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

Science Score: 23.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • CITATION.cff file
  • codemeta.json file
  • .zenodo.json file
  • DOI references
    Found 3 DOI reference(s) in README
  • Academic publication links
    Links to: arxiv.org, zenodo.org
  • Committers with academic emails
  • Institutional organization owner
  • JOSS paper metadata
  • Scientific vocabulary similarity
    Low similarity (11.7%) to scientific vocabulary

Keywords

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

Keywords from Contributors

distributed parallel gbdt gbm gbrt xgboost data-mining gradient-boosting lightgbm
Last synced: 6 months ago · JSON representation

Repository

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.

Basic Info
  • Host: GitHub
  • Owner: RGF-team
  • Language: C++
  • Default Branch: master
  • Homepage:
  • Size: 5.25 MB
Statistics
  • Stars: 382
  • Watchers: 17
  • Forks: 58
  • Open Issues: 9
  • Releases: 0
Topics
decision-forest decision-trees ensemble-model kaggle machine-learning ml regularized-greedy-forest rgf
Created over 9 years ago · Last pushed about 4 years ago
Metadata Files
Readme

README.md

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.

Owner

  • Name: RGF-team
  • Login: RGF-team
  • Kind: organization

GitHub Events

Total
  • Watch event: 7
Last Year
  • Watch event: 7

Committers

Last synced: over 2 years ago

All Time
  • Total Commits: 470
  • Total Committers: 10
  • Avg Commits per committer: 47.0
  • Development Distribution Score (DDS): 0.594
Past Year
  • Commits: 0
  • Committers: 0
  • Avg Commits per committer: 0.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
fukatani n****a@g****m 191
Nikita Titov n****8@m****u 148
Nikita Titov n****2@h****m 109
Lampros Mouselimis m****s@g****m 10
James Lamb j****0@g****m 7
Eyad Sibai e****i@g****m 1
nicknoproblems n****0@g****m 1
Andrew Kane a****1@g****m 1
Vadim Markovtsev v****m@s****h 1
Sean Szurko s****4 1
Committer Domains (Top 20 + Academic)

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 22
  • Total pull requests: 81
  • Average time to close issues: 4 months
  • Average time to close pull requests: 5 days
  • Total issue authors: 13
  • Total pull request authors: 6
  • Average comments per issue: 8.23
  • Average comments per pull request: 2.46
  • Merged pull requests: 75
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 1
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 1
  • Average comments per issue: 0
  • Average comments per pull request: 0.0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Top Authors
Issue Authors
  • fukatani (4)
  • jameslamb (4)
  • StrikerRUS (3)
  • similang (2)
  • wangbingnan136 (1)
  • tunguyen52 (1)
  • yuanjie-ai (1)
  • vsedelnik (1)
  • albertnanda (1)
  • galeese (1)
  • casperkaae (1)
  • bvphillips (1)
  • scolemann (1)
Pull Request Authors
  • StrikerRUS (61)
  • fukatani (8)
  • mlampros (7)
  • ankane (2)
  • jameslamb (2)
  • b1nb1n88 (1)
Top Labels
Issue Labels
help wanted (1) enhancement (1)
Pull Request Labels

Packages

  • Total packages: 3
  • Total downloads:
    • pypi 1,161 last-month
    • cran 393 last-month
  • Total docker downloads: 42,302
  • Total dependent packages: 1
    (may contain duplicates)
  • Total dependent repositories: 52
    (may contain duplicates)
  • Total versions: 40
  • Total maintainers: 3
pypi.org: rgf-python

Scikit-learn Wrapper for Regularized Greedy Forest

  • Versions: 28
  • Dependent Packages: 1
  • Dependent Repositories: 51
  • Downloads: 1,161 Last month
  • Docker Downloads: 297
Rankings
Docker downloads count: 1.8%
Dependent repos count: 2.1%
Stargazers count: 3.4%
Average: 4.2%
Dependent packages count: 4.7%
Forks count: 5.7%
Downloads: 7.8%
Maintainers (2)
Last synced: 6 months ago
cran.r-project.org: RGF

Regularized Greedy Forest

  • Versions: 11
  • Dependent Packages: 0
  • Dependent Repositories: 1
  • Downloads: 393 Last month
  • Docker Downloads: 42,005
Rankings
Docker downloads count: 0.6%
Stargazers count: 1.0%
Forks count: 1.3%
Average: 13.9%
Dependent repos count: 23.9%
Downloads: 27.8%
Dependent packages count: 28.7%
Maintainers (1)
Last synced: 6 months ago
formulae.brew.sh: rgf

Regularized Greedy Forest library

  • Versions: 1
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 0 Last month
Rankings
Dependent packages count: 19.0%
Forks count: 23.8%
Stargazers count: 24.7%
Average: 42.1%
Dependent repos count: 50.7%
Downloads: 92.0%
Last synced: 6 months ago

Dependencies

R-package/DESCRIPTION cran
  • R >= 3.2.0 depends
  • Matrix * imports
  • R6 * imports
  • reticulate * imports
  • covr * suggests
  • knitr * suggests
  • rmarkdown * suggests
  • testthat * suggests
python-package/setup.py pypi
  • joblib *