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lightgbm

A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.

https://github.com/microsoft/lightgbm

Science Score: 46.0%

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

  • ○
    CITATION.cff file
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    Found codemeta.json file
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    Found .zenodo.json file
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    DOI references
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    Academic publication links
    Links to: arxiv.org
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    Committers with academic emails
    11 of 327 committers (3.4%) from academic institutions
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    Institutional organization owner
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    JOSS paper metadata
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    Scientific vocabulary similarity
    Low similarity (12.2%) to scientific vocabulary

Keywords

data-mining decision-trees distributed gbdt gbm gbrt gradient-boosting kaggle lightgbm machine-learning microsoft parallel python r

Keywords from Contributors

xgboost tensor closember mlops autograd deep-neural-networks langchain transformer flexible alignment
Last synced: 6 months ago · JSON representation

Repository

A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.

Basic Info
Statistics
  • Stars: 17,573
  • Watchers: 435
  • Forks: 3,933
  • Open Issues: 468
  • Releases: 34
Topics
data-mining decision-trees distributed gbdt gbm gbrt gradient-boosting kaggle lightgbm machine-learning microsoft parallel python r
Created over 9 years ago · Last pushed 6 months ago
Metadata Files
Readme Contributing License Code of conduct Codeowners Security

README.md

Light Gradient Boosting Machine

Python-package GitHub Actions Build Status R-package GitHub Actions Build Status CUDA Version GitHub Actions Build Status Static Analysis GitHub Actions Build Status Azure Pipelines Build Status Appveyor Build Status Documentation Status Link checks License EffVer Versioning Python Versions PyPI Version conda Version CRAN Version NuGet Version

LightGBM is a gradient boosting framework that uses tree based learning algorithms. It is designed to be distributed and efficient with the following advantages:

  • Faster training speed and higher efficiency.
  • Lower memory usage.
  • Better accuracy.
  • Support of parallel, distributed, and GPU learning.
  • Capable of handling large-scale data.

For further details, please refer to Features.

Benefiting from these advantages, LightGBM is being widely-used in many winning solutions of machine learning competitions.

Comparison experiments on public datasets show that LightGBM can outperform existing boosting frameworks on both efficiency and accuracy, with significantly lower memory consumption. What's more, distributed learning experiments show that LightGBM can achieve a linear speed-up by using multiple machines for training in specific settings.

Get Started and Documentation

Our primary documentation is at https://lightgbm.readthedocs.io/ and is generated from this repository. If you are new to LightGBM, follow the installation instructions on that site.

Next you may want to read:

Documentation for contributors:

News

Please refer to changelogs at GitHub releases page.

External (Unofficial) Repositories

Projects listed here offer alternative ways to use LightGBM. They are not maintained or officially endorsed by the LightGBM development team.

JPMML (Java PMML converter): https://github.com/jpmml/jpmml-lightgbm

Nyoka (Python PMML converter): https://github.com/SoftwareAG/nyoka

Treelite (model compiler for efficient deployment): https://github.com/dmlc/treelite

lleaves (LLVM-based model compiler for efficient inference): https://github.com/siboehm/lleaves

Hummingbird (model compiler into tensor computations): https://github.com/microsoft/hummingbird

GBNet (use LightGBM as a PyTorch Module): https://github.com/mthorrell/gbnet

cuML Forest Inference Library (GPU-accelerated inference): https://github.com/rapidsai/cuml

daal4py (Intel CPU-accelerated inference): https://github.com/intel/scikit-learn-intelex/tree/master/daal4py

m2cgen (model appliers for various languages): https://github.com/BayesWitnesses/m2cgen

leaves (Go model applier): https://github.com/dmitryikh/leaves

ONNXMLTools (ONNX converter): https://github.com/onnx/onnxmltools

SHAP (model output explainer): https://github.com/slundberg/shap

Shapash (model visualization and interpretation): https://github.com/MAIF/shapash

dtreeviz (decision tree visualization and model interpretation): https://github.com/parrt/dtreeviz

supertree (interactive visualization of decision trees): https://github.com/mljar/supertree

