Classifying data with the boosting method
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.
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 commencing the recipe. ...
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