decision tree_ctree.txt

install.packages("party")
install.packages("caret")
library(party)
library(caret)

data_url <- c("https://archive.ics.uci.edu/ml/machine-learning-databases/car/car.data")
download.file(url = data_url, destfile = "car.data")
car_df <- read.csv("car.data", sep = ',', header = FALSE)
str(car_df)
dim(car_df)
summary(car_df)
set.seed(3000)
intrain <- createDataPartition(y = car_df$V7, p= 0.7, list = FALSE)	
training <- car_df[intrain,]
testing <- car_df[-intrain,]
dim(training); 
dim(testing);
anyNA(car_df)
summary(car_df)

trctrl<-ctree_control(teststat = c("quad", "max"),testtype = c("Teststatistic"))
set.seed(12345)
ctree_fit<- ctree(V7~., data = training, control=trctrl)
ctree_fit
plot(ctree_fit)

set.seed(54321)
pred <- predict(ctree_fit, newdata=testing)
library(caret)
confusionMatrix(pred, testing$V7)




ctree_fit<- train(V7~., data = training, method='ctree', tuneLength=10,trControl=trainControl(method='repeatedcv', number=10, classProbs=TRUE))