{"id":13471114,"url":"https://github.com/RGF-team/rgf","last_synced_at":"2025-03-26T13:30:50.177Z","repository":{"id":9769960,"uuid":"60709988","full_name":"RGF-team/rgf","owner":"RGF-team","description":"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.","archived":false,"fork":false,"pushed_at":"2022-01-08T13:46:30.000Z","size":5508,"stargazers_count":378,"open_issues_count":9,"forks_count":58,"subscribers_count":18,"default_branch":"master","last_synced_at":"2024-10-30T02:58:17.750Z","etag":null,"topics":["decision-forest","decision-trees","ensemble-model","kaggle","machine-learning","ml","regularized-greedy-forest","rgf"],"latest_commit_sha":null,"homepage":"","language":"C++","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/RGF-team.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2016-06-08T15:48:42.000Z","updated_at":"2024-10-18T10:43:21.000Z","dependencies_parsed_at":"2022-08-07T05:01:18.299Z","dependency_job_id":null,"html_url":"https://github.com/RGF-team/rgf","commit_stats":null,"previous_names":["fukatani/rgf_python"],"tags_count":26,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/RGF-team%2Frgf","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/RGF-team%2Frgf/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/RGF-team%2Frgf/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/RGF-team%2Frgf/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/RGF-team","download_url":"https://codeload.github.com/RGF-team/rgf/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":245662738,"owners_count":20652073,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["decision-forest","decision-trees","ensemble-model","kaggle","machine-learning","ml","regularized-greedy-forest","rgf"],"created_at":"2024-07-31T16:00:40.035Z","updated_at":"2025-03-26T13:30:47.285Z","avatar_url":"https://github.com/RGF-team.png","language":"C++","funding_links":[],"categories":["C++","Python","Uncategorized"],"sub_categories":["General-Purpose Machine Learning","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)\n[![DOI](https://zenodo.org/badge/DOI/10.1109/TPAMI.2013.159.svg)](https://doi.org/10.1109/TPAMI.2013.159)\n[![arXiv.org](https://img.shields.io/badge/arXiv-1109.0887-b31b1b.svg)](https://arxiv.org/abs/1109.0887)\n[![Python Versions](https://img.shields.io/pypi/pyversions/rgf_python.svg)](https://pypi.org/project/rgf_python)\n[![PyPI Version](https://img.shields.io/pypi/v/rgf_python.svg)](https://pypi.org/project/rgf_python)\n[![CRAN Version](https://r-pkg.org/badges/version/RGF)](https://cran.r-project.org/package=RGF)\n\n# Regularized Greedy Forest\n\nRegularized Greedy Forest (RGF) is a tree ensemble machine learning method described in [this paper](https://arxiv.org/abs/1109.0887).\nRGF 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.\nUnlike the traditional boosted decision tree approach, RGF works directly with the underlying forest structure.\nRGF 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.\n\nThis repository contains the following implementations of the RGF algorithm:\n\n- [RGF](https://github.com/RGF-team/rgf/tree/master/RGF): original implementation from the paper;\n- [FastRGF](https://github.com/RGF-team/rgf/tree/master/FastRGF): multi-core implementation with some simplifications;\n- [rgf_python](https://github.com/RGF-team/rgf/tree/master/python-package): wrapper of both RGF and FastRGF implementations for Python;\n- [R package](https://github.com/RGF-team/rgf/tree/master/R-package): wrapper of rgf_python for R.\n\nYou 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 ).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FRGF-team%2Frgf","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FRGF-team%2Frgf","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FRGF-team%2Frgf/lists"}