The adabag package implements both boosting and bagging methods. For the bagging method, the package implements Breiman's Bagging algorithm, which first generates multiple versions of classifiers and then obtains an aggregated classifier. In this recipe, we will illustrate how to use the bagging method from adabag to generate a classification model using the telecom churn dataset.
Classifying data with the bagging method
Getting ready
In this recipe, we will continue to use the telecom churn dataset as the input data source for the bagging method. For those who have not prepared the dataset, please refer to Chapter 7, Classification 1 - Tree, Lazy, and Probabilistic, for detailed information.
