MyBank - Cart- Classification and Decision Trees

1. Classification and Regression Tree - CART

setwd("C:/Users/adminsa/Desktop/Pos Graduacao/Machine Learning/Mybank")

ploan <- read.table("My Bank Case Study-dataset.csv", sep = ",", header = TRUE)

dim(ploan)

[1] 20000    40

names(ploan)

 [1] "CUST_ID"                  "TARGET"                   "AGE"                      "GENDER"                   "BALANCE"                 
 [6] "OCCUPATION"               "AGE_BKT"                  "SCR"                      "HOLDING_PERIOD"           "ACC_TYPE"                
[11] "ACC_OP_DATE"              "LEN_OF_RLTN_IN_MNTH"      "NO_OF_L_CR_TXNS"          "NO_OF_L_DR_TXNS"          "TOT_NO_OF_L_TXNS"        
[16] "NO_OF_BR_CSH_WDL_DR_TXNS" "NO_OF_ATM_DR_TXNS"        "NO_OF_NET_DR_TXNS"        "NO_OF_MOB_DR_TXNS"        "NO_OF_CHQ_DR_TXNS"       
[21] "FLG_HAS_CC"               "AMT_ATM_DR"               "AMT_BR_CSH_WDL_DR"        "AMT_CHQ_DR"               "AMT_NET_DR"              
[26] "AMT_MOB_DR"               "AMT_L_DR"                 "FLG_HAS_ANY_CHGS"         "AMT_OTH_BK_ATM_USG_CHGS"  "AMT_MIN_BAL_NMC_CHGS"    
[31] "NO_OF_IW_CHQ_BNC_TXNS"    "NO_OF_OW_CHQ_BNC_TXNS"    "AVG_AMT_PER_ATM_TXN"      "AVG_AMT_PER_CSH_WDL_TXN"  "AVG_AMT_PER_CHQ_TXN"     
[36] "AVG_AMT_PER_NET_TXN"      "AVG_AMT_PER_MOB_TXN"      "FLG_HAS_NOMINEE"          "FLG_HAS_OLD_LOAN"         "random"                  

str(ploan)

