Data Preprocessing : Comparing Oversampling and Undersampling Algorithms for Class Imbalance Treatment
John Pauline Pineda
April 30, 2023
- 1.
Table of Contents
- 1.1 Sample Data
- 1.2 Data Quality Assessment
- 1.3 Data Preprocessing
- 1.4 Data Exploration
- 1.5
Oversampling and Undersampling Algorithms Applied for Class Imbalance
using Logistic Regression
- 1.5.1 Original Data (LR)
- 1.5.2 Undersampling - Random Downsampling (LR_US_DOWNSAMPLE)
- 1.5.3 Oversampling - Random Upsampling (LR_US_UPSAMPLE)
- 1.5.4 Undersampling - Near Miss Algorithm (LR_US_NEARMISS)
- 1.5.5 Undersampling - Tomek Links (LR_US_TOMEK)
- 1.5.6 Oversampling - Adaptive Synthetic Algorithm (LR_OS_ADASYN)
- 1.5.7 Oversampling - Borderline Synthetic Minority Oversampling Technique (LR_OS_BSMOTE)
- 1.5.8 Oversampling - Synthetic Minority Oversampling Technique (LR_OS_SMOTE)
- 1.5.9 Oversampling - Random Oversampling Examples (LR_OS_ROSE)
- 1.6 Consolidated Findings
- 2. Summary
- 3. References
1. Table of Contents
This project explores different oversampling and undersampling methods to address imbalanced classification problems using various helpful packages in R. The algorithms applied to augment imbalanced data prior to model training by updating the original data set to minimize the effect of the disproportionate ratio of instances in each class included Near Miss, Tomek Links, Adaptive Synthetic Algorithm, Borderline Synthetic Minority Oversampling Technique, Synthetic Minority Oversampling Technique and Random Oversampling Examples. The derived class distributions were compared to the original data set and those applied with both random undersampling and oversampling methods. Using the Logistic Regression model structure, the corresponding logistic curves estimated from both the original and updated data were subjectively assessed in terms of skewness, data sparsity and class overlap. Considering the differences in their intended applications dependent on the quality and characteristics of the data being evaluated, a comparison of each method’s strengths and limitations was briefly discussed. All results were consolidated in a Summary presented at the end of the document.
Oversampling and undersampling algorithms address imbalanced classification problems by augmenting the data set used for model training based on its inherent characteristics to achieve a more reasonably balanced distribution between the majority and minority classes. In oversampling, new instances are created from the minor class instead of merely creating copies, by selecting two or more similar instances (using a distance measure) and perturbing an instance one attribute at a time by a random amount within the difference to the neighboring instances. In undersampling, a subset of instances from the majority class is removed based on relationships between nearest neighbors by having the minimum distance in the feature space with other instances belonging to the minority classes. The algorithms applied in this study (mostly contained in the caret, themis and ROSE packages attempt to augment imbalanced data prior to model training by updating the original data set to minimize the effect of the disproportionate ratio of instances in each class.
1.1 Sample Data
The Sonar dataset from the mlbench package was used for this illustrated example. The original dataset was transformed to simulate class imbalance. Only a pair of predictors was explored in the study to facilitate a clearer visualization of the class distribution.
Preliminary dataset assessment:
[A] 136 rows (observations)
[A.1] Train Set = 136 observations with class ratio of 80:20
[B] 3 columns (variables)
[B.1] 1/3 response = Class variable (factor)
[B.1.1] Levels = Class=R < Class=M
[B.2] 2/3 predictors = All remaining variables (2/2 numeric)
Code Chunk | Output
##################################
# Loading R libraries
##################################
library(AppliedPredictiveModeling)
library(caret)
library(rpart)
library(lattice)
library(dplyr)
library(tidyr)
library(moments)
library(skimr)
library(RANN)
library(mlbench)
library(pls)
library(corrplot)
library(lares)
library(DMwR2)
library(gridExtra)
library(rattle)
library(rpart.plot)
library(RColorBrewer)
library(stats)
library(nnet)
library(elasticnet)
library(earth)
library(party)
library(kernlab)
library(randomForest)
library(Cubist)
library(pROC)
library(ggpubr)
library(mda)
library(klaR)
library(pamr)
library(themis)
library(ROSE)
##################################
# Loading source and
# formulating the train set
##################################
data(Sonar)
Sonar.Original <- Sonar
Sonar.M <- Sonar[Sonar$Class=="M",]
Sonar.R <- Sonar[Sonar$Class=="R",]
set.seed(12345678)
Sonar.R.Reduced <- Sonar.R[sample(1:nrow(Sonar.R),25),]
Sonar <- as.data.frame(rbind(Sonar.M,Sonar.R.Reduced))
Sonar$Class <- factor(Sonar$Class,
levels=c("M","R"))
Sonar_Train <- Sonar[,c("Class","V1","V11")]
##################################
# Performing a general exploration of the train set
##################################
dim(Sonar_Train)## [1] 136 3str(Sonar_Train)## 'data.frame': 136 obs. of 3 variables:
## $ Class: Factor w/ 2 levels "M","R": 1 1 1 1 1 1 1 1 1 1 ...
## $ V1 : num 0.0491 0.1313 0.0201 0.0629 0.0335 ...
## $ V11 : num 0.0947 0.2907 0.2251 0.5466 0.5533 ...summary(Sonar_Train)## Class V1 V11
## M:111 Min. :0.00150 Min. :0.0523
## R: 25 1st Qu.:0.01550 1st Qu.:0.1780
## Median :0.02365 Median :0.2503
## Mean :0.03188 Mean :0.2642
## 3rd Qu.:0.03925 3rd Qu.:0.3222
## Max. :0.13710 Max. :0.7342##################################
# Formulating a data type assessment summary
##################################
PDA <- Sonar_Train
(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 Class factor
## 2 2 V1 numeric
## 3 3 V11 numeric1.2 Data Quality Assessment
[A] No missing observations noted for any variable.
[B] No low variance noted with First.Second.Mode.Ratio>5.
[C] No low variance noted for any variable with Unique.Count.Ratio<0.01.
[D] No high skewness noted for any variable with Skewness>3 or Skewness<(-3).
Code Chunk | Output
##################################
# Loading dataset
##################################
DQA <- Sonar_Train
##################################
# 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 Class factor 136 0 1.000
## 2 2 V1 numeric 136 0 1.000
## 3 3 V11 numeric 136 0 1.000##################################
# Listing all predictors
##################################
DQA.Predictors <- DQA[,!names(DQA) %in% c("Class")]
##################################
# Listing all numeric predictors
##################################
DQA.Predictors.Numeric <- DQA.Predictors[,sapply(DQA.Predictors, is.numeric)]
if (length(names(DQA.Predictors.Numeric))>0) {
print(paste0("There are ",
(length(names(DQA.Predictors.Numeric))),
" numeric predictor variable(s)."))
} else {
print("There are no numeric predictor variables.")
}## [1] "There are 2 numeric predictor variable(s)."##################################
# Listing all factor predictors
##################################
DQA.Predictors.Factor <- DQA.Predictors[,sapply(DQA.Predictors, is.factor)]
if (length(names(DQA.Predictors.Factor))>0) {
print(paste0("There are ",
(length(names(DQA.Predictors.Factor))),
" factor predictor variable(s)."))
} else {
print("There are no factor predictor variables.")
}## [1] "There are no factor predictor variables."##################################
# Formulating a data quality assessment summary for factor predictors
##################################
if (length(names(DQA.Predictors.Factor))>0) {
##################################
# Formulating a function to determine the first mode
##################################
FirstModes <- function(x) {
ux <- unique(na.omit(x))
tab <- tabulate(match(x, ux))
ux[tab == max(tab)]
}
##################################
# Formulating a function to determine the second mode
##################################
SecondModes <- function(x) {
ux <- unique(na.omit(x))
tab <- tabulate(match(x, ux))
fm = ux[tab == max(tab)]
sm = x[!(x %in% fm)]
usm <- unique(sm)
tabsm <- tabulate(match(sm, usm))
ifelse(is.na(usm[tabsm == max(tabsm)])==TRUE,
return("x"),
return(usm[tabsm == max(tabsm)]))
}
(DQA.Predictors.Factor.Summary <- data.frame(
Column.Name= names(DQA.Predictors.Factor),
Column.Type=sapply(DQA.Predictors.Factor, function(x) class(x)),
Unique.Count=sapply(DQA.Predictors.Factor, function(x) length(unique(x))),
First.Mode.Value=sapply(DQA.Predictors.Factor, function(x) as.character(FirstModes(x)[1])),
Second.Mode.Value=sapply(DQA.Predictors.Factor, function(x) as.character(SecondModes(x)[1])),
First.Mode.Count=sapply(DQA.Predictors.Factor, function(x) sum(na.omit(x) == FirstModes(x)[1])),
Second.Mode.Count=sapply(DQA.Predictors.Factor, function(x) sum(na.omit(x) == SecondModes(x)[1])),
Unique.Count.Ratio=sapply(DQA.Predictors.Factor, function(x) format(round((length(unique(x))/nrow(DQA.Predictors.Factor)),3), nsmall=3)),
First.Second.Mode.Ratio=sapply(DQA.Predictors.Factor, function(x) format(round((sum(na.omit(x) == FirstModes(x)[1])/sum(na.omit(x) == SecondModes(x)[1])),3), nsmall=3)),
row.names=NULL)
)
}
##################################
# Formulating a data quality assessment summary for numeric predictors
##################################
if (length(names(DQA.Predictors.Numeric))>0) {
##################################
# Formulating a function to determine the first mode
##################################
FirstModes <- function(x) {
ux <- unique(na.omit(x))
tab <- tabulate(match(x, ux))
ux[tab == max(tab)]
}
##################################
# Formulating a function to determine the second mode
##################################
SecondModes <- function(x) {
ux <- unique(na.omit(x))
tab <- tabulate(match(x, ux))
fm = ux[tab == max(tab)]
sm = na.omit(x)[!(na.omit(x) %in% fm)]
usm <- unique(sm)
tabsm <- tabulate(match(sm, usm))
ifelse(is.na(usm[tabsm == max(tabsm)])==TRUE,
return(0.00001),
return(usm[tabsm == max(tabsm)]))
}
(DQA.Predictors.Numeric.Summary <- data.frame(
Column.Name= names(DQA.Predictors.Numeric),
Column.Type=sapply(DQA.Predictors.Numeric, function(x) class(x)),
Unique.Count=sapply(DQA.Predictors.Numeric, function(x) length(unique(x))),
Unique.Count.Ratio=sapply(DQA.Predictors.Numeric, function(x) format(round((length(unique(x))/nrow(DQA.Predictors.Numeric)),3), nsmall=3)),
First.Mode.Value=sapply(DQA.Predictors.Numeric, function(x) format(round((FirstModes(x)[1]),3),nsmall=3)),
Second.Mode.Value=sapply(DQA.Predictors.Numeric, function(x) format(round((SecondModes(x)[1]),3),nsmall=3)),
First.Mode.Count=sapply(DQA.Predictors.Numeric, function(x) sum(na.omit(x) == FirstModes(x)[1])),
Second.Mode.Count=sapply(DQA.Predictors.Numeric, function(x) sum(na.omit(x) == SecondModes(x)[1])),
First.Second.Mode.Ratio=sapply(DQA.Predictors.Numeric, function(x) format(round((sum(na.omit(x) == FirstModes(x)[1])/sum(na.omit(x) == SecondModes(x)[1])),3), nsmall=3)),
Minimum=sapply(DQA.Predictors.Numeric, function(x) format(round(min(x,na.rm = TRUE),3), nsmall=3)),
Mean=sapply(DQA.Predictors.Numeric, function(x) format(round(mean(x,na.rm = TRUE),3), nsmall=3)),
Median=sapply(DQA.Predictors.Numeric, function(x) format(round(median(x,na.rm = TRUE),3), nsmall=3)),
Maximum=sapply(DQA.Predictors.Numeric, function(x) format(round(max(x,na.rm = TRUE),3), nsmall=3)),
Skewness=sapply(DQA.Predictors.Numeric, function(x) format(round(skewness(x,na.rm = TRUE),3), nsmall=3)),
Kurtosis=sapply(DQA.Predictors.Numeric, function(x) format(round(kurtosis(x,na.rm = TRUE),3), nsmall=3)),
Percentile25th=sapply(DQA.Predictors.Numeric, function(x) format(round(quantile(x,probs=0.25,na.rm = TRUE),3), nsmall=3)),
Percentile75th=sapply(DQA.Predictors.Numeric, function(x) format(round(quantile(x,probs=0.75,na.rm = TRUE),3), nsmall=3)),
row.names=NULL)
)
}## Column.Name Column.Type Unique.Count Unique.Count.Ratio First.Mode.Value
## 1 V1 numeric 122 0.897 0.020
## 2 V11 numeric 134 0.985 0.213
## Second.Mode.Value First.Mode.Count Second.Mode.Count First.Second.Mode.Ratio
## 1 0.034 3 2 1.500
## 2 0.095 2 1 2.000
## Minimum Mean Median Maximum Skewness Kurtosis Percentile25th Percentile75th
## 1 0.002 0.032 0.024 0.137 1.915 6.988 0.015 0.039
## 2 0.052 0.264 0.250 0.734 0.909 4.151 0.178 0.322##################################
# Identifying potential data quality issues
##################################
##################################
# Checking for missing observations
##################################
if ((nrow(DQA.Summary[DQA.Summary$NA.Count>0,]))>0){
print(paste0("Missing observations noted for ",
(nrow(DQA.Summary[DQA.Summary$NA.Count>0,])),
" variable(s) with NA.Count>0 and Fill.Rate<1.0."))
DQA.Summary[DQA.Summary$NA.Count>0,]
} else {
print("No missing observations noted.")
}## [1] "No missing observations noted."##################################
# Checking for zero or near-zero variance predictors
##################################
if (length(names(DQA.Predictors.Factor))==0) {
print("No factor predictors noted.")
} else if (nrow(DQA.Predictors.Factor.Summary[as.numeric(as.character(DQA.Predictors.Factor.Summary$First.Second.Mode.Ratio))>5,])>0){
print(paste0("Low variance observed for ",
(nrow(DQA.Predictors.Factor.Summary[as.numeric(as.character(DQA.Predictors.Factor.Summary$First.Second.Mode.Ratio))>5,])),
" factor variable(s) with First.Second.Mode.Ratio>5."))
DQA.Predictors.Factor.Summary[as.numeric(as.character(DQA.Predictors.Factor.Summary$First.Second.Mode.Ratio))>5,]
} else {
print("No low variance factor predictors due to high first-second mode ratio noted.")
}## [1] "No factor predictors noted."if (length(names(DQA.Predictors.Numeric))==0) {
print("No numeric predictors noted.")
} else if (nrow(DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$First.Second.Mode.Ratio))>5,])>0){
print(paste0("Low variance observed for ",
(nrow(DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$First.Second.Mode.Ratio))>5,])),
" numeric variable(s) with First.Second.Mode.Ratio>5."))
DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$First.Second.Mode.Ratio))>5,]
} else {
print("No low variance numeric predictors due to high first-second mode ratio noted.")
}## [1] "No low variance numeric predictors due to high first-second mode ratio noted."if (length(names(DQA.Predictors.Numeric))==0) {
print("No numeric predictors noted.")
} else if (nrow(DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$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] "No skewed numeric predictors noted."1.3 Data Preprocessing
1.3.1 Outlier
[A] Outliers noted for 2 variables with the numeric data 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). Outlier treatment for numerical stability remains optional depending on potential model requirements for the subsequent steps.
[A.1] V1 variable (8 outliers detected)
[A.2] V11 variable (5 outliers detected)
Code Chunk | Output
##################################
# Loading dataset
##################################
DPA <- Sonar_Train
##################################
# Listing all predictors
##################################
DPA.Predictors <- DPA[,!names(DPA) %in% c("Class")]
##################################
# 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])$out
OutlierCount <- length(Outliers)
OutlierCountList <- append(OutlierCountList,OutlierCount)
OutlierIndices <- which(DPA.Predictors.Numeric[,i] %in% c(Outliers))
boxplot(DPA.Predictors.Numeric[,i],
ylab = names(DPA.Predictors.Numeric)[i],
main = names(DPA.Predictors.Numeric)[i],
horizontal=TRUE)
mtext(paste0(OutlierCount, " Outlier(s) Detected"))
}
OutlierCountSummary <- as.data.frame(cbind(names(DPA.Predictors.Numeric),(OutlierCountList)))
names(OutlierCountSummary) <- c("NumericPredictors","OutlierCount")
OutlierCountSummary$OutlierCount <- as.numeric(as.character(OutlierCountSummary$OutlierCount))
NumericPredictorWithOutlierCount <- nrow(OutlierCountSummary[OutlierCountSummary$OutlierCount>0,])
print(paste0(NumericPredictorWithOutlierCount, " numeric variable(s) were noted with outlier(s)." ))## [1] "2 numeric variable(s) were noted with outlier(s)."##################################
# Gathering descriptive statistics
##################################
(DPA_Skimmed <- skim(DPA.Predictors.Numeric))| Name | DPA.Predictors.Numeric |
| Number of rows | 136 |
| Number of columns | 2 |
| _______________________ | |
| Column type frequency: | |
| numeric | 2 |
| ________________________ | |
| Group variables | None |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| V1 | 0 | 1 | 0.03 | 0.03 | 0.00 | 0.02 | 0.02 | 0.04 | 0.14 | ▇▃▁▁▁ |
| V11 | 0 | 1 | 0.26 | 0.13 | 0.05 | 0.18 | 0.25 | 0.32 | 0.73 | ▅▇▂▁▁ |
###################################
# Verifying the data dimensions
###################################
dim(DPA.Predictors.Numeric)## [1] 136 21.3.2 Zero and Near-Zero Variance
[A] No low variance noted for any variable from the previous data quality assessment using a lower threshold.
[B] No low variance noted for any variables 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
##################################
# Loading dataset
##################################
DPA <- Sonar_Train
##################################
# Gathering descriptive statistics
##################################
(DPA_Skimmed <- skim(DPA))| Name | DPA |
| Number of rows | 136 |
| Number of columns | 3 |
| _______________________ | |
| Column type frequency: | |
| factor | 1 |
| numeric | 2 |
| ________________________ | |
| Group variables | None |
Variable type: factor
| skim_variable | n_missing | complete_rate | ordered | n_unique | top_counts |
|---|---|---|---|---|---|
| Class | 0 | 1 | FALSE | 2 | M: 111, R: 25 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| V1 | 0 | 1 | 0.03 | 0.03 | 0.00 | 0.02 | 0.02 | 0.04 | 0.14 | ▇▃▁▁▁ |
| V11 | 0 | 1 | 0.26 | 0.13 | 0.05 | 0.18 | 0.25 | 0.32 | 0.73 | ▅▇▂▁▁ |
##################################
# Identifying columns with low variance
###################################
DPA_LowVariance <- nearZeroVar(DPA,
freqCut = 95/5,
uniqueCut = 10,
saveMetrics= TRUE)
(DPA_LowVariance[DPA_LowVariance$nzv,])## [1] freqRatio percentUnique zeroVar nzv
## <0 rows> (or 0-length row.names)if ((nrow(DPA_LowVariance[DPA_LowVariance$nzv,]))==0){
print("No low variance 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$nzv,]))
##################################
# Filtering out columns with low variance
#################################
DPA_ExcludedLowVariance <- DPA[,!names(DPA) %in% rownames(DPA_LowVariance[DPA_LowVariance$nzv,])]
##################################
# Gathering descriptive statistics
##################################
(DPA_ExcludedLowVariance_Skimmed <- skim(DPA_ExcludedLowVariance))
###################################
# Verifying the data dimensions
###################################
dim(DPA_ExcludedLowVariance)
} ## [1] "No low variance predictors noted."1.3.3 Collinearity
[A] No high correlation > 95% were noted for any variable as confirmed using the preprocessing summaries from the caret and lares packages.
Code Chunk | Output
##################################
# Loading dataset
##################################
DPA <- Sonar_Train
##################################
# Listing all predictors
##################################
DPA.Predictors <- DPA[,!names(DPA) %in% c("Class")]
##################################
# Listing all numeric predictors
##################################
DPA.Predictors.Numeric <- DPA.Predictors[,sapply(DPA.Predictors, is.numeric)]
##################################
# Visualizing pairwise correlation between predictors
##################################
DPA_CorrelationTest <- cor.mtest(DPA.Predictors.Numeric,
method = "pearson",
conf.level = .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")
##################################
# 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)]) > 0.95))## [1] 0if (DPA_HighlyCorrelatedCount == 0) {
print("No highly correlated predictors noted.")
} else {
print(paste0("High correlation observed for ",
(DPA_HighlyCorrelatedCount),
" pairs of numeric variable(s) with Correlation.Coefficient>0.95."))
(DPA_HighlyCorrelatedPairs <- corr_cross(DPA.Predictors.Numeric,
max_pvalue = 0.05,
top = DPA_HighlyCorrelatedCount,
rm.na = TRUE,
grid = FALSE
))
}## [1] "No highly correlated predictors noted."if (DPA_HighlyCorrelatedCount > 0) {
DPA_HighlyCorrelated <- findCorrelation(DPA_Correlation, cutoff = 0.95)
(DPA_HighlyCorrelatedForRemoval <- length(DPA_HighlyCorrelated))
print(paste0("High correlation can be resolved by removing ",
(DPA_HighlyCorrelatedForRemoval),
" numeric variable(s)."))
for (j in 1:DPA_HighlyCorrelatedForRemoval) {
DPA_HighlyCorrelatedRemovedVariable <- colnames(DPA.Predictors.Numeric)[DPA_HighlyCorrelated[j]]
print(paste0("Variable ",
j,
" for removal: ",
DPA_HighlyCorrelatedRemovedVariable))
}
##################################
# Filtering out columns with high correlation
#################################
DPA_ExcludedHighCorrelation <- DPA[,-DPA_HighlyCorrelated]
##################################
# Gathering descriptive statistics
##################################
(DPA_ExcludedHighCorrelation_Skimmed <- skim(DPA_ExcludedHighCorrelation))
###################################
# Verifying the data dimensions
###################################
dim(DPA_ExcludedHighCorrelation)
}1.3.4 Linear Dependencies
[A] No linear dependencies noted for any subset of 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
##################################
# Loading dataset
##################################
DPA <- Sonar_Train
##################################
# Listing all predictors
##################################
DPA.Predictors <- DPA[,!names(DPA) %in% c("Class")]
##################################
# Listing all numeric predictors
##################################
DPA.Predictors.Numeric <- DPA.Predictors[,sapply(DPA.Predictors, is.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$remove[j]]
print(paste0("Variable ",
j,
" for removal: ",
DPA_LinearlyDependentRemovedVariable))
}
##################################
# Filtering out columns with linear dependency
#################################
DPA_ExcludedLinearlyDependent <- DPA[,-DPA_LinearlyDependent$remove]
##################################
# Gathering descriptive statistics
##################################
(DPA_ExcludedLinearlyDependent_Skimmed <- skim(DPA_ExcludedLinearlyDependent))
###################################
# Verifying the data dimensions
###################################
dim(DPA_ExcludedLinearlyDependent)
} else {
###################################
# Verifying the data dimensions
###################################
dim(DPA)
}## [1] 136 31.3.5 Shape Transformation
[A] A number of numeric variables in the dataset were observed to be right-skewed which required shape transformation for data distribution stability. Considering that all numeric variables were strictly positive values, the BoxCox method from the caret package was used to transform their distributional shapes.
Code Chunk | Output
##################################
# Loading dataset
##################################
DPA <- Sonar_Train
##################################
# Listing all predictors
##################################
DPA.Predictors <- DPA[,!names(DPA) %in% c("Class")]
##################################
# Listing all numeric predictors
##################################
DPA.Predictors.Numeric <- DPA.Predictors[,sapply(DPA.Predictors, is.numeric)]
##################################
# Applying a Box-Cox transformation
##################################
DPA_BoxCox <- preProcess(DPA.Predictors.Numeric, method = c("BoxCox"))
DPA_BoxCoxTransformed <- predict(DPA_BoxCox, DPA.Predictors.Numeric)
##################################
# Gathering descriptive statistics
##################################
(DPA_BoxCoxTransformedSkimmed <- skim(DPA_BoxCoxTransformed))| Name | DPA_BoxCoxTransformed |
| Number of rows | 136 |
| Number of columns | 2 |
| _______________________ | |
| Column type frequency: | |
| numeric | 2 |
| ________________________ | |
| Group variables | None |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| V1 | 0 | 1 | -3.73 | 0.78 | -6.50 | -4.17 | -3.74 | -3.24 | -1.99 | ▁▁▇▇▂ |
| V11 | 0 | 1 | -1.07 | 0.29 | -1.73 | -1.25 | -1.06 | -0.91 | -0.29 | ▂▃▇▂▁ |
###################################
# Verifying the data dimensions
###################################
dim(DPA_BoxCoxTransformed)## [1] 136 21.3.6 Centering and Scaling
[A] To maintain numerical stability during modelling, centering and scaling transformations were applied on the transformed numeric variables. The center method from the caret package was implemented which subtracts the average value of a numeric variable to all the values. As a result of centering, the variables had zero mean values. In addition, the scale method, also from the caret package, was applied which performs a center transformation with each value of the variable divided by its standard deviation. Scaling the data coerced the values to have a common standard deviation of one.
Code Chunk | Output
##################################
# Loading dataset
##################################
DPA <- Sonar_Train
##################################
# Listing all predictors
##################################
DPA.Predictors <- DPA[,!names(DPA) %in% c("Class")]
##################################
# Listing all numeric predictors
##################################
DPA.Predictors.Numeric <- DPA.Predictors[,sapply(DPA.Predictors, is.numeric)]
##################################
# Applying a Box-Cox transformation
##################################
DPA_BoxCox <- preProcess(DPA.Predictors.Numeric, method = c("BoxCox"))
DPA_BoxCoxTransformed <- predict(DPA_BoxCox, DPA.Predictors.Numeric)
##################################
# Applying a center and scale data transformation
##################################
DPA.Predictors.Numeric_BoxCoxTransformed_CenteredScaled <- preProcess(DPA_BoxCoxTransformed, method = c("center","scale"))
DPA.Predictors.Numeric_BoxCoxTransformed_CenteredScaledTransformed <- predict(DPA.Predictors.Numeric_BoxCoxTransformed_CenteredScaled, DPA_BoxCoxTransformed)
##################################
# Gathering descriptive statistics
##################################
(DPA.Predictors.Numeric_BoxCoxTransformed_CenteredScaledTransformedSkimmed <- skim(DPA.Predictors.Numeric_BoxCoxTransformed_CenteredScaledTransformed))| Name | DPA.Predictors.Numeric_Bo… |
| Number of rows | 136 |
| Number of columns | 2 |
| _______________________ | |
| Column type frequency: | |
| numeric | 2 |
| ________________________ | |
| Group variables | None |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| V1 | 0 | 1 | 0 | 1 | -3.56 | -0.56 | -0.02 | 0.63 | 2.23 | ▁▁▇▇▂ |
| V11 | 0 | 1 | 0 | 1 | -2.27 | -0.59 | 0.04 | 0.57 | 2.71 | ▂▃▇▂▁ |
###################################
# Verifying the data dimensions
###################################
dim(DPA.Predictors.Numeric_BoxCoxTransformed_CenteredScaledTransformed)## [1] 136 21.3.7 Pre-Processed Dataset
[A] 136 rows (observations)
[A.1] Train Set = 136 observations with class ratio of 80:20
[B] 3 columns (variables)
[B.1] 1/3 response = Class variable (factor)
[B.1.1] Levels = Class=R < Class=M
[B.2] 2/3 predictors = All remaining variables (2/2 numeric)
[C] Pre-processing actions applied:
[C.1] Centering, scaling and shape transformation applied to improve data quality
[C.2] No outlier treatment applied since the high values noted were contextually valid and sensible
[C.3] No predictors removed due to zero or near-zero variance
[C.4] No predictors removed due to high correlation
[C.5] No predictors removed due to linear dependencies
Code Chunk | Output
##################################
# Creating the pre-modelling
# train set
##################################
Class <- DPA$Class
PMA.Predictors.Numeric <- DPA.Predictors.Numeric_BoxCoxTransformed_CenteredScaledTransformed
PMA_BoxCoxTransformed_CenteredScaledTransformed <- cbind(Class,PMA.Predictors.Numeric)
PMA_PreModelling_Train <- PMA_BoxCoxTransformed_CenteredScaledTransformed
##################################
# Gathering descriptive statistics
##################################
(PMA_PreModelling_Train_Skimmed <- skim(PMA_PreModelling_Train))| Name | PMA_PreModelling_Train |
| Number of rows | 136 |
| Number of columns | 3 |
| _______________________ | |
| Column type frequency: | |
| factor | 1 |
| numeric | 2 |
| ________________________ | |
| Group variables | None |
Variable type: factor
| skim_variable | n_missing | complete_rate | ordered | n_unique | top_counts |
|---|---|---|---|---|---|
| Class | 0 | 1 | FALSE | 2 | M: 111, R: 25 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| V1 | 0 | 1 | 0 | 1 | -3.56 | -0.56 | -0.02 | 0.63 | 2.23 | ▁▁▇▇▂ |
| V11 | 0 | 1 | 0 | 1 | -2.27 | -0.59 | 0.04 | 0.57 | 2.71 | ▂▃▇▂▁ |
###################################
# Verifying the data dimensions
# for the train set
###################################
dim(PMA_PreModelling_Train)## [1] 136 31.4 Data Exploration
[A] Both V1 and V11 numeric variables demonstrated differential relationships with the Class response variable.
Code Chunk | Output
##################################
# Loading dataset
##################################
EDA <- PMA_PreModelling_Train
##################################
# Listing all predictors
##################################
EDA.Predictors <- EDA[,!names(EDA) %in% c("Class")]
##################################
# Listing all numeric predictors
##################################
EDA.Predictors.Numeric <- EDA.Predictors[,sapply(EDA.Predictors, is.numeric)]
ncol(EDA.Predictors.Numeric)## [1] 2names(EDA.Predictors.Numeric)## [1] "V1" "V11"##################################
# Formulating the box plots
##################################
featurePlot(x = EDA.Predictors.Numeric,
y = EDA$Class,
plot = "box",
scales = list(x = list(relation="free", rot = 90),
y = list(relation="free")),
adjust = 1.5,
pch = "|")
1.5 Oversampling and Undersampling Algorithms Applied for Class Imbalance using Logistic Regression
1.5.1 Original Data (LR)
Logistic Regression models the relationship between the probability of an event (among two outcome levels) by having the log-odds of the event be a linear combination of a set of predictors weighted by their respective parameter estimates. The parameters are estimated via maximum likelihood estimation by testing different values through multiple iterations to optimize for the best fit of log odds. All of these iterations produce the log likelihood function, and logistic regression seeks to maximize this function to find the best parameter estimates. Given the optimal parameters, the conditional probabilities for each observation can be calculated, logged, and summed together to yield a predicted probability.
[A] The class ratio of the original data was noted at 80:20.
[A.1] Majority Class = Class=M with 111 instances
[A.2] Minority Class = Class=R with 25 instances
[B] The logistic regression model from the stats package was implemented. The Class response was regressed against the V1 and V11 predictors.
[C] The logistic curve formulated by plotting the predicted probabilities against the classification index using the logit values showed a skewed logistic profile with a longer tail for the predicted points belonging to the majority class.
