https://github.com/RGF-team/rgf

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.
https://github.com/RGF-team/rgf

decision-forest decision-trees ensemble-model kaggle machine-learning ml regularized-greedy-forest rgf

Last synced: 25 days ago
JSON representation

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.

• Host: GitHub
• URL: https://github.com/RGF-team/rgf
• Owner: RGF-team
• Created: 2016-06-08T15:48:42.000Z (almost 8 years ago)
• Default Branch: master
• Last Pushed: 2022-01-08T13:46:30.000Z (over 2 years ago)
• Last Synced: 2024-04-22T03:18:58.162Z (about 1 month ago)
• Topics: decision-forest, decision-trees, ensemble-model, kaggle, machine-learning, ml, regularized-greedy-forest, rgf
• Language: C++
• Homepage:
• Size: 5.25 MB
• Stars: 371
• Watchers: 18
• Forks: 55
• Open Issues: 8
• Metadata Files:
  • Readme: README.md

Lists

• awesome-python-data-science - rgf_python - Python Wrapper of Regularized Greedy Forest. <img height="20" src="img/sklearn_big.png" alt="sklearn"> (Machine Learning / Random Forests)
• AI - rgf_python - Python bindings for Regularized Greedy Forest (Tree) Library. (Python / General-Purpose Machine Learning)
• awesome-machine-learning - rgf_python - Python bindings for Regularized Greedy Forest (Tree) Library. (Python / General-Purpose Machine Learning)
• awesome-stars - RGF-team/rgf - 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. (C++)
• awesome-machine-learning - rgf_python - Python bindings for Regularized Greedy Forest (Tree) Library. (Python / General-Purpose Machine Learning)
• awesome-machine-learnings - rgf_python - Python bindings for Regularized Greedy Forest (Tree) Library. (Python / General-Purpose Machine Learning)
• awesome-machine-learning - rgf_python - Python bindings for Regularized Greedy Forest (Tree) Library. (Python / General-Purpose Machine Learning)
• awesome-machine-learning - rgf_python - Python bindings for Regularized Greedy Forest (Tree) Library. (Python / General-Purpose Machine Learning)
• awesome-machine-learning - rgf_python - Python bindings for Regularized Greedy Forest (Tree) Library. (Python / General-Purpose Machine Learning)
• awesome-machine-learning - rgf_python - Python bindings for Regularized Greedy Forest (Tree) Library. (Python / General-Purpose Machine Learning)
• awesome-python-machine-learning - Regularized Greedy Forest - Regularized Greedy Forest (RGF) is a tree ensemble machine learning method (Uncategorized / Uncategorized)

README

        
[![Python and R tests](https://github.com/RGF-team/rgf/workflows/Python%20and%20R%20tests/badge.svg?branch=master)](https://github.com/RGF-team/rgf/actions)

[![DOI](https://zenodo.org/badge/DOI/10.1109/TPAMI.2013.159.svg)](https://doi.org/10.1109/TPAMI.2013.159)

[![arXiv.org](https://img.shields.io/badge/arXiv-1109.0887-b31b1b.svg)](https://arxiv.org/abs/1109.0887)

[![Python Versions](https://img.shields.io/pypi/pyversions/rgf_python.svg)](https://pypi.org/project/rgf_python)

[![PyPI Version](https://img.shields.io/pypi/v/rgf_python.svg)](https://pypi.org/project/rgf_python)

[![CRAN Version](https://r-pkg.org/badges/version/RGF)](https://cran.r-project.org/package=RGF)

# Regularized Greedy Forest

Regularized Greedy Forest (RGF) is a tree ensemble machine learning method described in [this paper](https://arxiv.org/abs/1109.0887).

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](https://github.com/RGF-team/rgf/tree/master/RGF): original implementation from the paper;

- [FastRGF](https://github.com/RGF-team/rgf/tree/master/FastRGF): multi-core implementation with some simplifications;

- [rgf_python](https://github.com/RGF-team/rgf/tree/master/python-package): wrapper of both RGF and FastRGF implementations for Python;

- [R package](https://github.com/RGF-team/rgf/tree/master/R-package): wrapper of rgf_python for R.

You may want to get interesting information about RGF from the posts collected in [Awesome RGF](https://github.com/RGF-team/rgf/blob/master/AWESOME_RGF.md ).