'data.frame':   20000 obs. of  40 variables:
 $ CUST_ID                 : Factor w/ 20000 levels "C1","C10","C100",..: 17699 16532 11027 17984 2363 11747 18115 15556 15216 12494 ...
 $ TARGET                  : int  0 0 0 0 0 0 0 0 0 0 ...
 $ AGE                     : int  27 47 40 53 36 42 30 53 42 30 ...
 $ GENDER                  : Factor w/ 3 levels "F","M","O": 2 2 2 2 2 1 2 1 1 2 ...
 $ BALANCE                 : num  3384 287489 18217 71720 1671623 ...
 $ OCCUPATION              : Factor w/ 4 levels "PROF","SAL","SELF-EMP",..: 3 2 3 2 1 1 1 2 3 1 ...
 $ AGE_BKT                 : Factor w/ 7 levels "<25",">50","26-30",..: 3 7 5 2 5 6 3 2 6 3 ...
 $ SCR                     : int  776 324 603 196 167 493 479 562 105 170 ...
 $ HOLDING_PERIOD          : int  30 28 2 13 24 26 14 25 15 13 ...
 $ ACC_TYPE                : Factor w/ 2 levels "CA","SA": 2 2 2 1 2 2 2 1 2 2 ...
 $ ACC_OP_DATE             : Factor w/ 4869 levels "01-01-00","01-01-01",..: 3270 1806 3575 993 2861 862 4533 3160 257 334 ...
 $ LEN_OF_RLTN_IN_MNTH     : int  146 104 61 107 185 192 177 99 88 111 ...
 $ NO_OF_L_CR_TXNS         : int  7 8 10 36 20 5 6 14 18 14 ...
 $ NO_OF_L_DR_TXNS         : int  3 2 5 14 1 2 6 3 14 8 ...
 $ TOT_NO_OF_L_TXNS        : int  10 10 15 50 21 7 12 17 32 22 ...
 $ NO_OF_BR_CSH_WDL_DR_TXNS: int  0 0 1 4 1 1 0 3 6 3 ...
 $ NO_OF_ATM_DR_TXNS       : int  1 1 1 2 0 1 1 0 2 1 ...
 $ NO_OF_NET_DR_TXNS       : int  2 1 1 3 0 0 1 0 4 0 ...
 $ NO_OF_MOB_DR_TXNS       : int  0 0 0 1 0 0 0 0 1 0 ...
 $ NO_OF_CHQ_DR_TXNS       : int  0 0 2 4 0 0 4 0 1 4 ...
 $ FLG_HAS_CC              : int  0 0 0 0 0 1 0 0 1 0 ...
 $ AMT_ATM_DR              : int  13100 6600 11200 26100 0 18500 6200 0 35400 18000 ...
 $ AMT_BR_CSH_WDL_DR       : int  0 0 561120 673590 808480 379310 0 945160 198430 869880 ...
 $ AMT_CHQ_DR              : int  0 0 49320 60780 0 0 10580 0 51490 32610 ...
 $ AMT_NET_DR              : num  973557 799813 997570 741506 0 ...
 $ AMT_MOB_DR              : int  0 0 0 71388 0 0 0 0 170332 0 ...
 $ AMT_L_DR                : num  986657 806413 1619210 1573364 808480 ...
 $ FLG_HAS_ANY_CHGS        : int  0 1 1 0 0 0 1 0 0 0 ...
 $ AMT_OTH_BK_ATM_USG_CHGS : int  0 0 0 0 0 0 0 0 0 0 ...
 $ AMT_MIN_BAL_NMC_CHGS    : int  0 0 0 0 0 0 0 0 0 0 ...
 $ NO_OF_IW_CHQ_BNC_TXNS   : int  0 0 0 0 0 0 0 0 0 0 ...
 $ NO_OF_OW_CHQ_BNC_TXNS   : int  0 0 1 0 0 0 0 0 0 0 ...
 $ AVG_AMT_PER_ATM_TXN     : num  13100 6600 11200 13050 0 ...
 $ AVG_AMT_PER_CSH_WDL_TXN : num  0 0 561120 168398 808480 ...
 $ AVG_AMT_PER_CHQ_TXN     : num  0 0 24660 15195 0 ...
 $ AVG_AMT_PER_NET_TXN     : num  486779 799813 997570 247169 0 ...
 $ AVG_AMT_PER_MOB_TXN     : num  0 0 0 71388 0 ...
 $ FLG_HAS_NOMINEE         : int  1 1 1 1 1 1 0 1 1 0 ...
 $ FLG_HAS_OLD_LOAN        : int  1 0 1 0 0 1 1 1 1 0 ...
 $ random                  : num  1.14e-05 1.11e-04 1.20e-04 1.37e-04 1.74e-04 ...

colSums(is.na(ploan))