Code Chunk | Output
##################################
# Creating a local object
# for the train set
##################################
PMA_PreModelling_Train_LR <- PMA_PreModelling_Train
PMA_PreModelling_Train_LR$Label <- rep("LR",nrow(PMA_PreModelling_Train_LR))
##################################
# Verifying the class distribution
# for the original data
##################################
table(PMA_PreModelling_Train_LR$Class) ##
## M R
## 111 25##################################
# Visualizing the imbalanced data set
##################################
ggplot(PMA_PreModelling_Train, aes(V1, V11, color = Class)) +
scale_colour_manual(values=c("#1846BA55","#B8000055")) +
geom_point(size=5) +
scale_x_continuous( limits=c(-4,4), breaks=seq(-4,4,by=1)) +
scale_y_continuous( limits=c(-4,4), breaks=seq(-4,4,by=1)) +
labs(title = "Original Imbalanced Data Set") +
theme_bw() +
theme(plot.title = element_text(color="black", size=14, face="bold", hjust=0.50),
axis.title.x = element_text(color="black", size=12, face="bold"),
axis.title.y = element_text(color="black", size=12, face="bold"),
legend.position="top")
##################################
# Formulating the structure of the
# Logistic Regression model
##################################
LR_Model <- glm(Class ~ V1 + V11,
data = PMA_PreModelling_Train_LR,
family = binomial)
##################################
# Consolidating the model results
##################################
summary(LR_Model)##
## Call:
## glm(formula = Class ~ V1 + V11, family = binomial, data = PMA_PreModelling_Train_LR)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.3152 0.3771 -6.139 8.28e-10 ***
## V1 -0.7399 0.3005 -2.462 0.0138 *
## V11 -1.5607 0.3450 -4.524 6.07e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 129.783 on 135 degrees of freedom
## Residual deviance: 88.923 on 133 degrees of freedom
## AIC: 94.923
##
## Number of Fisher Scoring iterations: 6LR_Model_Coef <- (as.data.frame(LR_Model$coefficients))
LR_Model_Coef$Coef <- rownames(LR_Model_Coef)
LR_Model_Coef$Model <- rep("LR",nrow(LR_Model_Coef))
colnames(LR_Model_Coef) <- c("Estimates","Coefficients","Model")
print(LR_Model_Coef, rownames=FALSE)## Estimates Coefficients Model
## (Intercept) -2.3152181 (Intercept) LR
## V1 -0.7399005 V1 LR
## V11 -1.5607048 V11 LR##################################
# Computing the model predictions
##################################
(LR_Model_Probabilities <- predict(LR_Model,
type = c("response")))## 98 99 100 101 102 103
## 0.3637496537 0.0113931997 0.1319142547 0.0021783355 0.0037538060 0.0174606489
## 104 105 106 107 108 109
## 0.0725273351 0.0164891503 0.1569862922 0.4818797362 0.2583425943 0.1252724633
## 110 111 112 113 114 115
## 0.4112142881 0.4680556213 0.1332256210 0.0344698396 0.1348432397 0.1615191484
## 116 117 118 119 120 121
## 0.0662797277 0.2436188082 0.0780323151 0.1104267965 0.0515700587 0.1445535907
## 122 123 124 125 126 127
## 0.1731460430 0.0558634929 0.1081612509 0.0165343094 0.0369004305 0.0031146384
## 128 129 130 131 132 133
## 0.0221776030 0.0200623323 0.0028456905 0.0126580025 0.0050535349 0.0081982533
## 134 135 136 137 138 139
## 0.0025368869 0.0014777580 0.0799936459 0.0023418015 0.0008311849 0.0846443282
## 140 141 142 143 144 145
## 0.2606104200 0.1780753538 0.0394327767 0.0403610789 0.0364744270 0.7351055523
## 146 147 148 149 150 151
## 0.0306057253 0.0228736619 0.0552988212 0.0205374459 0.2190542623 0.2301893864
## 152 153 154 155 156 157
## 0.1183496315 0.1771452296 0.3898752302 0.5789364640 0.6505131954 0.1549759240
## 158 159 160 161 162 163
## 0.1418070654 0.0908007706 0.0533759775 0.0797056569 0.0996424369 0.1113892912
## 164 165 166 167 168 169
## 0.2325914484 0.0352838163 0.0868721918 0.2269151196 0.5811364344 0.6804246275
## 170 171 172 173 174 175
## 0.2377989316 0.1546431081 0.0039867863 0.0484288031 0.0350025026 0.0115543490
## 176 177 178 179 180 181
## 0.0124263395 0.0040952633 0.0623582127 0.2442298264 0.0289787190 0.0051456066
## 182 183 184 185 186 187
## 0.0192102410 0.0354944115 0.0420803608 0.0020214950 0.0046310125 0.0336330699
## 188 189 190 191 192 193
## 0.0359277781 0.1172950153 0.1419944753 0.1265297826 0.1047718509 0.2755585363
## 194 195 196 197 198 199
## 0.1390569085 0.0450510742 0.1550648867 0.2872791732 0.0821006438 0.0726099031
## 200 201 202 203 204 205
## 0.0792285656 0.0532267024 0.0299770042 0.0173915161 0.0553203581 0.0345232209
## 206 207 208 95 57 27
## 0.0333086574 0.0446830571 0.0622826685 0.8967905106 0.2407267465 0.6874636823
## 18 68 92 43 87 64
## 0.4352384578 0.4517614566 0.0687256353 0.6033006196 0.1583196380 0.7793277803
## 16 12 61 13 34 66
## 0.0727076286 0.8563207774 0.3983590501 0.7017484676 0.3096802387 0.8196244700
## 49 94 91 72 23 39
## 0.3431752805 0.0384599213 0.0861723774 0.8744789160 0.4935529921 0.5807289286
## 29 73 77 32
## 0.0739144263 0.5017946378 0.5358622581 0.3382071510##################################
# Creating a classification index
# based from the model predictions
##################################
(LR_Model_Indices <- predict(LR_Model,
type = c("link")))## 98 99 100 101 102 103
## -0.559126246 -4.463280018 -1.884138369 -6.127013509 -5.581224150 -4.030190682
## 104 105 106 107 108 109
## -2.548499795 -4.088426063 -1.680824728 -0.072512812 -1.054600830 -1.943421380
## 110 111 112 113 114 115
## -0.358947840 -0.127951795 -1.872734622 -3.332592613 -1.858797800 -1.646968043
## 116 117 118 119 120 121
## -2.645292815 -1.132940729 -2.469387136 -2.086388975 -2.911866678 -1.777973140
## 122 123 124 125 126 127
## -1.563492668 -2.827359673 -2.109662164 -4.085645174 -3.261933584 -5.768522713
## 128 129 130 131 132 133
## -3.786245149 -3.888644925 -5.859099785 -4.356726856 -5.282600962 -4.795602114
## 134 135 136 137 138 139
## -5.974277480 -6.515750374 -2.442433371 -6.054490239 -7.091826715 -2.380854599
## 140 141 142 143 144 145
## -1.042798305 -1.529441923 -3.192926600 -3.168691163 -3.273987644 1.020682662
## 146 147 148 149 150 151
## -3.455484324 -3.754629841 -2.838117074 -3.864754157 -1.271186197 -1.207242139
## 152 153 154 155 156 157
## -2.008152348 -1.535809819 -0.447836712 0.318408998 0.621295777 -1.696095344
## 158 159 160 161 162 163
## -1.800361500 -2.303896473 -2.875541209 -2.446352996 -2.201203830 -2.076628048
## 164 165 166 167 168 169
## -1.193735847 -3.308409550 -2.352437879 -1.225812824 0.327440308 0.755723902
## 170 171 172 173 174 175
## -1.164784899 -1.698638961 -5.520775067 -2.978019769 -3.316705945 -4.449071761
## 176 177 178 179 180 181
## -4.375432715 -5.493820583 -2.710472604 -1.129627633 -3.511786651 -5.264453127
## 182 183 184 185 186 187
## -3.932914599 -3.302240366 -3.125182749 -6.201894377 -5.370337994 -3.358033801
## 188 189 190 191 192 193
## -3.289655451 -2.018298780 -1.798822388 -1.931996317 -2.145293464 -0.966600884
## 194 195 196 197 198 199
## -1.823145144 -3.053861030 -1.695416182 -0.908635325 -2.414141893 -2.547272974
## 200 201 202 203 204 205
## -2.452874924 -2.878499479 -3.476889218 -4.034228249 -2.837704887 -3.330989885
## 206 207 208 95 57 27
## -3.368061909 -3.062448770 -2.711765358 2.162061490 -1.148699272 0.788288323
## 18 68 92 43 87 64
## -0.260509533 -0.193556201 -2.606431655 0.419236847 -1.670784310 1.261753296
## 16 12 61 13 34 66
## -2.545822600 1.785061852 -0.412307104 0.855637833 -0.801614636 1.513805316
## 49 94 91 72 23 39
## -0.649175855 -3.218919553 -2.361292279 1.941154440 -0.025789461 0.325766429
## 29 73 77 32
## -2.528058620 0.007178582 0.143695781 -0.671294010max(LR_Model_Indices)## [1] 2.162061min(LR_Model_Indices)## [1] -7.091827##################################
# Consolidating the model probabilities
# and classification index
# based from the model predictions
##################################
LR_Model_Predictions <- as.data.frame(PMA_PreModelling_Train_LR)
LR_Model_Predictions$LR_Prob <- LR_Model_Probabilities
LR_Model_Predictions$LR_LP <- LR_Model_Indices
LR_Model_Predictions$Class <- as.factor(LR_Model_Predictions$Class)
LR_Model_Predictions$Label <- rep("LR",nrow(LR_Model_Predictions))
##################################
# Formulating the probability curve
# using the consolidated model predictions
##################################
LR_Model_Predictions %>%
ggplot(aes(x = LR_LP ,
y = LR_Prob,
color = Class)) +
scale_colour_manual(values=c("#1846BA55","#B8000055")) +
geom_point(size=5) +
geom_line(color="black") +
xlab("Sonar Object Classification Index (Logit Values)") +
ylab("Estimated Rock Detection Probability") +
labs(color = "Class") +
scale_x_continuous( limits=c(-10,5), breaks=seq(-10,5,by=1)) +
scale_y_continuous( limits=c(0,1), breaks=seq(0,1,by=0.1),labels = scales::percent) +
ggtitle("Estimated Rock Detection Probabilities Based on Classification Index : Logistic Regression") +
theme_bw() +
theme(plot.title = element_text(color="black", size=14, face="bold", hjust=0.50),
axis.title.x = element_text(color="black", size=12, face="bold"),
axis.title.y = element_text(color="black", size=12, face="bold"),
legend.position="top")
1.5.2 Undersampling - Random Downsampling (LR_US_DOWNSAMPLE)
Random Downsampling performs a random removal of rows for the majority class instances to make the occurrence of levels with the minority class equal.
Logistic Regression models the relationship between the probability of an event (among two outcome levels) by having the log-odds of the event be a linear combination of a set of predictors weighted by their respective parameter estimates. The parameters are estimated via maximum likelihood estimation by testing different values through multiple iterations to optimize for the best fit of log odds. All of these iterations produce the log likelihood function, and logistic regression seeks to maximize this function to find the best parameter estimates. Given the optimal parameters, the conditional probabilities for each observation can be calculated, logged, and summed together to yield a predicted probability.
[A] The class ratio of the original data was noted at 80:20.
[A.1] Majority Class = Class=M with 111 instances
[A.2] Minority Class = Class=R with 25 instances
[B] The class ratio of the undersampled data was noted at 50:50, although the number of instances was significantly decreased by 63%.
[B.1] Majority Class = Class=M with 25 instances
[B.2] Minority Class = Class=R with 25 instances
[C] The logistic regression model from the stats package was implemented. The Class response was regressed against the V1 and V11 predictors.
[D] The logistic curve formulated by plotting the predicted probabilities against the classification index using the logit values showed a sufficiently balanced logistic profile for the predicted points from both the majority and minority classes, although the distribution of instances was relatively sparse.
Code Chunk | Output
##################################
# Visualizing the imbalanced data set
##################################
ggplot(PMA_PreModelling_Train, aes(V1, V11, color = Class)) +
scale_colour_manual(values=c("#1846BA55","#B8000055")) +
geom_point(size=5) +
scale_x_continuous( limits=c(-4,4), breaks=seq(-4,4,by=1)) +
scale_y_continuous( limits=c(-4,4), breaks=seq(-4,4,by=1)) +
labs(title = "Without Undersampling - Random Downsampling") +
theme_bw() +
theme(plot.title = element_text(color="black", size=14, face="bold", hjust=0.50),
axis.title.x = element_text(color="black", size=12, face="bold"),
axis.title.y = element_text(color="black", size=12, face="bold"),
legend.position="top")
##################################
# Implementing US_DOWNSAMPLE
# Visualizing the undersampled data using US_DOWNSAMPLE
##################################
recipe(Class ~ V1 + V11, data = PMA_PreModelling_Train) %>%
step_downsample(Class, seed=123456789) %>%
prep() %>%
bake(new_data = NULL) %>%
ggplot(aes(V1, V11, color = Class)) +
scale_x_continuous( limits=c(-4,4), breaks=seq(-4,4,by=1)) +
scale_y_continuous( limits=c(-4,4), breaks=seq(-4,4,by=1)) +
scale_colour_manual(values=c("#1846BA55","#B8000055")) +
geom_point(size=5) +
labs(title = "With Undersampling - Random Downsample") +
theme_bw() +
theme(plot.title = element_text(color="black", size=14, face="bold", hjust=0.50),
axis.title.x = element_text(color="black", size=12, face="bold"),
axis.title.y = element_text(color="black", size=12, face="bold"),
legend.position="top")
US_DOWNSAMPLE <- recipe(Class ~ V1 + V11, data = PMA_PreModelling_Train) %>%
step_downsample(Class, seed=123456789) %>%
prep()
PMA_PreModelling_Train_LR_US_DOWNSAMPLE <- US_DOWNSAMPLE %>%
bake(new_data = NULL)
(PMA_PreModelling_Train_LR_US_DOWNSAMPLE <- as.data.frame(PMA_PreModelling_Train_LR_US_DOWNSAMPLE))## V1 V11 Class
## 1 -0.295964244 1.50754826 M
## 2 -0.371994173 0.60103638 M
## 3 -0.789109306 -0.36302243 M
## 4 -0.256318859 -0.63813480 M
## 5 -0.779287302 0.73036007 M
## 6 0.537644499 -0.19150300 M
## 7 0.043933590 0.30731955 M
## 8 0.424204681 0.54321732 M
## 9 1.395977603 1.55085071 M
## 10 1.852613244 0.04399440 M
## 11 -0.507041623 0.38985083 M
## 12 -0.555415865 0.01776805 M
## 13 -2.093160440 0.59563328 M
## 14 0.207991857 -0.39104999 M
## 15 0.278484686 -2.26945660 M
## 16 -0.068999246 0.63930609 M
## 17 -0.935159101 0.03686239 M
## 18 0.424204681 1.89154083 M
## 19 0.625610136 0.17668535 M
## 20 0.256869245 1.19827720 M
## 21 0.695599819 -0.11827986 M
## 22 -0.499153812 0.87301303 M
## 23 0.345301912 -0.27257795 M
## 24 1.231717734 1.85842121 M
## 25 -0.957451233 0.02573746 M
## 26 -2.902302684 -1.49282840 R
## 27 -0.588710342 -0.46833442 R
## 28 -0.597170869 -1.70542147 R
## 29 -0.289270938 -1.17938825 R
## 30 0.544629613 -1.61762409 R
## 31 0.064360561 0.15607894 R
## 32 -0.168320027 -1.67226688 R
## 33 -0.316256467 -0.26298086 R
## 34 -1.638716948 -1.51501038 R
## 35 0.274190661 0.01776805 R
## 36 -0.860055136 -2.21946189 R
## 37 -0.789109306 -0.84516218 R
## 38 -1.427539411 -1.35491277 R
## 39 0.779483548 -1.33935885 R
## 40 -0.400799099 -2.26338258 R
## 41 -0.302692686 -0.92399268 R
## 42 0.827857790 0.18656255 R
## 43 -0.829167731 0.42261407 R
## 44 -2.434915803 -1.57286451 R
## 45 -1.138280881 -0.92728240 R
## 46 -0.860055136 -1.28443852 R
## 47 -1.125398710 0.66990478 R
## 48 -0.186674974 -1.39954451 R
## 49 -0.008605128 -1.57143555 R
## 50 -1.348878827 -0.41384379 RPMA_PreModelling_Train_LR_US_DOWNSAMPLE$Label <- rep("LR_US_DOWNSAMPLE",nrow(PMA_PreModelling_Train_LR_US_DOWNSAMPLE))
##################################
# Verifying the class distribution
# for the undersampled data using US_DOWNSAMPLE
##################################
table(PMA_PreModelling_Train_LR_US_DOWNSAMPLE$Class) ##
## M R
## 25 25##################################
# Formulating the structure of the
# Logistic Regression model
##################################
LR_US_DOWNSAMPLE_Model <- glm(Class ~ V1 + V11,
data = PMA_PreModelling_Train_LR_US_DOWNSAMPLE,
family = binomial)
##################################
# Consolidating the model results
##################################
summary(LR_US_DOWNSAMPLE_Model)##
## Call:
## glm(formula = Class ~ V1 + V11, family = binomial, data = PMA_PreModelling_Train_LR_US_DOWNSAMPLE)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.7625 0.4369 -1.745 0.080920 .
## V1 -0.7448 0.5075 -1.468 0.142179
## V11 -1.7181 0.5063 -3.393 0.000691 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 69.315 on 49 degrees of freedom
## Residual deviance: 43.344 on 47 degrees of freedom
## AIC: 49.344
##
## Number of Fisher Scoring iterations: 5LR_US_DOWNSAMPLE_Model_Coef <- (as.data.frame(LR_US_DOWNSAMPLE_Model$coefficients))
LR_US_DOWNSAMPLE_Model_Coef$Coef <- rownames(LR_US_DOWNSAMPLE_Model_Coef)
LR_US_DOWNSAMPLE_Model_Coef$Model <- rep("LR_US_DOWNSAMPLE",nrow(LR_US_DOWNSAMPLE_Model_Coef))
colnames(LR_US_DOWNSAMPLE_Model_Coef) <- c("Estimates","Coefficients","Model")
print(LR_US_DOWNSAMPLE_Model_Coef, rownames=FALSE)## Estimates Coefficients Model
## (Intercept) -0.7625171 (Intercept) LR_US_DOWNSAMPLE
## V1 -0.7448227 V1 LR_US_DOWNSAMPLE
## V11 -1.7180819 V11 LR_US_DOWNSAMPLE##################################
# Computing the model predictions
##################################
(LR_US_DOWNSAMPLE_Model_Probabilities <- predict(LR_US_DOWNSAMPLE_Model,
type = c("response")))## 1 2 3 4 5 6
## 0.041799701 0.179744842 0.610385203 0.628260800 0.192025135 0.302810692
## 7 8 9 10 11 12
## 0.210279740 0.117973061 0.011354052 0.098148202 0.258331895 0.406281809
## 13 14 15 16 17 18
## 0.443534294 0.438912131 0.949269888 0.140697941 0.467714486 0.013018286
## 19 20 21 22 23 24
## 0.177694571 0.046860883 0.253998128 0.131171064 0.365540908 0.007593438
## 25 26 27 28 29 30
## 0.476615880 0.981366508 0.617890633 0.931650474 0.814455352 0.833555873
## 31 32 33 34 35 36
## 0.253769009 0.903431085 0.481223964 0.955252856 0.269478992 0.975667022
## 37 38 39 40 41 42
## 0.781987655 0.932679562 0.722728010 0.968467123 0.740857535 0.154510435
## 43 44 45 46 47 48
## 0.295042156 0.977098243 0.842698840 0.889421156 0.254406742 0.855827057
## 49 50
## 0.874763232 0.721753452##################################
# Creating a classification index
# based from the model predictions
##################################
(LR_US_DOWNSAMPLE_Model_Indices <- predict(LR_US_DOWNSAMPLE_Model,
type = c("link")))## 1 2 3 4 5 6
## -3.13216766 -1.51807716 0.44893169 0.52476284 -1.43690467 -0.83394915
## 7 8 9 10 11 12
## -1.32324004 -2.01176629 -4.46676157 -2.21797160 -1.05465667 -0.37935775
## 13 14 15 16 17 18
## -0.22683042 -0.24557829 2.92917349 -1.80950517 -0.12932199 -4.32829655
## 19 20 21 22 23 24
## -1.53204571 -3.01257759 -1.07740121 -1.89064395 -0.55139462 -4.87284841
## 25 26 27 28 29 30
## -0.09360477 3.96398538 0.48060460 2.61232309 1.47922407 1.61104105
## 31 32 33 34 35 36
## -1.07861075 2.23594294 -0.07513948 3.06094845 -0.99726756 3.69128882
## 37 38 39 40 41 42
## 1.27728727 2.62859781 0.95803402 3.42468383 1.05043038 -1.69965419
## 43 44 45 46 47 48
## -0.87101976 3.75337359 1.67844746 2.08484208 -1.07524589 1.78105475
## 49 50
## 1.94374717 0.95317602max(LR_US_DOWNSAMPLE_Model_Indices)## [1] 3.963985min(LR_US_DOWNSAMPLE_Model_Indices)## [1] -4.872848##################################
# Consolidating the model probabilities
# and classification index
# based from the model predictions
##################################
LR_US_DOWNSAMPLE_Model_Predictions <- as.data.frame(PMA_PreModelling_Train_LR_US_DOWNSAMPLE)
LR_US_DOWNSAMPLE_Model_Predictions$LR_US_DOWNSAMPLE_Prob <- LR_US_DOWNSAMPLE_Model_Probabilities
LR_US_DOWNSAMPLE_Model_Predictions$LR_US_DOWNSAMPLE_LP <- LR_US_DOWNSAMPLE_Model_Indices
LR_US_DOWNSAMPLE_Model_Predictions$Class <- as.factor(LR_US_DOWNSAMPLE_Model_Predictions$Class)
LR_US_DOWNSAMPLE_Model_Predictions$Label <- rep("LR_US_DOWNSAMPLE",nrow(LR_US_DOWNSAMPLE_Model_Predictions))
##################################
# Formulating the probability curve
# using the consolidated model predictions
##################################
LR_US_DOWNSAMPLE_Model_Predictions %>%
ggplot(aes(x = LR_US_DOWNSAMPLE_LP ,
y = LR_US_DOWNSAMPLE_Prob,
color = Class)) +
scale_colour_manual(values=c("#1846BA55","#B8000055")) +
geom_point(size=5) +
geom_line(color="black") +
xlab("Sonar Object Classification Index (Logit Values)") +
ylab("Estimated Rock Detection Probability") +
labs(color = "Class") +
scale_x_continuous( limits=c(-10,5), breaks=seq(-10,5,by=1)) +
scale_y_continuous( limits=c(0,1), breaks=seq(0,1,by=0.1),labels = scales::percent) +
ggtitle("Estimated Rock Detection Probabilities Based on Classification Index : Logistic Regression (US_DOWNSAMPLE)") +
theme_bw() +
theme(plot.title = element_text(color="black", size=14, face="bold", hjust=0.50),
axis.title.x = element_text(color="black", size=12, face="bold"),
axis.title.y = element_text(color="black", size=12, face="bold"),
legend.position="top")
1.5.3 Oversampling - Random Upsampling (LR_US_UPSAMPLE)
Random Upsampling performs a random replication of rows for the minority class instances to make the occurrence of levels with the majority class equal.
Logistic Regression models the relationship between the probability of an event (among two outcome levels) by having the log-odds of the event be a linear combination of a set of predictors weighted by their respective parameter estimates. The parameters are estimated via maximum likelihood estimation by testing different values through multiple iterations to optimize for the best fit of log odds. All of these iterations produce the log likelihood function, and logistic regression seeks to maximize this function to find the best parameter estimates. Given the optimal parameters, the conditional probabilities for each observation can be calculated, logged, and summed together to yield a predicted probability.
[A] The class ratio of the original data was noted at 80:20.
[A.1] Majority Class = Class=M with 111 instances
[A.2] Minority Class = Class=R with 25 instances
[B] The class ratio of the oversampled data was noted at 50:50, although majority of the added instances were not unique values but replicates of the rows from the minority class.
[B.1] Majority Class = Class=M with 111 instances
[B.2] Minority Class = Class=R with 111 instances
[C] The logistic regression model from the stats package was implemented. The Class response was regressed against the V1 and V11 predictors.
[D] The logistic curve formulated by plotting the predicted probabilities against the classification index using the logit values showed a sufficiently balanced logistic profile for the predicted points from both the majority and minority classes, although the ratio of the uniques values and number of instances was relatively low.