SynapseML (LightGBM on Spark): https://github.com/microsoft/SynapseML

Kubeflow Fairing (LightGBM on Kubernetes): https://github.com/kubeflow/fairing

Kubeflow Operator (LightGBM on Kubernetes): https://github.com/kubeflow/xgboost-operator

lightgbmray (LightGBM on Ray): https://github.com/ray-project/lightgbmray

Mars (LightGBM on Mars): https://github.com/mars-project/mars

ML.NET (.NET/C#-package): https://github.com/dotnet/machinelearning

LightGBM.NET (.NET/C#-package): https://github.com/rca22/LightGBM.Net

LightGBM Ruby (Ruby gem): https://github.com/ankane/lightgbm-ruby

LightGBM4j (Java high-level binding): https://github.com/metarank/lightgbm4j

LightGBM4J (JVM interface for LightGBM written in Scala): https://github.com/seek-oss/lightgbm4j

Julia-package: https://github.com/IQVIA-ML/LightGBM.jl

lightgbm3 (Rust binding): https://github.com/Mottl/lightgbm3-rs

MLServer (inference server for LightGBM): https://github.com/SeldonIO/MLServer

MLflow (experiment tracking, model monitoring framework): https://github.com/mlflow/mlflow

FLAML (AutoML library for hyperparameter optimization): https://github.com/microsoft/FLAML

MLJAR AutoML (AutoML on tabular data): https://github.com/mljar/mljar-supervised

Optuna (hyperparameter optimization framework): https://github.com/optuna/optuna

LightGBMLSS (probabilistic modelling with LightGBM): https://github.com/StatMixedML/LightGBMLSS

mlforecast (time series forecasting with LightGBM): https://github.com/Nixtla/mlforecast

skforecast (time series forecasting with LightGBM): https://github.com/JoaquinAmatRodrigo/skforecast

{bonsai} (R {parsnip}-compliant interface): https://github.com/tidymodels/bonsai

{mlr3extralearners} (R {mlr3}-compliant interface): https://github.com/mlr-org/mlr3extralearners

lightgbm-transform (feature transformation binding): https://github.com/microsoft/lightgbm-transform

postgresml (LightGBM training and prediction in SQL, via a Postgres extension): https://github.com/postgresml/postgresml

pyodide (run lightgbm Python-package in a web browser): https://github.com/pyodide/pyodide

vaex-ml (Python DataFrame library with its own interface to LightGBM): https://github.com/vaexio/vaex

Support

How to Contribute

Check CONTRIBUTING page.

Microsoft Open Source Code of Conduct

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.

Reference Papers

Yu Shi, Guolin Ke, Zhuoming Chen, Shuxin Zheng, Tie-Yan Liu. "Quantized Training of Gradient Boosting Decision Trees" (link). Advances in Neural Information Processing Systems 35 (NeurIPS 2022), pp. 18822-18833.

Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, Tie-Yan Liu. "LightGBM: A Highly Efficient Gradient Boosting Decision Tree". Advances in Neural Information Processing Systems 30 (NIPS 2017), pp. 3149-3157.

Qi Meng, Guolin Ke, Taifeng Wang, Wei Chen, Qiwei Ye, Zhi-Ming Ma, Tie-Yan Liu. "A Communication-Efficient Parallel Algorithm for Decision Tree". Advances in Neural Information Processing Systems 29 (NIPS 2016), pp. 1279-1287.

Huan Zhang, Si Si and Cho-Jui Hsieh. "GPU Acceleration for Large-scale Tree Boosting". SysML Conference, 2018.

License

This project is licensed under the terms of the MIT license. See LICENSE for additional details.

Owner

  • Name: Microsoft
  • Login: microsoft
  • Kind: organization
  • Email: opensource@microsoft.com
  • Location: Redmond, WA