                 CUST_ID                   TARGET                      AGE                   GENDER                  BALANCE 
                       0                        0                        0                        0                        0 
              OCCUPATION                  AGE_BKT                      SCR           HOLDING_PERIOD                 ACC_TYPE 
                       0                        0                        0                        0                        0 
             ACC_OP_DATE      LEN_OF_RLTN_IN_MNTH          NO_OF_L_CR_TXNS          NO_OF_L_DR_TXNS         TOT_NO_OF_L_TXNS 
                       0                        0                        0                        0                        0 
NO_OF_BR_CSH_WDL_DR_TXNS        NO_OF_ATM_DR_TXNS        NO_OF_NET_DR_TXNS        NO_OF_MOB_DR_TXNS        NO_OF_CHQ_DR_TXNS 
                       0                        0                        0                        0                        0 
              FLG_HAS_CC               AMT_ATM_DR        AMT_BR_CSH_WDL_DR               AMT_CHQ_DR               AMT_NET_DR 
                       0                        0                        0                        0                        0 
              AMT_MOB_DR                 AMT_L_DR         FLG_HAS_ANY_CHGS  AMT_OTH_BK_ATM_USG_CHGS     AMT_MIN_BAL_NMC_CHGS 
                       0                        0                        0                        0                        0 
   NO_OF_IW_CHQ_BNC_TXNS    NO_OF_OW_CHQ_BNC_TXNS      AVG_AMT_PER_ATM_TXN  AVG_AMT_PER_CSH_WDL_TXN      AVG_AMT_PER_CHQ_TXN 
                       0                        0                        0                        0                        0 
     AVG_AMT_PER_NET_TXN      AVG_AMT_PER_MOB_TXN          FLG_HAS_NOMINEE         FLG_HAS_OLD_LOAN                   random 
                       0                        0                        0                        0                        0 

ploan$FLG_HAS_CC <- as.factor(ploan$FLG_HAS_CC)
ploan$FLG_HAS_ANY_CHGS <- as.factor(ploan$FLG_HAS_ANY_CHGS)
ploan$FLG_HAS_NOMINEE <- as.factor(ploan$FLG_HAS_NOMINEE)
ploan$FLG_HAS_OLD_LOAN <- as.factor(ploan$FLG_HAS_OLD_LOAN)

library(caret)
set.seed(111)

trainIndex <- createDataPartition(ploan$TARGET,p=0.7,list = FALSE,times = 1)

train.data <- ploan[trainIndex, ]
length(which(train.data$TARGET ==  1))*100/nrow(train.data)

[1] 12.77143

dim(train.data)

[1] 14000    40

test.data  <- ploan[-trainIndex,]
length(which(test.data$TARGET ==  1))*100/nrow(test.data)

[1] 12.06667

dim(test.data)

[1] 6000   40

Model Building - CART (Unbalanced Dataset)-Setting the control parameter inputs for rpart

library(rpart)

package 㤼㸱rpart㤼㸲 was built under R version 3.6.3

r.ctrl <- rpart.control(minsplit = 100,
                        minbucket = 10,
                        cp = 0,
                        xval = 10
                        )

cart.dev <- train.data
names(cart.dev)

 [1] "CUST_ID"                  "TARGET"                   "AGE"                      "GENDER"                   "BALANCE"                 
 [6] "OCCUPATION"               "AGE_BKT"                  "SCR"                      "HOLDING_PERIOD"           "ACC_TYPE"                
[11] "ACC_OP_DATE"              "LEN_OF_RLTN_IN_MNTH"      "NO_OF_L_CR_TXNS"          "NO_OF_L_DR_TXNS"          "TOT_NO_OF_L_TXNS"        
[16] "NO_OF_BR_CSH_WDL_DR_TXNS" "NO_OF_ATM_DR_TXNS"        "NO_OF_NET_DR_TXNS"        "NO_OF_MOB_DR_TXNS"        "NO_OF_CHQ_DR_TXNS"       
[21] "FLG_HAS_CC"               "AMT_ATM_DR"               "AMT_BR_CSH_WDL_DR"        "AMT_CHQ_DR"               "AMT_NET_DR"              
[26] "AMT_MOB_DR"               "AMT_L_DR"                 "FLG_HAS_ANY_CHGS"         "AMT_OTH_BK_ATM_USG_CHGS"  "AMT_MIN_BAL_NMC_CHGS"    
[31] "NO_OF_IW_CHQ_BNC_TXNS"    "NO_OF_OW_CHQ_BNC_TXNS"    "AVG_AMT_PER_ATM_TXN"      "AVG_AMT_PER_CSH_WDL_TXN"  "AVG_AMT_PER_CHQ_TXN"     
[36] "AVG_AMT_PER_NET_TXN"      "AVG_AMT_PER_MOB_TXN"      "FLG_HAS_NOMINEE"          "FLG_HAS_OLD_LOAN"         "random"                  

m1 <- rpart(formula = TARGET~.,
            data = cart.dev[,-c(1,11)],
            method = "class",
            control = r.ctrl)

printcp(m1)

