Skip to content
/ rgf Public

Home repository for the Regularized Greedy Forest (RGF) library. It includes original implementation from the paper and multithreaded one written in C++, along with various language-specific wrappers.

Notifications You must be signed in to change notification settings

RGF-team/rgf

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Python and R tests DOI arXiv.org 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.