rd Qu.:3.4135 3rd Qu.: 2.8314
## South America: 5 Max. :4.3858 Max. : 4.4466
## CLTECH NCOMOR y y_hat
## Min. : 0.20 Min. :2.511 Min. :51.20 Min. :54.85
## 1st Qu.: 49.02 1st Qu.:3.887 1st Qu.:67.42 1st Qu.:68.50
## Median : 90.20 Median :4.742 Median :73.51 Median :73.19
## Mean : 72.08 Mean :4.752 Mean :72.61 Mean :72.58
## 3rd Qu.:100.00 3rd Qu.:5.631 3rd Qu.:78.47 3rd Qu.:78.61
## Max. :100.00 Max. :7.406 Max. :86.20 Max. :84.60
## residuals abs_residuals label ids
## Min. :-7.20802 Min. :0.01395 Length:72 Min. : 1.00
## 1st Qu.:-1.69056 1st Qu.:0.73760 Class :character 1st Qu.:18.75
## Median : 0.19924 Median :1.59409 Mode :character Median :36.50
## Mean : 0.02669 Mean :1.91950 Mean :36.50
## 3rd Qu.: 1.50013 3rd Qu.:2.74804 3rd Qu.:54.25
## Max. : 7.49735 Max. :7.49735 Max. :72.00plot(RF_DALEX_Diagnostics,
variable = "y",
yvariable = "y_hat") +
geom_point(size=3) +
scale_x_continuous("Observed LIFEXP") +
scale_y_continuous("Predicted LIFEXP") +
geom_abline(slope = 1) +
ggtitle("RF: Observed and Predicted LIFEXP")
##################################
# Formulating the DALEX object
# for the Best NN model
# as applied to the model test data
##################################
NN_DALEX <- DALEX::explain(NN_Tune,
data = MT.Model.Predictors,
y = MT$LIFEXP,
verbose = FALSE,
label = "NN")
(NN_DALEX_Performance <- model_performance(NN_DALEX))## Measures for: regression
## mse : 5.300062
## rmse : 2.302186
## r2 : 0.9005332
## mad : 1.293056
##
## Residuals:
## 0% 10% 20% 30% 40% 50% 60%
## -6.0612264 -2.8063521 -1.3495125 -1.0121289 -0.3547678 0.2159281 0.6061218
## 70% 80% 90% 100%
## 1.1206919 1.8356939 2.8346540 7.8217567(NN_DALEX_Diagnostics <- model_diagnostics(NN_DALEX))## GENDER CONTIN INFMOR PERCAP
## Male :43 Africa :17 Min. :0.6419 Min. :-1.4775
## Female:29 Asia :21 1st Qu.:1.7090 1st Qu.: 0.7926
## Europe :16 Median :2.6602 Median : 1.7266
## North America:11 Mean :2.5559 Mean : 1.7439
## Oceania : 2 3rd Qu.:3.4135 3rd Qu.: 2.8314
## South America: 5 Max. :4.3858 Max. : 4.4466
## CLTECH NCOMOR y y_hat
## Min. : 0.20 Min. :2.511 Min. :51.20 Min. :52.40
## 1st Qu.: 49.02 1st Qu.:3.887 1st Qu.:67.42 1st Qu.:66.97
## Median : 90.20 Median :4.742 Median :73.51 Median :73.22
## Mean : 72.08 Mean :4.752 Mean :72.61 Mean :72.48
## 3rd Qu.:100.00 3rd Qu.:5.631 3rd Qu.:78.47 3rd Qu.:78.83
## Max. :100.00 Max. :7.406 Max. :86.20 Max. :84.49
## residuals abs_residuals label ids
## Min. :-6.0612 Min. :0.06061 Length:72 Min. : 1.00
## 1st Qu.:-1.2126 1st Qu.:0.72930 Class :character 1st Qu.:18.75
## Median : 0.2159 Median :1.29306 Mode :character Median :36.50
## Mean : 0.1293 Mean :1.77153 Mean :36.50
## 3rd Qu.: 1.5601 3rd Qu.:2.67075 3rd Qu.:54.25
## Max. : 7.8218 Max. :7.82176 Max. :72.00plot(NN_DALEX_Diagnostics,
variable = "y",
yvariable = "y_hat") +
geom_point(size=3) +
scale_x_continuous("Observed LIFEXP") +
scale_y_continuous("Predicted LIFEXP") +
geom_abline(slope = 1) +
ggtitle("NN: Observed and Predicted LIFEXP")
##################################
# Formulating the DALEX object
# for the Best PLS model
# as applied to the model test data
##################################
PLS_DALEX <- DALEX::explain(PLS_Tune,
data = MT.Model.Predictors,
y = MT$LIFEXP,
verbose = FALSE,
label = "PLS")
(PLS_DALEX_Performance <- model_performance(PLS_DALEX))## Measures for: regression