Classification tree:
rpart(formula = TARGET ~ ., data = cart.dev[, -c(1, 11)], method = "class", 
    control = r.ctrl)

Variables actually used in tree construction:
 [1] AGE                     AGE_BKT                 AMT_ATM_DR              AMT_BR_CSH_WDL_DR       AMT_CHQ_DR             
 [6] AMT_L_DR                AMT_MOB_DR              AMT_NET_DR              AVG_AMT_PER_ATM_TXN     AVG_AMT_PER_CHQ_TXN    
[11] AVG_AMT_PER_CSH_WDL_TXN AVG_AMT_PER_NET_TXN     BALANCE                 FLG_HAS_CC              GENDER                 
[16] HOLDING_PERIOD          LEN_OF_RLTN_IN_MNTH     NO_OF_ATM_DR_TXNS       NO_OF_IW_CHQ_BNC_TXNS   NO_OF_L_CR_TXNS        
[21] NO_OF_L_DR_TXNS         OCCUPATION              SCR                     TOT_NO_OF_L_TXNS       

Root node error: 1788/14000 = 0.12771

n= 14000 

           CP nsplit rel error  xerror     xstd
1  0.00643177      0   1.00000 1.00000 0.022087
2  0.00531320      2   0.98714 0.99273 0.022019
3  0.00335570      4   0.97651 0.99217 0.022013
4  0.00279642      5   0.97315 0.98937 0.021987
5  0.00268456      6   0.97036 0.99161 0.022008
6  0.00260999     15   0.94295 0.99161 0.022008
7  0.00223714     19   0.93233 0.98937 0.021987
8  0.00195749     20   0.93009 0.99664 0.022056
9  0.00167785     22   0.92617 1.00727 0.022156
10 0.00111857     43   0.88255 1.01119 0.022192
11 0.00095877     48   0.87696 1.01063 0.022187
12 0.00065250     55   0.87025 1.01566 0.022234
13 0.00055928     61   0.86633 1.02125 0.022286
14 0.00047939     75   0.85682 1.02125 0.022286
15 0.00037286     82   0.85347 1.01957 0.022271
16 0.00000000     85   0.85235 1.02740 0.022343

library(rattle)

package 㤼㸱rattle㤼㸲 was built under R version 3.6.3Rattle: A free graphical interface for data science with R.
Version 5.3.0 Copyright (c) 2006-2018 Togaware Pty Ltd.
Type 'rattle()' to shake, rattle, and roll your data.

Attaching package: 㤼㸱rattle㤼㸲

The following object is masked from 㤼㸱package:randomForest㤼㸲:

    importance

The following object is masked from 㤼㸱package:VIM㤼㸲:

    wine

library(RColorBrewer) 

fancyRpartPlot(m1)

plotcp(m1)

ptree<- prune(m1, cp= 0.0022 ,"CP") 
printcp(ptree)

Classification tree:
rpart(formula = TARGET ~ ., data = cart.dev[, -c(1, 11)], method = "class", 
    control = r.ctrl)

Variables actually used in tree construction:
 [1] AGE_BKT             AMT_L_DR            AMT_MOB_DR          AVG_AMT_PER_ATM_TXN AVG_AMT_PER_CHQ_TXN AVG_AMT_PER_NET_TXN
 [7] BALANCE             GENDER              HOLDING_PERIOD      LEN_OF_RLTN_IN_MNTH NO_OF_L_CR_TXNS     NO_OF_L_DR_TXNS    
[13] OCCUPATION          SCR                

