Regularized Greedy Forest (RGF) is a tree ensemble machine learning method described in this paper. RGF can deliver better results than gradient boosted decision trees (GBDT) on a number of datasets and it has been used to win a few Kaggle competitions. Unlike the traditional boosted decision tree approach, RGF works directly with the underlying forest structure. RGF integrates two ideas: one is to include tree-structured regularization into the learning formulation; and the other is to employ the fully-corrective regularized greedy algorithm.

This repository contains the following implementations of the RGF algorithm:

• RGF: original implementation from the paper;
• FastRGF: multi-core implementation with some simplifications;
• rgf_python: wrapper of both RGF and FastRGF implementations for Python;
• R package: wrapper of rgf_python for R.

You may want to get interesting information about RGF from the posts collected in Awesome RGF.