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.
Classifying data with the boosting method
Getting ready
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...