Code Chunk | Output
##################################
# Visualizing the imbalanced data set
##################################
ggplot(PMA_PreModelling_Train, aes(V1, V11, color = Class)) +
scale_colour_manual(values=c("#1846BA55","#B8000055")) +
geom_point(size=5) +
scale_x_continuous( limits=c(-4,4), breaks=seq(-4,4,by=1)) +
scale_y_continuous( limits=c(-4,4), breaks=seq(-4,4,by=1)) +
labs(title = "Without Oversampling - Random Upsampling") +
theme_bw() +
theme(plot.title = element_text(color="black", size=14, face="bold", hjust=0.50),
axis.title.x = element_text(color="black", size=12, face="bold"),
axis.title.y = element_text(color="black", size=12, face="bold"),
legend.position="top")
##################################
# Implementing OS_UPSAMPLE
# Visualizing the oversampled data using OS_UPSAMPLE
##################################
recipe(Class ~ V1 + V11, data = PMA_PreModelling_Train) %>%
step_upsample(Class, seed=123456789) %>%
prep() %>%
bake(new_data = NULL) %>%
ggplot(aes(V1, V11, color = Class)) +
scale_colour_manual(values=c("#1846BA55","#B8000055")) +
geom_point(size=5) +
scale_x_continuous( limits=c(-4,4), breaks=seq(-4,4,by=1)) +
scale_y_continuous( limits=c(-4,4), breaks=seq(-4,4,by=1)) +
labs(title = "With Undersampling - Random Upsample") +
theme_bw() +
theme(plot.title = element_text(color="black", size=14, face="bold", hjust=0.50),
axis.title.x = element_text(color="black", size=12, face="bold"),
axis.title.y = element_text(color="black", size=12, face="bold"),
legend.position="top")
OS_UPSAMPLE <- recipe(Class ~ V1 + V11, data = PMA_PreModelling_Train) %>%
step_upsample(Class, seed=123456789) %>%
prep()
PMA_PreModelling_Train_LR_OS_UPSAMPLE <- OS_UPSAMPLE %>%
bake(new_data = NULL)
(PMA_PreModelling_Train_LR_OS_UPSAMPLE <- as.data.frame(PMA_PreModelling_Train_LR_OS_UPSAMPLE))## V1 V11 Class
## 1 0.914240973 -1.55861587 M
## 2 2.175018299 0.34520609 M
## 3 -0.230553865 -0.16690717 M
## 4 1.231717734 1.85842121 M
## 5 0.424204681 1.89154083 M
## 6 1.143139503 0.55690423 M
## 7 -0.507041623 0.38985083 M
## 8 0.312328639 0.98808941 M
## 9 -0.935159101 0.03686239 M
## 10 0.408807916 -1.63079048 M
## 11 0.738227771 -1.15770286 M
## 12 1.169078303 -0.79246144 M
## 13 0.118911997 -1.30982705 M
## 14 -0.174409184 -1.31877657 M
## 15 1.012209983 -0.76338471 M
## 16 0.813818599 0.26605270 M
## 17 0.207991857 -0.39104999 M
## 18 -0.957451233 0.02573746 M
## 19 0.695599819 -0.11827986 M
## 20 -1.204708453 -0.18639848 M
## 21 -0.068999246 0.13149291 M
## 22 0.527094931 -0.39650480 M
## 23 0.104263091 0.33286517 M
## 24 0.465616266 -0.56497212 M
## 25 -0.507041623 -0.24127887 M
## 26 0.043933590 0.30731955 M
## 27 0.147716923 -0.20173692 M
## 28 0.612463710 0.84401925 M
## 29 -0.068999246 0.63930609 M
## 30 1.395977603 1.55085071 M
## 31 -0.180527407 1.02812486 M
## 32 0.565359423 0.74012531 M
## 33 2.230423635 1.21329169 M
## 34 0.782380194 0.93715691 M
## 35 2.005116912 0.95072168 M
## 36 1.784287809 0.74337479 M
## 37 1.523834205 1.62207084 M
## 38 1.928176420 1.77732114 M
## 39 -1.204708453 0.65264083 M
## 40 1.934080462 1.47897609 M
## 41 0.744203377 2.70773324 M
## 42 1.424344244 -0.63319889 M
## 43 -0.491314245 -0.58236264 M
## 44 0.689392711 -0.83030322 M
## 45 1.381555338 -0.09258957 M
## 46 1.002499594 0.07158504 M
## 47 0.977896818 0.15071601 M
## 48 0.278484686 -2.26945660 M
## 49 1.406688795 0.06372536 M
## 50 1.852613244 0.04399440 M
## 51 1.281675939 -0.27257795 M
## 52 1.390588252 0.33359231 M
## 53 -0.192852168 -0.57752146 M
## 54 -0.180527407 -0.62433567 M
## 55 -0.052243905 -0.17198030 M
## 56 -0.779287302 -0.12994977 M
## 57 -0.041194136 -1.17696945 M
## 58 -0.924156756 -1.24933498 M
## 59 -0.168320027 -1.80173334 M
## 60 -2.093160440 0.59563328 M
## 61 -0.230553865 -0.22058604 M
## 62 -1.038676203 0.48516249 M
## 63 -0.030238811 0.37335493 M
## 64 0.089444831 0.04161877 M
## 65 0.303951059 -0.21715050 M
## 66 -0.132380328 -0.09011428 M
## 67 -1.546463816 0.01457488 M
## 68 -0.499153812 0.87301303 M
## 69 -0.108968439 0.07550792 M
## 70 0.686277848 -1.02337267 M
## 71 -0.721885235 -1.35101476 M
## 72 -3.557061230 -0.28132844 M
## 73 -0.789109306 -0.36302243 M
## 74 -0.750264961 -0.03937819 M
## 75 -0.379134945 2.23365699 M
## 76 -0.371994173 0.60103638 M
## 77 0.401039618 0.45156422 M
## 78 -0.295964244 1.50754826 M
## 79 0.256869245 1.19827720 M
## 80 1.243886483 1.44694263 M
## 81 -0.230553865 0.36255507 M
## 82 -0.256318859 -0.63813480 M
## 83 0.632133128 0.46700244 M
## 84 0.324793230 1.73570326 M
## 85 0.723165726 0.69367752 M
## 86 -1.191144673 1.19711994 M
## 87 -1.177722925 1.07730973 M
## 88 0.142960831 2.42255907 M
## 89 0.443194144 1.74741590 M
## 90 -0.180527407 0.75375436 M
## 91 0.544629613 0.36615870 M
## 92 -1.274767709 0.41409622 M
## 93 -0.539087424 -0.07530230 M
## 94 -0.555415865 0.01776805 M
## 95 0.345301912 -0.27257795 M
## 96 -1.868589923 0.02175517 M
## 97 -0.217863016 -0.21200423 M
## 98 0.625610136 0.17668535 M
## 99 -0.799007156 -0.01833539 M
## 100 -2.013850697 0.05347991 M
## 101 0.537644499 -0.19150300 M
## 102 -0.013979414 0.15531332 M
## 103 -0.935159101 0.53154288 M
## 104 -0.779287302 0.73036007 M
## 105 0.424204681 0.54321732 M
## 106 0.157176488 1.02691760 M
## 107 -0.323092564 0.48794823 M
## 108 0.377448180 0.47190071 M
## 109 0.992715078 0.20396774 M
## 110 0.295518362 0.33867807 M
## 111 0.099342681 0.20698569 M
## 112 -2.434915803 -1.57286451 R
## 113 -0.829167731 0.42261407 R
## 114 -0.829167731 0.42261407 R
## 115 -2.434915803 -1.57286451 R
## 116 -1.348878827 -0.41384379 R
## 117 -0.008605128 -1.57143555 R
## 118 -0.168320027 -1.67226688 R
## 119 -1.348878827 -0.41384379 R
## 120 -0.316256467 -0.26298086 R
## 121 -1.427539411 -1.35491277 R
## 122 -0.597170869 -1.70542147 R
## 123 -0.186674974 -1.39954451 R
## 124 -0.400799099 -2.26338258 R
## 125 0.544629613 -1.61762409 R
## 126 -0.789109306 -0.84516218 R
## 127 -0.316256467 -0.26298086 R
## 128 -0.597170869 -1.70542147 R
## 129 -0.588710342 -0.46833442 R
## 130 -0.186674974 -1.39954451 R
## 131 -2.434915803 -1.57286451 R
## 132 0.544629613 -1.61762409 R
## 133 0.827857790 0.18656255 R
## 134 -0.186674974 -1.39954451 R
## 135 -0.588710342 -0.46833442 R
## 136 0.544629613 -1.61762409 R
## 137 -0.186674974 -1.39954451 R
## 138 0.064360561 0.15607894 R
## 139 -0.789109306 -0.84516218 R
## 140 -0.302692686 -0.92399268 R
## 141 -0.860055136 -1.28443852 R
## 142 -0.289270938 -1.17938825 R
## 143 -0.302692686 -0.92399268 R
## 144 0.544629613 -1.61762409 R
## 145 -0.860055136 -1.28443852 R
## 146 -0.186674974 -1.39954451 R
## 147 0.064360561 0.15607894 R
## 148 -0.008605128 -1.57143555 R
## 149 -1.427539411 -1.35491277 R
## 150 -0.588710342 -0.46833442 R
## 151 0.779483548 -1.33935885 R
## 152 -0.860055136 -1.28443852 R
## 153 0.827857790 0.18656255 R
## 154 -0.186674974 -1.39954451 R
## 155 -0.186674974 -1.39954451 R
## 156 -0.588710342 -0.46833442 R
## 157 -2.434915803 -1.57286451 R
## 158 0.779483548 -1.33935885 R
## 159 -0.168320027 -1.67226688 R
## 160 -0.860055136 -2.21946189 R
## 161 -0.588710342 -0.46833442 R
## 162 -2.434915803 -1.57286451 R
## 163 -1.138280881 -0.92728240 R
## 164 -0.008605128 -1.57143555 R
## 165 -0.316256467 -0.26298086 R
## 166 -0.860055136 -2.21946189 R
## 167 0.544629613 -1.61762409 R
## 168 -0.289270938 -1.17938825 R
## 169 -1.638716948 -1.51501038 R
## 170 -1.638716948 -1.51501038 R
## 171 -0.829167731 0.42261407 R
## 172 -0.302692686 -0.92399268 R
## 173 -2.902302684 -1.49282840 R
## 174 -0.588710342 -0.46833442 R
## 175 -0.588710342 -0.46833442 R
## 176 0.064360561 0.15607894 R
## 177 -0.316256467 -0.26298086 R
## 178 -0.186674974 -1.39954451 R
## 179 -1.348878827 -0.41384379 R
## 180 -2.434915803 -1.57286451 R
## 181 -1.138280881 -0.92728240 R
## 182 -0.302692686 -0.92399268 R
## 183 -0.400799099 -2.26338258 R
## 184 -1.638716948 -1.51501038 R
## 185 -1.125398710 0.66990478 R
## 186 -0.860055136 -1.28443852 R
## 187 -0.789109306 -0.84516218 R
## 188 -0.588710342 -0.46833442 R
## 189 -1.138280881 -0.92728240 R
## 190 -0.302692686 -0.92399268 R
## 191 0.779483548 -1.33935885 R
## 192 0.779483548 -1.33935885 R
## 193 -0.860055136 -1.28443852 R
## 194 -1.638716948 -1.51501038 R
## 195 -0.597170869 -1.70542147 R
## 196 0.827857790 0.18656255 R
## 197 -0.302692686 -0.92399268 R
## 198 -1.138280881 -0.92728240 R
## 199 -0.400799099 -2.26338258 R
## 200 -1.348878827 -0.41384379 R
## 201 -0.302692686 -0.92399268 R
## 202 -0.860055136 -2.21946189 R
## 203 -1.348878827 -0.41384379 R
## 204 -0.789109306 -0.84516218 R
## 205 -0.829167731 0.42261407 R
## 206 -0.168320027 -1.67226688 R
## 207 -0.860055136 -2.21946189 R
## 208 -0.400799099 -2.26338258 R
## 209 -1.427539411 -1.35491277 R
## 210 -0.860055136 -2.21946189 R
## 211 -0.597170869 -1.70542147 R
## 212 -0.400799099 -2.26338258 R
## 213 -0.860055136 -1.28443852 R
## 214 -0.302692686 -0.92399268 R
## 215 -1.427539411 -1.35491277 R
## 216 -1.348878827 -0.41384379 R
## 217 0.064360561 0.15607894 R
## 218 -0.168320027 -1.67226688 R
## 219 -0.168320027 -1.67226688 R
## 220 -0.860055136 -1.28443852 R
## 221 -1.638716948 -1.51501038 R
## 222 -0.588710342 -0.46833442 RPMA_PreModelling_Train_LR_OS_UPSAMPLE$Label <- rep("LR_OS_UPSAMPLE",nrow(PMA_PreModelling_Train_LR_OS_UPSAMPLE))
##################################
# Verifying the class distribution
# for the oversampled data using OS_UPSAMPLE
##################################
table(PMA_PreModelling_Train_LR_OS_UPSAMPLE$Class) ##
## M R
## 111 111##################################
# Formulating the structure of the
# Logistic Regression model
##################################
LR_OS_UPSAMPLE_Model <- glm(Class ~ V1 + V11,
data = PMA_PreModelling_Train_LR_OS_UPSAMPLE,
family = binomial)
##################################
# Consolidating the model results
##################################
summary(LR_OS_UPSAMPLE_Model)##
## Call:
## glm(formula = Class ~ V1 + V11, family = binomial, data = PMA_PreModelling_Train_LR_OS_UPSAMPLE)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.1647 0.2470 -4.715 2.42e-06 ***
## V1 -0.9110 0.2482 -3.670 0.000243 ***
## V11 -1.8812 0.2634 -7.142 9.17e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 307.76 on 221 degrees of freedom
## Residual deviance: 181.78 on 219 degrees of freedom
## AIC: 187.78
##
## Number of Fisher Scoring iterations: 5LR_OS_UPSAMPLE_Model_Coef <- (as.data.frame(LR_OS_UPSAMPLE_Model$coefficients))
LR_OS_UPSAMPLE_Model_Coef$Coef <- rownames(LR_OS_UPSAMPLE_Model_Coef)
LR_OS_UPSAMPLE_Model_Coef$Model <- rep("LR_OS_UPSAMPLE",nrow(LR_OS_UPSAMPLE_Model_Coef))
colnames(LR_OS_UPSAMPLE_Model_Coef) <- c("Estimates","Coefficients","Model")
print(LR_OS_UPSAMPLE_Model_Coef, rownames=FALSE)## Estimates Coefficients Model
## (Intercept) -1.1646747 (Intercept) LR_OS_UPSAMPLE
## V1 -0.9110338 V1 LR_OS_UPSAMPLE
## V11 -1.8811681 V11 LR_OS_UPSAMPLE##################################
# Computing the model predictions
##################################
(LR_OS_UPSAMPLE_Model_Probabilities <- predict(LR_OS_UPSAMPLE_Model,
type = c("response")))## 1 2 3 4 5 6
## 0.7179750734 0.0219760571 0.3450992033 0.0030705786 0.0060031731 0.0371925877
## 7 8 9 10 11 12
## 0.1921472883 0.0352984771 0.4056307613 0.8221055779 0.5843383511 0.3232247505
## 13 14 15 16 17 18
## 0.7669193985 0.8138250288 0.3428144171 0.0826722516 0.3501155120 0.4156101313
## 19 20 21 22 23 24
## 0.1713790099 0.5704046636 0.2060053981 0.2892593757 0.1317198352 0.3714364633
## 25 26 27 28 29 30
## 0.4381049807 0.1439553447 0.2850090965 0.0352163999 0.0907545671 0.0047075528
## 31 32 33 34 35 36
## 0.0504845805 0.0442755776 0.0041558586 0.0255708393 0.0083271556 0.0149403855
## 37 38 39 40 41 42
## 0.0036683904 0.0018985803 0.2150293746 0.0033053895 0.0009708468 0.2190609887
## 43 44 45 46 47 48
## 0.5934995135 0.4425560636 0.0954235210 0.0986203238 0.0879365600 0.9453623871
## 49 50 51 52 53 54
## 0.0713526975 0.0504392980 0.1394866952 0.0448266150 0.5243395342 0.5434581342
## 55 56 57 58 59 60
## 0.3114058838 0.4476277500 0.7478035602 0.8836514796 0.9151368708 0.4065560302
## 61 62 63 64 65 66
## 0.3682636851 0.2439622644 0.1371140479 0.2100782444 0.2624859141 0.2943080924
## 67 68 69 70 71 72
## 0.5539858259 0.0868900350 0.2301536474 0.5337583288 0.8843692868 0.9311925798
## 73 74 75 76 77 78
## 0.5590079462 0.3996122680 0.0065536738 0.1238543705 0.0847490662 0.0234079574
## 79 80 81 82 83 84
## 0.0252623769 0.0065622381 0.1629201398 0.5669159737 0.0679205488 0.0087864263
## 85 86 87 88 89 90
## 0.0419497741 0.0885487004 0.1073279679 0.0028654573 0.0077243548 0.0817993402
## 91 92 93 94 95 96
## 0.0870930602 0.3138237976 0.3700758345 0.3335668084 0.2755803841 0.6216949898
## 97 98 99 100 101 102
## 0.3618419434 0.1123457119 0.4007694305 0.6386262692 0.2151364084 0.1909097870
## 103 104 105 106 107 108
## 0.2120463101 0.1383994535 0.0708949509 0.0376997727 0.1432887529 0.0834577765
## 109 110 111 112 113 114
## 0.0792362717 0.1119463627 0.1618463380 0.9822321769 0.2307141816 0.2307141816
## 115 116 117 118 119 120
## 0.9822321769 0.6990355243 0.8580636277 0.8942101026 0.6990355243 0.4056824362
## 121 122 123 124 125 126
## 0.9361166156 0.9300561845 0.8372859227 0.9694768165 0.7993396439 0.7584357774
## 127 128 129 130 131 132
## 0.4056824362 0.9300561845 0.5628350830 0.8372859227 0.9822321769 0.7993396439
## 133 134 135 136 137 138
## 0.0936520778 0.8372859227 0.5628350830 0.7993396439 0.8372859227 0.1799155074
## 139 140 141 142 143 144
## 0.7584357774 0.7004148673 0.8844343440 0.7887635614 0.7004148673 0.7993396439
## 145 146 147 148 149 150
## 0.8844343440 0.8372859227 0.1799155074 0.8580636277 0.9361166156 0.5628350830
## 151 152 153 154 155 156
## 0.6558261080 0.8844343440 0.0936520778 0.8372859227 0.8372859227 0.5628350830
## 157 158 159 160 161 162
## 0.9822321769 0.6558261080 0.8942101026 0.9779909273 0.5628350830 0.9822321769
## 163 164 165 166 167 168
## 0.8343411495 0.8580636277 0.4056824362 0.9779909273 0.7993396439 0.7887635614
## 169 170 171 172 173 174
## 0.9600071925 0.9600071925 0.2307141816 0.7004148673 0.9864494168 0.5628350830
## 175 176 177 178 179 180
## 0.5628350830 0.1799155074 0.4056824362 0.8372859227 0.6990355243 0.9822321769
## 181 182 183 184 185 186
## 0.8343411495 0.7004148673 0.9694768165 0.9600071925 0.1978792907 0.8844343440
## 187 188 189 190 191 192
## 0.7584357774 0.5628350830 0.8343411495 0.7004148673 0.6558261080 0.6558261080
## 193 194 195 196 197 198
## 0.8844343440 0.9600071925 0.9300561845 0.0936520778 0.7004148673 0.8343411495
## 199 200 201 202 203 204
## 0.9694768165 0.6990355243 0.7004148673 0.9779909273 0.6990355243 0.7584357774
## 205 206 207 208 209 210
## 0.2307141816 0.8942101026 0.9779909273 0.9694768165 0.9361166156 0.9779909273
## 211 212 213 214 215 216
## 0.9300561845 0.9694768165 0.8844343440 0.7004148673 0.9361166156 0.6990355243
## 217 218 219 220 221 222
## 0.1799155074 0.8942101026 0.8942101026 0.8844343440 0.9600071925 0.5628350830##################################
# Creating a classification index
# based from the model predictions
##################################
(LR_OS_UPSAMPLE_Model_Indices <- predict(LR_OS_UPSAMPLE_Model,
type = c("link")))## 1 2 3 4 5 6
## 0.93443939 -3.79558061 -0.64065185 -5.78281396 -5.10944583 -3.25374392
## 7 8 9 10 11 12
## -1.43611755 -3.30797893 -0.38205745 1.53067859 0.34060860 -0.73899133
## 13 14 15 16 17 18
## 1.19099739 1.47505845 -0.65077721 -2.40658081 -0.61853150 -0.34082071
## 19 20 21 22 23 24
## -1.57588533 0.28350236 -1.34917429 -0.89898378 -1.88583723 -0.52605929
## 25 26 27 28 29 30
## -0.24885647 -1.78281940 -0.91974872 -3.31039195 -2.30445626 -5.35386842
## 31 32 33 34 35 36
## -2.93428382 -3.07203638 -5.47907171 -3.64039921 -4.77987133 -4.18863418
## 37 38 39 40 41 42
## -5.60432715 -6.26474851 -1.29487165 -5.70889009 -6.93637061 -1.27114688
## 43 44 45 46 47 48
## 0.37845127 -0.23079478 -2.24914176 -2.21264920 -2.33909390 2.85084581
## 49 50 51 52 53 54
## -2.56609386 -2.93522886 -1.81955986 -3.05909083 0.09743515 0.17427227
## 55 56 57 58 59 60
## -0.79355485 -0.21026021 1.08693202 2.02747256 2.37803393 -0.37822105
## 61 62 63 64 65 66
## -0.53967287 -1.13107773 -1.83946948 -1.32445384 -1.03308777 -0.87455160
## 67 68 69 70 71 72
## 0.21678837 -2.35221296 -1.20744383 0.13523906 2.03447311 2.60515452
## 73 74 75 76 77 78
## 0.23713683 -0.40708092 -5.02115426 -1.95642588 -2.37950354 -3.73099298
## 79 80 81 82 83 84
## -3.65285212 -5.01983969 -1.63665934 0.26927933 -2.61907944 -4.72572196
## 85 86 87 88 89 90
## -3.12842716 -2.33148548 -2.11832998 -5.85215776 -4.85562264 -2.41814677
## 91 92 93 94 95 96
## -2.34965674 -0.78230278 -0.53189150 -0.69209673 -0.96649144 0.49674883
## 97 98 99 100 101 102
## -0.56737849 -2.06700152 -0.40226017 0.56940673 -1.29423765 -1.44410943
## 103 104 105 106 107 108
## -1.31263462 -1.82864767 -2.57302259 -3.23967242 -1.78823908 -2.39626730
## 109 110 111 112 113 114
## -2.45276930 -2.07101229 -1.64455410 4.01243858 -1.20428294 -1.20428294
## 115 116 117 118 119 120
## 4.01243858 0.84270933 1.79929938 2.13448574 0.84270933 -0.38184312
## 121 122 123 124 125 126
## 2.68468076 2.58755271 1.63817108 3.45827005 1.38217223 1.14412276
## 127 128 129 130 131 132
## -0.38184312 2.58755271 0.25267615 1.63817108 4.01243858 1.38217223
## 133 134 135 136 137 138
## -2.26983664 1.63817108 0.25267615 1.38217223 1.63817108 -1.51692004
## 139 140 141 142 143 144
## 1.14412276 0.84927420 2.03510946 1.31748854 0.84927420 1.38217223
## 145 146 147 148 149 150
## 2.03510946 1.63817108 -1.51692004 1.79929938 2.68468076 0.25267615
## 151 152 153 154 155 156
## 0.64474864 2.03510946 -2.26983664 1.63817108 1.63817108 0.25267615
## 157 158 159 160 161 162
## 4.01243858 0.64474864 2.13448574 3.79404563 0.25267615 4.01243858
## 163 164 165 166 167 168
## 1.61671181 1.79929938 -0.38184312 3.79404563 1.38217223 1.31748854
## 169 170 171 172 173 174
## 3.17824115 3.17824115 -1.20428294 0.84927420 4.28768246 0.25267615
## 175 176 177 178 179 180
## 0.25267615 -1.51692004 -0.38184312 1.63817108 0.84270933 4.01243858
## 181 182 183 184 185 186
## 1.61671181 0.84927420 3.45827005 3.17824115 -1.39960190 2.03510946
## 187 188 189 190 191 192
## 1.14412276 0.25267615 1.61671181 0.84927420 0.64474864 0.64474864
## 193 194 195 196 197 198
## 2.03510946 3.17824115 2.58755271 -2.26983664 0.84927420 1.61671181
## 199 200 201 202 203 204
## 3.45827005 0.84270933 0.84927420 3.79404563 0.84270933 1.14412276
## 205 206 207 208 209 210
## -1.20428294 2.13448574 3.79404563 3.45827005 2.68468076 3.79404563
## 211 212 213 214 215 216
## 2.58755271 3.45827005 2.03510946 0.84927420 2.68468076 0.84270933
## 217 218 219 220 221 222
## -1.51692004 2.13448574 2.13448574 2.03510946 3.17824115 0.25267615max(LR_OS_UPSAMPLE_Model_Indices)## [1] 4.287682min(LR_OS_UPSAMPLE_Model_Indices)## [1] -6.936371##################################
# Consolidating the model probabilities
# and classification index
# based from the model predictions
##################################
LR_OS_UPSAMPLE_Model_Predictions <- as.data.frame(PMA_PreModelling_Train_LR_OS_UPSAMPLE)
LR_OS_UPSAMPLE_Model_Predictions$LR_OS_UPSAMPLE_Prob <- LR_OS_UPSAMPLE_Model_Probabilities
LR_OS_UPSAMPLE_Model_Predictions$LR_OS_UPSAMPLE_LP <- LR_OS_UPSAMPLE_Model_Indices
LR_OS_UPSAMPLE_Model_Predictions$Class <- as.factor(LR_OS_UPSAMPLE_Model_Predictions$Class)
LR_OS_UPSAMPLE_Model_Predictions$Label <- rep("LR_OS_UPSAMPLE",nrow(LR_OS_UPSAMPLE_Model_Predictions))
##################################
# Formulating the probability curve
# using the consolidated model predictions
##################################
LR_OS_UPSAMPLE_Model_Predictions %>%
ggplot(aes(x = LR_OS_UPSAMPLE_LP ,
y = LR_OS_UPSAMPLE_Prob,
color = Class)) +
scale_colour_manual(values=c("#1846BA55","#B8000055")) +
geom_point(size=5) +
geom_line(color="black") +
xlab("Sonar Object Classification Index (Logit Values)") +
ylab("Estimated Rock Detection Probability") +
labs(color = "Class") +
scale_x_continuous( limits=c(-10,5), breaks=seq(-10,5,by=1)) +
scale_y_continuous( limits=c(0,1), breaks=seq(0,1,by=0.1),labels = scales::percent) +
ggtitle("Estimated Rock Detection Probabilities Based on Classification Index : Logistic Regression (OS_UPSAMPLE)") +
theme_bw() +
theme(plot.title = element_text(color="black", size=14, face="bold", hjust=0.50),
axis.title.x = element_text(color="black", size=12, face="bold"),
axis.title.y = element_text(color="black", size=12, face="bold"),
legend.position="top")
1.5.4 Undersampling - Near Miss Algorithm (LR_US_NEARMISS)
Near Miss Algorithm removes majority class instances by undersampling points in the majority class which have the smallest mean distance to the defined nearest points in the minority class and based on their distance to other points in the same class.
Logistic Regression models the relationship between the probability of an event (among two outcome levels) by having the log-odds of the event be a linear combination of a set of predictors weighted by their respective parameter estimates. The parameters are estimated via maximum likelihood estimation by testing different values through multiple iterations to optimize for the best fit of log odds. All of these iterations produce the log likelihood function, and logistic regression seeks to maximize this function to find the best parameter estimates. Given the optimal parameters, the conditional probabilities for each observation can be calculated, logged, and summed together to yield a predicted probability.
[A] The class ratio of the original data was noted at 80:20.
[A.1] Majority Class = Class=M with 111 instances
[A.2] Minority Class = Class=R with 25 instances
[B] The class ratio of the undersampled data was noted at 50:50, although the number of instances was significantly decreased by 63%.
[B.1] Majority Class = Class=M with 25 instances
[B.2] Minority Class = Class=R with 25 instances
[C] The logistic regression model from the stats package was implemented. The Class response was regressed against the V1 and V11 predictors.
[D] The logistic curve formulated by plotting the predicted probabilities against the classification index using the logit values showed a sufficiently balanced logistic profile for the predicted points from both the majority and minority classes, although the distribution of instances was relatively sparse.
Code Chunk | Output
##################################
# Visualizing the imbalanced data set
##################################
ggplot(PMA_PreModelling_Train, aes(V1, V11, color = Class)) +
scale_colour_manual(values=c("#1846BA55","#B8000055")) +
geom_point(size=5) +
scale_x_continuous( limits=c(-4,4), breaks=seq(-4,4,by=1)) +
scale_y_continuous( limits=c(-4,4), breaks=seq(-4,4,by=1)) +
labs(title = "Without Undersampling - Near Miss Algorithm") +
theme_bw() +
theme(plot.title = element_text(color="black", size=14, face="bold", hjust=0.50),
axis.title.x = element_text(color="black", size=12, face="bold"),
axis.title.y = element_text(color="black", size=12, face="bold"),
legend.position="top")
##################################
# Implementing US_NEARMISS
# Visualizing the undersampled data using US_NEARMISS
##################################
recipe(Class ~ V1 + V11, data = PMA_PreModelling_Train) %>%
step_nearmiss(Class, seed=123456789) %>%
prep() %>%
bake(new_data = NULL) %>%
ggplot(aes(V1, V11, color = Class)) +
scale_colour_manual(values=c("#1846BA55","#B8000055")) +
geom_point(size=5) +
scale_x_continuous( limits=c(-4,4), breaks=seq(-4,4,by=1)) +
scale_y_continuous( limits=c(-4,4), breaks=seq(-4,4,by=1)) +
labs(title = "With Undersampling - Near Miss Algorithm") +
theme_bw() +
theme(plot.title = element_text(color="black", size=14, face="bold", hjust=0.50),
axis.title.x = element_text(color="black", size=12, face="bold"),
axis.title.y = element_text(color="black", size=12, face="bold"),
legend.position="top")
US_NEARMISS <- recipe(Class ~ V1 + V11, data = PMA_PreModelling_Train) %>%
step_nearmiss(Class, seed=123456789) %>%
prep()
PMA_PreModelling_Train_LR_US_NEARMISS <- US_NEARMISS %>%
bake(new_data = NULL)
(PMA_PreModelling_Train_LR_US_NEARMISS <- as.data.frame(PMA_PreModelling_Train_LR_US_NEARMISS))## V1 V11 Class
## 1 0.914240973 -1.55861587 M
## 2 1.143139503 0.55690423 M
## 3 -0.935159101 0.03686239 M
## 4 1.169078303 -0.79246144 M
## 5 -1.204708453 -0.18639848 M
## 6 -0.068999246 0.13149291 M
## 7 0.565359423 0.74012531 M
## 8 2.230423635 1.21329169 M
## 9 0.782380194 0.93715691 M
## 10 1.784287809 0.74337479 M
## 11 1.934080462 1.47897609 M
## 12 0.744203377 2.70773324 M
## 13 1.002499594 0.07158504 M
## 14 1.281675939 -0.27257795 M
## 15 1.390588252 0.33359231 M
## 16 -0.180527407 -0.62433567 M
## 17 -0.168320027 -1.80173334 M
## 18 -0.132380328 -0.09011428 M
## 19 -0.789109306 -0.36302243 M
## 20 0.401039618 0.45156422 M
## 21 1.243886483 1.44694263 M
## 22 0.142960831 2.42255907 M
## 23 0.625610136 0.17668535 M
## 24 0.537644499 -0.19150300 M
## 25 0.157176488 1.02691760 M
## 26 -2.902302684 -1.49282840 R
## 27 -0.588710342 -0.46833442 R
## 28 -0.597170869 -1.70542147 R
## 29 -0.289270938 -1.17938825 R
## 30 0.544629613 -1.61762409 R
## 31 0.064360561 0.15607894 R
## 32 -0.168320027 -1.67226688 R
## 33 -0.316256467 -0.26298086 R
## 34 -1.638716948 -1.51501038 R
## 35 0.274190661 0.01776805 R
## 36 -0.860055136 -2.21946189 R
## 37 -0.789109306 -0.84516218 R
## 38 -1.427539411 -1.35491277 R
## 39 0.779483548 -1.33935885 R
## 40 -0.400799099 -2.26338258 R
## 41 -0.302692686 -0.92399268 R
## 42 0.827857790 0.18656255 R
## 43 -0.829167731 0.42261407 R
## 44 -2.434915803 -1.57286451 R
## 45 -1.138280881 -0.92728240 R
## 46 -0.860055136 -1.28443852 R
## 47 -1.125398710 0.66990478 R
## 48 -0.186674974 -1.39954451 R
## 49 -0.008605128 -1.57143555 R
## 50 -1.348878827 -0.41384379 RPMA_PreModelling_Train_LR_US_NEARMISS$Label <- rep("LR_US_NEARMISS",nrow(PMA_PreModelling_Train_LR_US_NEARMISS))
##################################
# Verifying the class distribution
# for the undersampled data using US_NEARMISS
##################################
table(PMA_PreModelling_Train_LR_US_NEARMISS$Class) ##
## M R
## 25 25##################################
# Formulating the structure of the
# Logistic Regression model
##################################
LR_US_NEARMISS_Model <- glm(Class ~ V1 + V11,
data = PMA_PreModelling_Train_LR_US_NEARMISS,
family = binomial)
##################################
# Consolidating the model results
##################################
summary(LR_US_NEARMISS_Model)##
## Call:
## glm(formula = Class ~ V1 + V11, family = binomial, data = PMA_PreModelling_Train_LR_US_NEARMISS)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.5276 0.4512 -1.169 0.2423
## V1 -1.3388 0.5264 -2.543 0.0110 *
## V11 -1.2227 0.4795 -2.550 0.0108 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 69.315 on 49 degrees of freedom
## Residual deviance: 40.340 on 47 degrees of freedom
## AIC: 46.34
##
## Number of Fisher Scoring iterations: 5LR_US_NEARMISS_Model_Coef <- (as.data.frame(LR_US_NEARMISS_Model$coefficients))
LR_US_NEARMISS_Model_Coef$Coef <- rownames(LR_US_NEARMISS_Model_Coef)
LR_US_NEARMISS_Model_Coef$Model <- rep("LR_US_NEARMISS",nrow(LR_US_NEARMISS_Model_Coef))
colnames(LR_US_NEARMISS_Model_Coef) <- c("Estimates","Coefficients","Model")
print(LR_US_NEARMISS_Model_Coef, rownames=FALSE)## Estimates Coefficients Model
## (Intercept) -0.5275579 (Intercept) LR_US_NEARMISS
## V1 -1.3388434 V1 LR_US_NEARMISS
## V11 -1.2227451 V11 LR_US_NEARMISS##################################
# Computing the model predictions
##################################
(LR_US_NEARMISS_Model_Probabilities <- predict(LR_US_NEARMISS_Model,
type = c("response")))## 1 2 3 4 5 6
## 0.538475404 0.060711209 0.663604862 0.245308514 0.788060819 0.355268290
## 7 8 9 10 11 12
## 0.100700058 0.006710875 0.061748607 0.021344245 0.007207500 0.007885623
## 13 14 15 16 17 18
## 0.123761916 0.128955243 0.057473709 0.617167616 0.869988474 0.440253186
## 19 20 21 22 23 24
## 0.725681187 0.165669455 0.018666733 0.024575272 0.170626580 0.266350095
## 25 26 27 28 29 30
## 0.119871133 0.994423044 0.697049432 0.913509256 0.786141426 0.672873462
## 31 32 33 34 35 36
## 0.309048509 0.851010973 0.554141527 0.971220746 0.285695645 0.965701640
## 37 38 39 40 41 42
## 0.826690849 0.954366549 0.516626398 0.941397563 0.732532124 0.134231886
## 43 44 45 46 47 48
## 0.516448710 0.990581586 0.893808927 0.899751107 0.539927799 0.807470453
## 49 50
## 0.803043836 0.856239957##################################
# Creating a classification index
# based from the model predictions
##################################
(LR_US_NEARMISS_Model_Indices <- predict(LR_US_NEARMISS_Model,
type = c("link")))## 1 2 3 4 5 6
## 0.15420647 -2.73899464 0.67940041 -1.12379238 1.31327592 -0.59596102
## 7 8 9 10 11 12
## -2.18947025 -4.99729239 -2.72094650 -3.82539779 -4.92539958 -4.83479722
## 13 14 15 16 17 18
## -1.95727815 -1.91022797 -2.79723621 0.47754340 1.90085685 -0.24013458
## 19 20 21 22 23 24
## 0.97281980 -1.61663509 -3.96216914 -3.68113228 -1.58119307 -1.01322036
## 25 26 27 28 29 30
## -1.99365106 5.18351957 0.83328668 2.35725611 1.30182178 0.72121023
## 31 32 33 34 35 36
## -0.80457137 1.74255237 0.21741854 3.51889902 -0.91638207 3.33775738
## 37 38 39 40 41 42
## 1.56235380 3.04040680 0.06653012 2.77658926 1.00750772 -1.86404831
## 43 44 45 46 47 48
## 0.06581859 4.65562548 2.13025197 2.19446215 0.16005199 1.43365683
## 49 50
## 1.40542811 1.78440512max(LR_US_NEARMISS_Model_Indices)## [1] 5.18352min(LR_US_NEARMISS_Model_Indices)## [1] -4.997292##################################
# Consolidating the model probabilities
# and classification index
# based from the model predictions
##################################
LR_US_NEARMISS_Model_Predictions <- as.data.frame(PMA_PreModelling_Train_LR_US_NEARMISS)
LR_US_NEARMISS_Model_Predictions$LR_US_NEARMISS_Prob <- LR_US_NEARMISS_Model_Probabilities
LR_US_NEARMISS_Model_Predictions$LR_US_NEARMISS_LP <- LR_US_NEARMISS_Model_Indices
LR_US_NEARMISS_Model_Predictions$Class <- as.factor(LR_US_NEARMISS_Model_Predictions$Class)
LR_US_NEARMISS_Model_Predictions$Label <- rep("LR_US_NEARMISS",nrow(LR_US_NEARMISS_Model_Predictions))
##################################
# Formulating the probability curve
# using the consolidated model predictions
##################################
LR_US_NEARMISS_Model_Predictions %>%
ggplot(aes(x = LR_US_NEARMISS_LP ,
y = LR_US_NEARMISS_Prob,
color = Class)) +
scale_colour_manual(values=c("#1846BA55","#B8000055")) +
geom_point(size=5) +
geom_line(color="black") +
xlab("Sonar Object Classification Index (Logit Values)") +
ylab("Estimated Rock Detection Probability") +
labs(color = "Class") +
scale_x_continuous( limits=c(-10,5), breaks=seq(-10,5,by=1)) +
scale_y_continuous( limits=c(0,1), breaks=seq(0,1,by=0.1),labels = scales::percent) +
ggtitle("Estimated Rock Detection Probabilities Based on Classification Index : Logistic Regression (US_NEARMISS)") +
theme_bw() +
theme(plot.title = element_text(color="black", size=14, face="bold", hjust=0.50),
axis.title.x = element_text(color="black", size=12, face="bold"),
axis.title.y = element_text(color="black", size=12, face="bold"),
legend.position="top")
1.5.5 Undersampling - Tomek Links (LR_US_TOMEK)
Tomek Links remove majority class instances of tomek links referring to a pair of points from different classes and are each others nearest neighbors
Logistic Regression models the relationship between the probability of an event (among two outcome levels) by having the log-odds of the event be a linear combination of a set of predictors weighted by their respective parameter estimates. The parameters are estimated via maximum likelihood estimation by testing different values through multiple iterations to optimize for the best fit of log odds. All of these iterations produce the log likelihood function, and logistic regression seeks to maximize this function to find the best parameter estimates. Given the optimal parameters, the conditional probabilities for each observation can be calculated, logged, and summed together to yield a predicted probability.