## mse : 7.220162
## rmse : 2.687036
## r2 : 0.8644985
## mad : 1.875765
##
## Residuals:
## 0% 10% 20% 30% 40% 50% 60%
## -6.7868694 -2.5089071 -1.8150301 -1.1801931 -0.4920039 0.1375484 0.7844976
## 70% 80% 90% 100%
## 2.0494752 2.6262401 3.8453344 6.0828174(PLS_DALEX_Diagnostics <- model_diagnostics(PLS_DALEX))## GENDER CONTIN INFMOR PERCAP
## Male :43 Africa :17 Min. :0.6419 Min. :-1.4775
## Female:29 Asia :21 1st Qu.:1.7090 1st Qu.: 0.7926
## Europe :16 Median :2.6602 Median : 1.7266
## North America:11 Mean :2.5559 Mean : 1.7439
## Oceania : 2 3rd Qu.:3.4135 3rd Qu.: 2.8314
## South America: 5 Max. :4.3858 Max. : 4.4466
## CLTECH NCOMOR y y_hat
## Min. : 0.20 Min. :2.511 Min. :51.20 Min. :57.14
## 1st Qu.: 49.02 1st Qu.:3.887 1st Qu.:67.42 1st Qu.:67.18
## Median : 90.20 Median :4.742 Median :73.51 Median :72.99
## Mean : 72.08 Mean :4.752 Mean :72.61 Mean :72.33
## 3rd Qu.:100.00 3rd Qu.:5.631 3rd Qu.:78.47 3rd Qu.:77.39
## Max. :100.00 Max. :7.406 Max. :86.20 Max. :84.75
## residuals abs_residuals label ids
## Min. :-6.7869 Min. :0.04887 Length:72 Min. : 1.00
## 1st Qu.:-1.4065 1st Qu.:0.82974 Class :character 1st Qu.:18.75
## Median : 0.1375 Median :1.87576 Mode :character Median :36.50
## Mean : 0.2816 Mean :2.15539 Mean :36.50
## 3rd Qu.: 2.4014 3rd Qu.:3.14434 3rd Qu.:54.25
## Max. : 6.0828 Max. :6.78687 Max. :72.00plot(PLS_DALEX_Diagnostics,
variable = "y",
yvariable = "y_hat") +
geom_point(size=3) +
scale_x_continuous("Observed LIFEXP") +
scale_y_continuous("Predicted LIFEXP") +
geom_abline(slope = 1) +
ggtitle("PLS: Observed and Predicted LIFEXP")
##################################
# Formulating the DALEX object
# for the Best CUBIST model
# as applied to the model test data
##################################
CUBIST_DALEX <- DALEX::explain(CUBIST_Tune,
data = MT.Model.Predictors,
y = MT$LIFEXP,
verbose = FALSE,
label = "CUBIST")
(CUBIST_DALEX_Performance <- model_performance(CUBIST_DALEX))## Measures for: regression
## mse : 4.955851
## rmse : 2.226174
## r2 : 0.9069931
## mad : 1.555514
##
## Residuals:
## 0% 10% 20% 30% 40% 50%
## -6.27483923 -2.34803998 -1.75093724 -1.08073022 -0.25740164 0.08468332
## 60% 70% 80% 90% 100%
## 0.74473043 1.16073038 1.70618640 2.84412369 5.15525385(CUBIST_DALEX_Diagnostics <- model_diagnostics(CUBIST_DALEX))## GENDER CONTIN INFMOR PERCAP
## Male :43 Africa :17 Min. :0.6419 Min. :-1.4775
## Female:29 Asia :21 1st Qu.:1.7090 1st Qu.: 0.7926
## Europe :16 Median :2.6602 Median : 1.7266
## North America:11 Mean :2.5559 Mean : 1.7439
## Oceania : 2 3rd Qu.:3.4135 3rd Qu.: 2.8314
## South America: 5 Max. :4.3858 Max. : 4.4466
## CLTECH NCOMOR y y_hat
## Min. : 0.20 Min. :2.511 Min. :51.20 Min. :55.56
## 1st Qu.: 49.02 1st Qu.:3.887 1st Qu.:67.42 1st Qu.:67.93
## Median : 90.20 Median :4.742 Median :73.51 Median :72.67
## Mean : 72.08 Mean :4.752 Mean :72.61 Mean :72.49
## 3rd Qu.:100.00 3rd Qu.:5.631 3rd Qu.:78.47 3rd Qu.:77.88
## Max. :100.00 Max. :7.406 Max. :86.20 Max. :84.71
## residuals abs_residuals label ids
## Min. :-6.27484 Min. :0.01256 Length:72 Min. : 1.00
## 1st Qu.:-1.54890 1st Qu.:0.71120 Class :character 1st Qu.:18.75
## Median : 0.08468 Median :1.55551 Mode :character Median :36.50
## Mean : 0.11581 Mean :1.73586 Mean :36.50
## 3rd Qu.: 1.40404 3rd Qu.:2.38064 3rd Qu.:54.25
## Max. : 5.15525 Max. :6.27484 Max. :72.00plot(CUBIST_DALEX_Diagnostics,
variable = "y",
yvariable = "y_hat") +
geom_point(size=3) +
scale_x_continuous("Observed LIFEXP") +
scale_y_continuous("Predicted LIFEXP") +
geom_abline(slope = 1) +
ggtitle("CUBIST: Observed and Predicted LIFEXP")
1.3.8 Model Selection
[A] The formulated model using GBM: Stochastic Gradient Boosting was selected as the final model among others for the following reasons:
[A.1] It demonstrated the best RMSE and R-Squared performance based on external validation.
[A.2] No excessive overfitting observed when comparing the internal and external validation performance.
[A.3] It generated the most stable residual distribution with the lowest variance.