Open source projects and samples from Microsoft

Committers

Last synced: 9 months ago

All Time
  • Total Commits: 3,622
  • Total Committers: 327
  • Avg Commits per committer: 11.076
  • Development Distribution Score (DDS): 0.759
Past Year
  • Commits: 196
  • Committers: 35
  • Avg Commits per committer: 5.6
  • Development Distribution Score (DDS): 0.541
Top Committers
Name Email Commits
Nikita Titov n****8@m****u 874
James Lamb j****0@g****m 863
Guolin Ke i@y****e 797
wxchan w****n 93
Laurae L****2 80
shiyu1994 s****4@q****m 66
José Morales j****2@g****m 58
Tsukasa OMOTO h****2@g****m 47
Qiwei Ye c****e@g****m 41
david-cortes d****a@g****m 34
Belinda Trotta b****a 25
Zhuyi Xue a****8@g****m 23
Oliver Borchert o****t@q****m 18
Ilya Matiach i****t@m****m 18
Alberto Ferreira A****F 15
Huan Zhang e****g@u****u 14
Chen Yufei c****f@g****m 13
xuehui x****i@m****m 13
cbecker c****r 9
zhangyafeikimi z****i@g****m 8
mjmckp m****p 8
Michael Mayer m****9@g****m 8
Frank Fineis f****s@g****m 8
Scott Votaw s****w@g****m 8
Allard van Mossel a****l@g****m 7
Yachen Yan y****n 7
Nick Miller 5****r 7
Thomas J. Fan t****n@g****m 7
Darío Hereñú m****a@g****m 6
NovusEdge 6****e 6
and 297 more...
Committer Domains (Top 20 + Academic)
qq.com: 6 mail.ru: 4 163.com: 4 microsoft.com: 3 foxmail.com: 3 126.com: 2 sina.com: 2 chrissmith.tech: 1 batflyer.net: 1 preferred.jp: 1 gatech.edu: 1 phor.net: 1 arbisoft.com: 1 virginia.edu: 1 alum.utoronto.ca: 1 cs.washington.edu: 1 i.ua: 1 ymail.com: 1 e.ntu.edu.sg: 1 zhihu.com: 1 ucdavis.edu: 1 hawaii.edu: 1 stanford.edu: 1 illinois.edu: 1 buaa.edu.cn: 1 uchicago.edu: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 889
  • Total pull requests: 1,095
  • Average time to close issues: 4 months
  • Average time to close pull requests: 25 days
  • Total issue authors: 559
  • Total pull request authors: 150
  • Average comments per issue: 4.64
  • Average comments per pull request: 3.39
  • Merged pull requests: 835
  • Bot issues: 0
  • Bot pull requests: 16
Past Year
  • Issues: 195
  • Pull requests: 300
  • Average time to close issues: 15 days
  • Average time to close pull requests: 13 days
  • Issue authors: 127
  • Pull request authors: 47
  • Average comments per issue: 1.2
  • Average comments per pull request: 2.03
  • Merged pull requests: 187
  • Bot issues: 0
  • Bot pull requests: 7
View more stats
Top Authors
Issue Authors
  • jameslamb (119)
  • StrikerRUS (25)
  • wil70 (18)
  • shiyu1994 (15)
  • david-cortes (9)
  • adfea9c0 (8)
  • mayer79 (8)
  • pseudotensor (7)
  • simpsus (6)
  • yurivict (6)
  • jaguerrerod (6)
  • memeplex (6)
  • mlondschien (5)
  • nitinmnsn (4)
  • nipnipj (4)
Pull Request Authors
  • jameslamb (486)
  • StrikerRUS (160)
  • borchero (43)
  • shiyu1994 (37)
  • nicklamiller (22)
  • jmoralez (20)
  • guolinke (19)
  • david-cortes (19)
  • dependabot[bot] (15)
  • vnherdeiro (13)
  • mayer79 (10)
  • neNasko1 (7)
  • kant (6)
  • suk1yak1 (6)
  • mlondschien (5)
Top Labels
Issue Labels
question (353) bug (119) awaiting response (85) maintenance (82) r-package (77) feature request (76) gpu (OpenCL) (56) help wanted (24) blocking (24) dask (21) good first issue (18) doc (14) gpu (CUDA) (11) duplicate (9) efficiency (6) awaiting review (5) feature (2) invalid (1) effectiveness (1)
Pull Request Labels
maintenance (573) fix (135) doc (112) feature (87) breaking (53) in progress (49) awaiting review (44) efficiency (36) blocking (17) awaiting response (10) invalid (7) help wanted (1) gpu (CUDA) (1) effectiveness (1)

Packages

  • Total packages: 14
  • Total downloads:
    • pypi 10,514,678 last-month
    • cran 6,984 last-month
    • nuget 2,625,483 total
    • homebrew 168 last-month
  • Total docker downloads: 25,576,765
  • Total dependent packages: 461
    (may contain duplicates)
  • Total dependent repositories: 5,881
    (may contain duplicates)
  • Total versions: 342
  • Total maintainers: 7
  • Total advisories: 1
pypi.org: lightgbm

