Machine Learning with R Cookbook - Second Edition

How to do it...

Perform the following steps to estimate the prediction errors of each classification method:

1. You can estimate the error rate of the bagging model:

        > churn.bagging= errorest(churn ~ ., data = trainset, model =         bagging)
        > churn.bagging
        Output
        Call:
        errorest.data.frame(formula = churn ~ ., data = trainset, model        = bagging)
       
           10-fold cross-validation estimator of misclassification error 
      
        Misclassification error:  0.0583

2. You can then estimate the error rate of the boosting method:

        > install.packages("ada")
        > library(ada)
        > churn.boosting= errorest(churn ~ ., data = trainset, model =         ada)
        > churn.boosting
        Output
        Call:
        errorest.data.frame(formula = churn ~ ., data = trainset, model = ada) 10-fold cross-validation estimator of misclassification ...