Code Chunk | Output
##################################
# Consolidating the performance
# on the model test data
##################################
plot(LR_DALEX_Performance,
GBM_DALEX_Performance,
RF_DALEX_Performance,
NN_DALEX_Performance,
PLS_DALEX_Performance,
CUBIST_DALEX_Performance)
plot(LR_DALEX_Performance,
GBM_DALEX_Performance,
RF_DALEX_Performance,
NN_DALEX_Performance,
PLS_DALEX_Performance,
CUBIST_DALEX_Performance,
geom = "boxplot")
plot(LR_DALEX_Performance,
GBM_DALEX_Performance,
RF_DALEX_Performance,
NN_DALEX_Performance,
PLS_DALEX_Performance,
CUBIST_DALEX_Performance,
geom = "histogram")
plot(LR_DALEX_Performance,
geom = "histogram") +
scale_x_continuous(limits=c(-8, 8)) +
scale_y_continuous(limits=c(0, 8))
plot(GBM_DALEX_Performance,
geom = "histogram") +
scale_x_continuous(limits=c(-8, 8)) +
scale_y_continuous(limits=c(0, 8))
plot(RF_DALEX_Performance,
geom = "histogram") +
scale_x_continuous(limits=c(-8, 8)) +
scale_y_continuous(limits=c(0, 8))
plot(NN_DALEX_Performance,
geom = "histogram") +
scale_x_continuous(limits=c(-8, 8)) +
scale_y_continuous(limits=c(0, 8))
plot(PLS_DALEX_Performance,
geom = "histogram") +
scale_x_continuous(limits=c(-8, 8)) +
scale_y_continuous(limits=c(0, 8))
plot(CUBIST_DALEX_Performance,
geom = "histogram") +
scale_x_continuous(limits=c(-8, 8)) +
scale_y_continuous(limits=c(0, 8))
##################################
# Consolidating the variable importance
# on the model test data
##################################
LR_DALEX_VariableImportance <- model_parts(LR_DALEX,
loss_function = loss_root_mean_square,
B = 200,
N = NULL)
GBM_DALEX_VariableImportance <- model_parts(GBM_DALEX,
loss_function = loss_root_mean_square,
B = 200,
N = NULL)
RF_DALEX_VariableImportance <- model_parts(RF_DALEX,
loss_function = loss_root_mean_square,
B = 200,
N = NULL)
NN_DALEX_VariableImportance <- model_parts(NN_DALEX,
loss_function = loss_root_mean_square,
B = 200,
N = NULL)
PLS_DALEX_VariableImportance <- model_parts(PLS_DALEX,
loss_function = loss_root_mean_square,
B = 200,
N = NULL)
CUBIST_DALEX_VariableImportance <- model_parts(CUBIST_DALEX,
loss_function = loss_root_mean_square,
B = 200,
N = NULL)
plot(LR_DALEX_VariableImportance,
GBM_DALEX_VariableImportance,
RF_DALEX_VariableImportance,
NN_DALEX_VariableImportance,
PLS_DALEX_VariableImportance,
CUBIST_DALEX_VariableImportance)
1.3.9 Model Presentation
1.3.9.1 Dataset Level Exploration : Variable Importance
[A] The predictors for the GBM: Stochastic Gradient Boosting model ranked based on importance were given as follows:
[A.1] INFMOR (numeric)
[A.2] NCOMOR (numeric)
[A.3] CONTIN (factor)
[A.4] GENDER (factor)
[A.5] CLTECH (numeric)
[A.6] PERCAP (numeric)
[A] The most dominant predictors for the model were the following:
[A.1] INFMOR (numeric)
[A.2] NCOMOR (numeric)
[B] Comparable contributions were observed for the following predictors:
[B.1] CONTIN (factor)
[B.2] GENDER (factor)
[B.3] CLTECH (numeric)
[C] The predictor which showed the least contribution in the model was:
[C.1] PERCAP (numeric)
Code Chunk | Output
##################################
# Summarizing the variable importance
# for the final model - GBM
##################################
GBM_DALEX_VariableImportance## variable mean_dropout_loss label
## 1 _full_model_ 2.100785 GBM
## 2 PERCAP 2.183325 GBM
## 3 CLTECH 2.521746 GBM
## 4 GENDER 2.540034 GBM
## 5 CONTIN 2.605090 GBM
## 6 NCOMOR 4.033273 GBM
## 7 INFMOR 6.362181 GBM
## 8 _baseline_ 10.113806 GBMplot(GBM_DALEX_VariableImportance)
1.3.9.2 Dataset Level Exploration : Partial Dependence Plots
[A] The average effects of the individual GBM: Stochastic Gradient Boosting model predictors on the response variable LIFEXP, as conditioned by the other predictors in the model, are described as follows:
[A.1] Lower values of INFMOR (numeric) lead to higher LIFEXP (numeric)
[A.2] Lower values of NCOMOR (numeric) lead to higher LIFEXP (numeric)
[A.3] CONTIN=Africa (numeric) lead to lower LIFEXP (numeric)
[A.4] GENDER=Female (factor) lead to higher LIFEXP (numeric)
[A.5] Higher values of CLTECH (numeric) lead to higher LIFEXP (numeric)
[A.6] Minimal effect for PERCAP (numeric) on LIFEXP (numeric)
Code Chunk | Output
##################################
# Formulating the partial dependence plots
# for the final model - GBM
# using the numeric variables
##################################
GBM_DALEX_PartialDependencePlot_INFMOR <- model_profile(GBM_DALEX,
variables = "INFMOR")
GBM_DALEX_PartialDependencePlot_NCOMOR <- model_profile(GBM_DALEX,
variables = "NCOMOR")
GBM_DALEX_PartialDependencePlot_CLTECH <- model_profile(GBM_DALEX,
variables = "CLTECH")
GBM_DALEX_PartialDependencePlot_PERCAP <- model_profile(GBM_DALEX,
variables = "PERCAP")
(GBM_DALEX_PDP_INFMOR <- plot(GBM_DALEX_PartialDependencePlot_INFMOR,
geom = "profiles"))
(GBM_DALEX_PDP_NCOMOR <- plot(GBM_DALEX_PartialDependencePlot_NCOMOR,
