> install.packages("randomForest") 
> library(randomForest)
> ir.index <- sample(nrow(iris), nrow(iris) * 0.2)
> ir.test = iris[ir.index,]
> ir.train = iris[-ir.index,]
> ir.rf <- randomForest(Species ~ ., data = ir.train)
> ir.rf

Call:
 randomForest(formula = Species ~ ., data = ir.train) 
               Type of random forest: classification
                     Number of trees: 500
No. of variables tried at each split: 2

        OOB estimate of  error rate: 5%
Confusion matrix:
           setosa versicolor virginica class.error
setosa         36          0         0  0.00000000
versicolor      0         37         3  0.07500000
virginica       0          3        41  0.06818182

> plot(ir.rf)
