Machine Learning with R Cookbook - Second Edition

How it works...

The algorithm of gradient boosting involves, first, the process computing the deviation of residuals for each partition, and then, determining the best data partitioning in each stage. Next, the successive model will fit the residuals from the previous stage and build a new model to reduce the residual variance (an error). The reduction of the residual variance follows the functional gradient descent technique, in which it minimizes the residual variance by going down its derivative, as show here:

Gradient descent method

In this recipe, we use the gradient boosting method from gbm to classify the telecom churn dataset. To begin ...