geom = "profiles"))
(GBM_DALEX_PDP_CLTECH <- plot(GBM_DALEX_PartialDependencePlot_CLTECH,
geom = "profiles"))
(GBM_DALEX_PDP_PERCAP <- plot(GBM_DALEX_PartialDependencePlot_PERCAP,
geom = "profiles"))
##################################
# Formulating the grouped partial dependence plots
# for the final model - GBM
# using the numeric variables
# stratified by GENDER
##################################
GBM_DALEX_GroupedPartialDependencePlot_INFMOR <- model_profile(GBM_DALEX,
variables = "INFMOR",
groups = "GENDER")
GBM_DALEX_GroupedPartialDependencePlot_NCOMOR <- model_profile(GBM_DALEX,
variables = "NCOMOR",
groups = "GENDER")
GBM_DALEX_GroupedPartialDependencePlot_CLTECH <- model_profile(GBM_DALEX,
variables = "CLTECH",
groups = "GENDER")
GBM_DALEX_GroupedPartialDependencePlot_PERCAP <- model_profile(GBM_DALEX,
variables = "PERCAP",
groups = "GENDER")
(GBM_DALEX_GPDP_INFMOR <- plot(GBM_DALEX_GroupedPartialDependencePlot_INFMOR,
geom = "profiles"))
(GBM_DALEX_GPDP_NCOMOR <- plot(GBM_DALEX_GroupedPartialDependencePlot_NCOMOR,
geom = "profiles"))
(GBM_DALEX_GPDP_CLTECH <- plot(GBM_DALEX_GroupedPartialDependencePlot_CLTECH,
geom = "profiles"))
(GBM_DALEX_GPDP_PERCAP <- plot(GBM_DALEX_GroupedPartialDependencePlot_PERCAP,
geom = "profiles"))
##################################
# Formulating the grouped partial dependence plots
# for the final model - GBM
# using the numeric variables
# stratified by CONTIN
##################################
GBM_DALEX_GroupedPartialDependencePlot_INFMOR <- model_profile(GBM_DALEX,
variables = "INFMOR",
groups = "CONTIN")
GBM_DALEX_GroupedPartialDependencePlot_NCOMOR <- model_profile(GBM_DALEX,
variables = "NCOMOR",
groups = "CONTIN")
GBM_DALEX_GroupedPartialDependencePlot_CLTECH <- model_profile(GBM_DALEX,
variables = "CLTECH",
groups = "CONTIN")
GBM_DALEX_GroupedPartialDependencePlot_PERCAP <- model_profile(GBM_DALEX,
variables = "PERCAP",
groups = "CONTIN")
(GBM_DALEX_GPDP_INFMOR <- plot(GBM_DALEX_GroupedPartialDependencePlot_INFMOR,
geom = "profiles"))
(GBM_DALEX_GPDP_NCOMOR <- plot(GBM_DALEX_GroupedPartialDependencePlot_NCOMOR,
geom = "profiles"))
(GBM_DALEX_GPDP_CLTECH <- plot(GBM_DALEX_GroupedPartialDependencePlot_CLTECH,
geom = "profiles"))
(GBM_DALEX_GPDP_PERCAP <- plot(GBM_DALEX_GroupedPartialDependencePlot_PERCAP,
geom = "profiles"))
##################################
# Formulating the partial dependence plots
# for the final model - GBM
# using the factor variables
##################################
GBM_DALEX_PartialDependencePlot_GENDER <- model_profile(GBM_DALEX,
variable_type = 'categorical',
variables = "GENDER")
GBM_DALEX_PartialDependencePlot_CONTIN <- model_profile(GBM_DALEX,
variable_type = 'categorical',
variables = "CONTIN")
(GBM_DALEX_PDP_GENDER <- plot(GBM_DALEX_PartialDependencePlot_GENDER,
geom = "profiles"))
(GBM_DALEX_PDP_CONTIN <- plot(GBM_DALEX_PartialDependencePlot_CONTIN,
geom = "profiles"))
1.3.9.3 Instance Level Exploration : Breakdown Plots
[A] The decomposition of the GBM: Stochastic Gradient Boosting model’s prediction for the response variable LIFEXP, into contributions that can be attributed to different explanatory variables were demonstrated using two illustrated instances:
[A.1] Instance 1
[A.1.1] COUNTRY=Philippines (character)
[A.1.2] INFMOR=2.944 (numeric)
[A.1.3] NCOMOR=4.704 (numeric)
[A.1.4] CONTIN=Asia (factor)
[A.1.5] GENDER=Female (factor)
[A.1.6] CLTECH=47.4 (numeric)
[A.1.7] PERCAP=1.249 (numeric)
[A.2] Instance 2
[A.2.1] COUNTRY=Philippines (character)
[A.2.2] INFMOR=3.174 (numeric)
[A.2.3] NCOMOR=5.951 (numeric)
[A.2.4] CONTIN=Asia (factor)
[A.2.5] GENDER=Male (factor)
[A.2.6] CLTECH=47.4 (numeric)
[A.2.7] PERCAP=1.249 (numeric)
[B] Variable attributions for Instance 1 were described as follows:
[B.1] Target response: LIFEXP=75.505 (numeric)
[B.2] Predicted response: 75.616
[B.3] Variable attributions conditioned on the previous predictor(s) are given below:
[B.3.1] Average response: 72.475
[B.3.2] GENDER=Female (factor): +1.286
[B.3.3] INFMOR=2.944 (numeric): +0.996
[B.3.4] CONTIN=Asia (factor): +0.629
[B.3.5] PERCAP=1.249 (numeric): +0.866
[B.3.6] NCOMOR=4.704 (numeric): -0.065
[B.3.7] CLTECH=47.4 (numeric): -0.571
[B.3.8] Predicted response: 75.616
[C] Variable attributions for Instance 2 were described as follows:
[C.1] Target response: LIFEXP=67.263 (numeric)
[C.2] Predicted response: 68.103
[C.3] Variable attributions conditioned on the previous predictor(s) are given below:
[C.3.1] Average response: 72.475
[C.3.2] NCOMOR=5.951 (numeric): -3.294
[C.3.3] INFMOR=3.174 (numeric): -1.208
[C.3.4] GENDER=Male (factor): -0.972
[C.3.5] CONTIN=Asia (factor): +0.675
[C.3.6] PERCAP=1.249 (numeric): +1.088
[C.3.7] CLTECH=47.4 (numeric): -0.662
[C.3.8] Predicted response: 68.103
Code Chunk | Output
##################################
# Formulating the sampled instances
# for illustration
##################################
