#define a data set
train <- data.frame(ClaimID = c(1,2,3),
RearEnd = c(TRUE, FALSE, TRUE),
Fraud = c(TRUE, FALSE, TRUE))
train
##   ClaimID RearEnd Fraud
## 1       1    TRUE  TRUE
## 2       2   FALSE FALSE
## 3       3    TRUE  TRUE
library(rpart)
mytree <- rpart(Fraud~RearEnd, data = train, method = "class")
mytree
## n= 3 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 3 1 TRUE (0.3333333 0.6666667) *
mytree <-rpart(Fraud~RearEnd,
                data = train, 
                method = "class",
                minsplit = 2,
                minbucket = 1)
mytree
## n= 3 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 3 1 TRUE (0.3333333 0.6666667)  
##   2) RearEnd< 0.5 1 0 FALSE (1.0000000 0.0000000) *
##   3) RearEnd>=0.5 2 0 TRUE (0.0000000 1.0000000) *
library(rattle)
## 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)
library(RColorBrewer)
fancyRpartPlot(mytree, caption = NULL)
mytree <-rpart(Fraud~RearEnd,
               data = train, 
               method = "class",
               parms = list(split = 'information'),
               minsplit = 2, minbucket = 1)
mytree
## n= 3 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 3 1 TRUE (0.3333333 0.6666667)  
##   2) RearEnd< 0.5 1 0 FALSE (1.0000000 0.0000000) *
##   3) RearEnd>=0.5 2 0 TRUE (0.0000000 1.0000000) *
fancyRpartPlot(mytree, caption = NULL)

#CHANGED Fraud 
train <- data.frame(ClaimID = c(1,2,3),
                    RearEnd = c(TRUE, FALSE, TRUE),
                    Fraud = c(TRUE, FALSE, FALSE))
train
##   ClaimID RearEnd Fraud
## 1       1    TRUE  TRUE
## 2       2   FALSE FALSE
## 3       3    TRUE FALSE
mytree
## n= 3 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 3 1 TRUE (0.3333333 0.6666667)  
##   2) RearEnd< 0.5 1 0 FALSE (1.0000000 0.0000000) *
##   3) RearEnd>=0.5 2 0 TRUE (0.0000000 1.0000000) *
mytree <-rpart(Fraud~RearEnd,
               data = train, 
               method = "class",
               minsplit = 2,
               minbucket = 1,
               cp = -1)
mytree
## n= 3 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 3 1 FALSE (0.6666667 0.3333333)  
##   2) RearEnd< 0.5 1 0 FALSE (1.0000000 0.0000000) *
##   3) RearEnd>=0.5 2 1 FALSE (0.5000000 0.5000000) *
fancyRpartPlot(mytree, caption = NULL)

train <- data.frame(ClaimID = 1:7,
                    RearEnd = c(TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE),
                    Whiplash = c(TRUE, TRUE, TRUE, TRUE, TRUE, FALSE, FALSE),
                    Fraud = c(TRUE, TRUE, TRUE, FALSE, FALSE, FALSE, FALSE))
train
##   ClaimID RearEnd Whiplash Fraud
## 1       1    TRUE     TRUE  TRUE
## 2       2    TRUE     TRUE  TRUE
## 3       3   FALSE     TRUE  TRUE
## 4       4   FALSE     TRUE FALSE
## 5       5   FALSE     TRUE FALSE
## 6       6   FALSE    FALSE FALSE
## 7       7   FALSE    FALSE FALSE
mytree <-rpart(Fraud~RearEnd + Whiplash,
               data = train, 
               method = "class",
               maxdepth = 1, #how deep the tree is
               minsplit = 2,
               minbucket = 1)
fancyRpartPlot(mytree, caption = NULL)

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",
                maxdepth = 1, 
                minsplit = 2,
                minbucket = 1,
                parms = list(loss = lossmatrix))
  
fancyRpartPlot(mytree, caption = NULL)

train <- data.frame(ClaimID = c(1,2,3,4,5),
                    Activity = factor(x = c("active", "very active", "very active", "inactive", "very inactive"),
                    levels = c("very inactive", "inactive", "active","very active"), order=TRUE),
                    Fraud = c(FALSE, TRUE, TRUE, FALSE, TRUE))
train
##   ClaimID      Activity Fraud
## 1       1        active FALSE
## 2       2   very active  TRUE
## 3       3   very active  TRUE
## 4       4      inactive FALSE
## 5       5 very inactive  TRUE
mytree <- rpart(Fraud~Activity,
                data = train, 
                method = "class",
                minsplit = 2,
                minbucket = 1)
fancyRpartPlot(mytree, caption = NULL)       

train <- data.frame(ClaimID = 1:10,
                    RearEnd = c(TRUE, TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, TRUE, TRUE, FALSE),
                    Whiplash = c(TRUE, TRUE, TRUE, TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, TRUE),
                    Activity = factor(x = c("active", "very active", "very active", 
                                            "inactive", "very inactive", "inactive", "very inactive", "active", "active", "very active"),
                                      levels = c("very inactive", "inactive", "active","very active"), order=TRUE),
                    Fraud = c(FALSE, TRUE, TRUE, FALSE, FALSE, TRUE, TRUE, FALSE, FALSE,TRUE))
train
##    ClaimID RearEnd Whiplash      Activity Fraud
## 1        1    TRUE     TRUE        active FALSE
## 2        2    TRUE     TRUE   very active  TRUE
## 3        3    TRUE     TRUE   very active  TRUE
## 4        4   FALSE     TRUE      inactive FALSE
## 5        5   FALSE     TRUE very inactive FALSE
## 6        6   FALSE    FALSE      inactive  TRUE
## 7        7   FALSE    FALSE very inactive  TRUE
## 8        8    TRUE    FALSE        active FALSE
## 9        9    TRUE    FALSE        active FALSE
## 10      10   FALSE     TRUE   very active  TRUE
mytree <- rpart(Fraud ~ RearEnd + Whiplash + Activity,
                data = train, 
                method = "class",
                minsplit = 2,
                minbucket = 1,
                cp = -1)
fancyRpartPlot(mytree, caption = NULL)  

#display attributes of mytree
names(mytree)
##  [1] "frame"               "where"               "call"               
##  [4] "terms"               "cptable"             "method"             
##  [7] "parms"               "control"             "functions"          
## [10] "numresp"             "splits"              "csplit"             
## [13] "variable.importance" "y"                   "ordered"
mytree$method
## [1] "class"
#the most important variable
mytree$variable.importance
##  Activity  Whiplash   RearEnd 
## 3.0000000 2.0000000 0.8571429
print(mytree$finalmodel)
## NULL
mytree$variable.importance
##  Activity  Whiplash   RearEnd 
## 3.0000000 2.0000000 0.8571429
printcp(mytree)
## 
## Classification tree:
## rpart(formula = Fraud ~ RearEnd + Whiplash + Activity, data = train, 
##     method = "class", minsplit = 2, minbucket = 1, cp = -1)
## 
## Variables actually used in tree construction:
## [1] Activity RearEnd  Whiplash
## 
## Root node error: 5/10 = 0.5
## 
## n= 10 
## 
##     CP nsplit rel error xerror    xstd
## 1  0.6      0       1.0    2.0 0.00000
## 2  0.2      1       0.4    0.4 0.25298
## 3 -1.0      3       0.0    0.4 0.25298
mytree <- prune(mytree, cp =0.21)
fancyRpartPlot(mytree)

Note that the echo = FALSE parameter was added to the code chunk to prevent printing of the R code that generated the plot.