Similar to the bagging
method, boosting starts with a simple or weak classifier and gradually improves it by reweighting the misclassified samples. Thus, the new classifier can learn from previous classifiers. The adabag
package provides implementation of the AdaBoost.M1 and SAMME algorithms. Therefore, one can use the boosting
method in adabag
to perform ensemble learning. In this recipe, we will use the boosting
method in adabag
to classify the telecom churn
dataset.
In this recipe, we will continue to use the telecom churn dataset as the input data source to perform classifications with the boosting
method. Also, you need to have the adabag
package loaded in R before commencing the recipe.