(Instance_1_Philippines_Female <- PME[PME$COUNTRY=="Philippines" & PME$GENDER=="Female",
c("GENDER","CONTIN","INFMOR","PERCAP","CLTECH","NCOMOR","LIFEXP")])## GENDER CONTIN INFMOR PERCAP CLTECH NCOMOR LIFEXP
## 141 Female Asia 2.944439 1.248566 47.4 4.704261 75.505(Instance_2_Philippines_Male <- PME[PME$COUNTRY=="Philippines" & PME$GENDER=="Male",
c("GENDER","CONTIN","INFMOR","PERCAP","CLTECH","NCOMOR","LIFEXP")])## GENDER CONTIN INFMOR PERCAP CLTECH NCOMOR LIFEXP
## 338 Male Asia 3.173878 1.248566 47.4 5.950788 67.263##################################
# Obtaining the breakdown plots
# for the individual instances
##################################
(Instance_1_GBM_BDP <- DALEX::predict_parts(explainer = GBM_DALEX,
new_observation = Instance_1_Philippines_Female[,c(1:6)],
type = "break_down"))## contribution
## GBM: intercept 72.475
## GBM: GENDER = Female 1.286
## GBM: INFMOR = 2.944 0.996
## GBM: CONTIN = Asia 0.629
## GBM: PERCAP = 1.249 0.866
## GBM: NCOMOR = 4.704 -0.065
## GBM: CLTECH = 47.4 -0.571
## GBM: prediction 75.616plot(Instance_1_GBM_BDP)
(Instance_2_GBM_BDP <- DALEX::predict_parts(explainer = GBM_DALEX,
new_observation = Instance_2_Philippines_Male[,c(1:6)],
type = "break_down"))## contribution
## GBM: intercept 72.475
## GBM: NCOMOR = 5.951 -3.294
## GBM: INFMOR = 3.174 -1.208
## GBM: GENDER = Male -0.972
## GBM: CONTIN = Asia 0.675
## GBM: PERCAP = 1.249 1.088
## GBM: CLTECH = 47.4 -0.662
## GBM: prediction 68.103plot(Instance_2_GBM_BDP)
1.3.9.4 Instance Level Exploration : Shapley Additive Explanations
[A] Using 25 random orderings of predictors, the average variable attributions for Instance 1, ranked from highest to lowest, were enumerated as follows:
[A.1] GENDER=Female (factor): +1.216
[A.2] INFMOR=2.944 (numeric): +1.017
[A.3] PERCAP=1.249 (numeric): +0.787
[A.4] CONTIN=Asia (factor): +0.734
[A.5] CLTECH=47.4 (numeric): -0.419
[A.6] NCOMOR=4.704 (numeric): -0.195
[B] Using 25 random orderings of predictors, the average variable attributions for Instance 2,ranked from highest to lowest, were described as follows:
[B.1] NCOMOR=5.951 (numeric): -2.957
[B.2] INFMOR=3.174 (numeric): -1.654
[B.3] GENDER=Male (factor): -0.889
[B.4] PERCAP=1.249 (numeric): +0.870
[B.5] CONTIN=Asia (factor): +0.768
[B.6] CLTECH=47.4 (numeric): -0.508
Code Chunk | Output
#################################
# Obtaining the shapley additive explanations
# for the individual instances
#################################
(Instance_1_GBM_SHAP <- DALEX::predict_parts(explainer = GBM_DALEX,
new_observation = Instance_1_Philippines_Female[,c(1:6)],
type = "shap",
B = 25))## min q1 median mean q3
## GBM: CLTECH = 47.4 -0.6461512 -0.5575544 -0.4095151 -0.4195469 -0.29357833
## GBM: CONTIN = Asia 0.5969206 0.6584082 0.7493046 0.7345593 0.81007457
## GBM: GENDER = Female 1.1254368 1.1763314 1.1903410 1.2163946 1.26665268
## GBM: INFMOR = 2.944 0.8087826 0.9515436 0.9875200 1.0176594 1.03214262
## GBM: NCOMOR = 4.704 -0.4485391 -0.3066485 -0.2115814 -0.1957897 -0.06458359
## GBM: PERCAP = 1.249 0.6416678 0.7081684 0.7807724 0.7874857 0.83103750
## max
## GBM: CLTECH = 47.4 -0.2418281
## GBM: CONTIN = Asia 0.8575527
## GBM: GENDER = Female 1.3370485
## GBM: INFMOR = 2.944 1.3780094
## GBM: NCOMOR = 4.704 0.0781626
## GBM: PERCAP = 1.249 0.9611060plot(Instance_1_GBM_SHAP)
(Instance_2_GBM_SHAP <- DALEX::predict_parts(explainer = GBM_DALEX,
new_observation = Instance_2_Philippines_Male[,c(1:6)],
type = "shap",
B = 25))## min q1 median mean q3
## GBM: CLTECH = 47.4 -0.6861096 -0.6619461 -0.5416852 -0.5087936 -0.3487904
## GBM: CONTIN = Asia 0.6115964 0.7027173 0.7892966 0.7685187 0.8219538
## GBM: GENDER = Male -1.0513274 -0.9924010 -0.8337821 -0.8894513 -0.8045019
## GBM: INFMOR = 3.174 -2.1214680 -1.6973253 -1.6586635 -1.6549659 -1.3896740
## GBM: NCOMOR = 5.951 -3.4994711 -3.1562885 -2.9604593 -2.9579765 -2.7187872
## GBM: PERCAP = 1.249 0.6669090 0.7717363 0.8397737 0.8704869 0.9583036
## max
## GBM: CLTECH = 47.4 -0.2694148
## GBM: CONTIN = Asia 0.9233503
## GBM: GENDER = Male -0.7663641
## GBM: INFMOR = 3.174 -1.2080360
## GBM: NCOMOR = 5.951 -2.4613331
## GBM: PERCAP = 1.249 1.1004550plot(Instance_2_GBM_SHAP)
1.3.9.5 Instance Level Exploration : Ceteris Paribus Profiles
[A] Assuming all other predictors remain unchanged, the effects of the individual predictors to the model predictions for Instance 1 were described as follows:
[A.1] Decreasing INFMOR (numeric) by 2 percent increases LIFEXP (numeric) by 5 years
[A.2] Decreasing PERCAP (numeric) by USD1K did not have a considerable effect on LIFEXP (numeric)
[A.3] Increasing CLTECH (numeric) by 50% increases LIFEXP (numeric) by 2 years
[A.4] Decreasing NCOMOR (numeric) by 2 percent increases LIFEXP (numeric) by 5 years
[A.5] Shifting factor level from GENDER=Female to GENDER=Male decreases LIFEXP (numeric) by 2 years
