Classifying data with gradient boosting
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 churn dataset.
Getting ready
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, Classification (I) – Tree, Lazy, and Probabilistic, for detailed information.
How to do it...
Perform the following steps to calculate and classify data with the gradient ...
Get R: Recipes for Analysis, Visualization and Machine Learning now with the O’Reilly learning platform.
O’Reilly members experience books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.
