> library(rpart)

> dt=rpart(Species~.,iris)

> plot(dt);text(dt);

> table(predict(dt,type="class"),iris$Species)

Try these statements in R. Explain their meanings. Explain the result of the contingency table.

> dt=rpart(Species~.,iris,control=rpart.control(cp=0.0,minsplit=0))

> plot(dt);text(dt);

> table(predict(dt,type="class"),iris$Species)

Explain the meaning of the first statement. Explain the result of the contingency table and its difference with the previous result.

> train_index = c(sample(50,30), sample(50,30)+50, sample(50,30)+100)

> iris_train=iris[train_index,]

> iris_test=iris[-train_index,]

> dt=rpart(Species~.,iris_train)

> plot(dt);text(dt);

> table(predict(dt,newdata=iris_test,type="class"),iris_test$Species)

Explain the meaning of these statements.

> library(randomForest)

> rf=randomForest(Species~., iris, ntree=1000, proximity=TRUE)

> table(predict(rf,type="class"),iris$Species)

Explain the meaning of these statements.