[A.6] Shifting factor level from CONTIN=Asia to CONTIN=Africa decreases LIFEXP (numeric) by 2 years
[B] Assuming all other predictors remain unchanged, the effects of the individual predictors to the model predictions for Instance 2 were described as follows:
[B.1] Decreasing INFMOR (numeric) by 2 percent increases LIFEXP (numeric) by 5 years
[B.2] Decreasing PERCAP (numeric) by USD1K did not have a considerable effect on LIFEXP (numeric)
[B.3] Increasing CLTECH (numeric) by 50% increases LIFEXP (numeric) by 2 years
[B.4] Decreasing NCOMOR (numeric) by 2 percent increases LIFEXP (numeric) by 5 years
[B.5] Shifting factor level from GENDER=Male to GENDER=Female increases LIFEXP (numeric) by 2 years
[B.6] Shifting factor level from CONTIN=Asia to CONTIN=Africa decreases LIFEXP (numeric) by 2 years
Code Chunk | Output
##################################
# Obtaining the ceteris paribus profiles
# for the individual instances
##################################
(Instance_1_GBM_CPP <- DALEX::predict_profile(explainer = GBM_DALEX,
new_observation = Instance_1_Philippines_Female[,c(1:6)]))## Top profiles :
## GENDER CONTIN INFMOR PERCAP CLTECH NCOMOR _yhat_ _vname_
## 141 Male Asia 2.944439 1.248566 47.4 4.704261 73.64589 GENDER
## 141.1 Female Asia 2.944439 1.248566 47.4 4.704261 75.61556 GENDER
## 1411 Female Africa 2.944439 1.248566 47.4 4.704261 73.69957 CONTIN
## 141.110 Female Asia 2.944439 1.248566 47.4 4.704261 75.61556 CONTIN
## 141.2 Female Europe 2.944439 1.248566 47.4 4.704261 75.13845 CONTIN
## 141.3 Female North America 2.944439 1.248566 47.4 4.704261 75.35803 CONTIN
## _ids_ _label_
## 141 141 GBM
## 141.1 141 GBM
## 1411 141 GBM
## 141.110 141 GBM
## 141.2 141 GBM
## 141.3 141 GBM
##
##
## Top observations:
## GENDER CONTIN INFMOR PERCAP CLTECH NCOMOR _yhat_ _label_ _ids_
## 141 Female Asia 2.944439 1.248566 47.4 4.704261 75.61556 GBM 1plot(Instance_1_GBM_CPP,
variables = c("INFMOR","PERCAP","CLTECH","NCOMOR")) +
ggtitle("Ceteris-paribus profile", "") +
ylim(55, 80)
plot(Instance_1_GBM_CPP,
variables = c("GENDER","CONTIN"),
variable_type = "categorical",
categorical_type = "bars") +
ggtitle("Ceteris-paribus profile", "")
(Instance_2_GBM_CPP <- DALEX::predict_profile(explainer = GBM_DALEX,
new_observation = Instance_2_Philippines_Male[,c(1:6)]))## Top profiles :
## GENDER CONTIN INFMOR PERCAP CLTECH NCOMOR _yhat_ _vname_
## 338 Male Asia 3.173878 1.248566 47.4 5.950788 68.10262 GENDER
## 338.1 Female Asia 3.173878 1.248566 47.4 5.950788 70.56651 GENDER
## 3381 Male Africa 3.173878 1.248566 47.4 5.950788 66.03975 CONTIN
## 338.110 Male Asia 3.173878 1.248566 47.4 5.950788 68.10262 CONTIN
## 338.2 Male Europe 3.173878 1.248566 47.4 5.950788 67.12975 CONTIN
## 338.3 Male North America 3.173878 1.248566 47.4 5.950788 68.09560 CONTIN
## _ids_ _label_
## 338 338 GBM
## 338.1 338 GBM
## 3381 338 GBM
## 338.110 338 GBM
## 338.2 338 GBM
## 338.3 338 GBM
##
##
## Top observations:
## GENDER CONTIN INFMOR PERCAP CLTECH NCOMOR _yhat_ _label_ _ids_
## 338 Male Asia 3.173878 1.248566 47.4 5.950788 68.10262 GBM 1plot(Instance_2_GBM_CPP,
variables = c("INFMOR","PERCAP","CLTECH","NCOMOR")) +
ggtitle("Ceteris-paribus profile", "") +
ylim(55, 80)
plot(Instance_2_GBM_CPP,
variables = c("GENDER","CONTIN"),
variable_type = "categorical",
categorical_type = "bars") +
ggtitle("Ceteris-paribus profile", "")
1.3.9.6 Instance Level Exploration : Local Fidelity Plots
[A] The histograms of residuals for the entire dataset and for a selected set of 50 neighbors for Instance 1 were almost parallel and very close to each other suggesting that the model predictions were stable around this particular instance of interest.
[B] The histograms of residuals for the entire dataset and for a selected set of 50 neighbors for Instance 2 were almost parallel and very close to each other suggesting that the model predictions were stable around this particular instance of interest.
Code Chunk | Output
Instance_1_GBM_LFP <- predict_diagnostics(explainer = GBM_DALEX,
new_observation = Instance_1_Philippines_Female[,c(1:6)],
neighbours = 50)
plot(Instance_1_GBM_LFP)
Instance_2_GBM_LFP <- predict_diagnostics(explainer = GBM_DALEX,
new_observation = Instance_2_Philippines_Male[,c(1:6)],
neighbours = 50)
plot(Instance_2_GBM_LFP)
1.3.9.7 Instance Level Exploration : Local Stability Plots
[A] The profiles obtained for the 5 nearest neighbors for Instance 1 were relatively close to each other, suggesting the stability of predictions. However, there were relatively more negative than positive residuals, which may have indicated a (local) positive bias of the predictions.
[B] The profiles obtained for the 5 nearest neighbors for Instance 2 were relatively close to each other, suggesting the stability of predictions. However, there were relatively more negative than positive residuals, which may have indicated a (local) positive bias of the predictions.
