Machine Learning with R Cookbook - Second Edition

How to do it...

Perform the following steps to classify data with random forest:

1. First, you have to install and load the randomForest package:

        > install.packages("randomForest")
        > library(randomForest)  

2. You can then fit the random forest classifier with a training set:

        > churn.rf = randomForest(churn ~ ., data = trainset, importance        = T)
        > churn.rf
        Output
        Call:
         randomForest(formula = churn ~ ., data = trainset, importance         = T) 
                       Type of random forest: classification
                             Number of trees: 500
        No. of variables tried at each split: 4
    
                OOB estimate of  error rate: 4.88%
        Confusion matrix:
            yes     no class.error
         yes 247   95 0.277777778
        no   18 1955 0.009123163

3. Next, make predictions based on the fitted model and testing dataset:

    > churn.prediction = predict(churn.rf, ...