[A] The class ratio of the original data was noted at 80:20.
[A.1] Majority Class = Class=M with 111 instances
[A.2] Minority Class = Class=R with 25 instances
[B] The class ratio of the undersampled data was noted at 85:15 which performed worse than the original data.
[B.1] Majority Class = Class=M with 102 instances
[B.2] Minority Class = Class=R with 16 instances
[C] The logistic regression model from the stats package was implemented. The Class response was regressed against the V1 and V11 predictors.
[D] The logistic curve formulated by plotting the predicted probabilities against the classification index using the logit values showed a skewed logistic profile with a longer tail for the predicted points belonging to the majority class.
Code Chunk | Output
##################################
# Visualizing the imbalanced data set
##################################
ggplot(PMA_PreModelling_Train, aes(V1, V11, color = Class)) +
scale_colour_manual(values=c("#1846BA55","#B8000055")) +
geom_point(size=5) +
scale_x_continuous( limits=c(-4,4), breaks=seq(-4,4,by=1)) +
scale_y_continuous( limits=c(-4,4), breaks=seq(-4,4,by=1)) +
labs(title = "Without Undersampling - Tomek Links") +
theme_bw() +
theme(plot.title = element_text(color="black", size=14, face="bold", hjust=0.50),
axis.title.x = element_text(color="black", size=12, face="bold"),
axis.title.y = element_text(color="black", size=12, face="bold"),
legend.position="top")
##################################
# Implementing US_TOMEK
# Visualizing the undersampled data using US_TOMEK
##################################
recipe(Class ~ V1 + V11, data = PMA_PreModelling_Train) %>%
step_tomek(Class, seed=123456789) %>%
prep() %>%
bake(new_data = NULL) %>%
ggplot(aes(V1, V11, color = Class)) +
scale_colour_manual(values=c("#1846BA55","#B8000055")) +
geom_point(size=5) +
scale_x_continuous( limits=c(-4,4), breaks=seq(-4,4,by=1)) +
scale_y_continuous( limits=c(-4,4), breaks=seq(-4,4,by=1)) +
labs(title = "With Undersampling - Tomek Links") +
theme_bw() +
theme(plot.title = element_text(color="black", size=14, face="bold", hjust=0.50),
axis.title.x = element_text(color="black", size=12, face="bold"),
axis.title.y = element_text(color="black", size=12, face="bold"),
legend.position="top")
US_TOMEK <- recipe(Class ~ V1 + V11, data = PMA_PreModelling_Train) %>%
step_tomek(Class, seed=123456789) %>%
prep()
PMA_PreModelling_Train_LR_US_TOMEK <- US_TOMEK %>%
bake(new_data = NULL)
(PMA_PreModelling_Train_LR_US_TOMEK <- as.data.frame(PMA_PreModelling_Train_LR_US_TOMEK))## V1 V11 Class
## 1 0.914240973 -1.55861587 M
## 2 2.175018299 0.34520609 M
## 3 -0.230553865 -0.16690717 M
## 4 1.231717734 1.85842121 M
## 5 0.424204681 1.89154083 M
## 6 1.143139503 0.55690423 M
## 7 -0.507041623 0.38985083 M
## 8 0.312328639 0.98808941 M
## 9 -0.935159101 0.03686239 M
## 10 0.738227771 -1.15770286 M
## 11 1.169078303 -0.79246144 M
## 12 0.118911997 -1.30982705 M
## 13 1.012209983 -0.76338471 M
## 14 0.207991857 -0.39104999 M
## 15 -0.957451233 0.02573746 M
## 16 0.695599819 -0.11827986 M
## 17 -0.068999246 0.13149291 M
## 18 0.527094931 -0.39650480 M
## 19 0.104263091 0.33286517 M
## 20 0.465616266 -0.56497212 M
## 21 -0.507041623 -0.24127887 M
## 22 0.043933590 0.30731955 M
## 23 0.147716923 -0.20173692 M
## 24 0.612463710 0.84401925 M
## 25 -0.068999246 0.63930609 M
## 26 1.395977603 1.55085071 M
## 27 -0.180527407 1.02812486 M
## 28 0.565359423 0.74012531 M
## 29 2.230423635 1.21329169 M
## 30 0.782380194 0.93715691 M
## 31 2.005116912 0.95072168 M
## 32 1.784287809 0.74337479 M
## 33 1.523834205 1.62207084 M
## 34 1.928176420 1.77732114 M
## 35 1.934080462 1.47897609 M
## 36 0.744203377 2.70773324 M
## 37 1.424344244 -0.63319889 M
## 38 0.689392711 -0.83030322 M
## 39 1.381555338 -0.09258957 M
## 40 1.002499594 0.07158504 M
## 41 0.977896818 0.15071601 M
## 42 0.278484686 -2.26945660 M
## 43 1.406688795 0.06372536 M
## 44 1.852613244 0.04399440 M
## 45 1.281675939 -0.27257795 M
## 46 1.390588252 0.33359231 M
## 47 -0.192852168 -0.57752146 M
## 48 -0.180527407 -0.62433567 M
## 49 -0.052243905 -0.17198030 M
## 50 -0.779287302 -0.12994977 M
## 51 -0.041194136 -1.17696945 M
## 52 -2.093160440 0.59563328 M
## 53 -0.230553865 -0.22058604 M
## 54 -1.038676203 0.48516249 M
## 55 -0.030238811 0.37335493 M
## 56 0.089444831 0.04161877 M
## 57 0.303951059 -0.21715050 M
## 58 -0.132380328 -0.09011428 M
## 59 -1.546463816 0.01457488 M
## 60 -0.499153812 0.87301303 M
## 61 -0.108968439 0.07550792 M
## 62 0.686277848 -1.02337267 M
## 63 -0.721885235 -1.35101476 M
## 64 -3.557061230 -0.28132844 M
## 65 -0.789109306 -0.36302243 M
## 66 -0.750264961 -0.03937819 M
## 67 -0.379134945 2.23365699 M
## 68 -0.371994173 0.60103638 M
## 69 0.401039618 0.45156422 M
## 70 -0.295964244 1.50754826 M
## 71 0.256869245 1.19827720 M
## 72 1.243886483 1.44694263 M
## 73 -0.230553865 0.36255507 M
## 74 -0.256318859 -0.63813480 M
## 75 0.632133128 0.46700244 M
## 76 0.324793230 1.73570326 M
## 77 0.723165726 0.69367752 M
## 78 -1.191144673 1.19711994 M
## 79 -1.177722925 1.07730973 M
## 80 0.142960831 2.42255907 M
## 81 0.443194144 1.74741590 M
## 82 -0.180527407 0.75375436 M
## 83 0.544629613 0.36615870 M
## 84 -1.274767709 0.41409622 M
## 85 -0.539087424 -0.07530230 M
## 86 -0.555415865 0.01776805 M
## 87 0.345301912 -0.27257795 M
## 88 -1.868589923 0.02175517 M
## 89 -0.217863016 -0.21200423 M
## 90 0.625610136 0.17668535 M
## 91 -0.799007156 -0.01833539 M
## 92 -2.013850697 0.05347991 M
## 93 0.537644499 -0.19150300 M
## 94 -0.013979414 0.15531332 M
## 95 -0.935159101 0.53154288 M
## 96 -0.779287302 0.73036007 M
## 97 0.424204681 0.54321732 M
## 98 0.157176488 1.02691760 M
## 99 -0.323092564 0.48794823 M
## 100 0.377448180 0.47190071 M
## 101 0.992715078 0.20396774 M
## 102 0.295518362 0.33867807 M
## 103 -2.902302684 -1.49282840 R
## 104 -0.597170869 -1.70542147 R
## 105 -0.289270938 -1.17938825 R
## 106 -0.316256467 -0.26298086 R
## 107 -1.638716948 -1.51501038 R
## 108 0.274190661 0.01776805 R
## 109 -0.860055136 -2.21946189 R
## 110 -0.789109306 -0.84516218 R
## 111 -1.427539411 -1.35491277 R
## 112 0.779483548 -1.33935885 R
## 113 -0.400799099 -2.26338258 R
## 114 -0.302692686 -0.92399268 R
## 115 -0.829167731 0.42261407 R
## 116 -2.434915803 -1.57286451 R
## 117 -1.138280881 -0.92728240 R
## 118 -0.008605128 -1.57143555 RPMA_PreModelling_Train_LR_US_TOMEK$Label <- rep("LR_US_TOMEK",nrow(PMA_PreModelling_Train_LR_US_TOMEK))
##################################
# Verifying the class distribution
# for the undersampled data using US_TOMEK
##################################
table(PMA_PreModelling_Train_LR_US_TOMEK$Class) ##
## M R
## 102 16##################################
# Formulating the structure of the
# Logistic Regression model
##################################
LR_US_TOMEK_Model <- glm(Class ~ V1 + V11,
data = PMA_PreModelling_Train_LR_US_TOMEK,
family = binomial)
##################################
# Consolidating the model results
##################################
summary(LR_US_TOMEK_Model)##
## Call:
## glm(formula = Class ~ V1 + V11, family = binomial, data = PMA_PreModelling_Train_LR_US_TOMEK)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.1192 0.5681 -5.491 4.00e-08 ***
## V1 -0.8550 0.3663 -2.334 0.0196 *
## V11 -2.1768 0.5199 -4.187 2.83e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 93.664 on 117 degrees of freedom
## Residual deviance: 51.097 on 115 degrees of freedom
## AIC: 57.097
##
## Number of Fisher Scoring iterations: 7LR_US_TOMEK_Model_Coef <- (as.data.frame(LR_US_TOMEK_Model$coefficients))
LR_US_TOMEK_Model_Coef$Coef <- rownames(LR_US_TOMEK_Model_Coef)
LR_US_TOMEK_Model_Coef$Model <- rep("LR_US_TOMEK",nrow(LR_US_TOMEK_Model_Coef))
colnames(LR_US_TOMEK_Model_Coef) <- c("Estimates","Coefficients","Model")
print(LR_US_TOMEK_Model_Coef, rownames=FALSE)## Estimates Coefficients Model
## (Intercept) -3.1192250 (Intercept) LR_US_TOMEK
## V1 -0.8549776 V1 LR_US_TOMEK
## V11 -2.1767958 V11 LR_US_TOMEK##################################
# Computing the model predictions
##################################
(LR_US_TOMEK_Model_Probabilities <- predict(LR_US_TOMEK_Model,
type = c("response")))## 1 2 3 4 5 6
## 3.756409e-01 3.235729e-03 7.183859e-02 2.697569e-04 5.004968e-04 4.923252e-03
## 7 8 9 10 11 12
## 2.835075e-02 3.922375e-03 8.317828e-02 2.261271e-01 8.365176e-02 4.086217e-01
## 13 14 15 16 17 18
## 8.924332e-02 7.974495e-02 8.653858e-02 3.057631e-02 3.401110e-02 6.257509e-02
## 19 20 21 22 23 24
## 1.920963e-02 9.216455e-02 1.033540e-01 2.133670e-02 5.698041e-02 4.151500e-03
## 25 26 27 28 29 30
## 1.152242e-02 4.577910e-04 5.470520e-03 5.412073e-03 4.676720e-04 2.934903e-03
## 31 32 33 34 35 36
## 1.003676e-03 1.902036e-03 3.514870e-04 1.774511e-04 3.379585e-04 6.444174e-05
## 37 38 39 40 41 42
## 4.932868e-02 1.299682e-01 1.632055e-02 1.579461e-02 1.360818e-02 8.296036e-01
## 43 44 45 46 47 48
## 1.142309e-02 8.171200e-03 2.604136e-02 6.468579e-03 1.548320e-01 1.671688e-01
## 49 50 51 52 53 54
## 6.296205e-02 1.024699e-01 3.723887e-01 6.747167e-02 8.003027e-02 3.601015e-02
## 55 56 57 58 59 60
## 1.972224e-02 3.604439e-02 5.183789e-02 5.679248e-02 1.383861e-01 1.002269e-02
## 61 62 63 64 65 66
## 3.952765e-02 1.856837e-01 6.079859e-01 6.305035e-01 1.605248e-01 8.378117e-02
## 67 68 69 70 71 72
## 4.723640e-04 1.615066e-02 1.160016e-02 2.133604e-03 2.606214e-03 6.535624e-04
## 73 74 75 76 77 78
## 2.386204e-02 1.807923e-01 9.227894e-03 7.647569e-04 5.233073e-03 8.953573e-03
## 79 80 81 82 83 84
## 1.145983e-02 2.004250e-04 6.737929e-04 9.896313e-03 1.234693e-02 5.065520e-02
## 85 86 87 88 89 90
## 7.625172e-02 6.398192e-02 5.619436e-02 1.723620e-01 7.788281e-02 1.731500e-02
## 91 92 93 94 95 96
## 8.346450e-02 1.803700e-01 4.061963e-02 3.090759e-02 2.998119e-02 1.724570e-02
## 97 98 99 100 101 102
## 9.337053e-03 4.114982e-03 1.974037e-02 1.132709e-02 1.198585e-02 1.615689e-02
## 103 104 105 106 107 108
## 9.316147e-01 7.509522e-01 4.244291e-01 9.309838e-02 8.291634e-01 3.253605e-02
## 109 110 111 112 113 114
## 9.203724e-01 3.532443e-01 7.409012e-01 2.952220e-01 8.957034e-01 2.996246e-01
## 115 116 117 118
## 3.454790e-02 9.157827e-01 4.681641e-01 5.765988e-01##################################
# Creating a classification index
# based from the model predictions
##################################
(LR_US_TOMEK_Model_Indices <- predict(LR_US_TOMEK_Model,
type = c("link")))## 1 2 3 4 5 6 7
## -0.5080921 -5.7302601 -2.5587838 -8.2177196 -7.5994087 -5.3088505 -3.5343414
## 8 9 10 11 12 13 14
## -5.5371279 -2.3999268 -1.2303105 -2.3937341 -0.3696661 -2.3229093 -2.4458174
## 15 16 17 18 19 20 21
## -2.3566509 -3.4564762 -3.3464654 -2.7067694 -3.9329471 -2.2874876 -2.1605009
## 22 23 24 25 26 27 28
## -3.8257592 -2.8063796 -5.4801254 -4.4518710 -7.6886399 -5.2028960 -5.2136964
## 29 30 31 32 33 34 35
## -7.6672756 -5.8281418 -6.9030821 -6.2629263 -7.9529862 -8.6366379 -7.9922495
## 36 37 38 39 40 41 42
## -9.6496846 -2.9586628 -1.9012398 -4.0988753 -4.1321657 -4.2833829 1.5828204
## 43 44 45 46 47 48 49
## -4.4606296 -4.7989347 -3.6216827 -5.0343092 -1.6971944 -1.6058269 -2.7001917
## 50 51 52 53 54 55 56
## -2.1700777 -0.5219828 -2.6261917 -2.4419359 -3.2872798 -3.9060890 -3.2862939
## 57 58 59 60 61 62 63
## -2.9064041 -2.8098824 -1.8287596 -4.5928308 -3.1904248 -1.4783039 0.4388540
## 64 65 66 67 68 69 70
## 0.5343773 -1.6543285 -2.3920470 -7.6572883 -4.1095118 -4.4450680 -6.1478070
## 71 72 73 74 75 76 77
## -5.9472473 -7.3324188 -3.7113150 -1.5109890 -4.6762536 -7.1751876 -5.2475099
## 78 79 80 81 82 83 84
## -4.7067087 -4.4573816 -8.5148698 -7.3019137 -4.6056475 -4.3819239 -2.9307301
## 85 86 87 88 89 90 91
## -2.4943996 -2.6830343 -2.8211039 -1.5689790 -2.4714671 -4.0387156 -2.3961794
## 92 93 94 95 96 97 98
## -1.5138426 -3.1620361 -3.4453583 -3.4767452 -4.0427966 -4.6643837 -5.4889973
## 99 100 101 102 103 104 105
## -3.9051518 -4.4691663 -4.4119703 -4.1091196 2.6117614 1.1036970 -0.3046174
## 106 107 108 109 110 111 112
## -2.2763772 1.5797095 -3.3923293 2.4474182 -0.6048087 1.0506577 -0.8701553
## 113 114 115 116 117 118
## 2.1503710 -0.8490861 -3.3302497 2.3863784 -0.1275159 0.3088265max(LR_US_TOMEK_Model_Indices)## [1] 2.611761min(LR_US_TOMEK_Model_Indices)## [1] -9.649685##################################
# Consolidating the model probabilities
# and classification index
# based from the model predictions
##################################
LR_US_TOMEK_Model_Predictions <- as.data.frame(PMA_PreModelling_Train_LR_US_TOMEK)
LR_US_TOMEK_Model_Predictions$LR_US_TOMEK_Prob <- LR_US_TOMEK_Model_Probabilities
LR_US_TOMEK_Model_Predictions$LR_US_TOMEK_LP <- LR_US_TOMEK_Model_Indices
LR_US_TOMEK_Model_Predictions$Class <- as.factor(LR_US_TOMEK_Model_Predictions$Class)
LR_US_TOMEK_Model_Predictions$Label <- rep("LR_US_TOMEK",nrow(LR_US_TOMEK_Model_Predictions))
##################################
# Formulating the probability curve
# using the consolidated model predictions
##################################
LR_US_TOMEK_Model_Predictions %>%
ggplot(aes(x = LR_US_TOMEK_LP ,
y = LR_US_TOMEK_Prob,
color = Class)) +
scale_colour_manual(values=c("#1846BA55","#B8000055")) +
geom_point(size=5) +
scale_x_continuous( limits=c(-4,4), breaks=seq(-4,4,by=1)) +
scale_y_continuous( limits=c(-4,4), breaks=seq(-4,4,by=1)) +
geom_line(color="black") +
xlab("Sonar Object Classification Index (Logit Values)") +
ylab("Estimated Rock Detection Probability") +
labs(color = "Class") +
scale_x_continuous( limits=c(-10,5), breaks=seq(-10,5,by=1)) +
scale_y_continuous( limits=c(0,1), breaks=seq(0,1,by=0.1),labels = scales::percent) +
ggtitle("Estimated Rock Detection Probabilities Based on Classification Index : Logistic Regression (US_TOMEK)") +
theme_bw() +
theme(plot.title = element_text(color="black", size=14, face="bold", hjust=0.50),
axis.title.x = element_text(color="black", size=12, face="bold"),
axis.title.y = element_text(color="black", size=12, face="bold"),
legend.position="top")
1.5.6 Oversampling - Adaptive Synthetic Algorithm (LR_OS_ADASYN)
Adaptive Synthetic Algorithm uses a weighted distribution for different minority class instances according to their level of difficulty in learning. Synthetic data is generated for minority class instances that are harder to learn compared to those minority instances that are easier to learn. The algorithm improves learning with respect to the data distributions by reducing the bias introduced by the class imbalance and adaptively shifting the classification decision boundary toward the difficult examples.
Logistic Regression models the relationship between the probability of an event (among two outcome levels) by having the log-odds of the event be a linear combination of a set of predictors weighted by their respective parameter estimates. The parameters are estimated via maximum likelihood estimation by testing different values through multiple iterations to optimize for the best fit of log odds. All of these iterations produce the log likelihood function, and logistic regression seeks to maximize this function to find the best parameter estimates. Given the optimal parameters, the conditional probabilities for each observation can be calculated, logged, and summed together to yield a predicted probability.
[A] The class ratio of the original data was noted at 80:20.
[A.1] Majority Class = Class=M with 111 instances
[A.2] Minority Class = Class=R with 25 instances
[B] The class ratio of the oversampled data was noted at 50:50 with majority of the added instances being unique values for the minority class.
[B.1] Majority Class = Class=M with 111 instances
[B.2] Minority Class = Class=R with 111 instances
[C] The logistic regression model from the stats package was implemented. The Class response was regressed against the V1 and V11 predictors.
[D] The logistic curve formulated by plotting the predicted probabilities against the classification index using the logit values showed a sufficiently balanced logistic profile for the predicted points from both the majority and minority classes. Although the ratio of the uniques values and number of instances was relatively high, the added instances tended to cluster at the center of the data distribution.
Code Chunk | Output
##################################
# Visualizing the imbalanced data set
##################################
ggplot(PMA_PreModelling_Train, aes(V1, V11, color = Class)) +
scale_colour_manual(values=c("#1846BA55","#B8000055")) +
geom_point(size=5) +
labs(title = "Without Oversampling - Adaptive Synthetic Algorithm") +
theme_bw() +
theme(plot.title = element_text(color="black", size=14, face="bold", hjust=0.50),
axis.title.x = element_text(color="black", size=12, face="bold"),
axis.title.y = element_text(color="black", size=12, face="bold"),
legend.position="top")
##################################
# Implementing OS_ADASYN
# Visualizing the oversampled data using OS_ADASYN
##################################
recipe(Class ~ V1 + V11, data = PMA_PreModelling_Train) %>%
step_adasyn(Class, seed=123456789) %>%
prep() %>%
bake(new_data = NULL) %>%
ggplot(aes(V1, V11, color = Class)) +
scale_colour_manual(values=c("#1846BA55","#B8000055")) +
geom_point(size=5) +
scale_x_continuous( limits=c(-4,4), breaks=seq(-4,4,by=1)) +
scale_y_continuous( limits=c(-4,4), breaks=seq(-4,4,by=1)) +
labs(title = "With Oversampling - Adaptive Synthetic Algorithm") +
theme_bw() +
theme(plot.title = element_text(color="black", size=14, face="bold", hjust=0.50),
axis.title.x = element_text(color="black", size=12, face="bold"),
axis.title.y = element_text(color="black", size=12, face="bold"),
legend.position="top")
OS_ADASYN <- recipe(Class ~ V1 + V11, data = PMA_PreModelling_Train) %>%
step_adasyn(Class, seed=123456789) %>%
prep()
PMA_PreModelling_Train_LR_OS_ADASYN <- OS_ADASYN %>%
bake(new_data = NULL)
(PMA_PreModelling_Train_LR_OS_ADASYN <- as.data.frame(PMA_PreModelling_Train_LR_OS_ADASYN))## V1 V11 Class
## 1 0.914240973 -1.558615871 M
## 2 2.175018299 0.345206089 M
## 3 -0.230553865 -0.166907171 M
## 4 1.231717734 1.858421213 M
## 5 0.424204681 1.891540835 M
## 6 1.143139503 0.556904232 M
## 7 -0.507041623 0.389850825 M
## 8 0.312328639 0.988089406 M
## 9 -0.935159101 0.036862390 M
## 10 0.408807916 -1.630790485 M
## 11 0.738227771 -1.157702863 M
## 12 1.169078303 -0.792461442 M
## 13 0.118911997 -1.309827049 M
## 14 -0.174409184 -1.318776567 M
## 15 1.012209983 -0.763384712 M
## 16 0.813818599 0.266052702 M
## 17 0.207991857 -0.391049992 M
## 18 -0.957451233 0.025737464 M
## 19 0.695599819 -0.118279862 M
## 20 -1.204708453 -0.186398477 M
## 21 -0.068999246 0.131492907 M
## 22 0.527094931 -0.396504803 M
## 23 0.104263091 0.332865171 M
## 24 0.465616266 -0.564972117 M
## 25 -0.507041623 -0.241278874 M
## 26 0.043933590 0.307319548 M
## 27 0.147716923 -0.201736917 M
## 28 0.612463710 0.844019251 M
## 29 -0.068999246 0.639306087 M
## 30 1.395977603 1.550850706 M
## 31 -0.180527407 1.028124858 M
## 32 0.565359423 0.740125313 M
## 33 2.230423635 1.213291690 M
## 34 0.782380194 0.937156911 M
## 35 2.005116912 0.950721679 M
## 36 1.784287809 0.743374785 M
## 37 1.523834205 1.622070841 M
## 38 1.928176420 1.777321144 M
## 39 -1.204708453 0.652640830 M
## 40 1.934080462 1.478976095 M
## 41 0.744203377 2.707733238 M
## 42 1.424344244 -0.633198890 M
## 43 -0.491314245 -0.582362640 M
## 44 0.689392711 -0.830303223 M
## 45 1.381555338 -0.092589575 M
## 46 1.002499594 0.071585037 M
## 47 0.977896818 0.150716010 M
## 48 0.278484686 -2.269456602 M
## 49 1.406688795 0.063725362 M
## 50 1.852613244 0.043994401 M
## 51 1.281675939 -0.272577947 M
## 52 1.390588252 0.333592312 M
## 53 -0.192852168 -0.577521460 M
## 54 -0.180527407 -0.624335667 M
## 55 -0.052243905 -0.171980296 M
## 56 -0.779287302 -0.129949769 M
## 57 -0.041194136 -1.176969454 M
## 58 -0.924156756 -1.249334984 M
## 59 -0.168320027 -1.801733345 M
## 60 -2.093160440 0.595633277 M
## 61 -0.230553865 -0.220586042 M
## 62 -1.038676203 0.485162490 M
## 63 -0.030238811 0.373354928 M
## 64 0.089444831 0.041618772 M
## 65 0.303951059 -0.217150498 M
## 66 -0.132380328 -0.090114281 M
## 67 -1.546463816 0.014574884 M
## 68 -0.499153812 0.873013033 M
## 69 -0.108968439 0.075507923 M
## 70 0.686277848 -1.023372673 M
## 71 -0.721885235 -1.351014760 M
## 72 -3.557061230 -0.281328435 M
## 73 -0.789109306 -0.363022432 M
## 74 -0.750264961 -0.039378188 M
## 75 -0.379134945 2.233656987 M
## 76 -0.371994173 0.601036378 M
## 77 0.401039618 0.451564218 M
## 78 -0.295964244 1.507548259 M
## 79 0.256869245 1.198277196 M
## 80 1.243886483 1.446942626 M
## 81 -0.230553865 0.362555066 M
## 82 -0.256318859 -0.638134800 M
## 83 0.632133128 0.467002438 M
## 84 0.324793230 1.735703263 M
## 85 0.723165726 0.693677522 M
## 86 -1.191144673 1.197119945 M
## 87 -1.177722925 1.077309728 M
## 88 0.142960831 2.422559073 M
## 89 0.443194144 1.747415902 M
## 90 -0.180527407 0.753754356 M
## 91 0.544629613 0.366158697 M
## 92 -1.274767709 0.414096217 M
## 93 -0.539087424 -0.075302304 M
## 94 -0.555415865 0.017768054 M
## 95 0.345301912 -0.272577947 M
## 96 -1.868589923 0.021755168 M
## 97 -0.217863016 -0.212004228 M
## 98 0.625610136 0.176685353 M
## 99 -0.799007156 -0.018335390 M
## 100 -2.013850697 0.053479912 M
## 101 0.537644499 -0.191503002 M
## 102 -0.013979414 0.155313323 M
## 103 -0.935159101 0.531542880 M
## 104 -0.779287302 0.730360068 M
## 105 0.424204681 0.543217319 M
## 106 0.157176488 1.026917595 M
## 107 -0.323092564 0.487948233 M
## 108 0.377448180 0.471900708 M
## 109 0.992715078 0.203967739 M
## 110 0.295518362 0.338678072 M
## 111 0.099342681 0.206985690 M
## 112 -2.902302684 -1.492828399 R
## 113 -0.588710342 -0.468334419 R
## 114 -0.597170869 -1.705421467 R
## 115 -0.289270938 -1.179388252 R
## 116 0.544629613 -1.617624095 R
## 117 0.064360561 0.156078935 R
## 118 -0.168320027 -1.672266879 R
## 119 -0.316256467 -0.262980859 R
## 120 -1.638716948 -1.515010380 R
## 121 0.274190661 0.017768054 R
## 122 -0.860055136 -2.219461886 R
## 123 -0.789109306 -0.845162176 R
## 124 -1.427539411 -1.354912772 R
## 125 0.779483548 -1.339358851 R
## 126 -0.400799099 -2.263382579 R
## 127 -0.302692686 -0.923992684 R
## 128 0.827857790 0.186562547 R
## 129 -0.829167731 0.422614069 R
## 130 -2.434915803 -1.572864509 R
## 131 -1.138280881 -0.927282397 R
## 132 -0.860055136 -1.284438518 R
## 133 -1.125398710 0.669904780 R
## 134 -0.186674974 -1.399544510 R
## 135 -0.008605128 -1.571435552 R
## 136 -1.348878827 -0.413843793 R
## 137 -0.390322558 -0.784388451 R
## 138 -0.295860799 -1.053993156 R
## 139 -0.017377454 -1.559181951 R
## 140 -0.191911171 -1.315384987 R
## 141 -0.290855568 -1.149235140 R
## 142 -0.035273087 -1.662069728 R
## 143 0.346506687 -1.632808882 R
## 144 0.682389831 -1.454399746 R
## 145 -0.356332622 -0.246153717 R
## 146 0.266972171 0.022526169 R
## 147 0.652376435 0.179556227 R
## 148 -0.342166539 0.277343988 R
## 149 0.104992266 0.129296282 R
## 150 0.128805610 0.158651987 R
## 151 -0.082002824 0.016138154 R
## 152 -0.096321170 0.002448115 R
## 153 -0.178557486 -1.673058340 R
## 154 -0.063390608 -1.606022761 R
## 155 -0.176865876 -1.545290531 R
## 156 -0.180838432 -1.486265301 R
## 157 -0.358706919 -2.156356393 R
## 158 -0.349557028 -0.303980855 R
## 159 -0.582661828 -0.590981902 R
## 160 0.038582729 -0.094260047 R
## 161 0.035702482 0.124526356 R
## 162 -0.574843937 -0.457883052 R
## 163 0.082506030 -0.073375165 R
## 164 -0.529153404 -0.423445246 R
## 165 -0.390201423 -0.354022656 R
## 166 -0.059449742 0.019763646 R
## 167 -0.557747942 -0.560307667 R
## 168 -0.308189318 -0.656122036 R
## 169 0.219359952 0.053910074 R
## 170 0.748334227 0.162318486 R
## 171 0.167225292 0.088274976 R
## 172 0.248268872 0.034854570 R
## 173 0.755598258 0.164533045 R
## 174 0.219609003 -0.008184720 R
## 175 -0.130244888 -0.642472049 R
## 176 -0.784077436 -2.070896097 R
## 177 -0.647882130 -1.804581595 R
## 178 -0.512560514 -2.252694339 R
## 179 -0.807753229 -0.960600033 R
## 180 -0.318364841 -0.921452795 R
## 181 -0.557028431 -1.000347302 R
## 182 -1.342933236 -1.229833763 R
## 183 -1.325016764 -1.203346635 R
## 184 -0.862607677 -0.903846499 R
## 185 0.045146002 -1.229443601 R
## 186 0.437074722 -1.360688788 R
## 187 -0.077403279 -1.640333136 R
## 188 -0.125638032 -1.657275187 R
## 189 -0.517275875 -0.889216525 R
## 190 -0.294167245 -1.086218926 R
## 191 -0.310113401 -0.562354563 R
## 192 -0.297566301 -1.021540043 R
## 193 -0.310870399 -0.525463291 R
## 194 0.703852721 0.148757571 R
## 195 -0.050300971 -0.158482083 R
## 196 0.450144116 0.038151940 R
## 197 0.112591704 -0.144113810 R
## 198 0.727653830 0.156013729 R
## 199 0.205014899 -0.058163811 R
## 200 0.820894004 -0.033103728 R
## 201 -0.725082580 0.036955271 R
## 202 -0.655946484 -0.219209453 R
## 203 -0.723124380 0.280868725 R
## 204 -1.121817738 0.666915420 R
## 205 -1.106401172 -0.023584008 R
## 206 -0.157000177 0.251678836 R
## 207 -1.123828064 0.666573667 R
## 208 -0.938882051 0.514202506 R
## 209 -0.930462819 0.256473662 R
## 210 -0.872827627 0.459060921 R
## 211 -0.171945746 -1.413762652 R
## 212 -0.180283482 -1.405714226 R
## 213 -0.016300987 -1.560685612 R
## 214 -0.083518808 -1.618730111 R
## 215 -0.505285997 -1.684504044 R
## 216 -1.174123534 -0.839897872 R
## 217 -1.279860500 -0.582110741 R
## 218 -1.377082750 -0.751266116 R
## 219 -0.970648251 -0.705281241 R
## 220 -1.190222435 -0.536093069 R
## 221 -1.196659723 -0.784954555 R
## 222 -1.253287340 -0.646896207 RPMA_PreModelling_Train_LR_OS_ADASYN$Label <- rep("LR_OS_ADASYN",nrow(PMA_PreModelling_Train_LR_OS_ADASYN))
##################################
# Verifying the class distribution
# for the oversampled data using OS_ADASYN
##################################
table(PMA_PreModelling_Train_LR_OS_ADASYN$Class) ##
## M R
## 111 111##################################
# Formulating the structure of the
# Logistic Regression model
##################################
LR_OS_ADASYN_Model <- glm(Class ~ V1 + V11,
data = PMA_PreModelling_Train_LR_OS_ADASYN,
family = binomial)
##################################
# Consolidating the model results
##################################
summary(LR_OS_ADASYN_Model)##
