Performing cross-validation with the boosting method

Similar to the bagging function, adabag provides a cross-validation function for the boosting method named boosting.cv. In this recipe, we will demonstrate how to perform cross-validation using boosting.cv from the package adabag.

Getting ready

In this recipe, we continue to use the telecom churn dataset as the input data source to perform a k-fold cross-validation with the boosting method.

How to do it...

Perform the following steps to retrieve the minimum estimation errors via cross-validation with the boosting method:

1. First, you can use boosting.cv to cross-validate the training dataset:

        > churn.boostcv = boosting.cv(churn ~ ., v=10, data=trainset,
        mfinal=5,control=rpart.control(cp=0.01))

2. You can then obtain the confusion matrix from the boosting results:

        > churn.boostcv$confusion
        Output
                       Observed Class
        Predicted Class  yes   no
                    no   119 1940
          ...