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.
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.
Perform the following steps to generate a classification model for the telecom churn
dataset:
- First, you need to install and load the
adabag
package (it might take a while to installadabag
):
> install.packages("adabag")> library(adabag)
- Next, you can use the
bagging
function to train...