Learns Gradient Boosted Trees with the objective of classification. The algorithm uses very shallow regression trees and a special form of boosting to build an ensemble of trees. The implementation follows the algorithm in section 4.6 of the paper "Greedy Function Approximation: A Gradient Boosting Machine" by Jerome H. Friedman (1999). For more information you can also take a look at this .

The used base learner for this ensemble method is a simple regression tree as it is used in the *Tree Ensemble* , *Random Forest* and *Simple Regression Tree* nodes. Per default a tree is build using binary splits for numeric and nominal attributes (the later can be changed to multiway splits). The built-in missing value handling tries to find the best direction for missing values to go to by testing each possible direction and selecting the one yielding the best result (i.e. largest gain).

In a regression tree the predicted value for a leaf node is the mean target value of the records within the leaf. Hence the predictions are best (with respect to the training data) if the variance of target values within a leaf is minimal. This is achieved by splits that minimize the sum of squared errors in their respective children.

#### Sampling

This node allows to perform row sampling (bagging) and attribute sampling (attribute bagging) similar to the random forest* and tree ensemble nodes. If sampling is used this is usually referred to as *Stochastic Gradient Boosted Trees* . The respective settings can be found in the *Advanced Options* tab.

(*) RANDOM FORESTS is a registered trademark of Minitab, LLC and is used with Minitab’s permission.