Root node error: 1788/14000 = 0.12771

n= 14000 

         CP nsplit rel error  xerror     xstd
1 0.0064318      0   1.00000 1.00000 0.022087
2 0.0053132      2   0.98714 0.99273 0.022019
3 0.0033557      4   0.97651 0.99217 0.022013
4 0.0027964      5   0.97315 0.98937 0.021987
5 0.0026846      6   0.97036 0.99161 0.022008
6 0.0026100     15   0.94295 0.99161 0.022008
7 0.0022371     19   0.93233 0.98937 0.021987
8 0.0022000     20   0.93009 0.99664 0.022056

fancyRpartPlot(ptree, 
               uniform = TRUE, 
               main = "Final Tree", 
               palettes = c("Blues", "Reds")
               )

Measurements KPIs for CART Rank Ordering, KS, Area Under Curve (AUC), Gini Coefficient, Classification Error

Lets Predict The Data:

cart.dev$predict.class = predict(ptree, cart.dev, type = "class")
cart.dev$predict.score = predict(ptree, cart.dev, type = "prob")

Deciling

library(StatMeasures)

decile <- function(x){
  deciles <- vector(length=10)
  for (i in seq(0.1,1,.1)){
    deciles[i*10] <- quantile(x, i, na.rm=T)
  }
      return (
    ifelse(x<deciles[1], 1,
           ifelse(x<deciles[2], 2,
                  ifelse(x<deciles[3], 3,
                         ifelse(x<deciles[4], 4,
                                ifelse(x<deciles[5], 5,
                                       ifelse(x<deciles[6], 6,
                                              ifelse(x<deciles[7], 7,
                                                     ifelse(x<deciles[8], 8,
                                                            ifelse(x<deciles[9], 9, 10
                                                            ))))))))))
}



cart.dev$deciles <- decile(cart.dev$predict.score[,2])

Ranking the Code

KS and Area under Curve

cart.dev - Confusion Matrix, using CARET and am excellent library that helps us not just bringing the accuracy, but others fine measurements such as: sensivity, Specificity

library(caret)
library(e1071)

class(cart.dev$TARGET)

[1] "integer"

class(cart.dev$predict.class)

[1] "factor"

cart.dev$TARGET = as.factor(cart.dev$TARGET)
cm.dev = confusionMatrix(cart.dev$predict.class, cart.dev$TARGET, positive = "1")
print(cm.dev)

Confusion Matrix and Statistics

          Reference
Prediction     0     1
         0 12085  1536
         1   127   252
                                          
               Accuracy : 0.8812          
                 95% CI : (0.8757, 0.8865)
    No Information Rate : 0.8723          
    P-Value [Acc > NIR] : 0.0007325       
                                          
                  Kappa : 0.1967          
                                          
 Mcnemar's Test P-Value : < 2.2e-16       
                                          
            Sensitivity : 0.14094         
            Specificity : 0.98960         
         Pos Pred Value : 0.66491         
         Neg Pred Value : 0.88723         
             Prevalence : 0.12771         
         Detection Rate : 0.01800         
   Detection Prevalence : 0.02707         
      Balanced Accuracy : 0.56527         
                                          
       'Positive' Class : 1               
                                          

library(rpart)
cart.holdout <- test.data
names(test.data)

