Gradient Boosted Trees Learner (Regression) (deprecated)

Learns Gradient Boosted Trees with the objective of regression. 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 4.4 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 .

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.

Input ports

1. Type: Table
   Input Data
   The data to learn from. It must contain at least one numeric target column and either a fingerprint (bitvector) column or another numeric or nominal column.

Output ports

1. Type: Gradient Boosting Model
   Gradient Boosting Model
   The trained model.