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.
