Besides adabag, the ipred package provides a bagging method for a classification tree. We demonstrate here how to use the bagging method of the ipred package to train a classification model:

1. First, you need to install and load the ipred package:

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

2. You can then use the bagging method to fit the classification method:

        > churn.bagging = bagging(churn ~ ., data = trainset, coob = T)
        > churn.bagging
        Output
        Bagging classification trees with 25 bootstrap replications 
    
        Call: bagging.data.frame(formula = churn ~ ., data = trainset, coob
         = T)
    
        Out-of-bag estimate of misclassification error:  0.0605  

3. Obtain an out of bag estimate of misclassification of the errors:

        > mean(predict(churn.bagging) != trainset$churn)
        Output
        [1] 0.06047516  

4. You can then use the predict function to obtain the predicted labels of the testing dataset:

        > churn.prediction = predict(churn.bagging, newdata=testset,
        type="class")  

5. Obtain the classification table from the labels of the testing dataset and prediction results:

        > prediction.table = table(churn.prediction, testset$churn)
        Output
        churn.prediction yes  no
                     no   31 869
                     yes 110   8