Gradient boosting ensembles weak learners and creates a new base learner that maximally correlates with the negative gradient of the loss function. One may apply this method on either regression or classification problems, and it will perform well in different datasets. In this recipe, we will introduce how to use
gbm to classify a telecom
In this recipe, we 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 5, Classification (I) – Tree, Lazy, and Probabilistic, for detailed information.
Perform the following steps to calculate and classify data with the gradient ...