R Notebook

The Model Evaluation

Load the libraries

library(ROCR)
library(e1071)

#read the data into a table from the file

cc <- read.csv("a2/UCI_Credit_Card.csv",header = TRUE, sep = ",")
head(cc,5)

#Convert to factorial

cc$default.payment.next.month <- factor(cc$default.payment.next.month,levels=c(0,1), labels=c("No","Yes"))
cc$PAY_0  <- factor(cc$PAY_0 , levels=c(-2,-1,0,1,2,3,4,5,6,7,8,9), labels=c("Pay duly", "Delay 1 month", "Delay 2 months", "Delay 3 months", "Delay 4 months",  "Delay 5 months", "Delay 6 months", "Delay 7 months",  "Delay 8 months", "Delay 9 months", "Delay 10 months", "Delay 11 months"))

cc$PAY_2  <- factor(cc$PAY_2 , levels=c(-2,-1,0,1,2,3,4,5,6,7,8,9), labels=c("Pay duly", "Delay 1 month", "Delay 2 months", "Delay 3 months", "Delay 4 months",  "Delay 5 months", "Delay 6 months", "Delay 7 months",  "Delay 8 months", "Delay 9 months", "Delay 10 months", "Delay 11 months"))

cc$PAY_3  <- factor(cc$PAY_3 , levels=c(-2,-1,0,1,2,3,4,5,6,7,8,9), labels=c("Pay duly", "Delay 1 month", "Delay 2 months", "Delay 3 months", "Delay 4 months",  "Delay 5 months", "Delay 6 months", "Delay 7 months",  "Delay 8 months", "Delay 9 months", "Delay 10 months", "Delay 11 months"))

cc$PAY_4  <- factor(cc$PAY_4 , levels=c(-2,-1,0,1,2,3,4,5,6,7,8,9), labels=c("Pay duly", "Delay 1 month", "Delay 2 months", "Delay 3 months", "Delay 4 months",  "Delay 5 months", "Delay 6 months", "Delay 7 months",  "Delay 8 months", "Delay 9 months", "Delay 10 months", "Delay 11 months"))

cc$PAY_5  <- factor(cc$PAY_5 , levels=c(-2,-1,0,1,2,3,4,5,6,7,8,9), labels=c("Pay duly", "Delay 1 month", "Delay 2 months", "Delay 3 months", "Delay 4 months",  "Delay 5 months", "Delay 6 months", "Delay 7 months",  "Delay 8 months", "Delay 9 months", "Delay 10 months", "Delay 11 months"))

cc$PAY_6  <- factor(cc$PAY_6 , levels=c(-2,-1,0,1,2,3,4,5,6,7,8,9), labels=c("Pay duly", "Delay 1 month", "Delay 2 months", "Delay 3 months", "Delay 4 months",  "Delay 5 months", "Delay 6 months", "Delay 7 months",  "Delay 8 months", "Delay 9 months", "Delay 10 months", "Delay 11 months"))

define the data drames fir the NB classifier

train <- as.data.frame(cc[1:5000,])
test <- as.data.frame(cc[5001:5100,])

#execute and display model

nbPay <- naiveBayes(default.payment.next.month ~ PAY_0 + PAY_2 + PAY_3, train)
nbPay

Naive Bayes Classifier for Discrete Predictors

Call:
naiveBayes.default(x = X, y = Y, laplace = laplace)

A-priori probabilities:
Y
    No    Yes 
0.7786 0.2214 

Conditional probabilities:
     PAY_0
Y         Pay duly Delay 1 month Delay 2 months Delay 3 months
  No  0.0875931158  0.2167993835   0.5371179039   0.1109684048
  Yes 0.0370370370  0.1806684734   0.2746160795   0.2177055104
     PAY_0
Y     Delay 4 months Delay 5 months Delay 6 months
  No    0.0416131518   0.0038530696   0.0005137426
  Yes   0.2493224932   0.0207768744   0.0108401084
     PAY_0
Y     Delay 7 months Delay 8 months Delay 9 months
  No    0.0000000000   0.0000000000   0.0000000000
  Yes   0.0027100271   0.0009033424   0.0027100271
     PAY_0
Y     Delay 10 months Delay 11 months
  No     0.0015412278    0.0000000000
  Yes    0.0027100271    0.0000000000

     PAY_2
Y         Pay duly Delay 1 month Delay 2 months Delay 3 months
  No  0.1235550989  0.2306704341   0.5586950938   0.0005137426
  Yes 0.1174345077  0.1725383921   0.3703703704   0.0000000000
     PAY_2
Y     Delay 4 months Delay 5 months Delay 6 months
  No    0.0796301053   0.0043668122   0.0007706139
  Yes   0.2953929539   0.0298102981   0.0063233966
     PAY_2
Y     Delay 7 months Delay 8 months Delay 9 months
  No    0.0002568713   0.0000000000   0.0015412278
  Yes   0.0009033424   0.0045167118   0.0027100271
     PAY_2
Y     Delay 10 months Delay 11 months
  No     0.0000000000    0.0000000000
  Yes    0.0000000000    0.0000000000

     PAY_3
Y         Pay duly Delay 1 month Delay 2 months Delay 3 months
  No  0.1281787824  0.2347803750   0.5440534292   0.0005137426
  Yes 0.1255645890  0.1607949413   0.3974706414   0.0000000000
     PAY_3
Y     Delay 4 months Delay 5 months Delay 6 months
  No    0.0863087593   0.0023118418   0.0015412278
  Yes   0.2818428184   0.0144534779   0.0054200542
     PAY_3
Y     Delay 7 months Delay 8 months Delay 9 months
  No    0.0005137426   0.0015412278   0.0002568713
  Yes   0.0054200542   0.0027100271   0.0063233966
     PAY_3
Y     Delay 10 months Delay 11 months
  No     0.0000000000    0.0000000000
  Yes    0.0000000000    0.0000000000

predict with test data and display results

results <- predict(nbPay,test)
results

  [1] No  No  No  No  No  No  No  No  No  No  No  No  Yes No  No 
 [16] No  No  No  No  Yes No  No  No  No  No  No  No  No  No  No 
 [31] No  Yes No  No  No  No  No  No  No  No  No  No  No  No  No 
 [46] No  No  No  Yes No  No  No  No  No  No  No  Yes No  No  No 
 [61] No  Yes No  No  No  No  No  No  No  No  No  No  No  No  No 
 [76] Yes Yes No  No  Yes Yes No  No  No  Yes No  No  No  No  No 
 [91] No  No  No  No  No  No  No  No  No  Yes
Levels: No Yes

Diagnostics of classifiers

pred <-prediction(as.numeric(results), as.numeric( test$default.payment.next.month), label.ordering = NULL)
perf <- performance(pred, "tpr", "fpr")
plot(perf, lwd=2, xlab="False Positive Rate", ylab="True Positive Rate")
abline(a=0, b=1, col="gray50", lty=3)

#AUC score of the ROC curve

auc <- performance(pred, "auc")
auc <- unlist(slot(auc,"y.values"))
auc

[1] 0.6066667