set.seed(15)
train.data <- final_data_Fpositive[sample(358,215),]
set.seed(15)
test.data <- final_data_Fpositive[-sample(358,215),]

CLtree <- rpart(TB~., data = train.data, method = "class")
plot(CLtree)

printcp(CLtree)
plotcp(CLtree)

ptree<- prune(CLtree, cp= CLtree$cptable[which.min(CLtree$cptable[,"xerror"]),"CP"])

plot(ptree, uniform=TRUE, main="Pruned Classification Tree")
text(ptree, cex = 0.7)

pred.pruned <- predict(ptree, test.data, "class")
mean(pred.pruned == test.data$TB)
table(test.data$TB,pred.pruned)