Results using CART Model 

The first step we follow in any modeling exercise is to split the data into training and validation. You can use the following code for the split. (We will use the same split for random forest as well)

train.flag <- createDataPartition(y=iris$Species,p=0.5,list=FALSE)

training <- iris[train.flag,]

Validation <- iris[-train.flag,]

CART model gave following result in the training and validation :

Misclassification rate in training data = 3/75

Misclassification rate in validation data = 4/75

As you can see, CART model gave decent result in terms of accuracy and stability. We will now model the random forest algorithm on the same training dataset and validate it using same validation dataset.