Machine Learning with R Cookbook - Second Edition

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Apart from the randomForest package, the party package also provides an implementation of random forest. In the following steps, we illustrate how to use the cforest function within the party package to perform classifications:

1. First, install and load the party package:

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

2. You can then use the cforest function to fit the classification model:

        > churn.cforest = cforest(churn ~ ., data = trainset, con-        trols=cforest_unbiased(ntree=1000, mtry=5))        > churn.cforest        Output        Random Forest using Conditional Inference Trees        Number of trees: 1000         Response: churn         Inputs: international_plan, voice_mail_plan, num-        ber_vmail_messages, total_day_minutes, total_day_calls, to- tal_day_charge, ...