Code Chunk | Output
Instance_1_GBM_LFP <- predict_diagnostics(explainer = GBM_DALEX,
new_observation = Instance_1_Philippines_Female[,c(1:6)],
neighbours = 5,
variables = c("INFMOR","NCOMOR","CLTECH","PERCAP"))
plot(Instance_1_GBM_LFP)
Instance_1_GBM_LFP <- predict_diagnostics(explainer = GBM_DALEX,
new_observation = Instance_1_Philippines_Female[,c(1:6)],
neighbours = 5,
variables = c("GENDER","CONTIN"))
plot(Instance_1_GBM_LFP)
Instance_2_GBM_LFP <- predict_diagnostics(explainer = GBM_DALEX,
new_observation = Instance_2_Philippines_Male[,c(1:6)],
neighbours = 5,
variables = c("INFMOR","NCOMOR","CLTECH","PERCAP"))
plot(Instance_2_GBM_LFP)
Instance_2_GBM_LFP <- predict_diagnostics(explainer = GBM_DALEX,
new_observation = Instance_2_Philippines_Male[,c(1:6)],
neighbours = 5,
variables = c("GENDER","CONTIN"))
plot(Instance_2_GBM_LFP)
2. Summary
A stochastic gradient boosted model provided a set of robust and reliable estimates of life expectancy, primarily characterized by healthcare access (infant mortality, non-communicable disease-related mortality), demographic (gender, continent) and socio-economic (access to clean cooking, GDP per capita) factors. As a black-box model, various model-agnostic methods were used to interpret the model predictions. The key drivers identified for high life expectancy levels ranked by feature importance with their conditioned effects indicated were given as follows:
[1] Infant mortality (-)
[2] Non-communicable disease-related mortality (-)
[3] Belonging to the Non-African continent (+)
[4] Belonging to the female gender (+)
[5] Access to clean fuels and technologies for cooking (+)
[6] Gross domestic product per capita (+)
Overall, progressive economies tend to have higher life expectancy potentially influenced by a complex interplay of various factors. Developed countries typically invest heavily in healthcare infrastructure, ensuring that their populations have access to quality medical services. Adequate healthcare contributes to early diagnosis, effective treatment, and improved overall health outcomes. Similarly, stable economies lead to higher standards of living. Economic stability is linked to improved living conditions, better nutrition, and reduced exposure to environmental hazards, all of which contribute to increased life expectancy. Interestingly, gender differences in life expectancy were generally observed across countries favoring women. However, it is essential to recognize that causation is multifaceted and each country’s unique circumstances might contribute differently to its life expectancy outcomes.
[A] From an initial dataset comprised of 394 observations and 22 predictors, an optimal subset of 364 observations and 6 predictors representing healthcare access, demographic and socio-economic factors were determined after conducting data quality assessment and feature selection, excluding cases or variables noted with irregularities and applying preprocessing operations most suitable for the downstream analysis.
[B] Multiple regression modelling algorithms with various hyperparameter combinations were formulated using Stochastic Gradient Boosting, Cubist Regression, Neural Network, Random Forest, Linear Regression and Partial Least Squares Regression. The best model with optimized hyperparameters from each algorithm were determined through internal resampling validation using 10-Fold Cross Validation of performance metrics Root Mean Square Error (RMSE) and R-Squared. All candidate models were compared based on internal and external validation performance, including their residual distributions.
[C] The final model selected among candidates used Stochastic Gradient Boosting with optimal hyperparameters: total number of trees (n.trees=300), maximum depth of each tree (interaction.depth=2), learning rate (shrinkage=0.1) and minimum number of observations in the terminal node (n.minobsinnode=5). This model demonstrated the best externally validated RMSE and R-Squared (RMSE=2.1007, R-Squared=0.9171); no excessive overfitting comparing the external and internal validation metrics (RMSE=2.0978, R-Squared=0.9288); and stable residual distribution with lowest variation.
[D] Owing to the black-box nature of the selected model, post-hoc exploration of the model results involved model agnostic methods including Dataset-Level Exploration using model-level global explanations (Permutated Mean Dropout Loss-Based Variable Importance, Partial Dependence Plots) and Instance-Level Exploration using prediction-level local explanations (Breakdown Plots, Shapley Additive Explanations, Ceteris Paribus Plots, Local Fidelity Plots, Local Stability Plots). These results helped provide dataset-level and instance-level insights on the importance, contribution and effect of the various predictors to model prediction.
The current results have limitations which can be further addressed by extending the study to include the following actions:
[1] Considering the detection and treatment of multivariate outliers to develop robust and reliable predictive models that accurately capture the underlying patterns in the data rather than noise or anomalies
[2] Conducting sensitivity analysis to assess the impact of outliers on model predictions and performance metrics - whether removing outliers improves model accuracy without sacrificing generalizability
[3] Accounting for potential non-linearity and interactions among independent variables to gain insights into their influence on model prediction
[4] Applying training and test data splitting prior to data transformation or scaling to avoid data snooping or inadvertently using information from the testing set in the model training process
[5] Exploring stacking model structures by leveraging the strengths of diverse learning algorithms and mitigating the weaknesses of individual models to produce more robust predictions
3. References
[Book] Explanatory Model Analysis: Explore, Explain, and Examine Predictive Models With examples in R and Python by Przemyslaw Biecek and Tomasz Burzykowski
[Book] Explainable Machine Learning: A Guide for Making Black Box Models Explainable by Christoph Molnar
[Book] Explainable AI: Interpreting, Explaining and Visualizing Deep Learning by Wojciech Samek, Gregoire Montavon, Andrea Vedaldi, Lars Kai Hansen and Klaus-Robert Muller
[Book] Applied Predictive Modeling by Max Kuhn and Kjell Johnson