## Call:
## glm(formula = Class ~ V1 + V11, family = binomial, data = PMA_PreModelling_Train_LR_OS_ADASYN)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.4101 0.1704 -2.406 0.0161 *
## V1 -0.5944 0.2092 -2.841 0.0045 **
## V11 -1.2753 0.2148 -5.938 2.89e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 307.76 on 221 degrees of freedom
## Residual deviance: 238.51 on 219 degrees of freedom
## AIC: 244.51
##
## Number of Fisher Scoring iterations: 4LR_OS_ADASYN_Model_Coef <- (as.data.frame(LR_OS_ADASYN_Model$coefficients))
LR_OS_ADASYN_Model_Coef$Coef <- rownames(LR_OS_ADASYN_Model_Coef)
LR_OS_ADASYN_Model_Coef$Model <- rep("LR_OS_ADASYN",nrow(LR_OS_ADASYN_Model_Coef))
colnames(LR_OS_ADASYN_Model_Coef) <- c("Estimates","Coefficients","Model")
print(LR_OS_ADASYN_Model_Coef, rownames=FALSE)## Estimates Coefficients Model
## (Intercept) -0.4101181 (Intercept) LR_OS_ADASYN
## V1 -0.5943822 V1 LR_OS_ADASYN
## V11 -1.2753114 V11 LR_OS_ADASYN##################################
# Computing the model predictions
##################################
(LR_OS_ADASYN_Model_Probabilities <- predict(LR_OS_ADASYN_Model,
type = c("response")))## 1 2 3 4 5 6 7
## 0.73772818 0.10497255 0.48494896 0.02896442 0.04416885 0.14187466 0.35298982
## 8 9 10 11 12 13 14
## 0.13518371 0.52465818 0.80638030 0.65192786 0.47642692 0.76667670 0.79825096
## 15 16 17 18 19 20 21
## 0.49045006 0.22563277 0.49124234 0.53149589 0.33788658 0.63266232 0.36893216
## 22 23 24 25 26 27 28
## 0.44577737 0.28975032 0.50841003 0.54957759 0.30403436 0.44012839 0.13580915
## 29 30 31 32 33 34 35
## 0.23425830 0.03850579 0.16603467 0.15576953 0.03615320 0.11201542 0.05655150
## 36 37 38 39 40 41 42
## 0.08175770 0.03278329 0.02139795 0.37136132 0.03089268 0.01331233 0.38955167
## 43 44 45 46 47 48 49
## 0.65126495 0.55947074 0.24727303 0.25025003 0.23441048 0.91040741 0.20957344
## 50 51 52 53 54 55 56
## 0.17258950 0.30485130 0.15948123 0.60850432 0.62090861 0.46015049 0.55448352
## 57 58 59 60 61 62 63
## 0.75312881 0.84972878 0.87949954 0.51859174 0.50205872 0.39855712 0.29561056
## 64 65 66 67 68 69 70
## 0.37370392 0.42217609 0.44608300 0.62022067 0.22674314 0.39134899 0.61942054
## 71 72 73 74 75 76 77
## 0.85093106 0.88724633 0.62758752 0.52149811 0.04594066 0.27777607 0.22716764
## 78 79 80 81 82 83 84
## 0.10369789 0.10997728 0.04766295 0.32399955 0.63553899 0.20078099 0.05642670
## 85 86 87 88 89 90 91
## 0.15127834 0.22638738 0.25274867 0.02699838 0.05205260 0.22027108 0.23133247
## 92 93 94 95 96 97 98
## 0.45499203 0.50158494 0.47436036 0.43346259 0.66212847 0.49743683 0.26750888
## 99 100 101 102 103 104 105
## 0.52203094 0.67231525 0.38096402 0.35437073 0.37001350 0.29351771 0.20504940
## 106 107 108 109 110 111 112
## 0.14025414 0.30145821 0.22508296 0.22092352 0.26547781 0.32450675 0.96153492
## 113 114 115 116 117 118 119
## 0.63113128 0.89281022 0.78004124 0.79069806 0.34357069 0.86087655 0.52828019
## 120 121 122 123 124 125 126
## 0.92386207 0.35531618 0.94938606 0.75708417 0.89718416 0.69734156 0.93788428
## 127 128 129 130 131 132 133
## 0.72074811 0.24230065 0.38787815 0.95448129 0.80984905 0.85057811 0.35536511
## 134 135 136 137 138 139 140
## 0.81543098 0.83188786 0.71492212 0.69470786 0.75211155 0.83042655 0.79922795
## 141 142 143 144 145 146 147
## 0.77353736 0.84947689 0.81248892 0.73867519 0.52886844 0.35490911 0.26369154
## 148 149 150 151 152 153 154
## 0.36344558 0.34583074 0.33425366 0.40565468 0.41193095 0.86172405 0.84233953
## 155 156 157 158 159 160 161
## 0.84100629 0.83101251 0.92778263 0.54619851 0.66594284 0.42242166 0.35660069
## 162 163 164 165 166 167 168
## 0.62609548 0.40960899 0.60931311 0.56790283 0.40131546 0.65384058 0.64790067
## 169 170 171 172 173 174 175
## 0.35222942 0.25694247 0.34930583 0.35385536 0.25558129 0.37046751 0.61931673
## 176 177 178 179 180 181 182
## 0.93684708 0.90690144 0.94088997 0.78500246 0.72196940 0.76794150 0.87615569
## 183 184 185 186 187 188 189
## 0.87125377 0.77821361 0.75601813 0.74372008 0.84913396 0.85546221 0.73718131
## 190 191 192 193 194 195 196
## 0.75951039 0.62043265 0.74450785 0.60939910 0.26538054 0.45559081 0.32600030
## 197 198 199 200 201 202 203
## 0.42720825 0.26084358 0.38750825 0.29821230 0.49343253 0.56447039 0.41617366
## 204 205 206 207 208 209 210
## 0.35575095 0.56895381 0.34575106 0.35612479 0.37570197 0.45409182 0.38301500
## 211 212 213 214 215 216 217
## 0.81683819 0.81604264 0.83060641 0.84604459 0.88477862 0.79557946 0.74894259
## 218 219 220 221 222
## 0.79681306 0.74388715 0.72730948 0.78620781 0.76129777##################################
# Creating a classification index
# based from the model predictions
##################################
(LR_OS_ADASYN_Model_Indices <- predict(LR_OS_ADASYN_Model,
type = c("link")))## 1 2 3 4 5 6
## 1.034194007 -2.143155516 -0.060222356 -3.512295020 -3.074561474 -1.799806198
## 7 8 9 10 11 12
## -0.605922800 -1.855882401 0.098712797 1.426659526 0.627524174 -0.094362275
## 13 14 15 16 17 18
## 1.189640156 1.375398467 -0.038204425 -1.233137433 -0.035034220 0.126150588
## 19 20 21 22 23 24
## -0.672726578 0.543655270 -0.536800580 -0.217746823 -0.896596981 0.033643295
## 25 26 27 28 29 30
## 0.198964127 -0.828159573 -0.240641004 -1.850543025 -1.184420539 -3.217679974
## 31 32 33 34 35 36
## -1.613995214 -1.690047948 -3.283166961 -2.070317881 -2.814390118 -2.418701365
## 37 38 39 40 41 42
## -3.384503515 -3.822829815 -0.526381159 -3.445856218 -4.305662512 -0.449197159
## 43 44 45 46 47 48
## 0.624604084 0.239014356 -1.113209442 -1.097279217 -1.183572402 2.318619541
## 49 50 51 52 53 54
## -1.327498549 -1.567384980 -0.824301675 -1.662093081 0.441029528 0.493406603
## 55 56 57 58 59 60
## -0.159736808 0.218802829 1.115369581 1.732475427 1.987699485 0.074401266
## 61 62 63 64 65 66
## 0.008234922 -0.411480728 -0.868288495 -0.516359404 -0.313846676 -0.216509810
## 67 68 69 70 71 72
## 0.490484945 -1.226793467 -0.441645313 0.487089454 1.741922218 2.062917134
## 73 74 75 76 77 78
## 0.521881088 0.086045493 -3.033375348 -0.955519951 -1.224373916 -2.156795762
## 79 80 81 82 83 84
## -2.090973219 -2.994764558 -0.735451606 0.556053884 -1.381420321 -2.816731639
## 85 86 87 88 89 90
## -1.724609805 -1.228823674 -1.084005982 -3.584608772 -2.902044296 -1.264087373
## 91 92 93 94 95 96
## -1.200802614 -0.180520511 0.006339761 -0.102648596 -0.267737627 0.672793868
## 97 98 99 100 101 102
## -0.010252778 -1.007298471 0.088180865 0.718675356 -0.485458441 -0.599881837
## 103 104 105 106 107 108
## -0.532158893 -0.878360151 -1.355029065 -1.813180759 -0.840363591 -1.236286943
## 109 110 111 112 113 114
## -1.260292650 -1.017688967 -0.733136834 3.218780081 0.537073094 2.119773149
## 115 116 117 118 119 120
## 1.265906734 1.329138280 -0.647422117 1.822589419 0.113241617 2.496016154
## 121 122 123 124 125 126
## -0.595751943 2.931588504 1.136759420 2.166321675 0.834670434 2.714627456
## 127 128 129 130 131 132
## 0.948175492 -1.140107373 -0.456240118 3.043044607 1.449029645 1.739142503
## 133 134 135 136 137 138
## -0.595538372 1.485693315 1.599066382 0.919411187 0.822222252 1.109905829
## 139 140 141 142 143 144
## 1.588653339 1.381476015 1.228394001 1.730504144 1.466264352 1.039094181
## 145 146 147 148 149 150
## 0.115602324 -0.597529479 -1.026869141 -0.560440357 -0.637416654 -0.689008549
## 151 152 153 154 155 156
## -0.381958248 -0.355988614 1.829683742 1.675739359 1.665734531 1.592820196
## 157 158 159 160 161 162
## 2.553116895 0.185322642 0.689891703 -0.312840064 -0.590148900 0.515502402
## 163 164 165 166 167 168
## -0.365582021 0.444425835 0.273299828 -0.399987029 0.635964130 0.609824089
## 169 170 171 172 173 174
## -0.609253877 -1.061921255 -0.622091916 -0.602135123 -1.069063118 -0.530211707
## 175 176 177 178 179 180
## 0.486649104 2.696961062 2.276375065 2.767425616 1.295060256 0.954251593
## 181 182 183 184 185 186
## 1.196724048 1.956518579 1.912090011 1.255286332 1.130971418 1.065394456
## 187 188 189 190 191 192
## 1.727824656 1.778100926 1.031369484 1.149997104 0.491384999 1.069531722
## 193 194 195 196 197 198
## 0.444787084 -1.018187850 -0.178106078 -0.726331346 -0.293250606 -1.041588666
## 199 200 201 202 203 204
## -0.457798326 -0.855825310 -0.026271400 0.259325140 -0.338500938 -0.593854473
## 205 206 207 208 209 210
## 0.277584017 -0.637768884 -0.592223728 -0.507831661 -0.184151359 -0.476770541
## 211 212 213 214 215 216
## 1.495071084 1.489762635 1.589931142 1.703909032 2.038482188 1.358891395
## 217 218 219 220 221 222
## 1.092980686 1.366493646 1.066271179 0.981014553 1.302216663 1.159807719max(LR_OS_ADASYN_Model_Indices)## [1] 3.21878min(LR_OS_ADASYN_Model_Indices)## [1] -4.305663##################################
# Consolidating the model probabilities
# and classification index
# based from the model predictions
##################################
LR_OS_ADASYN_Model_Predictions <- as.data.frame(PMA_PreModelling_Train_LR_OS_ADASYN)
LR_OS_ADASYN_Model_Predictions$LR_OS_ADASYN_Prob <- LR_OS_ADASYN_Model_Probabilities
LR_OS_ADASYN_Model_Predictions$LR_OS_ADASYN_LP <- LR_OS_ADASYN_Model_Indices
LR_OS_ADASYN_Model_Predictions$Class <- as.factor(LR_OS_ADASYN_Model_Predictions$Class)
LR_OS_ADASYN_Model_Predictions$Label <- rep("LR_OS_ADASYN",nrow(LR_OS_ADASYN_Model_Predictions))
##################################
# Formulating the probability curve
# using the consolidated model predictions
##################################
LR_OS_ADASYN_Model_Predictions %>%
ggplot(aes(x = LR_OS_ADASYN_LP ,
y = LR_OS_ADASYN_Prob,
color = Class)) +
scale_colour_manual(values=c("#1846BA55","#B8000055")) +
geom_point(size=5) +
geom_line(color="black") +
xlab("Sonar Object Classification Index (Logit Values)") +
ylab("Estimated Rock Detection Probability") +
labs(color = "Class") +
scale_x_continuous( limits=c(-10,5), breaks=seq(-10,5,by=1)) +
scale_y_continuous( limits=c(0,1), breaks=seq(0,1,by=0.1),labels = scales::percent) +
ggtitle("Estimated Rock Detection Probabilities Based on Classification Index : Logistic Regression (OS_ADASYN)") +
theme_bw() +
theme(plot.title = element_text(color="black", size=14, face="bold", hjust=0.50),
axis.title.x = element_text(color="black", size=12, face="bold"),
axis.title.y = element_text(color="black", size=12, face="bold"),
legend.position="top")
1.5.7 Oversampling - Borderline Synthetic Minority Oversampling Technique (LR_OS_BSMOTE)
Borderline Synthetic Minority Oversampling Technique generates new instances of the minority class using nearest neighbors of these cases in the border region between classes. The algorithm initially classifies instances of the minority class into the noise, safe and danger categories. If the surrounding nearest neighbors are from the majority class, instances are denoted as noise data which will have have adverse effects on the data distribution and are thus not considered using synthetic data generation. Instances with more than half of the surrounding neighbors are from the same minority class are denoted as safe. Oversampling is only performed within minority class instances in the danger category which covers those with more than half of the nearest neighbors from the majority class.
Logistic Regression models the relationship between the probability of an event (among two outcome levels) by having the log-odds of the event be a linear combination of a set of predictors weighted by their respective parameter estimates. The parameters are estimated via maximum likelihood estimation by testing different values through multiple iterations to optimize for the best fit of log odds. All of these iterations produce the log likelihood function, and logistic regression seeks to maximize this function to find the best parameter estimates. Given the optimal parameters, the conditional probabilities for each observation can be calculated, logged, and summed together to yield a predicted probability.
[A] The class ratio of the original data was noted at 80:20.
[A.1] Majority Class = Class=M with 111 instances
[A.2] Minority Class = Class=R with 25 instances
[B] The class ratio of the oversampled data was noted at 50:50 with majority of the added instances being unique values for the minority class.
[B.1] Majority Class = Class=M with 111 instances
[B.2] Minority Class = Class=R with 111 instances
[C] The logistic regression model from the stats package was implemented. The Class response was regressed against the V1 and V11 predictors.
[D] The logistic curve formulated by plotting the predicted probabilities against the classification index using the logit values showed a sufficiently balanced logistic profile for the predicted points from both the majority and minority classes. Minimal overlap between both classes was observed driving better differentiation although a minimal skew was still present due to a longer tail for the predicted points belonging to the majority class.
Code Chunk | Output
##################################
# Visualizing the imbalanced data set
##################################
ggplot(PMA_PreModelling_Train, aes(V1, V11, color = Class)) +
scale_colour_manual(values=c("#1846BA55","#B8000055")) +
geom_point(size=5) +
scale_x_continuous( limits=c(-4,4), breaks=seq(-4,4,by=1)) +
scale_y_continuous( limits=c(-4,4), breaks=seq(-4,4,by=1)) +
labs(title = "Without Oversampling - Borderline Synthetic Minority Oversampling Technique") +
theme_bw() +
theme(plot.title = element_text(color="black", size=14, face="bold", hjust=0.50),
axis.title.x = element_text(color="black", size=12, face="bold"),
axis.title.y = element_text(color="black", size=12, face="bold"),
legend.position="top")
##################################
# Implementing OS_BSMOTE
# Visualizing the oversampled data using OS_BSMOTE
##################################
recipe(Class ~ V1 + V11, data = PMA_PreModelling_Train) %>%
step_bsmote(Class, seed=123456789) %>%
prep() %>%
bake(new_data = NULL) %>%
ggplot(aes(V1, V11, color = Class)) +
scale_colour_manual(values=c("#1846BA55","#B8000055")) +
geom_point(size=5) +
scale_x_continuous( limits=c(-4,4), breaks=seq(-4,4,by=1)) +
scale_y_continuous( limits=c(-4,4), breaks=seq(-4,4,by=1)) +
labs(title = "With Oversampling - Borderline Synthetic Minority Oversampling Technique") +
theme_bw() +
theme(plot.title = element_text(color="black", size=14, face="bold", hjust=0.50),
axis.title.x = element_text(color="black", size=12, face="bold"),
axis.title.y = element_text(color="black", size=12, face="bold"),
legend.position="top")
OS_BSMOTE <- recipe(Class ~ V1 + V11, data = PMA_PreModelling_Train) %>%
step_bsmote(Class, seed=123456789) %>%
prep()
PMA_PreModelling_Train_LR_OS_BSMOTE <- OS_BSMOTE %>%
bake(new_data = NULL)
(PMA_PreModelling_Train_LR_OS_BSMOTE <- as.data.frame(PMA_PreModelling_Train_LR_OS_BSMOTE))## V1 V11 Class
## 1 0.914240973 -1.55861587 M
## 2 2.175018299 0.34520609 M
## 3 -0.230553865 -0.16690717 M
## 4 1.231717734 1.85842121 M
## 5 0.424204681 1.89154083 M
## 6 1.143139503 0.55690423 M
## 7 -0.507041623 0.38985083 M
## 8 0.312328639 0.98808941 M
## 9 -0.935159101 0.03686239 M
## 10 0.408807916 -1.63079048 M
## 11 0.738227771 -1.15770286 M
## 12 1.169078303 -0.79246144 M
## 13 0.118911997 -1.30982705 M
## 14 -0.174409184 -1.31877657 M
## 15 1.012209983 -0.76338471 M
## 16 0.813818599 0.26605270 M
## 17 0.207991857 -0.39104999 M
## 18 -0.957451233 0.02573746 M
## 19 0.695599819 -0.11827986 M
## 20 -1.204708453 -0.18639848 M
## 21 -0.068999246 0.13149291 M
## 22 0.527094931 -0.39650480 M
## 23 0.104263091 0.33286517 M
## 24 0.465616266 -0.56497212 M
## 25 -0.507041623 -0.24127887 M
## 26 0.043933590 0.30731955 M
## 27 0.147716923 -0.20173692 M
## 28 0.612463710 0.84401925 M
## 29 -0.068999246 0.63930609 M
## 30 1.395977603 1.55085071 M
## 31 -0.180527407 1.02812486 M
## 32 0.565359423 0.74012531 M
## 33 2.230423635 1.21329169 M
## 34 0.782380194 0.93715691 M
## 35 2.005116912 0.95072168 M
## 36 1.784287809 0.74337479 M
## 37 1.523834205 1.62207084 M
## 38 1.928176420 1.77732114 M
## 39 -1.204708453 0.65264083 M
## 40 1.934080462 1.47897609 M
## 41 0.744203377 2.70773324 M
## 42 1.424344244 -0.63319889 M
## 43 -0.491314245 -0.58236264 M
## 44 0.689392711 -0.83030322 M
## 45 1.381555338 -0.09258957 M
## 46 1.002499594 0.07158504 M
## 47 0.977896818 0.15071601 M
## 48 0.278484686 -2.26945660 M
## 49 1.406688795 0.06372536 M
## 50 1.852613244 0.04399440 M
## 51 1.281675939 -0.27257795 M
## 52 1.390588252 0.33359231 M
## 53 -0.192852168 -0.57752146 M
## 54 -0.180527407 -0.62433567 M
## 55 -0.052243905 -0.17198030 M
## 56 -0.779287302 -0.12994977 M
## 57 -0.041194136 -1.17696945 M
## 58 -0.924156756 -1.24933498 M
## 59 -0.168320027 -1.80173334 M
## 60 -2.093160440 0.59563328 M
## 61 -0.230553865 -0.22058604 M
## 62 -1.038676203 0.48516249 M
## 63 -0.030238811 0.37335493 M
## 64 0.089444831 0.04161877 M
## 65 0.303951059 -0.21715050 M
## 66 -0.132380328 -0.09011428 M
## 67 -1.546463816 0.01457488 M
## 68 -0.499153812 0.87301303 M
## 69 -0.108968439 0.07550792 M
## 70 0.686277848 -1.02337267 M
## 71 -0.721885235 -1.35101476 M
## 72 -3.557061230 -0.28132844 M
## 73 -0.789109306 -0.36302243 M
## 74 -0.750264961 -0.03937819 M
## 75 -0.379134945 2.23365699 M
## 76 -0.371994173 0.60103638 M
## 77 0.401039618 0.45156422 M
## 78 -0.295964244 1.50754826 M
## 79 0.256869245 1.19827720 M
## 80 1.243886483 1.44694263 M
## 81 -0.230553865 0.36255507 M
## 82 -0.256318859 -0.63813480 M
## 83 0.632133128 0.46700244 M
## 84 0.324793230 1.73570326 M
## 85 0.723165726 0.69367752 M
## 86 -1.191144673 1.19711994 M
## 87 -1.177722925 1.07730973 M
## 88 0.142960831 2.42255907 M
## 89 0.443194144 1.74741590 M
## 90 -0.180527407 0.75375436 M
## 91 0.544629613 0.36615870 M
## 92 -1.274767709 0.41409622 M
## 93 -0.539087424 -0.07530230 M
## 94 -0.555415865 0.01776805 M
## 95 0.345301912 -0.27257795 M
## 96 -1.868589923 0.02175517 M
## 97 -0.217863016 -0.21200423 M
## 98 0.625610136 0.17668535 M
## 99 -0.799007156 -0.01833539 M
## 100 -2.013850697 0.05347991 M
## 101 0.537644499 -0.19150300 M
## 102 -0.013979414 0.15531332 M
## 103 -0.935159101 0.53154288 M
## 104 -0.779287302 0.73036007 M
## 105 0.424204681 0.54321732 M
## 106 0.157176488 1.02691760 M
## 107 -0.323092564 0.48794823 M
## 108 0.377448180 0.47190071 M
## 109 0.992715078 0.20396774 M
## 110 0.295518362 0.33867807 M
## 111 0.099342681 0.20698569 M
## 112 -2.902302684 -1.49282840 R
## 113 -0.588710342 -0.46833442 R
## 114 -0.597170869 -1.70542147 R
## 115 -0.289270938 -1.17938825 R
## 116 0.544629613 -1.61762409 R
## 117 0.064360561 0.15607894 R
## 118 -0.168320027 -1.67226688 R
## 119 -0.316256467 -0.26298086 R
## 120 -1.638716948 -1.51501038 R
## 121 0.274190661 0.01776805 R
## 122 -0.860055136 -2.21946189 R
## 123 -0.789109306 -0.84516218 R
## 124 -1.427539411 -1.35491277 R
## 125 0.779483548 -1.33935885 R
## 126 -0.400799099 -2.26338258 R
## 127 -0.302692686 -0.92399268 R
## 128 0.827857790 0.18656255 R
## 129 -0.829167731 0.42261407 R
## 130 -2.434915803 -1.57286451 R
## 131 -1.138280881 -0.92728240 R
## 132 -0.860055136 -1.28443852 R
## 133 -1.125398710 0.66990478 R
## 134 -0.186674974 -1.39954451 R
## 135 -0.008605128 -1.57143555 R
## 136 -1.348878827 -0.41384379 R
## 137 -2.110893443 -0.94312757 R
## 138 -2.045433423 -1.50787056 R
## 139 -2.727906546 -1.49588989 R
## 140 -1.331613148 -0.98926479 R
## 141 -2.884342616 -1.48707040 R
## 142 -1.692543814 -0.65254827 R
## 143 -2.543559671 -1.55426015 R
## 144 -0.200710576 -1.41000300 R
## 145 -0.579994800 -1.75422471 R
## 146 -0.726952717 -1.49758877 R
## 147 -0.427587806 -2.18726646 R
## 148 -0.234409462 -1.43511339 R
## 149 -0.638609937 -1.78645087 R
## 150 -0.424385927 -2.19636412 R
## 151 -0.539891272 -1.86817291 R
## 152 -0.209046173 -1.77581970 R
## 153 -0.198503193 -1.74901247 R
## 154 -0.085576490 -1.62002917 R
## 155 -0.158312844 -1.66594914 R
## 156 -0.255554034 -1.31678566 R
## 157 -0.101553943 -1.63011607 R
## 158 -0.175951690 -1.55887373 R
## 159 -1.536305519 -1.43737038 R
## 160 -1.482954163 -1.39692372 R
## 161 -1.569819776 -1.57734131 R
## 162 -2.179855466 -1.55433108 R
## 163 -1.554224066 -1.59145067 R
## 164 -1.806165345 -1.52717767 R
## 165 -1.620621314 -1.53138141 R
## 166 -0.960871406 -2.12825390 R
## 167 -0.769103007 -2.22816004 R
## 168 -0.703740137 -2.09580937 R
## 169 -1.116503328 -1.82876902 R
## 170 -0.405250123 -1.85968973 R
## 171 -0.708435397 -2.09952354 R
## 172 -0.659278473 -1.82686585 R
## 173 -0.626623939 -2.03480683 R
## 174 -0.336673771 -1.14769148 R
## 175 -0.711430840 -0.69909654 R
## 176 -0.754841345 -0.85071577 R
## 177 -0.842929035 -0.85781982 R
## 178 -0.495358659 -0.89276851 R
## 179 -0.831157170 -1.10551056 R
## 180 -0.652182669 -0.58768701 R
## 181 -1.157096603 -1.32132725 R
## 182 -1.122671198 -1.31705206 R
## 183 -1.243061435 -1.08218655 R
## 184 -0.939653544 -1.29432362 R
## 185 -1.367768189 -1.34748995 R
## 186 -1.542847586 -1.44233004 R
## 187 -1.000539548 -1.01397735 R
## 188 -0.773256346 -2.22776284 R
## 189 -0.226926303 -1.82128283 R
## 190 -0.350132468 -2.05897887 R
## 191 -0.691041322 -2.23562543 R
## 192 -0.250215562 -1.88049954 R
## 193 -0.065672120 -1.67211873 R
## 194 -0.090517759 -1.71595384 R
## 195 -1.736178623 -1.42168866 R
## 196 -2.664395207 -1.53356807 R
## 197 -1.663602473 -1.51681863 R
## 198 -2.158723504 -1.55279557 R
## 199 -2.306693552 -1.43602548 R
## 200 -1.551549377 -0.63013419 R
## 201 -2.133114929 -1.25078205 R
## 202 -1.307953653 -1.17812109 R
## 203 -0.959054286 -1.15735412 R
## 204 -0.801121364 -0.84798724 R
## 205 -1.361056552 -1.25662668 R
## 206 -0.979418863 -0.88992030 R
## 207 -1.031861506 -0.83841126 R
## 208 -1.114796484 -0.95742913 R
## 209 -0.732133486 -1.26089511 R
## 210 -0.999679111 -1.30177802 R
## 211 -0.858361528 -1.27395218 R
## 212 -1.042843755 -1.04979426 R
## 213 -0.827023587 -1.07991659 R
## 214 -0.811668799 -0.98484412 R
## 215 -1.087905903 -0.99194835 R
## 216 -0.185382331 -1.41875093 R
## 217 -0.417948424 -1.57187563 R
## 218 -0.079660886 -1.50284534 R
## 219 -0.022012673 -1.55849323 R
## 220 -0.284049367 -1.19059300 R
## 221 -0.174278835 -1.58372942 R
## 222 -0.277331844 -1.02794573 RPMA_PreModelling_Train_LR_OS_BSMOTE$Label <- rep("LR_OS_BSMOTE",nrow(PMA_PreModelling_Train_LR_OS_BSMOTE))
##################################
# Verifying the class distribution
# for the oversampled data using OS_BSMOTE
##################################
table(PMA_PreModelling_Train_LR_OS_BSMOTE$Class) ##
## M R
## 111 111##################################
# Formulating the structure of the
# Logistic Regression model
##################################
LR_OS_BSMOTE_Model <- glm(Class ~ V1 + V11,
data = PMA_PreModelling_Train_LR_OS_BSMOTE,
family = binomial)
##################################
# Consolidating the model results
##################################
summary(LR_OS_BSMOTE_Model)##
## Call:
## glm(formula = Class ~ V1 + V11, family = binomial, data = PMA_PreModelling_Train_LR_OS_BSMOTE)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.3748 0.4136 -5.742 9.36e-09 ***
## V1 -1.3384 0.3235 -4.138 3.50e-05 ***
## V11 -2.5707 0.3536 -7.271 3.58e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 307.76 on 221 degrees of freedom
## Residual deviance: 126.59 on 219 degrees of freedom
## AIC: 132.59
##
## Number of Fisher Scoring iterations: 6LR_OS_BSMOTE_Model_Coef <- (as.data.frame(LR_OS_BSMOTE_Model$coefficients))
LR_OS_BSMOTE_Model_Coef$Coef <- rownames(LR_OS_BSMOTE_Model_Coef)
LR_OS_BSMOTE_Model_Coef$Model <- rep("LR_OS_BSMOTE",nrow(LR_OS_BSMOTE_Model_Coef))
colnames(LR_OS_BSMOTE_Model_Coef) <- c("Estimates","Coefficients","Model")
print(LR_OS_BSMOTE_Model_Coef, rownames=FALSE)## Estimates Coefficients Model
## (Intercept) -2.374787 (Intercept) LR_OS_BSMOTE
## V1 -1.338444 V1 LR_OS_BSMOTE
## V11 -2.570724 V11 LR_OS_BSMOTE##################################
# Computing the model predictions
##################################
(LR_OS_BSMOTE_Model_Probabilities <- predict(LR_OS_BSMOTE_Model,
type = c("response")))## 1 2 3 4 5 6
## 6.006859e-01 2.079892e-03 1.628557e-01 1.505806e-04 4.074472e-04 4.789811e-03
## 7 8 9 10 11 12
## 6.307140e-02 4.806695e-03 2.283076e-01 7.808042e-01 4.045050e-01 1.298446e-01
## 13 14 15 16 17 18
## 6.970438e-01 7.771026e-01 1.459006e-01 1.555055e-02 1.613942e-01 2.387661e-01
## 19 20 21 22 23 24
## 4.734715e-02 4.296759e-01 6.783271e-02 1.129486e-01 3.324495e-02 1.757225e-01
## 25 26 27 28 29 30
## 2.542867e-01 3.828633e-02 1.136601e-01 4.659037e-03 1.934256e-02 2.664560e-04
## 31 32 33 34 35 36
## 8.357633e-03 6.469618e-03 2.077176e-04 2.926166e-03 5.513619e-04 1.261781e-03
## 37 38 39 40 41 42
## 1.869932e-04 7.303152e-05 8.016464e-02 1.560020e-04 3.258266e-05 6.577898e-02
## 43 44 45 46 47 48
## 4.451968e-01 2.381189e-01 1.823696e-02 1.982859e-02 1.677255e-02 9.563376e-01
## 49 50 51 52 53 54
## 1.187490e-02 6.912386e-03 3.262472e-02 6.098557e-03 3.470533e-01 3.709446e-01
## 55 56 57 58 59 60
## 1.343831e-01 2.693959e-01 6.695195e-01 8.883345e-01 9.228887e-01 2.489130e-01
## 61 62 63 64 65 66
## 1.825541e-01 9.693127e-02 3.577391e-02 6.904229e-02 9.767097e-02 1.228251e-01
## 67 68 69 70 71 72
## 4.152266e-01 1.887292e-02 8.143187e-02 3.401731e-01 8.874048e-01 9.572771e-01
## 73 74 75 76 77 78
## 4.048304e-01 2.193652e-01 4.954973e-04 3.161516e-02 1.675101e-02 2.859779e-03
## 79 80 81 82 83 84
## 3.021328e-03 4.265516e-04 4.750576e-02 4.034164e-01 1.187473e-02 6.945722e-04
## 85 86 87 88 89 90
## 5.905442e-03 2.067431e-02 2.743980e-02 1.516378e-04 5.752660e-04 1.677652e-02
## 91 92 93 94 95 96
## 1.720777e-02 1.501903e-01 1.885182e-01 1.574837e-01 1.056248e-01 5.175650e-01
## 97 98 99 100 101 102
## 1.767992e-01 2.493234e-02 2.212795e-01 5.456617e-01 6.900384e-02 5.978552e-02
## 103 104 105 106 107 108
## 7.659193e-02 3.881689e-02 1.288117e-02 5.351133e-03 3.928910e-02 1.641340e-02
## 109 110 111 112 113 114
## 1.437443e-02 2.555662e-02 4.565748e-02 9.952622e-01 4.054342e-01 9.431421e-01
## 115 116 117 118 119 120
## 7.396726e-01 7.416806e-01 5.405582e-02 8.956161e-01 2.183267e-01 9.761822e-01
## 121 122 123 124 125 126
## 5.800597e-02 9.888154e-01 7.014203e-01 9.534252e-01 5.062597e-01 9.816606e-01
## 127 128 129 130 131 132
## 6.000517e-01 1.866196e-02 8.695320e-02 9.928083e-01 8.223755e-01 8.887747e-01
## 133 134 135 136 137 138
## 6.974294e-02 8.134993e-01 8.424349e-01 6.211635e-01 9.465990e-01 9.857863e-01
## 139 140 141 142 143 144
## 9.940706e-01 8.755153e-01 9.950755e-01 8.275139e-01 9.934726e-01 8.203295e-01
## 145 146 147 148 149 150
## 9.483940e-01 9.204214e-01 9.785506e-01 8.359281e-01 9.557410e-01 9.789479e-01
## 151 152 153 154 155 156
## 9.589239e-01 9.220227e-01 9.158404e-01 8.703888e-01 8.928129e-01 7.945136e-01
## 157 158 159 160 161 162
## 8.756336e-01 8.662439e-01 9.669637e-01 9.608783e-01 9.777136e-01 9.894245e-01
## 163 164 165 166 167 168
## 9.780466e-01 9.814510e-01 9.765941e-01 9.876594e-01 9.876613e-01 9.811992e-01
## 169 170 171 172 173 174
## 9.785606e-01 9.501862e-01 9.814891e-01 9.609841e-01 9.757512e-01 7.361844e-01
## 175 176 177 178 179 180
## 5.925718e-01 6.947628e-01 7.228503e-01 6.418228e-01 8.291525e-01 5.022260e-01
## 181 182 183 184 185 186
## 9.289512e-01 9.250913e-01 8.880508e-01 9.011649e-01 9.488224e-01 9.676439e-01
## 187 188 189 190 191 192
## 8.279311e-01 9.877165e-01 9.315634e-01 9.672930e-01 9.865775e-01 9.423702e-01
## 193 194 195 196 197 198
## 8.820166e-01 8.963778e-01 9.735002e-01 9.941401e-01 9.770490e-01 9.890819e-01
## 199 200 201 202 203 204
## 9.879220e-01 7.894770e-01 9.757711e-01 9.171668e-01 8.680804e-01 7.062853e-01
## 205 206 207 208 209 210
## 9.356769e-01 7.727403e-01 7.616280e-01 8.290050e-01 8.637211e-01 9.096805e-01
## 211 212 213 214 215 216
## 8.858527e-01 8.480944e-01 8.188099e-01 7.761464e-01 8.363531e-01 8.206201e-01
## 217 218 219 220 221 222
## 9.025137e-01 8.313514e-01 8.403899e-01 7.438513e-01 8.732280e-01 6.545208e-01##################################
# Creating a classification index
# based from the model predictions
##################################
