Scikit-learn Wrapper for Regularized Greedy Forest

Machine, Learning, decision-forest, decision-trees, ensemble-model, kaggle, machine-learning, ml, regularized-greedy-forest, rgf
pip install rgf-python==3.12.0


Python and R tests DOI Python Versions PyPI Version CRAN Version

Regularized Greedy Forest

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.