Decision Trees

# Create the training data frame
train <- data.frame(ClaimID = c(1:7),
                    RearEnd = c(TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE),
                    Fraud = c(TRUE,TRUE,TRUE,FALSE,FALSE,FALSE,FALSE),
                    Whiplash = c(TRUE,TRUE,TRUE,TRUE,TRUE,FALSE,FALSE))

# Display the training data
train

##   ClaimID RearEnd Fraud Whiplash
## 1       1    TRUE  TRUE     TRUE
## 2       2    TRUE  TRUE     TRUE
## 3       3   FALSE  TRUE     TRUE
## 4       4   FALSE FALSE     TRUE
## 5       5   FALSE FALSE     TRUE
## 6       6   FALSE FALSE    FALSE
## 7       7   FALSE FALSE    FALSE

# Load the rpart package
library(rpart)

## Warning: package 'rpart' was built under R version 4.3.3

library(rattle)

## Warning: package 'rattle' was built under R version 4.3.3

## Loading required package: tibble

## Loading required package: bitops

## Rattle: A free graphical interface for data science with R.
## Version 5.5.1 Copyright (c) 2006-2021 Togaware Pty Ltd.
## Type 'rattle()' to shake, rattle, and roll your data.

library(rpart.plot)

## Warning: package 'rpart.plot' was built under R version 4.3.3

library(RColorBrewer)
# Train the decision tree model
lossmatrix<-matrix(c(0,1,3,0), byrow = TRUE, nrow = 2 )
lossmatrix

##      [,1] [,2]
## [1,]    0    1
## [2,]    3    0

mytree <- rpart(Fraud ~RearEnd+Whiplash, data = train, method = "class",minsplit=2,minbucket=1,parms=list(loss=lossmatrix))

# Display the decision tree
mytree

## n= 7 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 7 4 TRUE (0.5714286 0.4285714)  
##   2) Whiplash< 0.5 2 0 FALSE (1.0000000 0.0000000) *
##   3) Whiplash>=0.5 5 2 TRUE (0.4000000 0.6000000) *

fancyRpartPlot(mytree, caption = NULL)