(LR_OS_BSMOTE_Model_Indices <- predict(LR_OS_BSMOTE_Model,
type = c("link")))## 1 2 3 4 5
## 0.408324003 -6.173357428 -1.637131633 -8.800861401 -7.805191778
## 6 7 8 9 10
## -5.336462967 -2.698339590 -5.332927345 -1.217892324 1.270358909
## 11 12 13 14 15
## -0.386729015 -1.902333371 0.833259849 1.248860586 -1.767122159
## 16 17 18 19 20
## -4.147986246 -1.647891134 -1.159456434 -3.001743931 -0.283173443
## 21 22 23 24 25
## -2.620467808 -2.060969930 -3.370042340 -1.545601145 -1.075879089
## 26 27 28 29 30
## -3.223623920 -2.053888243 -5.364276686 -3.925915527 -8.230035045
## 31 32 33 34 35
## -4.776187215 -5.034147554 -8.479123183 -5.831131646 -7.502567613
## 36 37 38 39 40
## -6.673968501 -8.584251291 -9.524546462 -2.440112242 -8.765485802
## 41 42 43 44 45
## -10.331697835 -2.653412674 -0.220096954 -1.163020255 -3.985899663
## 46 47 48 49 50
## -3.900602432 -4.071096916 3.086623816 -4.421382008 -4.967504071
## 51 52 53 54 55
## -3.389516181 -5.093585877 -0.632017084 -0.528166665 -1.862747933
## 56 57 58 59 60
## -0.997689924 0.706012696 2.073840593 2.482259349 -1.104418259
## 61 62 63 64 65
## -1.499138052 -2.231796468 -3.294107073 -2.601494709 -2.223374824
## 66 67 68 69 70
## -1.965944774 -0.342400130 -3.950973850 -2.423049316 -0.662522844
## 71 72 73 74 75
## 2.064502155 3.109357894 -0.385378145 -1.269369274 -7.609452965
## 76 77 78 79 80
## -3.421992894 -4.072403649 -5.854146889 -5.799032924 -7.759350558
## 81 82 83 84 85
## -2.998233112 -0.391250280 -4.421396791 -7.271519668 -5.125957993
## 86 87 88 89 90
## -3.857972335 -3.567937550 -8.793864147 -7.460102633 -4.070856278
## 91 92 93 94 95
## -4.045036769 -1.733109397 -1.459667592 -1.677071125 -2.136231934
## 96 97 98 99 100
## 0.070289110 -1.538185522 -3.666340925 -1.258225807 0.183156955
## 101 102 103 104 105
## -2.602093063 -2.755344499 -2.489579517 -3.209309392 -4.339023651
## 106 107 108 109 110
## -5.225081444 -3.196726510 -4.093107340 -4.227825845 -3.640970185
## 111 112 113 114 115
## -3.039855195 5.347432700 -0.382872854 2.808660917 1.044267677
## 116 117 118 119 120
## 1.054722007 -2.862166349 2.149436749 -1.275444526 3.713217647
## 121 122 123 124 125
## -2.787453045 4.481973033 0.854070247 3.019001507 0.025039910
## 126 127 128 129 130
## 3.980192518 0.405680323 -3.962429636 -2.351417088 4.927612286
## 131 132 133 134 135
## 1.532525319 2.078285669 -2.590644755 1.472909778 1.676457742
## 136 137 138 139 140
## 0.494489753 2.875046379 4.239230380 5.121883467 1.950629387
## 141 142 143 144 145
## 5.308591914 1.568109532 5.025199351 1.518581542 2.911131389
## 146 147 148 149 150
## 2.448086088 3.820374146 1.628237508 3.072429056 3.839476193
## 151 152 153 154 155
## 3.150384469 2.470152182 2.387126982 1.904400400 2.119801522
## 156 157 158 159 160
## 1.352350344 1.951715959 1.868148771 3.376554627 3.201169629
## 161 162 163 164 165
## 3.781238284 4.538583963 3.796635579 3.968616703 3.731083062
## 166 167 168 169 170
## 4.382439385 4.382599258 3.954877615 3.820850921 2.948366914
## 171 172 173 174 175
## 3.970710070 3.203988514 3.694841183 1.026230052 0.374607475
## 176 177 178 179 180
## 0.822481260 0.958644246 0.583284207 1.579632898 0.008903908
## 181 182 183 184 185
## 2.570689819 2.513622999 2.070984108 2.210235552 2.919919051
## 186 187 188 189 190
## 3.398060745 1.571035089 4.387137173 2.610956908 3.386912469
## 191 192 193 194 195
## 4.297309508 2.794358127 2.011667419 2.157609899 3.603760225
## 196 197 198 199 200
## 5.133737324 3.751174055 4.506352641 4.404218553 1.321775934
## 201 202 203 204 205
## 3.695683464 2.404459967 1.884091524 0.877410183 2.677351471
## 206 207 208 209 210
## 1.223849729 1.161625750 1.578591698 1.846546094 2.309739633
## 211 212 213 214 215
## 2.049061392 1.719732291 1.508305278 1.243348656 1.631339559
## 216 217 218 219 220
## 1.520554047 2.225472173 1.595235372 1.661131855 1.066083204
## 221 222
## 1.929806883 0.638970888max(LR_OS_BSMOTE_Model_Indices)## [1] 5.347433min(LR_OS_BSMOTE_Model_Indices)## [1] -10.3317##################################
# Consolidating the model probabilities
# and classification index
# based from the model predictions
##################################
LR_OS_BSMOTE_Model_Predictions <- as.data.frame(PMA_PreModelling_Train_LR_OS_BSMOTE)
LR_OS_BSMOTE_Model_Predictions$LR_OS_BSMOTE_Prob <- LR_OS_BSMOTE_Model_Probabilities
LR_OS_BSMOTE_Model_Predictions$LR_OS_BSMOTE_LP <- LR_OS_BSMOTE_Model_Indices
LR_OS_BSMOTE_Model_Predictions$Class <- as.factor(LR_OS_BSMOTE_Model_Predictions$Class)
LR_OS_BSMOTE_Model_Predictions$Label <- rep("LR_OS_BSMOTE",nrow(LR_OS_BSMOTE_Model_Predictions))
##################################
# Formulating the probability curve
# using the consolidated model predictions
##################################
LR_OS_BSMOTE_Model_Predictions %>%
ggplot(aes(x = LR_OS_BSMOTE_LP ,
y = LR_OS_BSMOTE_Prob,
color = Class)) +
scale_colour_manual(values=c("#1846BA55","#B8000055")) +
geom_point(size=5) +
scale_x_continuous( limits=c(-4,4), breaks=seq(-4,4,by=1)) +
scale_y_continuous( limits=c(-4,4), breaks=seq(-4,4,by=1)) +
geom_line(color="black") +
xlab("Sonar Object Classification Index (Logit Values)") +
ylab("Estimated Rock Detection Probability") +
labs(color = "Class") +
scale_x_continuous( limits=c(-10,5), breaks=seq(-10,5,by=1)) +
scale_y_continuous( limits=c(0,1), breaks=seq(0,1,by=0.1),labels = scales::percent) +
ggtitle("Estimated Rock Detection Probabilities Based on Classification Index : Logistic Regression (OS_BSMOTE)") +
theme_bw() +
theme(plot.title = element_text(color="black", size=14, face="bold", hjust=0.50),
axis.title.x = element_text(color="black", size=12, face="bold"),
axis.title.y = element_text(color="black", size=12, face="bold"),
legend.position="top")
1.5.8 Oversampling - Synthetic Minority Oversampling Technique (LR_OS_SMOTE)
Synthetic Minority Oversampling Technique generates new minority instances between existing instances. The new instances created are not just the copy of existing minority cases, instead the algorithm takes sample of feature space for each target class and its neighbors and then generates new instances that combine the features of the target cases with features of its neighbors.
Logistic Regression models the relationship between the probability of an event (among two outcome levels) by having the log-odds of the event be a linear combination of a set of predictors weighted by their respective parameter estimates. The parameters are estimated via maximum likelihood estimation by testing different values through multiple iterations to optimize for the best fit of log odds. All of these iterations produce the log likelihood function, and logistic regression seeks to maximize this function to find the best parameter estimates. Given the optimal parameters, the conditional probabilities for each observation can be calculated, logged, and summed together to yield a predicted probability.
[A] The class ratio of the original data was noted at 80:20.
[A.1] Majority Class = Class=M with 111 instances
[A.2] Minority Class = Class=R with 25 instances
[B] The class ratio of the oversampled data was noted at 50:50 with majority of the added instances being unique values for the minority class.
[B.1] Majority Class = Class=M with 111 instances
[B.2] Minority Class = Class=R with 111 instances
[C] The logistic regression model from the stats package was implemented. The Class response was regressed against the V1 and V11 predictors.
[D] The logistic curve formulated by plotting the predicted probabilities against the classification index using the logit values showed a sufficiently balanced logistic profile for the predicted points from both the majority and minority classes. Although the ratio of the unique values and number of instances was relatively high, a significant overlap between both classes was observed.
Code Chunk | Output
##################################
# Visualizing the imbalanced data set
##################################
ggplot(PMA_PreModelling_Train, aes(V1, V11, color = Class)) +
scale_colour_manual(values=c("#1846BA55","#B8000055")) +
geom_point(size=5) +
scale_x_continuous( limits=c(-4,4), breaks=seq(-4,4,by=1)) +
scale_y_continuous( limits=c(-4,4), breaks=seq(-4,4,by=1)) +
labs(title = "Without Oversampling - Synthetic Minority Oversampling Technique") +
theme_bw() +
theme(plot.title = element_text(color="black", size=14, face="bold", hjust=0.50),
axis.title.x = element_text(color="black", size=12, face="bold"),
axis.title.y = element_text(color="black", size=12, face="bold"),
legend.position="top")
##################################
# Implementing OS_SMOTE
# Visualizing the oversampled data using OS_SMOTE
##################################
recipe(Class ~ V1 + V11, data = PMA_PreModelling_Train) %>%
step_smote(Class, seed=123456789) %>%
prep() %>%
bake(new_data = NULL) %>%
ggplot(aes(V1, V11, color = Class)) +
scale_colour_manual(values=c("#1846BA55","#B8000055")) +
geom_point(size=5) +
scale_x_continuous( limits=c(-4,4), breaks=seq(-4,4,by=1)) +
scale_y_continuous( limits=c(-4,4), breaks=seq(-4,4,by=1)) +
labs(title = "With Oversampling - Synthetic Minority Oversampling Technique") +
theme_bw() +
theme(plot.title = element_text(color="black", size=14, face="bold", hjust=0.50),
axis.title.x = element_text(color="black", size=12, face="bold"),
axis.title.y = element_text(color="black", size=12, face="bold"),
legend.position="top")
OS_SMOTE <- recipe(Class ~ V1 + V11, data = PMA_PreModelling_Train) %>%
step_smote(Class, seed=123456789) %>%
prep()
PMA_PreModelling_Train_LR_OS_SMOTE <- OS_SMOTE %>%
bake(new_data = NULL)
(PMA_PreModelling_Train_LR_OS_SMOTE <- as.data.frame(PMA_PreModelling_Train_LR_OS_SMOTE))## V1 V11 Class
## 1 0.914240973 -1.558615871 M
## 2 2.175018299 0.345206089 M
## 3 -0.230553865 -0.166907171 M
## 4 1.231717734 1.858421213 M
## 5 0.424204681 1.891540835 M
## 6 1.143139503 0.556904232 M
## 7 -0.507041623 0.389850825 M
## 8 0.312328639 0.988089406 M
## 9 -0.935159101 0.036862390 M
## 10 0.408807916 -1.630790485 M
## 11 0.738227771 -1.157702863 M
## 12 1.169078303 -0.792461442 M
## 13 0.118911997 -1.309827049 M
## 14 -0.174409184 -1.318776567 M
## 15 1.012209983 -0.763384712 M
## 16 0.813818599 0.266052702 M
## 17 0.207991857 -0.391049992 M
## 18 -0.957451233 0.025737464 M
## 19 0.695599819 -0.118279862 M
## 20 -1.204708453 -0.186398477 M
## 21 -0.068999246 0.131492907 M
## 22 0.527094931 -0.396504803 M
## 23 0.104263091 0.332865171 M
## 24 0.465616266 -0.564972117 M
## 25 -0.507041623 -0.241278874 M
## 26 0.043933590 0.307319548 M
## 27 0.147716923 -0.201736917 M
## 28 0.612463710 0.844019251 M
## 29 -0.068999246 0.639306087 M
## 30 1.395977603 1.550850706 M
## 31 -0.180527407 1.028124858 M
## 32 0.565359423 0.740125313 M
## 33 2.230423635 1.213291690 M
## 34 0.782380194 0.937156911 M
## 35 2.005116912 0.950721679 M
## 36 1.784287809 0.743374785 M
## 37 1.523834205 1.622070841 M
## 38 1.928176420 1.777321144 M
## 39 -1.204708453 0.652640830 M
## 40 1.934080462 1.478976095 M
## 41 0.744203377 2.707733238 M
## 42 1.424344244 -0.633198890 M
## 43 -0.491314245 -0.582362640 M
## 44 0.689392711 -0.830303223 M
## 45 1.381555338 -0.092589575 M
## 46 1.002499594 0.071585037 M
## 47 0.977896818 0.150716010 M
## 48 0.278484686 -2.269456602 M
## 49 1.406688795 0.063725362 M
## 50 1.852613244 0.043994401 M
## 51 1.281675939 -0.272577947 M
## 52 1.390588252 0.333592312 M
## 53 -0.192852168 -0.577521460 M
## 54 -0.180527407 -0.624335667 M
## 55 -0.052243905 -0.171980296 M
## 56 -0.779287302 -0.129949769 M
## 57 -0.041194136 -1.176969454 M
## 58 -0.924156756 -1.249334984 M
## 59 -0.168320027 -1.801733345 M
## 60 -2.093160440 0.595633277 M
## 61 -0.230553865 -0.220586042 M
## 62 -1.038676203 0.485162490 M
## 63 -0.030238811 0.373354928 M
## 64 0.089444831 0.041618772 M
## 65 0.303951059 -0.217150498 M
## 66 -0.132380328 -0.090114281 M
## 67 -1.546463816 0.014574884 M
## 68 -0.499153812 0.873013033 M
## 69 -0.108968439 0.075507923 M
## 70 0.686277848 -1.023372673 M
## 71 -0.721885235 -1.351014760 M
## 72 -3.557061230 -0.281328435 M
## 73 -0.789109306 -0.363022432 M
## 74 -0.750264961 -0.039378188 M
## 75 -0.379134945 2.233656987 M
## 76 -0.371994173 0.601036378 M
## 77 0.401039618 0.451564218 M
## 78 -0.295964244 1.507548259 M
## 79 0.256869245 1.198277196 M
## 80 1.243886483 1.446942626 M
## 81 -0.230553865 0.362555066 M
## 82 -0.256318859 -0.638134800 M
## 83 0.632133128 0.467002438 M
## 84 0.324793230 1.735703263 M
## 85 0.723165726 0.693677522 M
## 86 -1.191144673 1.197119945 M
## 87 -1.177722925 1.077309728 M
## 88 0.142960831 2.422559073 M
## 89 0.443194144 1.747415902 M
## 90 -0.180527407 0.753754356 M
## 91 0.544629613 0.366158697 M
## 92 -1.274767709 0.414096217 M
## 93 -0.539087424 -0.075302304 M
## 94 -0.555415865 0.017768054 M
## 95 0.345301912 -0.272577947 M
## 96 -1.868589923 0.021755168 M
## 97 -0.217863016 -0.212004228 M
## 98 0.625610136 0.176685353 M
## 99 -0.799007156 -0.018335390 M
## 100 -2.013850697 0.053479912 M
## 101 0.537644499 -0.191503002 M
## 102 -0.013979414 0.155313323 M
## 103 -0.935159101 0.531542880 M
## 104 -0.779287302 0.730360068 M
## 105 0.424204681 0.543217319 M
## 106 0.157176488 1.026917595 M
## 107 -0.323092564 0.487948233 M
## 108 0.377448180 0.471900708 M
## 109 0.992715078 0.203967739 M
## 110 0.295518362 0.338678072 M
## 111 0.099342681 0.206985690 M
## 112 -2.902302684 -1.492828399 R
## 113 -0.588710342 -0.468334419 R
## 114 -0.597170869 -1.705421467 R
## 115 -0.289270938 -1.179388252 R
## 116 0.544629613 -1.617624095 R
## 117 0.064360561 0.156078935 R
## 118 -0.168320027 -1.672266879 R
## 119 -0.316256467 -0.262980859 R
## 120 -1.638716948 -1.515010380 R
## 121 0.274190661 0.017768054 R
## 122 -0.860055136 -2.219461886 R
## 123 -0.789109306 -0.845162176 R
## 124 -1.427539411 -1.354912772 R
## 125 0.779483548 -1.339358851 R
## 126 -0.400799099 -2.263382579 R
## 127 -0.302692686 -0.923992684 R
## 128 0.827857790 0.186562547 R
## 129 -0.829167731 0.422614069 R
## 130 -2.434915803 -1.572864509 R
## 131 -1.138280881 -0.927282397 R
## 132 -0.860055136 -1.284438518 R
## 133 -1.125398710 0.669904780 R
## 134 -0.186674974 -1.399544510 R
## 135 -0.008605128 -1.571435552 R
## 136 -1.348878827 -0.413843793 R
## 137 -1.599032158 -1.370950282 R
## 138 -2.657432813 -1.322745508 R
## 139 -1.790490183 -1.512346033 R
## 140 -0.810443562 -0.452440072 R
## 141 -0.538605201 -0.548157539 R
## 142 -0.660062049 -0.527920440 R
## 143 -0.982531013 -0.440104448 R
## 144 -0.584866894 -1.740381378 R
## 145 -0.455540713 -2.107842443 R
## 146 -0.515081007 -1.938667573 R
## 147 -0.418862256 -1.691636374 R
## 148 -0.239516596 -1.286153950 R
## 149 -0.200058557 -1.542931405 R
## 150 -0.280193092 -1.198868010 R
## 151 0.168622255 -1.586231937 R
## 152 0.570113701 -1.587429430 R
## 153 0.594021690 -1.559102196 R
## 154 0.043595498 0.162273052 R
## 155 0.729004935 0.182615719 R
## 156 -0.112595852 0.208864171 R
## 157 -0.265229694 -1.679758994 R
## 158 -0.273378159 -1.939394197 R
## 159 -0.321171380 -2.060916205 R
## 160 -0.133312506 -1.650165905 R
## 161 0.134694842 -0.048560153 R
## 162 -0.475824201 -0.459442307 R
## 163 0.218194825 -0.008857141 R
## 164 -1.947338832 -1.537435745 R
## 165 -1.603461320 -1.473605052 R
## 166 -1.761439442 -1.523927754 R
## 167 -0.074193522 -0.550968323 R
## 168 0.602335677 0.117808437 R
## 169 -0.315403865 -0.314371272 R
## 170 -0.641190054 -2.240392925 R
## 171 -0.860055136 -1.717141529 R
## 172 -0.418893061 -1.870481935 R
## 173 -0.634044436 -1.777523552 R
## 174 -0.796581793 -0.891429685 R
## 175 -0.827847489 -1.085017958 R
## 176 -0.655076888 -0.593129261 R
## 177 -0.846646467 -1.201415865 R
## 178 -1.003113777 -1.302204563 R
## 179 -1.477508813 -1.392795503 R
## 180 -1.024062139 -1.032758823 R
## 181 -1.564325317 -1.458612705 R
## 182 -0.046092087 -1.390787072 R
## 183 -0.066053607 -1.212799303 R
## 184 0.616583983 -1.532369401 R
## 185 -0.286655632 -1.973154324 R
## 186 -0.020863313 -1.593062643 R
## 187 -0.264751485 -2.023354050 R
## 188 -0.360121149 -0.914685611 R
## 189 -0.291775634 -1.131727665 R
## 190 -0.270452304 -1.056144682 R
## 191 -0.234639216 -1.202941057 R
## 192 -0.084662664 -0.235306944 R
## 193 -0.539978141 -0.445804920 R
## 194 -0.263127896 -0.317813628 R
## 195 -0.719767227 0.017260698 R
## 196 -0.729847094 0.289854799 R
## 197 -0.854171908 0.443487311 R
## 198 -2.209147026 -1.524018122 R
## 199 -2.549911790 -1.553172404 R
## 200 -2.408990100 -1.545196549 R
## 201 -0.955492262 -1.161926657 R
## 202 -0.882407262 -0.713601583 R
## 203 -1.281912481 -0.577108003 R
## 204 -0.852329589 -0.860030693 R
## 205 -0.879649125 -1.259285873 R
## 206 -0.820084244 -1.036950160 R
## 207 -0.817419038 -1.020447965 R
## 208 -1.332052173 -0.332244311 R
## 209 -0.357437358 -0.215502108 R
## 210 -0.321886669 0.322888990 R
## 211 -1.300027287 -0.176942239 R
## 212 -0.158828325 -1.426424914 R
## 213 -0.255897661 -1.251002529 R
## 214 -0.177300888 -1.538827013 R
## 215 -0.255677280 -1.116706900 R
## 216 -0.162403256 -1.606447414 R
## 217 -0.161098689 -1.424233330 R
## 218 -0.099380030 -1.483810427 R
## 219 -1.358596322 -0.530100664 R
## 220 -0.896995634 0.313447296 R
## 221 -1.278607918 -0.418880974 R
## 222 -1.404025395 -1.073598925 RPMA_PreModelling_Train_LR_OS_SMOTE$Label <- rep("LR_OS_SMOTE",nrow(PMA_PreModelling_Train_LR_OS_SMOTE))
##################################
# Verifying the class distribution
# for the oversampled data using OS_SMOTE
##################################
table(PMA_PreModelling_Train_LR_OS_SMOTE$Class) ##
## M R
## 111 111##################################
# Formulating the structure of the
# Logistic Regression model
##################################
LR_OS_SMOTE_Model <- glm(Class ~ V1 + V11,
data = PMA_PreModelling_Train_LR_OS_SMOTE,
family = binomial)
##################################
# Consolidating the model results
##################################
summary(LR_OS_SMOTE_Model)##
## Call:
## glm(formula = Class ~ V1 + V11, family = binomial, data = PMA_PreModelling_Train_LR_OS_SMOTE)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.0415 0.2324 -4.481 7.43e-06 ***
## V1 -0.9329 0.2481 -3.760 0.00017 ***
## V11 -1.7443 0.2506 -6.961 3.38e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 307.76 on 221 degrees of freedom
## Residual deviance: 189.34 on 219 degrees of freedom
## AIC: 195.34
##
## Number of Fisher Scoring iterations: 5LR_OS_SMOTE_Model_Coef <- (as.data.frame(LR_OS_SMOTE_Model$coefficients))
LR_OS_SMOTE_Model_Coef$Coef <- rownames(LR_OS_SMOTE_Model_Coef)
LR_OS_SMOTE_Model_Coef$Model <- rep("LR_OS_SMOTE",nrow(LR_OS_SMOTE_Model_Coef))
colnames(LR_OS_SMOTE_Model_Coef) <- c("Estimates","Coefficients","Model")
print(LR_OS_SMOTE_Model_Coef, rownames=FALSE)## Estimates Coefficients Model
## (Intercept) -1.0415029 (Intercept) LR_OS_SMOTE
## V1 -0.9328753 V1 LR_OS_SMOTE
## V11 -1.7442581 V11 LR_OS_SMOTE##################################
# Computing the model predictions
##################################
(LR_OS_SMOTE_Model_Probabilities <- predict(LR_OS_SMOTE_Model,
type = c("response")))## 1 2 3 4 5 6
## 0.695138300 0.024779368 0.369282594 0.004354817 0.008692463 0.043969471
## 7 8 9 10 11 12
## 0.222960465 0.044944135 0.441910102 0.805596919 0.571789299 0.320853998
## 13 14 15 16 17 18
## 0.756252682 0.805571240 0.342037247 0.094083981 0.365066375 0.451846444
## 19 20 21 22 23 24
## 0.184808902 0.600480342 0.230322198 0.301195793 0.151952308 0.379797538
## 25 26 27 28 29 30
## 0.463155988 0.165407411 0.304190208 0.043728465 0.109852360 0.006375734
## 31 32 33 34 35 36
## 0.064984856 0.054172728 0.005280164 0.032108175 0.010248414 0.017939314
## 37 38 39 40 41 42
## 0.005004700 0.002624183 0.258068057 0.004383673 0.001564322 0.219981147
## 43 44 45 46 47 48
## 0.606499788 0.441184242 0.102585458 0.108944043 0.098265568 0.934454823
## 49 50 51 52 53 54
## 0.078354180 0.054862637 0.146577492 0.051143629 0.536373281 0.553768583
## 55 56 57 58 59 60
## 0.333413190 0.478049348 0.740748894 0.880774271 0.905356078 0.468097562
## 61 62 63 64 65 66
## 0.391344086 0.285203257 0.159156007 0.231913539 0.279632581 0.318466956
## 67 68 69 70 71 72
## 0.592843463 0.109231538 0.255107619 0.525804921 0.879578471 0.940894174
## 73 74 75 76 77 78
## 0.581234332 0.432191970 0.010111774 0.148952395 0.099457455 0.032453398
## 79 80 81 82 83 84
## 0.033206650 0.008786310 0.188648708 0.577051822 0.079747910 0.012468425
## 85 86 87 88 89 90
## 0.050879338 0.117284577 0.139201194 0.004494323 0.010955560 0.100848196
## 91 92 93 94 95 96
## 0.100810641 0.360177402 0.399570525 0.364853162 0.291485499 0.660093722
## 97 98 99 100 101 102
## 0.384977539 0.126385323 0.434344509 0.677845021 0.229873159 0.214271334
## 103 104 105 106 107 108
## 0.250440555 0.169600465 0.084343448 0.048368075 0.169214359 0.098258011
## 109 110 111 112 113 114
## 0.089207115 0.129213957 0.183140465 0.986209131 0.580441619 0.923457474
## 115 116 117 118 119 120
## 0.783385955 0.781081414 0.202008970 0.884155454 0.428550245 0.958104757
## 121 122 123 124 125 126
## 0.209443900 0.974222449 0.762931417 0.934228287 0.638191570 0.963747328
## 127 128 129 130 131 132
## 0.701102490 0.105344361 0.267931877 0.981538824 0.837234144 0.880924439
## 133 134 135 136 137 138
## 0.238643107 0.828325850 0.846513532 0.718833604 0.944876905 0.976904705
## 139 140 141 142 143 144
## 0.963269404 0.623331302 0.602783546 0.621302933 0.655370721 0.926883871
## 145 146 147 148 149 150
## 0.955215764 0.943775731 0.908868243 0.806171172 0.862522074 0.787683378
## 151 152 153 154 155 156
## 0.827497084 0.767730195 0.754697624 0.203393521 0.115056286 0.214034101
## 157 158 159 160 161 162
## 0.894339710 0.930624937 0.945467349 0.876660196 0.253041716 0.550766391
## 163 164 165 166 167 168
## 0.226251030 0.969433469 0.953676457 0.963024518 0.497185366 0.140771687
## 169 170 171 172 173 174
## 0.450431899 0.969659778 0.940249029 0.931621958 0.934038188 0.778421640
## 175 176 177 178 179 180
## 0.835253446 0.646610366 0.863408362 0.897122923 0.940823077 0.847511773
## 181 182 183 184 185 186
## 0.950828270 0.806493833 0.756862021 0.741953895 0.935087595 0.852792408
## 187 188 189 190 191 192
## 0.939045080 0.708868092 0.769362054 0.741347551 0.781704288 0.365380305
## 193 194 195 196 197 198
## 0.559671314 0.439870312 0.401274633 0.296039750 0.265373614 0.975340798
## 199 200 201 202 203 204
## 0.982814940 0.980179251 0.867214606 0.736213169 0.761511998 0.777944845
## 205 206 207 208 209 210
## 0.878213438 0.822334132 0.817719367 0.685821378 0.417713421 0.213421318
## 211 212 213 214 215 216
## 0.617722703 0.831278762 0.798876785 0.859120974 0.758566641 0.871250176
## 217 218 219 220 221 222
## 0.831039532 0.837447101 0.759607794 0.320505259 0.707218982 0.894816419##################################
# Creating a classification index
# based from the model predictions
##################################
(LR_OS_SMOTE_Model_Indices <- predict(LR_OS_SMOTE_Model,
type = c("link")))## 1 2 3 4 5 6
## 0.82425259 -3.67265237 -0.53529572 -5.43210828 -4.73656844 -3.07929429
## 7 8 9 10 11 12
## -1.24849676 -3.05634956 -0.23341359 1.42164977 0.28915520 -0.74984995
## 13 14 15 16 17 18
## 1.13224345 1.42148582 -0.65422868 -2.26475881 -0.55344128 -0.19321306
## 19 20 21 22 23 24
## -1.48410023 0.40746693 -1.20649280 -0.84161007 -1.71937016 -0.49040766
## 25 26 27 28 29 30
## -0.14764367 -1.61853210 -0.82742315 -3.08504263 -2.09225005 -5.04885991
## 31 32 33 34 35 36
## -2.66640847 -2.85988236 -5.23850398 -3.40600965 -4.57033103 -4.00265851
## 37 38 39 40 41 42
## -5.29236048 -5.94035796 -1.05603418 -5.42547492 -6.45873755 -1.26577624
## 43 44 45 46 47 48
## 0.43262277 -0.23635725 -2.16882171 -2.10157285 -2.21664637 2.65722365
## 49 50 51 52 53 54
## -2.46492169 -2.84649772 -1.76170050 -2.92061950 0.14575059 0.21590919
## 55 56 57 58 59 60
## -0.69278785 -0.08785908 1.04986458 1.99978279 2.25820667 -0.12778335
## 61 62 63 64 65 66
## -0.44166592 -0.91879612 -1.66452124 -1.19753768 -0.94628485 -0.76082602
## 67 68 69 70 71 72
## 0.37573267 -2.09861470 -1.07155426 0.10331148 1.98844445 2.76750117
## 73 74 75 76 77 78
## 0.32784250 -0.27291352 -4.58389159 -1.74284131 -2.20326744 -3.39495845
## 79 80 81 82 83 84
## -3.37123462 -4.72573535 -1.45881452 0.31068241 -2.44577712 -4.37200900
## 85 86 87 88 89 90
## -2.92607903 -2.01839960 -1.82194048 -5.40043585 -4.50289216 -2.18783550
## 91 92 93 94 95 96
## -2.18824973 -0.57459425 -0.40725491 -0.55436124 -0.88818027 0.66371190
## 97 98 99 100 101 102
## -0.46847380 -1.93330405 -0.26414720 0.74388595 -1.20902755 -1.29936840
## 103 104 105 106 107 108
## -1.09626404 -1.58846149 -2.38474422 -2.97933833 -1.59120550 -2.21673166
## 109 110 111 112 113 114
## -2.32335472 -1.90792669 -1.49521373 4.26986170 0.32458654 2.49027827
## 115 116 117 118 119 120
## 1.28550832 1.27197948 -1.37378525 2.03238373 -0.28776857 3.12978483
## 121 122 123 124 125 126
## -1.32828070 3.63213568 1.16881866 2.65353097 0.56752363 3.28031608
## 127 128 129 130 131 132
## 0.85255334 -2.13920427 -1.00514081 3.97345164 1.63779107 2.00121359
## 133 134 135 136 137 138
## -1.16013307 1.57380841 1.70751378 0.93868325 2.84148588 3.74476018
## 139 140 141 142 143 144
## 3.26672303 0.50371215 0.41707681 0.49508219 0.64273177 2.53977930
## 145 146 147 148 149 150
## 3.06008104 2.82054007 2.29989380 1.42532065 1.83639738 1.31101754
## 151 152 153 154 155 156
## 1.56799145 1.19553871 1.12382553 -1.36521818 -2.04010260 -1.30077805
## 157 158 159 160 161 162
## 2.13585655 2.59632886 2.85287973 1.96117628 -1.08245498 0.20376771
## 163 164 165 166 167 168
## -1.22960236 3.45680620 3.02467415 3.25982382 -0.01125866 -1.80889534
## 169 170 171 172 173 174
## -0.19892580 3.46447098 2.75595933 2.61187535 2.65044135 1.25649204
## 175 176 177 178 179 180
## 1.62332695 0.60417267 1.84389204 2.16565804 2.76622345 1.71521733
## 181 182 183 184 185 186
## 2.96201461 1.42738687 1.13555187 1.05614883 2.66760147 1.75667237
## 187 188 189 190 191 192
## 2.73472891 0.88989300 1.20471252 1.05298428 1.27562590 -0.55208717
## 193 194 195 196 197 198
## 0.23982821 -0.24168840 -0.40015694 -0.86622815 -1.01822335 3.67763681
## 199 200 201 202 203 204
## 4.04638045 3.90100613 1.87655223 1.02637839 1.16098692 1.25372983
## 205 206 207 208 209 210
## 1.97561964 1.53224216 1.50097177 0.78065553 -0.33216713 -1.30442453
## 211 212 213 214 215 216
## 0.47989340 1.59471732 1.37928899 1.80800819 1.14483716 1.91205799
## 217 218 219 220 221 222
## 1.59301260 1.63935462 1.15053046 -0.75145081 0.88191540 2.14091136max(LR_OS_SMOTE_Model_Indices)## [1] 4.269862min(LR_OS_SMOTE_Model_Indices)## [1] -6.458738##################################
# Consolidating the model probabilities
# and classification index
# based from the model predictions
##################################
