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# depth of Tree
# how many leaves should be in each bucket
#predict using decision trees
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
# to grow our decisin tree we need to intsallRpar

library(rpart)# bring the rpart into your r environment 
## Warning: package 'rpart' was built under R version 4.3.3
mytree<-rpart(Fraud~RearEnd, data=train,method="class")
# dependent varable is Fraud~ RearEnd
mytree
## n= 3 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 3 1 TRUE (0.3333333 0.6666667) *
#minsplit is the minimum no of observation that must exist in a node in other for spit to be attmpted
#minbuket is the minimum number of observation in any terminal node

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)
## 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)
# plot mytree
fancyRpartPlot(mytree, caption=NULL)
#if rearend is 0 ,yes (false) no fraud
#if rearend is 0 ,no that means rearend is 1 (True) fraud


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)

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<-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,
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")),                                    
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
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"), ordered=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)

names(mytree)
##  [1] "frame"               "where"               "call"               
##  [4] "terms"               "cptable"             "method"             
##  [7] "parms"               "control"             "functions"          
## [10] "numresp"             "splits"              "csplit"             
## [13] "variable.importance" "y"                   "ordered"
mytree$variable.importance
##  Activity  Whiplash   RearEnd 
## 3.0000000 2.0000000 0.8571429
mytree$method
## [1] "class"
print(mytree$method)
## [1] "class"
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)