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)
## Warning: package 'rpart' was built under R version 4.3.3
mytree <- rpart(FRAUD~RearEnd, data = train, method= "class")
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
## Warning: package 'tibble' was built under R version 4.3.3
## 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)
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", parms = list(split='information'), minsplit=2, minbucket=1)
mytree
## n= 3
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 3 1 FALSE (0.6666667 0.3333333) *
mytree <- rpart(FRAUD~RearEnd, data = train, method = "class", minsplit=2, minbucket=1, cp = -1)
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)
)
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)
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 <NA> 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 <NA> TRUE
## 7 7 FALSE FALSE <NA> TRUE
## 8 8 TRUE FALSE active FALSE
## 9 9 TRUE FALSE active FALSE
## 10 10 FALSE TRUE very active TRUE
#Grow a full tree
mytree <- rpart(
FRAUD ~ RearEnd + Whiplash + Activity, data= train, method = "class", minsplit=2, minbucket=1, cp= -1
)
fancyRpartPlot(mytree, caption = NULL)
mytree$variable.importance
## Activity Whiplash RearEnd
## 4.0357143 2.0000000 0.8571429
names(mytree)
## [1] "frame" "where" "call"
## [4] "terms" "cptable" "method"
## [7] "parms" "control" "functions"
## [10] "numresp" "splits" "csplit"
## [13] "variable.importance" "y" "ordered"
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)
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