LR_OS_SMOTE_Model_Predictions <- as.data.frame(PMA_PreModelling_Train_LR_OS_SMOTE)
LR_OS_SMOTE_Model_Predictions$LR_OS_SMOTE_Prob <- LR_OS_SMOTE_Model_Probabilities
LR_OS_SMOTE_Model_Predictions$LR_OS_SMOTE_LP <- LR_OS_SMOTE_Model_Indices
LR_OS_SMOTE_Model_Predictions$Class <- as.factor(LR_OS_SMOTE_Model_Predictions$Class)
LR_OS_SMOTE_Model_Predictions$Label <- rep("LR_OS_SMOTE",nrow(LR_OS_SMOTE_Model_Predictions))
##################################
# Formulating the probability curve
# using the consolidated model predictions
##################################
LR_OS_SMOTE_Model_Predictions %>%
ggplot(aes(x = LR_OS_SMOTE_LP ,
y = LR_OS_SMOTE_Prob,
color = Class)) +
scale_colour_manual(values=c("#1846BA55","#B8000055")) +
geom_point(size=5) +
geom_line(color="black") +
xlab("Sonar Object Classification Index (Logit Values)") +
ylab("Estimated Rock Detection Probability") +
labs(color = "Class") +
scale_x_continuous( limits=c(-10,5), breaks=seq(-10,5,by=1)) +
scale_y_continuous( limits=c(0,1), breaks=seq(0,1,by=0.1),labels = scales::percent) +
ggtitle("Estimated Rock Detection Probabilities Based on Classification Index : Logistic Regression (OS_SMOTE)") +
theme_bw() +
theme(plot.title = element_text(color="black", size=14, face="bold", hjust=0.50),
axis.title.x = element_text(color="black", size=12, face="bold"),
axis.title.y = element_text(color="black", size=12, face="bold"),
legend.position="top")
1.5.9 Oversampling - Random Oversampling Examples (LR_OS_ROSE)
Random Oversampling Examples generates synthetic data by enlarging the feature space of minority and majority class instances. The algorithm draws new examples from a conditional kernel density estimate of the two classes. The kernel is a normal product function centered at each of the instances with diagonal covariance matrix defined with the width of the neighborhood which is the asymptotically optimal smoothing matrix under the assumption of multivariate normality.
Logistic Regression models the relationship between the probability of an event (among two outcome levels) by having the log-odds of the event be a linear combination of a set of predictors weighted by their respective parameter estimates. The parameters are estimated via maximum likelihood estimation by testing different values through multiple iterations to optimize for the best fit of log odds. All of these iterations produce the log likelihood function, and logistic regression seeks to maximize this function to find the best parameter estimates. Given the optimal parameters, the conditional probabilities for each observation can be calculated, logged, and summed together to yield a predicted probability.
[A] The class ratio of the original data was noted at 80:20.
[A.1] Majority Class = Class=M with 111 instances
[A.2] Minority Class = Class=R with 25 instances
[B] The class ratio of the oversampled data was noted at 50:50 with majority of the added instances being unique values for the minority class.
[B.1] Majority Class = Class=M with 110 instances
[B.2] Minority Class = Class=R with 112 instances
[C] The logistic regression model from the stats package was implemented. The Class response was regressed against the V1 and V11 predictors.
[D] The logistic curve formulated by plotting the predicted probabilities against the classification index using the logit values showed a sufficiently balanced logistic profile for the predicted points from both the majority and minority classes. Although the ratio of the unique values and number of instances was relatively high, a significant overlap between both classes was observed.
Code Chunk | Output
##################################
# Visualizing the imbalanced data set
##################################
ggplot(PMA_PreModelling_Train, aes(V1, V11, color = Class)) +
scale_colour_manual(values=c("#1846BA55","#B8000055")) +
geom_point(size=5) +
scale_x_continuous( limits=c(-4,4), breaks=seq(-4,4,by=1)) +
scale_y_continuous( limits=c(-4,4), breaks=seq(-4,4,by=1)) +
labs(title = "Without Oversampling - Random Oversampling Examples") +
theme_bw() +
theme(plot.title = element_text(color="black", size=14, face="bold", hjust=0.50),
axis.title.x = element_text(color="black", size=12, face="bold"),
axis.title.y = element_text(color="black", size=12, face="bold"),
legend.position="top")
##################################
# Implementing OS_ROSE
# Visualizing the oversampled data using OS_ROSE
##################################
recipe(Class ~ V1 + V11, data = PMA_PreModelling_Train) %>%
step_rose(Class, seed=123456789) %>%
prep() %>%
bake(new_data = NULL) %>%
ggplot(aes(V1, V11, color = Class)) +
scale_colour_manual(values=c("#1846BA55","#B8000055")) +
geom_point(size=5) +
scale_x_continuous( limits=c(-4,4), breaks=seq(-4,4,by=1)) +
scale_y_continuous( limits=c(-4,4), breaks=seq(-4,4,by=1)) +
labs(title = "With Oversampling - Random Oversampling Examples") +
theme_bw() +
theme(plot.title = element_text(color="black", size=14, face="bold", hjust=0.50),
axis.title.x = element_text(color="black", size=12, face="bold"),
axis.title.y = element_text(color="black", size=12, face="bold"),
legend.position="top")
OS_ROSE <- recipe(Class ~ V1 + V11, data = PMA_PreModelling_Train) %>%
step_rose(Class, seed=123456789) %>%
prep()
PMA_PreModelling_Train_LR_OS_ROSE <- OS_ROSE %>%
bake(new_data = NULL)
(PMA_PreModelling_Train_LR_OS_ROSE <- as.data.frame(PMA_PreModelling_Train_LR_OS_ROSE))## V1 V11 Class
## 1 0.453829274 -0.420221366 M
## 2 0.127920224 0.686476420 M
## 3 1.075595629 -0.235554656 M
## 4 2.137901379 -1.178271820 M
## 5 1.092110905 0.456926231 M
## 6 0.857402441 1.398298653 M
## 7 -1.907459325 -0.021176979 M
## 8 0.309616027 -0.332458449 M
## 9 0.693229730 -2.341819879 M
## 10 1.716214396 -0.555582217 M
## 11 -0.770935156 -0.187998880 M
## 12 -1.496837087 1.378598435 M
## 13 -0.106000306 1.534889807 M
## 14 -0.916802330 -1.457363172 M
## 15 0.157063822 0.775286340 M
## 16 -0.064112224 -1.717694803 M
## 17 -0.112461451 -0.079932050 M
## 18 0.125906565 -1.196069844 M
## 19 -0.946156465 0.057997744 M
## 20 -1.748418397 -0.678614715 M
## 21 0.690260404 0.140155995 M
## 22 -0.560768697 0.384716767 M
## 23 0.250861459 -0.498669324 M
## 24 -2.438990535 0.731196382 M
## 25 1.529501791 -1.522171442 M
## 26 2.558447121 1.985131848 M
## 27 -0.387603764 0.364725766 M
## 28 -0.961430471 -0.770858863 M
## 29 0.863038442 2.196168690 M
## 30 1.039157671 0.133492560 M
## 31 -1.697870849 -1.143428379 M
## 32 -0.610125646 -0.127814520 M
## 33 -0.749263527 -0.150861865 M
## 34 -0.279227167 0.078775816 M
## 35 0.194985084 0.683020200 M
## 36 0.210531378 1.454231491 M
## 37 -0.147592914 0.362929787 M
## 38 -0.146821909 -1.873438319 M
## 39 -0.268162038 0.392433523 M
## 40 1.087154437 -1.567477864 M
## 41 -3.258143427 -0.310093040 M
## 42 0.221639048 2.228731592 M
## 43 -1.339735538 1.934676727 M
## 44 1.124251223 2.209600237 M
## 45 -1.920302036 0.658912503 M
## 46 0.320103472 0.002252489 M
## 47 -0.373684831 0.377203482 M
## 48 0.416501911 0.873626212 M
## 49 -0.337904242 0.283054835 M
## 50 -0.488958504 0.548829476 M
## 51 0.837780447 0.174524037 M
## 52 -0.818636727 1.462313516 M
## 53 0.522647205 0.301663855 M
## 54 0.077677433 -0.395192354 M
## 55 0.808685352 0.089522951 M
## 56 0.121157987 -1.487662966 M
## 57 0.930609853 -0.001536725 M
## 58 1.606666273 -0.460572860 M
## 59 1.879030954 1.415353475 M
## 60 -0.259700391 1.886523777 M
## 61 -1.163566178 -0.350578696 M
## 62 -0.473028462 -0.152114025 M
## 63 -0.270091603 0.103561905 M
## 64 -0.381874467 1.008824831 M
## 65 0.108606139 2.879619917 M
## 66 1.200948103 -0.411763531 M
## 67 -0.143328184 -1.493160633 M
## 68 1.381475831 0.613253958 M
## 69 1.396345117 0.826444551 M
## 70 0.096154862 -1.106932315 M
## 71 0.697201801 0.765645056 M
## 72 0.280450179 -0.779498730 M
## 73 -0.819993172 0.582228816 M
## 74 0.113508607 1.089077908 M
## 75 0.324148150 1.032218252 M
## 76 1.331741998 -0.388969403 M
## 77 -0.504012381 -0.624175476 M
## 78 -0.278104210 0.190564663 M
## 79 1.514692613 0.102141162 M
## 80 -1.332434679 0.332392981 M
## 81 0.748018715 2.546676685 M
## 82 -1.401265796 0.653245938 M
## 83 -3.555046462 -0.437014632 M
## 84 -0.797376264 0.590342435 M
## 85 -1.153805197 0.874627800 M
## 86 1.207193590 -0.900335700 M
## 87 -0.892535628 -0.053046654 M
## 88 0.025673058 -0.103210450 M
## 89 0.708863775 0.305835422 M
## 90 -0.479304016 -0.828521934 M
## 91 0.531855505 -1.020076195 M
## 92 0.843587712 -0.127072250 M
## 93 0.321447635 -0.242383581 M
## 94 -0.104688089 -0.955018789 M
## 95 -1.223682265 -0.993723911 M
## 96 -0.060035584 0.193030555 M
## 97 -0.184412189 -0.355696298 M
## 98 0.739698901 -0.008225890 M
## 99 1.751934661 0.060003736 M
## 100 0.012753639 -0.683233510 M
## 101 0.704272106 1.556907557 M
## 102 0.349253294 -1.364315460 M
## 103 -0.054249920 -0.279637524 M
## 104 0.422806210 1.856196052 M
## 105 -0.378916586 -1.774888727 M
## 106 0.896085827 -0.207757879 M
## 107 0.724946782 -1.974278177 M
## 108 1.177536942 1.427597354 M
## 109 0.417306877 0.674306556 M
## 110 0.119668249 0.770502124 M
## 111 -0.295729614 -1.403169748 R
## 112 -0.003104477 -1.303129281 R
## 113 -1.119225438 0.383400942 R
## 114 -1.205336093 -0.269591889 R
## 115 -0.031385116 -0.727154048 R
## 116 -0.262140197 -2.421432946 R
## 117 -0.004372316 -0.720426193 R
## 118 -0.652857082 -0.170061116 R
## 119 -0.243474578 0.765583966 R
## 120 -0.862558574 -2.394683708 R
## 121 -0.084987329 -0.315983611 R
## 122 0.335663272 -1.450942265 R
## 123 -0.547564677 -1.300644608 R
## 124 -0.085770445 -0.386014589 R
## 125 1.158708949 0.194640216 R
## 126 0.838758747 0.838624142 R
## 127 0.129363261 -1.283480849 R
## 128 1.185903335 0.067746005 R
## 129 -0.411610808 -1.398984345 R
## 130 -1.584645597 -3.066092331 R
## 131 -1.195552409 -0.055875719 R
## 132 -2.077490954 -1.016881169 R
## 133 1.123569804 -1.294911355 R
## 134 -1.696277599 1.577388230 R
## 135 -2.606714342 -2.430876734 R
## 136 -0.936576706 -0.967689409 R
## 137 -0.653422361 -0.737952271 R
## 138 1.399478608 -2.336802156 R
## 139 -0.439107621 -0.751356400 R
## 140 0.682169711 -0.645106739 R
## 141 -0.391226812 -1.228763737 R
## 142 -0.175404459 -1.171822064 R
## 143 0.149961923 -0.632053488 R
## 144 -1.306586950 0.989132759 R
## 145 0.192756837 -1.588485717 R
## 146 -1.637705898 -1.115797066 R
## 147 -1.035313367 -0.044642241 R
## 148 -0.454662737 0.327593121 R
## 149 -0.398721880 -2.531360086 R
## 150 -0.521649915 -1.203285944 R
## 151 -1.036925438 -1.419544783 R
## 152 -0.766892871 -1.525224785 R
## 153 -2.249201887 -1.166084343 R
## 154 -0.868992543 0.374276284 R
## 155 -3.122277505 -1.914384957 R
## 156 -1.711225949 0.095212802 R
## 157 0.846502796 -0.576583436 R
## 158 1.436542798 -0.686131512 R
## 159 -0.842933030 -0.712232667 R
## 160 -3.122732053 -1.680571529 R
## 161 0.635603354 -1.077316478 R
## 162 -0.654996459 -2.908798364 R
## 163 0.418427304 -0.144379560 R
## 164 -1.041169997 -1.922001515 R
## 165 -0.441540025 -0.259070590 R
## 166 -1.655681734 0.576523808 R
## 167 0.953177477 -1.711512314 R
## 168 -3.210760176 -1.284814307 R
## 169 -1.217750244 -1.591163354 R
## 170 0.259282153 -0.574268729 R
## 171 -0.921207702 -0.875473417 R
## 172 -0.865572661 -2.663360159 R
## 173 -0.947803456 -0.948766125 R
## 174 -0.460461844 0.286061177 R
## 175 0.419542632 -1.381617010 R
## 176 -0.621694247 -1.065107860 R
## 177 0.222233446 -0.308422959 R
## 178 -0.643581074 0.498325789 R
## 179 -0.260344829 0.290918714 R
## 180 -1.089617028 1.086009599 R
## 181 -0.474076557 -1.474554870 R
## 182 -1.113328689 -0.745280710 R
## 183 -1.367306542 -1.446652698 R
## 184 -0.227162341 -1.792986420 R
## 185 0.869250328 0.385747269 R
## 186 -0.828656357 1.245036664 R
## 187 -0.712628500 -2.041821701 R
## 188 -0.314242395 -0.861387324 R
## 189 -1.931922510 -1.283036428 R
## 190 -1.152889515 0.238829964 R
## 191 -1.757284170 -0.475428583 R
## 192 0.091169879 -0.475918031 R
## 193 0.853052045 -0.195251645 R
## 194 -1.030858895 -1.444796537 R
## 195 -0.458061630 -2.392889498 R
## 196 -0.757335077 -0.976940053 R
## 197 -1.825122741 -1.449896879 R
## 198 0.548521112 -1.625042438 R
## 199 0.815956664 0.127893714 R
## 200 -0.802403884 -1.389200876 R
## 201 -0.504679908 0.215394678 R
## 202 -0.828050004 0.276042407 R
## 203 -0.015700504 -1.362342936 R
## 204 -1.790699584 1.276762042 R
## 205 -0.319616516 -0.271949278 R
## 206 -0.497131472 -0.653931589 R
## 207 -0.434923007 -1.431687637 R
## 208 -2.126603097 -1.447703954 R
## 209 -1.508349941 -1.887116236 R
## 210 -2.644025006 -1.683878336 R
## 211 -1.292563792 0.557925461 R
## 212 0.956404116 0.448577768 R
## 213 0.851149954 -0.762481606 R
## 214 -0.031079759 -1.607449284 R
## 215 -0.131473949 -1.989738433 R
## 216 -0.987534049 -1.880077457 R
## 217 1.350301010 -0.132513674 R
## 218 -1.488226564 -1.509045712 R
## 219 1.732090761 -1.215001202 R
## 220 -1.010449867 -0.143822342 R
## 221 -0.443012681 -2.279874936 R
## 222 -0.918668643 -1.722236931 RPMA_PreModelling_Train_LR_OS_ROSE$Label <- rep("LR_OS_ROSE",nrow(PMA_PreModelling_Train_LR_OS_ROSE))
##################################
# Verifying the class distribution
# for the oversampled data using OS_ROSE
##################################
table(PMA_PreModelling_Train_LR_OS_ROSE$Class) ##
## M R
## 110 112##################################
# Formulating the structure of the
# Logistic Regression model
##################################
LR_OS_ROSE_Model <- glm(Class ~ V1 + V11,
data = PMA_PreModelling_Train_LR_OS_ROSE,
family = binomial)
##################################
# Consolidating the model results
##################################
summary(LR_OS_ROSE_Model)##
## Call:
## glm(formula = Class ~ V1 + V11, family = binomial, data = PMA_PreModelling_Train_LR_OS_ROSE)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.4555 0.1701 -2.678 0.007398 **
## V1 -0.5284 0.1593 -3.317 0.000908 ***
## V11 -0.9132 0.1650 -5.535 3.11e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 307.74 on 221 degrees of freedom
## Residual deviance: 249.98 on 219 degrees of freedom
## AIC: 255.98
##
## Number of Fisher Scoring iterations: 4LR_OS_ROSE_Model_Coef <- (as.data.frame(LR_OS_ROSE_Model$coefficients))
LR_OS_ROSE_Model_Coef$Coef <- rownames(LR_OS_ROSE_Model_Coef)
LR_OS_ROSE_Model_Coef$Model <- rep("LR_OS_ROSE",nrow(LR_OS_ROSE_Model_Coef))
colnames(LR_OS_ROSE_Model_Coef) <- c("Estimates","Coefficients","Model")
print(LR_OS_ROSE_Model_Coef, rownames=FALSE)## Estimates Coefficients Model
## (Intercept) -0.4555392 (Intercept) LR_OS_ROSE
## V1 -0.5283578 V1 LR_OS_ROSE
## V11 -0.9131847 V11 LR_OS_ROSE##################################
# Computing the model predictions
##################################
(LR_OS_ROSE_Model_Probabilities <- predict(LR_OS_ROSE_Model,
type = c("response")))## 1 2 3 4 5 6 7
## 0.42272822 0.24048787 0.30816041 0.37539602 0.19002887 0.10106385 0.63913697
## 8 9 10 11 12 13 14
## 0.42176519 0.78863241 0.29839231 0.53082787 0.28422724 0.14170857 0.79571130
## 15 16 17 18 19 20 21
## 0.22330577 0.75894563 0.41991396 0.63880235 0.49785184 0.74799586 0.27924052
## 22 23 24 25 26 27 28
## 0.37505987 0.46687195 0.54125677 0.53154810 0.02608136 0.35805690 0.68056655
## 29 30 31 32 33 34 35
## 0.05131761 0.24480950 0.81543281 0.49588602 0.51951627 0.40614035 0.23464164
## 36 37 38 39 40 41 42
## 0.13070302 0.32982442 0.79130846 0.33800037 0.59903625 0.82478350 0.06863251
## 43 44 45 46 47 48 49
## 0.18028811 0.04447561 0.48934125 0.34825343 0.35375905 0.18642890 0.36923822
## 50 51 52 53 54 55 56
## 0.33217220 0.25777606 0.20450560 0.26753625 0.46612769 0.27596505 0.69824553
## 57 58 59 60 61 62 63
## 0.27972403 0.29238147 0.06060725 0.11495790 0.61760221 0.48333050 0.39953443
## 64 65 66 67 68 69 70
## 0.23594946 0.04138595 0.32870381 0.72784589 0.14861986 0.12477099 0.62351399
## 71 72 73 74 75 76 77
## 0.17900745 0.52700098 0.36494339 0.18093261 0.17230032 0.30917596 0.59405330
## 78 79 80 81 82 83 84
## 0.38163331 0.20601548 0.48623520 0.04006559 0.42269894 0.86079066 0.36046859
## 85 86 87 88 89 90 91
## 0.34420429 0.43261336 0.51611451 0.40736875 0.24799075 0.63513227 0.54858837
## 92 93 94 95 96 97 98
## 0.31319703 0.40034265 0.61582913 0.74997077 0.35432276 0.49167898 0.30177638
## 99 100 101 102 103 104 105
## 0.19216831 0.54032242 0.09540200 0.64698261 0.45722602 0.08517833 0.79664657
## 106 107 108 109 110 111 112
## 0.32316339 0.72398394 0.08460516 0.21554948 0.22751830 0.72751777 0.67614219
## 113 114 115 116 117 118 119
## 0.44662821 0.60527575 0.55603100 0.86923142 0.55098545 0.51117317 0.26385700
## 120 121 122 123 124 125 126
## 0.89907501 0.46951683 0.66643143 0.73526461 0.48557423 0.22348101 0.15915581
## 127 128 129 130 131 132 133
## 0.65659701 0.24158944 0.73874767 0.96013708 0.55654831 0.82788619 0.53327752
## 134 135 136 137 138 139 140
## 0.26899165 0.95857559 0.71565046 0.63728241 0.71889102 0.61362934 0.44352519
## 141 142 143 144 145 146 147
## 0.70542292 0.66978816 0.51060062 0.33884294 0.70954950 0.80669624 0.53301273
## 148 149 150 151 152 153 154
## 0.37414679 0.88762701 0.71481811 0.80037425 0.79289528 0.85786807 0.41625077
## 155 156 157 158 159 160 161
## 0.94989962 0.58944065 0.40702723 0.35709778 0.65480568 0.93871938 0.54795805
## 162 163 164 165 166 167 168
## 0.92735848 0.36707788 0.86409346 0.50358274 0.47322076 0.64651810 0.91790101
## 169 170 171 172 173 174 175
## 0.83765665 0.48297670 0.69649384 0.91937851 0.71333541 0.38379354 0.64209386
## 176 177 178 179 180 181 182
## 0.69963871 0.42768298 0.36110724 0.35809396 0.29493003 0.75795197 0.69280709
## 183 184 185 186 187 188 189
## 0.83032735 0.78613971 0.21975873 0.23963883 0.85638009 0.62177733 0.85028733
## 190 191 192 193 194 195 196
## 0.48388136 0.71240426 0.48272976 0.32564362 0.80352755 0.87778623 0.69777637
## 197 198 199 200 201 202 203
## 0.86209523 0.67669167 0.26826426 0.77504386 0.40478111 0.43288003 0.68929534
## 204 205 206 207 208 209 210
## 0.33730469 0.49041932 0.59971660 0.74680630 0.87975259 0.88742407 0.92266675
## 211 212 213 214 215 216 217
## 0.42994096 0.20253953 0.44794817 0.73667934 0.80704537 0.85607802 0.25961475
## 218 219 220 221 222
## 0.84668245 0.43507308 0.55222785 0.86535253 0.83237685##################################
# Creating a classification index
# based from the model predictions
##################################
(LR_OS_ROSE_Model_Indices <- predict(LR_OS_ROSE_Model,
type = c("link")))## 1 2 3 4 5 6
## -0.311583732 -1.150006656 -0.808733670 -0.509136303 -1.449822617 -2.185459486
## 7 8 9 10 11 12
## 0.571620329 -0.315531291 1.316701585 -0.854965324 0.123468107 -0.923588677
## 13 14 15 16 17 18
## -1.801171064 1.359702258 -1.246504767 1.146907641 -0.323126605 0.570169780
## 19 20 21 22 23 24
## -0.008592705 1.087951913 -0.948232019 -0.510570169 -0.132706621 0.165403137
## 25 26 27 28 29 30
## 0.126360262 -3.620106864 -0.583807739 0.756376634 -2.917040046 -1.126489672
## 31 32 33 34 35 36
## 1.485705461 -0.016456294 0.078104777 -0.379944235 -1.182284732 -1.894757113
## 37 38 39 40 41 42
## -0.708979289 1.332830551 -0.672218011 0.401451079 1.549098576 -2.607887604
## 43 44 45 46 47 48
## -1.514396714 -3.067319346 -0.042641454 -0.626725333 -0.602556377 -1.473383381
## 49 50 51 52 53 54
## -0.535486224 -0.698376867 -1.057559759 -1.358368477 -1.007158785 -0.135697076
## 55 56 57 58 59 60
## -0.964565444 0.838957115 -0.945830902 -0.883845811 -2.740819106 -2.041069195
## 61 62 63 64 65 66
## 0.479383185 -0.066702727 -0.407405360 -1.175016290 -3.142547062 -0.714053376
## 67 68 69 70 71 72
## 0.983720838 -1.745466933 -1.948005631 0.504490295 -1.523086620 0.108109072
## 73 74 75 76 77 78
## -0.553971878 -1.510041697 -1.569411378 -0.803974603 0.380747178 -0.482621426
## 79 80 81 82 83 84
## -1.349112661 -0.055073129 -3.176347023 -0.311703691 1.821872477 -0.573330931
## 85 86 87 88 89 90
## -0.644613971 -0.271196583 0.064480356 -0.374853574 -1.109357179 0.554298385
## 91 92 93 94 95 96
## 0.194968769 -0.785214949 -0.404037608 0.471882126 1.098456379 -0.600091505
## 97 98 99 100 101 102
## -0.033287169 -0.838853164 -1.435982099 0.161640704 -2.249391106 0.605802116
## 103 104 105 106 107 108
## -0.171515133 -2.373982082 1.365465607 -0.739271854 0.964310162 -2.381360181
## 109 110 111 112 113 114
## -1.291793024 -1.222377651 0.982065023 0.736098817 -0.214303591 0.427496730
## 115 116 117 118 119 120
## 0.225069325 1.894180197 0.204653127 0.044700141 -1.026017110 2.186988947
## 121 122 123 124 125 126
## -0.122084091 0.692088786 1.021499657 -0.057719106 -1.245494630 -1.664522726
## 127 128 129 130 131 132
## 0.648165803 -1.143985141 1.039469713 3.181629377 0.227165101 1.570719734
## 133 134 135 136 137 138
## 0.133307162 -0.999744528 3.141578211 0.922987602 0.563588342 0.938967358
## 139 140 141 142 143 144
## 0.462593918 -0.226867299 0.873256809 0.707227114 0.042408819 -0.668454716
## 145 146 147 148 149 150
## 0.893197099 1.428684346 0.132243309 -0.514467642 2.066727983 0.918900944
## 151 152 153 154 155 156
## 1.388635067 1.342466612 1.797694609 -0.338183600 2.942327636 0.361653519
## 157 158 159 160 161 162
## -0.376268405 -0.587983024 0.640231037 2.729052948 0.192423733 2.546803534
## 163 164 165 166 167 168
## -0.544773350 1.849713529 0.014331213 -0.107219562 0.603768915 2.414163843
## 169 170 171 172 173 174
## 1.640894726 -0.068119539 0.830657032 2.433932696 0.911638889 -0.473477302
## 175 176 177 178 179 180
## 0.584463706 0.845578033 -0.291310863 -0.570561627 -0.583646521 -0.871558923
## 181 182 183 184 185 186
## 1.141483829 0.813275667 1.587949042 1.301811601 -1.267072947 -1.154660623
## 187 188 189 190 191 192
## 1.785544023 0.497098959 1.736856426 -0.064496902 0.907089741 -0.069108460
## 193 194 195 196 197 198
## -0.727955121 1.408489278 1.971631378 0.836731432 1.832802349 0.738609301
## 199 200 201 202 203 204
## -1.003446893 1.237014177 -0.385582774 -0.270110234 0.796827032 -0.675328688
## 205 206 207 208 209 210
## -0.038327405 0.404284424 1.081651042 1.990089307 2.064695003 2.479144060
## 211 212 213 214 215 216
## -0.282092041 -1.370497185 -0.208964397 1.028780156 1.430924825 1.783090147
## 217 218 219 220 221 222
## -1.047971859 1.708814429 -0.261182377 0.209676238 1.860476975 1.602567012max(LR_OS_ROSE_Model_Indices)## [1] 3.181629min(LR_OS_ROSE_Model_Indices)## [1] -3.620107##################################
# Consolidating the model probabilities
# and classification index
# based from the model predictions
##################################
LR_OS_ROSE_Model_Predictions <- as.data.frame(PMA_PreModelling_Train_LR_OS_ROSE)
LR_OS_ROSE_Model_Predictions$LR_OS_ROSE_Prob <- LR_OS_ROSE_Model_Probabilities
LR_OS_ROSE_Model_Predictions$LR_OS_ROSE_LP <- LR_OS_ROSE_Model_Indices
LR_OS_ROSE_Model_Predictions$Class <- as.factor(LR_OS_ROSE_Model_Predictions$Class)
LR_OS_ROSE_Model_Predictions$Label <- rep("LR_OS_ROSE",nrow(LR_OS_ROSE_Model_Predictions))
##################################
# Formulating the probability curve
# using the consolidated model predictions
##################################
LR_OS_ROSE_Model_Predictions %>%
ggplot(aes(x = LR_OS_ROSE_LP ,
y = LR_OS_ROSE_Prob,
color = Class)) +
scale_colour_manual(values=c("#1846BA55","#B8000055")) +
geom_point(size=5) +
geom_line(color="black") +
xlab("Sonar Object Classification Index (Logit Values)") +
ylab("Estimated Rock Detection Probability") +
labs(color = "Class") +
scale_x_continuous( limits=c(-10,5), breaks=seq(-10,5,by=1)) +
scale_y_continuous( limits=c(0,1), breaks=seq(0,1,by=0.1),labels = scales::percent) +
ggtitle("Estimated Rock Detection Probabilities Based on Classification Index : Logistic Regression (OS_ROSE)") +
theme_bw() +
theme(plot.title = element_text(color="black", size=14, face="bold", hjust=0.50),
axis.title.x = element_text(color="black", size=12, face="bold"),
axis.title.y = element_text(color="black", size=12, face="bold"),
legend.position="top")
1.6 Consolidated Findings
[A] The option to apply either an undersampling and oversampling method to augment imbalanced data prior to model training is predominantly driven by the quality and characteristics of the data set at hand. For large-sized data sets, undersampling methods may still prove effective in achieving balanced class distributions while maintaining a sufficient number of instances and reasonable computation complexity. For small- to medium-sized data sets, oversampling methods may help to maximize data augmentation while avoiding data sparsity in the resulting data update. The effectiveness of certain algorithms may vary depending on how discriminative the predictors are in distinguishing between classes.
