To begin, load the essential libraries and register the number of cores for parallel processing:

library(doMC) 
registerDoMC(cores = 4)  
library(caret) 
#setting the random seed for replication 
set.seed(1234) 
# setting the working directory where the data is located 
setwd("~/Desktop/chapter 15") 
# reading the data 
mydata <- read.csv("WA_Fn-UseC_-HR-Employee-Attrition.csv") 
#removing the non-discriminatory features identified during EDA 
mydata$EmployeeNumber=mydata$Over18=mydata$EmployeeCount=mydata$StandardHours = NULL 
#setting up cross-validation 
cvcontrol <- trainControl(method="repeatedcv", repeats=10, number = 10, allowParallel=TRUE) 
# model creation with treebag , observe that the number of bags is set as 10 
train.bagg <- train(Attrition ~ ., data=mydata, method="treebag",B=10, trControl=cvcontrol, importance=TRUE) 
train.bagg 

This will result in the following output:

Bagged CART  
1470 samples 
  30 predictors 
   2 classes: 'No', 'Yes'  
No pre-processing 
Resampling: Cross-Validated (10 fold, repeated 10 times)  
Summary of sample sizes: 1324, 1323, 1323, 1322, 1323, 1322, ...  
Resampling results: 
  Accuracy  Kappa     
  0.854478  0.2971994 

We can see that we achieved a better accuracy of 85.4% compared to 84% accuracy that was obtained with the KNN algorithm.