[Book] The Elements of Statistical Learning by Trevor Hastie , Robert Tibshirani and Jerome Friedman
[Book] Pattern Recognition and Neural Networks by Brian Ripley
[Book] Regression Modeling Strategies by Frank Harrel
[R Package] DALEX by Przemyslaw Biecek, Szymon Maksymiuk and Hubert Baniecki
[R Package] iml by Christoph Molnar
[R Package] ALEPlot by Dan Apley
[R Package] randomForest by Leo Breiman, Adele Cutler, Andy Liaw and Matthew Wiener
[R Package] auditor by Alicja Gosiewska, Przemyslaw Biecek, Hubert Baniecki and Tomasz Mikołajczyk
[R Package] fastshap by Brandon Greenwell
[R Package] rms by Frank Harrell
[R Package] EIX by Szymon Maksymiuk, Ewelina Karbowiak and Przemyslaw Biecek
[R Package] parsnip by Max Kuhn and Davis Vaughan
[R Package] h2o by Tomas Fryda, Erin LeDell, Navdeep Gill, Spencer Aiello, Anqi Fu, Arno Candel, Cliff Click, Tom Kraljevic, Tomas Nykodym, Patrick Aboyoun, Michal Kurka, Michal Malohlava, Sebastien Poirier and Wendy Wong
[R Package] tidymodels by Max Kuhn and Hadley Wickham
[R Package] e1071 by David Meyer, Evgenia Dimitriadou, Kurt Hornik, Andreas Weingessel and Friedrich Leisch
[R Package] lime by Emil Hvitfeldt, Thomas Lin Pedersen and Michael Benesty
[R Package] ExplainPrediction by Marko Robnik-Sikonja
[R Package] localModel by Przemyslaw Biecek and Mateusz Staniak
[R Package] skimr by Elin Waring
[R Package] corrplot by Taiyun Wei
[R Package] lares by Bernardo Lares
[R Package] minerva by Michele Filosi
[R Package] CORElearn by Marko Robnik-Sikonja and Petr Savicky
[R Package] caret by Max Kuhn
[R Package] gbm by Brandon Greenwell, Bradley Boehmke, Jay Cunningham and GBM Developers
[R Package] randomForest by Andy Liaw
[R Package] nnet by Brian Ripley
[R Package] pls by Kristian Hovde Liland
[R Package] Cubist by Max Kuhn
[R Package] patchwork by Thomas Lin Pedersen
[Article] Life Expectancy by Max Roser, Esteban Ortiz-Ospina and Hannah Ritchie
[Article] Interpretation Methods for Black-Box Machine Learning Models in Insurance Rating-Type Applications by Gabe Taylor, Sunish Menon, Huimin Ru, Ray Wright, Xin Hunt and Ralph Abbey
[Article] 4 Model-Agnostic Interpretability Techniques for Complex Models by Funda Gunes
[Article] How Can We Provide Post-Hoc Explanations for Black-Box AI Models? by Joy Lin
[Article] Explaining black-box models using attribute importance, PDPs, and LIME by Nikolay Manchev
[Article] An Introduction to Explainable AI with Shapley Values by SHAP Team
[Article] A Gentle Introduction to SHAP Values in R by Pablo Casas
[Article] SHAP Values with Examples Applied to a Multi-Classification Problem byHarpo Maxx
[Article] SHAP Values - Interpret Predictions Of ML Models using Game-Theoretic Approach by Sunny Solanki
[Article] How to Interpret Machine Learning (ML) Models with SHAP Values by Xiaoyou Wang
[Article] Partial Dependence and Individual Conditional Expectation plots by Sci-Kit Learn Team
[Article] Interpret Model Predictions with Partial Dependence and Individual Conditional Expectation plots by Ilknur Kaynar Kabul
[Article] Correlation in R: Pearson and Spearman Correlation Matrix by Daniel Johnson
[Article] Correlation (Pearson, Kendall, Spearman) by Statistics Solutions Team
[Article] A Comparison of the Pearson and Spearman Correlation Methods by Minitab Support Team
[Article] How to Perform Lowess Smoothing in R (Step-by-Step) by Statology Team
[Article] Maximal Information Coefficient by R Bloggers Team
[Article] Methods for Forecasts of Continuous Variables by WWRP/WGNE Joint Working Group on Forecast Verification Research Team
[Article] Generalized Boosting Model by BCCVL Team
[Article] An Introduction to Partial Least Squares by Statology Team
[Article] Random Forest by BCCVL Team
[Article] Artificial Neural Network by BCCVL Team
[Article] Cubist Regression Models by Max Kuhn
[Publication] Determinants of Life Expectancy at Birth: A Longitudinal Study on OECD Countries by Paolo Roffia, Alessandro Bucciol and Sara Hashlamoun
[Publication] Robust Locally Weighted Regression and Smoothing Scatterplots by William Cleveland (Journal of the American Statistical Association)
[Publication] Mathematical Contributions to the Theory of Evolution: Regression, Heredity and Panmixia by Karl Pearson (Royal Society)
[Publication] The Proof and Measurement of Association between Two Things by Charles Spearman (The American Journal of Psychology)
[Publication] Detecting Novel Associations in Large Data Sets by David Reshef, Yakir Reshef, Hilary Finucane, Sharon Grossman, Gilean Mcvean, Peter Turnbaugh, Eric Lander, Michael Mitzenmacher and Pardis Sabeti (Science)
[Publication] Stochastic Gradient Boosting by Jerome Friedman (Computational Statistics and Data Analysis)
[Publication] Random Forest by Leo Breiman (Machine Learning)
[Publication] The Collinearity Problem in Linear Regression. The Partial Least Squares (PLS) Approach to Generalized Inverses by Svante Wold, Axel Ruhe, Herman Wold, and William Dunn (Society for Industrial and Applied Mathematics)
[Publication] Learning With Continuous Classes by Ross Quinlan (Proceedings of the 5th Australian Joint Conference On Artificial Intelligence)
[Publication] A Survey of Methods for Explaining Black Box Models by Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri, Franco Turini, Fosca Giannotti and Dino Pedreschi (ACM Computing Surveys)
[Publication] iml: An R package for Interpretable Machine Learning by Christoph Molnar (Journal of Open Source Software)
[Publication] All Models Are Wrong, but Many Are Useful: Learning a Variable’s Importance by Studying an Entire Class of Prediction Models Simultaneously by Aaron Fisher, Cynthia Rudin and Francesca Dominici (Journal of Machine Learning Research)
[Publication] Greedy Function Approximation: A Gradient Boosting Machine by Jerome Friedman (Annals of Statistics)
[Publication] Peeking Inside the Black Box: Visualizing Statistical Learning With Plots of Individual Conditional Expectation by Alex Goldstein, Adam Kapelner, Justin Bleich and Emil Pitkin (Journal of Computational and Graphical Statistics)
[Publication] An Efficient Explanation of Individual Classifications Using Game Theory by Erik Strumbelj and Igor Kononenko (Journal of Machine Learning Research)
[Publication] Explaining Classifications For Individual Instances by Marco Robnik-Sikonja and Igor Kononenko (IEEE Transactions on Knowledge and Data Engineering)