 [1] "CUST_ID"                  "TARGET"                   "AGE"                      "GENDER"                   "BALANCE"                 
 [6] "OCCUPATION"               "AGE_BKT"                  "SCR"                      "HOLDING_PERIOD"           "ACC_TYPE"                
[11] "ACC_OP_DATE"              "LEN_OF_RLTN_IN_MNTH"      "NO_OF_L_CR_TXNS"          "NO_OF_L_DR_TXNS"          "TOT_NO_OF_L_TXNS"        
[16] "NO_OF_BR_CSH_WDL_DR_TXNS" "NO_OF_ATM_DR_TXNS"        "NO_OF_NET_DR_TXNS"        "NO_OF_MOB_DR_TXNS"        "NO_OF_CHQ_DR_TXNS"       
[21] "FLG_HAS_CC"               "AMT_ATM_DR"               "AMT_BR_CSH_WDL_DR"        "AMT_CHQ_DR"               "AMT_NET_DR"              
[26] "AMT_MOB_DR"               "AMT_L_DR"                 "FLG_HAS_ANY_CHGS"         "AMT_OTH_BK_ATM_USG_CHGS"  "AMT_MIN_BAL_NMC_CHGS"    
[31] "NO_OF_IW_CHQ_BNC_TXNS"    "NO_OF_OW_CHQ_BNC_TXNS"    "AVG_AMT_PER_ATM_TXN"      "AVG_AMT_PER_CSH_WDL_TXN"  "AVG_AMT_PER_CHQ_TXN"     
[36] "AVG_AMT_PER_NET_TXN"      "AVG_AMT_PER_MOB_TXN"      "FLG_HAS_NOMINEE"          "FLG_HAS_OLD_LOAN"         "random"                  

names(cart.holdout)

 [1] "CUST_ID"                  "TARGET"                   "AGE"                      "GENDER"                   "BALANCE"                 
 [6] "OCCUPATION"               "AGE_BKT"                  "SCR"                      "HOLDING_PERIOD"           "ACC_TYPE"                
[11] "ACC_OP_DATE"              "LEN_OF_RLTN_IN_MNTH"      "NO_OF_L_CR_TXNS"          "NO_OF_L_DR_TXNS"          "TOT_NO_OF_L_TXNS"        
[16] "NO_OF_BR_CSH_WDL_DR_TXNS" "NO_OF_ATM_DR_TXNS"        "NO_OF_NET_DR_TXNS"        "NO_OF_MOB_DR_TXNS"        "NO_OF_CHQ_DR_TXNS"       
[21] "FLG_HAS_CC"               "AMT_ATM_DR"               "AMT_BR_CSH_WDL_DR"        "AMT_CHQ_DR"               "AMT_NET_DR"              
[26] "AMT_MOB_DR"               "AMT_L_DR"                 "FLG_HAS_ANY_CHGS"         "AMT_OTH_BK_ATM_USG_CHGS"  "AMT_MIN_BAL_NMC_CHGS"    
[31] "NO_OF_IW_CHQ_BNC_TXNS"    "NO_OF_OW_CHQ_BNC_TXNS"    "AVG_AMT_PER_ATM_TXN"      "AVG_AMT_PER_CSH_WDL_TXN"  "AVG_AMT_PER_CHQ_TXN"     
[36] "AVG_AMT_PER_NET_TXN"      "AVG_AMT_PER_MOB_TXN"      "FLG_HAS_NOMINEE"          "FLG_HAS_OLD_LOAN"         "random"                  

m2 <- rpart(formula = TARGET~.,
            data = cart.holdout[,-c(1,11)],
            method = "class",
            control = r.ctrl)

printcp(m2)

Classification tree:
rpart(formula = TARGET ~ ., data = cart.holdout[, -c(1, 11)], 
    method = "class", control = r.ctrl)

Variables actually used in tree construction:
[1] AMT_ATM_DR        AMT_BR_CSH_WDL_DR BALANCE           FLG_HAS_CC        HOLDING_PERIOD    NO_OF_L_DR_TXNS   OCCUPATION       
[8] SCR               TOT_NO_OF_L_TXNS 

Root node error: 724/6000 = 0.12067

n= 6000 

          CP nsplit rel error xerror     xstd
1 0.00966851      0   1.00000 1.0000 0.034850
2 0.00055249      4   0.95994 1.0166 0.035098
3 0.00046041      9   0.95718 1.0497 0.035584
4 0.00000000     12   0.95580 1.0497 0.035584

library(rattle)
library(RColorBrewer) 

fancyRpartPlot(m2)

cart.holdout$predict.class = predict(ptree, cart.holdout, type = "class")
cart.holdout$predict.score = predict(ptree, cart.holdout, type = "prob")

