#My fancyRpartplot function is not working wen trying to knit so i commented them out
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<- set(DependentVariable - IndependentVariable, 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) *
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) *
train <- data.frame(claiID =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
## claiID 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
)
mytree
## n= 7
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 7 3 FALSE (0.5714286 0.4285714)
## 2) RearEnd< 0.5 5 1 FALSE (0.8000000 0.2000000) *
## 3) RearEnd>=0.5 2 0 TRUE (0.0000000 1.0000000) *
#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"), level =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)
mytree$variable.importance
## Activity Whiplash RearEnd
## 3.0000000 2.0000000 0.8571429
names(mytree)
## [1] "frame" "where" "call"
## [4] "terms" "cptable" "method"
## [7] "parms" "control" "functions"
## [10] "numresp" "splits" "csplit"
## [13] "variable.importance" "y" "ordered"
mytree$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)