[B] Using the logistic regression model, the algorithm which demonstrated relatively better performance among other undersampling methods is the following:
[B.1] LR_US_NEARMISS: Undersampling - Near Miss Algorithm
[B.1.1] A sufficiently balanced logistic profile for the predicted points from both the majority and minority classes was achieved.
[B.1.2] As an undersampling method applied on a relatively small-sized original data set, the distribution of instances was relatively sparse.
[C] Using the logistic regression model, the algorithm which demonstrated relatively better performance among other oversampling methods is the following:
[C.1] LR_OS_BSMOTE: Oversampling - Borderline Synthetic Minority Oversampling Technique
[C.1.1] A sufficiently balanced logistic profile for the predicted points from both the majority and minority classes was achieved.
[C.1.2] Minimal overlap between both classes was obtained although a minimal skew was still observed due to a longer tail for the predicted points belonging to the majority class.
Code Chunk | Output
##################################
# Visualizing the imbalanced data set
# Visualizing the undersampled and oversampled data
##################################
LR_ClassDistribution <- PMA_PreModelling_Train_LR %>%
ggplot(aes(x = V1 ,
y = V11,
color = Class)) +
scale_colour_manual(values=c("#1846BA55","#B8000055")) +
geom_point(size=5) +
scale_x_continuous( limits=c(-4,4), breaks=seq(-4,4,by=1)) +
scale_y_continuous( limits=c(-4,4), breaks=seq(-4,4,by=1)) +
theme_bw() +
facet_grid(. ~ Label) +
theme(plot.title = element_text(color="black", size=14, face="bold", hjust=0.50),
axis.title.x = element_text(color="black", size=10, face="bold"),
axis.title.y = element_text(color="black", size=10, face="bold"),
legend.position="top")
LR_US_DOWNSAMPLE_ClassDistribution <- PMA_PreModelling_Train_LR_US_DOWNSAMPLE %>%
ggplot(aes(x = V1,
y = V11,
color = Class)) +
scale_colour_manual(values=c("#1846BA55","#B8000055")) +
geom_point(size=5) +
scale_x_continuous( limits=c(-4,4), breaks=seq(-4,4,by=1)) +
scale_y_continuous( limits=c(-4,4), breaks=seq(-4,4,by=1)) +
theme_bw() +
facet_grid(. ~ Label) +
theme(plot.title = element_text(color="black", size=14, face="bold", hjust=0.50),
axis.title.x = element_text(color="black", size=12, face="bold"),
axis.title.y = element_text(color="black", size=12, face="bold"),
legend.position="top")
LR_OS_UPSAMPLE_ClassDistribution <- PMA_PreModelling_Train_LR_OS_UPSAMPLE %>%
ggplot(aes(x = V1,
y = V11,
color = Class)) +
scale_colour_manual(values=c("#1846BA55","#B8000055")) +
geom_point(size=5) +
scale_x_continuous( limits=c(-4,4), breaks=seq(-4,4,by=1)) +
scale_y_continuous( limits=c(-4,4), breaks=seq(-4,4,by=1)) +
theme_bw() +
facet_grid(. ~ Label) +
theme(plot.title = element_text(color="black", size=14, face="bold", hjust=0.50),
axis.title.x = element_text(color="black", size=12, face="bold"),
axis.title.y = element_text(color="black", size=12, face="bold"),
legend.position="top")
LR_US_NEARMISS_ClassDistribution <- PMA_PreModelling_Train_LR_US_NEARMISS %>%
ggplot(aes(x = V1,
y = V11,
color = Class)) +
scale_colour_manual(values=c("#1846BA55","#B8000055")) +
geom_point(size=5) +
scale_x_continuous( limits=c(-4,4), breaks=seq(-4,4,by=1)) +
scale_y_continuous( limits=c(-4,4), breaks=seq(-4,4,by=1)) +
theme_bw() +
facet_grid(. ~ Label) +
theme(plot.title = element_text(color="black", size=14, face="bold", hjust=0.50),
axis.title.x = element_text(color="black", size=12, face="bold"),
axis.title.y = element_text(color="black", size=12, face="bold"),
legend.position="top")
LR_US_TOMEK_ClassDistribution <- PMA_PreModelling_Train_LR_US_TOMEK %>%
ggplot(aes(x = V1,
y = V11,
color = Class)) +
scale_colour_manual(values=c("#1846BA55","#B8000055")) +
geom_point(size=5) +
scale_x_continuous( limits=c(-4,4), breaks=seq(-4,4,by=1)) +
scale_y_continuous( limits=c(-4,4), breaks=seq(-4,4,by=1)) +
theme_bw() +
facet_grid(. ~ Label) +
theme(plot.title = element_text(color="black", size=14, face="bold", hjust=0.50),
axis.title.x = element_text(color="black", size=12, face="bold"),
axis.title.y = element_text(color="black", size=12, face="bold"),
legend.position="top")
LR_OS_ADASYN_ClassDistribution <- PMA_PreModelling_Train_LR_OS_ADASYN %>%
ggplot(aes(x = V1,
y = V11,
color = Class)) +
scale_colour_manual(values=c("#1846BA55","#B8000055")) +
geom_point(size=5) +
scale_x_continuous( limits=c(-4,4), breaks=seq(-4,4,by=1)) +
scale_y_continuous( limits=c(-4,4), breaks=seq(-4,4,by=1)) +
theme_bw() +
facet_grid(. ~ Label) +
theme(plot.title = element_text(color="black", size=14, face="bold", hjust=0.50),
axis.title.x = element_text(color="black", size=12, face="bold"),
axis.title.y = element_text(color="black", size=12, face="bold"),
legend.position="top")
LR_OS_BSMOTE_ClassDistribution <- PMA_PreModelling_Train_LR_OS_BSMOTE %>%
ggplot(aes(x = V1,
y = V11,
color = Class)) +
scale_colour_manual(values=c("#1846BA55","#B8000055")) +
geom_point(size=5) +
scale_x_continuous( limits=c(-4,4), breaks=seq(-4,4,by=1)) +
scale_y_continuous( limits=c(-4,4), breaks=seq(-4,4,by=1)) +
theme_bw() +
facet_grid(. ~ Label) +
theme(plot.title = element_text(color="black", size=14, face="bold", hjust=0.50),
axis.title.x = element_text(color="black", size=12, face="bold"),
axis.title.y = element_text(color="black", size=12, face="bold"),
legend.position="top")
LR_OS_SMOTE_ClassDistribution <- PMA_PreModelling_Train_LR_OS_SMOTE %>%
ggplot(aes(x = V1,
y = V11,
color = Class)) +
scale_colour_manual(values=c("#1846BA55","#B8000055")) +
geom_point(size=5) +
scale_x_continuous( limits=c(-4,4), breaks=seq(-4,4,by=1)) +
scale_y_continuous( limits=c(-4,4), breaks=seq(-4,4,by=1)) +
theme_bw() +
facet_grid(. ~ Label) +
theme(plot.title = element_text(color="black", size=14, face="bold", hjust=0.50),
axis.title.x = element_text(color="black", size=12, face="bold"),
axis.title.y = element_text(color="black", size=12, face="bold"),
legend.position="top")
LR_OS_ROSE_ClassDistribution <- PMA_PreModelling_Train_LR_OS_ROSE %>%
ggplot(aes(x = V1,
y = V11,
color = Class)) +
scale_colour_manual(values=c("#1846BA55","#B8000055")) +
geom_point(size=5) +
scale_x_continuous( limits=c(-4,4), breaks=seq(-4,4,by=1)) +
scale_y_continuous( limits=c(-4,4), breaks=seq(-4,4,by=1)) +
theme_bw() +
facet_grid(. ~ Label) +
theme(plot.title = element_text(color="black", size=14, face="bold", hjust=0.50),
axis.title.x = element_text(color="black", size=12, face="bold"),
axis.title.y = element_text(color="black", size=12, face="bold"),
legend.position="top")
RDD_ClassDistribution <- ggarrange(LR_ClassDistribution,
LR_US_DOWNSAMPLE_ClassDistribution,
LR_OS_UPSAMPLE_ClassDistribution,
LR_US_NEARMISS_ClassDistribution,
LR_US_TOMEK_ClassDistribution,
LR_OS_ADASYN_ClassDistribution,
LR_OS_BSMOTE_ClassDistribution,
LR_OS_SMOTE_ClassDistribution,
LR_OS_ROSE_ClassDistribution,
ncol=3, nrow=3)
annotate_figure(RDD_ClassDistribution,
top = text_grob("Class Distribution",
color = "black",
face = "bold",
size = 14))
##################################
# Replotting the logistic curves
##################################
LR_LogisticCurvePlot <- LR_Model_Predictions %>%
ggplot(aes(x = LR_LP ,
y = LR_Prob,
color = Class)) +
scale_colour_manual(values=c("#1846BA55","#B8000055")) +
geom_point(size=5) +
geom_line(color="black") +
xlab("Sonar Object Classification Index (Logit Values)") +
ylab("Estimated Rock Detection Probability") +
labs(color = "Class") +
scale_x_continuous( limits=c(-10,5), breaks=seq(-10,5,by=1)) +
scale_y_continuous( limits=c(0,1), breaks=seq(0,1,by=0.1),labels = scales::percent) +
facet_grid(. ~ Label) +
theme_bw() +
theme(plot.title = element_text(color="black", size=14, face="bold", hjust=0.50),
axis.title.x = element_text(color="black", size=10, face="bold"),
axis.title.y = element_text(color="black", size=10, face="bold"),
legend.position="top")
LR_US_DOWNSAMPLE_LogisticCurvePlot <- LR_US_DOWNSAMPLE_Model_Predictions %>%
ggplot(aes(x = LR_US_DOWNSAMPLE_LP ,
y = LR_US_DOWNSAMPLE_Prob,
color = Class)) +
scale_colour_manual(values=c("#1846BA55","#B8000055")) +
geom_point(size=5) +
geom_line(color="black") +
xlab("Sonar Object Classification Index (Logit Values)") +
ylab("Estimated Rock Detection Probability") +
labs(color = "Class") +
scale_x_continuous( limits=c(-10,5), breaks=seq(-10,5,by=1)) +
scale_y_continuous( limits=c(0,1), breaks=seq(0,1,by=0.1),labels = scales::percent) +
facet_grid(. ~ Label) +
theme_bw() +
theme(plot.title = element_text(color="black", size=14, face="bold", hjust=0.50),
axis.title.x = element_text(color="black", size=10, face="bold"),
axis.title.y = element_text(color="black", size=10, face="bold"),
legend.position="top")
LR_OS_UPSAMPLE_LogisticCurvePlot <- LR_OS_UPSAMPLE_Model_Predictions %>%
ggplot(aes(x = LR_OS_UPSAMPLE_LP ,
y = LR_OS_UPSAMPLE_Prob,
color = Class)) +
scale_colour_manual(values=c("#1846BA55","#B8000055")) +
geom_point(size=5) +
geom_line(color="black") +
xlab("Sonar Object Classification Index (Logit Values)") +
ylab("Estimated Rock Detection Probability") +
labs(color = "Class") +
scale_x_continuous( limits=c(-10,5), breaks=seq(-10,5,by=1)) +
scale_y_continuous( limits=c(0,1), breaks=seq(0,1,by=0.1),labels = scales::percent) +
facet_grid(. ~ Label) +
theme_bw() +
theme(plot.title = element_text(color="black", size=14, face="bold", hjust=0.50),
axis.title.x = element_text(color="black", size=10, face="bold"),
axis.title.y = element_text(color="black", size=10, face="bold"),
legend.position="top")
LR_US_NEARMISS_LogisticCurvePlot <- LR_US_NEARMISS_Model_Predictions %>%
ggplot(aes(x = LR_US_NEARMISS_LP ,
y = LR_US_NEARMISS_Prob,
color = Class)) +
scale_colour_manual(values=c("#1846BA55","#B8000055")) +
geom_point(size=5) +
geom_line(color="black") +
xlab("Sonar Object Classification Index (Logit Values)") +
ylab("Estimated Rock Detection Probability") +
labs(color = "Class") +
scale_x_continuous( limits=c(-10,5), breaks=seq(-10,5,by=1)) +
scale_y_continuous( limits=c(0,1), breaks=seq(0,1,by=0.1),labels = scales::percent) +
facet_grid(. ~ Label) +
theme_bw() +
theme(plot.title = element_text(color="black", size=14, face="bold", hjust=0.50),
axis.title.x = element_text(color="black", size=10, face="bold"),
axis.title.y = element_text(color="black", size=10, face="bold"),
legend.position="top")
LR_US_TOMEK_LogisticCurvePlot <- LR_US_TOMEK_Model_Predictions %>%
ggplot(aes(x = LR_US_TOMEK_LP ,
y = LR_US_TOMEK_Prob,
color = Class)) +
scale_colour_manual(values=c("#1846BA55","#B8000055")) +
geom_point(size=5) +
geom_line(color="black") +
xlab("Sonar Object Classification Index (Logit Values)") +
ylab("Estimated Rock Detection Probability") +
labs(color = "Class") +
scale_x_continuous( limits=c(-10,5), breaks=seq(-10,5,by=1)) +
scale_y_continuous( limits=c(0,1), breaks=seq(0,1,by=0.1),labels = scales::percent) +
facet_grid(. ~ Label) +
theme_bw() +
theme(plot.title = element_text(color="black", size=14, face="bold", hjust=0.50),
axis.title.x = element_text(color="black", size=10, face="bold"),
axis.title.y = element_text(color="black", size=10, face="bold"),
legend.position="top")
LR_OS_ADASYN_LogisticCurvePlot <- LR_OS_ADASYN_Model_Predictions %>%
ggplot(aes(x = LR_OS_ADASYN_LP ,
y = LR_OS_ADASYN_Prob,
color = Class)) +
scale_colour_manual(values=c("#1846BA55","#B8000055")) +
geom_point(size=5) +
geom_line(color="black") +
xlab("Sonar Object Classification Index (Logit Values)") +
ylab("Estimated Rock Detection Probability") +
labs(color = "Class") +
scale_x_continuous( limits=c(-10,5), breaks=seq(-10,5,by=1)) +
scale_y_continuous( limits=c(0,1), breaks=seq(0,1,by=0.1),labels = scales::percent) +
facet_grid(. ~ Label) +
theme_bw() +
theme(plot.title = element_text(color="black", size=14, face="bold", hjust=0.50),
axis.title.x = element_text(color="black", size=10, face="bold"),
axis.title.y = element_text(color="black", size=10, face="bold"),
legend.position="top")
LR_OS_BSMOTE_LogisticCurvePlot <- LR_OS_BSMOTE_Model_Predictions %>%
ggplot(aes(x = LR_OS_BSMOTE_LP ,
y = LR_OS_BSMOTE_Prob,
color = Class)) +
scale_colour_manual(values=c("#1846BA55","#B8000055")) +
geom_point(size=5) +
geom_line(color="black") +
xlab("Sonar Object Classification Index (Logit Values)") +
ylab("Estimated Rock Detection Probability") +
labs(color = "Class") +
scale_x_continuous( limits=c(-10,5), breaks=seq(-10,5,by=1)) +
scale_y_continuous( limits=c(0,1), breaks=seq(0,1,by=0.1),labels = scales::percent) +
facet_grid(. ~ Label) +
theme_bw() +
theme(plot.title = element_text(color="black", size=14, face="bold", hjust=0.50),
axis.title.x = element_text(color="black", size=10, face="bold"),
axis.title.y = element_text(color="black", size=10, face="bold"),
legend.position="top")
LR_OS_SMOTE_LogisticCurvePlot <- LR_OS_SMOTE_Model_Predictions %>%
ggplot(aes(x = LR_OS_SMOTE_LP ,
y = LR_OS_SMOTE_Prob,
color = Class)) +
scale_colour_manual(values=c("#1846BA55","#B8000055")) +
geom_point(size=5) +
geom_line(color="black") +
xlab("Sonar Object Classification Index (Logit Values)") +
ylab("Estimated Rock Detection Probability") +
labs(color = "Class") +
scale_x_continuous( limits=c(-10,5), breaks=seq(-10,5,by=1)) +
scale_y_continuous( limits=c(0,1), breaks=seq(0,1,by=0.1),labels = scales::percent) +
facet_grid(. ~ Label) +
theme_bw() +
theme(plot.title = element_text(color="black", size=14, face="bold", hjust=0.50),
axis.title.x = element_text(color="black", size=10, face="bold"),
axis.title.y = element_text(color="black", size=10, face="bold"),
legend.position="top")
LR_OS_ROSE_LogisticCurvePlot <- LR_OS_ROSE_Model_Predictions %>%
ggplot(aes(x = LR_OS_ROSE_LP ,
y = LR_OS_ROSE_Prob,
color = Class)) +
scale_colour_manual(values=c("#1846BA55","#B8000055")) +
geom_point(size=5) +
geom_line(color="black") +
xlab("Sonar Object Classification Index (Logit Values)") +
ylab("Estimated Rock Detection Probability") +
labs(color = "Class") +
scale_x_continuous( limits=c(-10,5), breaks=seq(-10,5,by=1)) +
scale_y_continuous( limits=c(0,1), breaks=seq(0,1,by=0.1),labels = scales::percent) +
facet_grid(. ~ Label) +
theme_bw() +
theme(plot.title = element_text(color="black", size=14, face="bold", hjust=0.50),
axis.title.x = element_text(color="black", size=10, face="bold"),
axis.title.y = element_text(color="black", size=10, face="bold"),
legend.position="top")
RLR_LogisticCurvePlot <- ggarrange(LR_LogisticCurvePlot,
LR_US_DOWNSAMPLE_LogisticCurvePlot,
LR_OS_UPSAMPLE_LogisticCurvePlot,
LR_US_NEARMISS_LogisticCurvePlot,
LR_US_TOMEK_LogisticCurvePlot,
LR_OS_ADASYN_LogisticCurvePlot,
LR_OS_BSMOTE_LogisticCurvePlot,
LR_OS_SMOTE_LogisticCurvePlot,
LR_OS_ROSE_LogisticCurvePlot,
ncol=3, nrow=3)
annotate_figure(RLR_LogisticCurvePlot,
top = text_grob("Estimated Rock Detection Probabilities Based on Classification Index",
color = "black",
face = "bold",
size = 14))
3. References
[Book] Applied Predictive Modeling by Max Kuhn and Kjell Johnson
[Book] An Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie and Rob Tibshirani
[Book] Multivariate Data Visualization with R by Deepayan Sarkar
[Book] Machine Learning by Samuel Jackson
[Book] Data Modeling Methods by Jacob Larget
[R Package] AppliedPredictiveModeling by Max Kuhn
[R Package] caret by Max Kuhn
[R Package] rpart by Terry Therneau and Beth Atkinson
[R Package] lattice by Deepayan Sarkar
[R Package] dplyr by Hadley Wickham
[R Package] moments by Lukasz Komsta and Frederick
[R Package] skimr by Elin Waring
[R Package] RANN by Sunil Arya, David Mount, Samuel Kemp and Gregory Jefferis
[R Package] corrplot by Taiyun Wei
[R Package] tidyverse by Hadley Wickham
[R Package] lares by Bernardo Lares
[R Package] DMwR2 by Luis Torgo
[R Package] gridExtra by Baptiste Auguie and Anton Antonov
[R Package] rattle by Graham Williams
[R Package] rpart.plot by Stephen Milborrow
[R Package] RColorBrewer by Erich Neuwirth
[R Package] stats by R Core Team
[R Package] pls by Kristian Hovde Liland
[R Package] nnet by Brian Ripley
[R Package] elasticnet by Hui Zou
[R Package] earth by Stephen Milborrow
[R Package] party by Torsten Hothorn
[R Package] kernlab by Alexandros Karatzoglou
[R Package] randomForest by Andy Liaw
[R Package] pROC by Xavier Robin
[R Package] mda by Trevor Hastie
[R Package] klaR by Christian Roever, Nils Raabe, Karsten Luebke, Uwe Ligges, Gero Szepannek, Marc Zentgraf and David Meyer
[R Package] pamr by Trevor Hastie, Rob Tibshirani, Balasubramanian Narasimhan and Gil Chu
[R Package] ROSE by Nicola Lunardon
[R Package] themis by Emil Hvitfeldt
[Article] The caret Package by Max Kuhn
[Article] A Short Introduction to the caret Package by Max Kuhn
[Article] Caret Package – A Practical Guide to Machine Learning in R by Selva Prabhakaran
[Article] Tuning Machine Learning Models Using the Caret R Package by Jason Brownlee
[Article] Lattice Graphs by Alboukadel Kassambara
[Article] A Tour of Machine Learning Algorithms by Jason Brownlee
[Article] Decision Tree Algorithm Examples In Data Mining by Software Testing Help Team
[Article] 4 Types of Classification Tasks in Machine Learning by Jason Brownlee
[Article] Spot-Check Classification Machine Learning Algorithms in Python with scikit-learn by Jason Brownlee
[Article] Feature Engineering and Selection: A Practical Approach for Predictive Models by Max Kuhn and Kjell Johnson
[Article] Machine Learning Tutorial: A Step-by-Step Guide for Beginners by Mayank Banoula
[Article] Classification Tree by BCCVL Team
[Article] A Gentle Introduction to Imbalanced Classification by Jason Brownlee
[Article] 8 Tactics to Combat Imbalanced Classes in Your Machine Learning Dataset by Jason Brownlee
[Article] Step-By-Step Framework for Imbalanced Classification Projects by Jason Brownlee
[Article] How to Handle Imbalanced Classes in Machine Learning by Elite Data Science Team
[Article] Best Ways To Handle Imbalanced Data in Machine Learning by Jaiganesh Nagidi
[Article] Handling Imbalanced Data for Classification by Geeks For Geeks Team
[Article] Random Oversampling and Undersampling for Imbalanced Classification by Jason Brownlee
[Publication] The Origins of Logistic Regression by JS Cramer (Econometrics eJournal)
[Publication] KNN Approach to Unbalanced Data Distributions: A Case Study Involving Information Extraction by Inderjeet Mani and J Zhang (Proceedings of Workshop on Learning from Imbalanced Datasets)
[Publication] Two Modifications of CNN by Ivan Tomek (IEEE Transactions on Systems, Man, and Cybernetics)
[Publication] ADASYN: Adaptive Synthetic Sampling Approach for Imbalanced Learning by Haibo He, Yang Bai, Edwardo Garcia and Shutao Li (IEEE World Congress on Computational Intelligence)
[Publication] Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning by Hui Han, Wenyuan Wang and Binghuan Mao (International Conference on Intelligent Computing)
[Publication] SMOTE: Synthetic Minority Over-Sampling Technique by Nitesh Chawla, Kevin Bowyer, Lawrence Hall and Philip Kegelmeyer (Journal of Artificial Intelligence Research)
[Publication] Training and Assessing Classification Rules with Imbalanced Data by Giovanna Menardi and Nicola Torelli (Data Mining and Knowledge Discovery)
[Publication] A Study of the Behavior of Several Methods for Balancing Machine Learning Training Data by Gustavo Batista, Ronaldo Prati and Maria Carolina Monard (ACM SIGKDD Explorations Newsletter)
[Publication] A Survey of Predictive Modelling under Imbalanced Distributiona by Paula Branco, Luis Torgo and Rita Ribeiro (Arxiv Cornell University)
[Course] Applied Data Mining and Statistical Learning by Penn State Eberly College of Science
[Course] Regression Methods by Penn State Eberly College of Science
[Course] Applied Regression Analysis by Penn State Eberly College of Science
[Course] Applied Data Mining and Statistical Learning by Penn State Eberly College of Science