Deciling, already done that wih Development subset, just add a new collumn in holdout subset

cart.holdout$deciles <- decile(cart.holdout$predict.score[,2])

View(cart.holdout)

Ranking the Code

library(data.table) 
library(scales)

tmp_DT.holdout = data.table(cart.holdout)

rank.holdout <- tmp_DT.holdout[, list(cnt=length(TARGET),
                      cnt_resp=sum(TARGET==1),
                      cnt_non_resp=sum(TARGET==0)
                      ), by=deciles][order(-deciles)]

rank.holdout$rrate <- round(rank.holdout$cnt_resp / rank.holdout$cnt,4); 
rank.holdout$cum_resp <- cumsum(rank.holdout$cnt_resp) 
rank.holdout$cum_non_resp <- cumsum(rank.holdout$cnt_non_resp) 
rank.holdout$cum_rel_resp <- round(rank.holdout$cum_resp / sum(rank.holdout$cnt_resp),4); 
rank.holdout$cum_rel_non_resp <- round(rank.holdout$cum_non_resp / sum(rank.holdout$cnt_non_resp),4); 
rank.holdout$ks <- abs(rank.holdout$cum_rel_resp - rank.holdout$cum_rel_non_resp) * 100; 
rank.holdout$rrate <- percent(rank.holdout$rrate) 
rank.holdout$cum_rel_resp <- percent(rank.holdout$cum_rel_resp) 
rank.holdout$cum_rel_non_resp <- percent(rank.holdout$cum_rel_non_resp) 
rank.holdout

NA

KS and Area under Curve

library(ROCR)
library(ineq)

pred.holdout <- prediction(cart.holdout$predict.score[,2], cart.holdout$TARGET) 
perf.holdout <- performance(pred.holdout, "tpr", "fpr") 
plot(perf.holdout)

KS.holdout <- max(attr(perf.holdout, 'y.values')[[1]]-attr(perf.holdout, 'x.values')[[1]]) 

auc.holdout <- performance(pred.holdout,"auc"); 
auc.holdout <- as.numeric(auc.holdout@y.values) 

gini.holdout = ineq(cart.holdout$predict.score[,2], type="Gini") 
with(cart.holdout, table(TARGET, predict.class)) 

      predict.class
TARGET    0    1
     0 5197   79
     1  625   99

plot(perf.holdout)

cart.holdout - Confusion Matrix, using CARET and an excellent library that helps us not just bringing the accuracy, but others fine measurements such as: sensivity, Specificity

library(caret)
library(e1071)
cart.holdout$TARGET = as.factor(cart.holdout$TARGET)
cm.holdout = confusionMatrix(cart.holdout$predict.class, cart.holdout$TARGET, positive = "1")
print(cm.holdout)

Confusion Matrix and Statistics

          Reference
Prediction    0    1
         0 5197  625
         1   79   99
                                          
               Accuracy : 0.8827          
                 95% CI : (0.8743, 0.8907)
    No Information Rate : 0.8793          
    P-Value [Acc > NIR] : 0.2204          
                                          
                  Kappa : 0.1805          
                                          
 Mcnemar's Test P-Value : <2e-16          
                                          
            Sensitivity : 0.13674         
            Specificity : 0.98503         
         Pos Pred Value : 0.55618         
         Neg Pred Value : 0.89265         
             Prevalence : 0.12067         
         Detection Rate : 0.01650         
   Detection Prevalence : 0.02967         
      Balanced Accuracy : 0.56088         
                                          
       'Positive' Class : 1               
                                          

class(cart.holdout$TARGET)

[1] "factor"

class(cart.holdout$predict.class)

[1] "factor"

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