LightGBM Python-package

  • Homepage: https://github.com/microsoft/LightGBM
  • Documentation: https://lightgbm.readthedocs.io/en/latest/
  • License: The MIT License (MIT) Copyright (c) Microsoft Corporation Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
  • Latest release: 4.6.0
    published about 1 year ago
  • Versions: 37
  • Dependent Packages: 413
  • Dependent Repositories: 5,625
  • Downloads: 10,514,612 Last month
  • Docker Downloads: 25,461,891
Rankings
Dependent packages count: 0.1%
Forks count: 0.1%
Downloads: 0.1%
Stargazers count: 0.1%
Dependent repos count: 0.1%
Average: 0.2%
Docker downloads count: 0.7%
Maintainers (3)
StrikerRUS jameslamb guolinke
Advisories (1)
Last synced: 6 months ago
conda-forge.org: lightgbm

A fast, distributed, high performance gradient boosting (GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.

  • Versions: 21
  • Dependent Packages: 24
  • Dependent Repositories: 100
Rankings
Forks count: 1.7%
Stargazers count: 2.1%
Average: 2.5%
Dependent packages count: 2.8%
Dependent repos count: 3.4%
Last synced: 6 months ago
proxy.golang.org: github.com/microsoft/LightGBM
  • Versions: 36
  • Dependent Packages: 0
  • Dependent Repositories: 1
Rankings
Forks count: 0.1%
Stargazers count: 0.5%
Average: 3.5%
Dependent repos count: 4.8%
Dependent packages count: 8.5%
Last synced: 6 months ago
cran.r-project.org: lightgbm

Light Gradient Boosting Machine

  • Versions: 17
  • Dependent Packages: 13
  • Dependent Repositories: 41
  • Downloads: 6,984 Last month
  • Docker Downloads: 114,874
Rankings
Stargazers count: 0.0%
Forks count: 0.0%
Average: 3.9%
Dependent repos count: 4.0%
Downloads: 5.7%
Dependent packages count: 6.5%
Docker downloads count: 7.4%
Maintainers (1)
jaylamb20@gmail.com
Last synced: 6 months ago
repo1.maven.org: com.microsoft.ml.lightgbm:lightgbmlib

A fast, distributed, high performance gradient boosting framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.

  • Versions: 19
  • Dependent Packages: 6
  • Dependent Repositories: 13
Rankings
Stargazers count: 1.6%
Forks count: 1.7%
Average: 4.9%
Dependent repos count: 6.7%
Dependent packages count: 9.6%
Last synced: 6 months ago
proxy.golang.org: github.com/Microsoft/lightGBM
  • Versions: 36
  • Dependent Packages: 0
  • Dependent Repositories: 0
Rankings
Dependent packages count: 5.5%
Average: 5.7%
Dependent repos count: 5.9%
Last synced: 6 months ago
spack.io: py-lightgbm

LightGBM is a gradient boosting framework that uses tree based learning algorithms.

  • Versions: 1
  • Dependent Packages: 1
  • Dependent Repositories: 0
Rankings
Dependent repos count: 0.0%
Forks count: 0.3%
Stargazers count: 0.6%
Average: 7.2%
Dependent packages count: 28.1%
Maintainers (1)
adamjstewart
Last synced: 6 months ago
proxy.golang.org: github.com/microsoft/lightgbm
  • Versions: 36
  • Dependent Packages: 0
  • Dependent Repositories: 0
Rankings
Dependent packages count: 7.0%
Average: 8.2%
Dependent repos count: 9.3%
Last synced: 6 months ago
proxy.golang.org: github.com/Microsoft/LightGBM
  • Versions: 36
  • Dependent Packages: 0
  • Dependent Repositories: 0
Rankings
Dependent packages count: 7.0%
Average: 8.2%
Dependent repos count: 9.3%
Last synced: 6 months ago
anaconda.org: lightgbm

A fast, distributed, high performance gradient boosting (GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.

  • Versions: 10
  • Dependent Packages: 2
  • Dependent Repositories: 100
Rankings
Forks count: 5.4%
Stargazers count: 6.2%
Average: 11.0%
Dependent packages count: 13.5%
Dependent repos count: 18.8%
Last synced: 6 months ago
nuget.org: lightgbm

A fast, distributed, high performance gradient boosting framework

  • Versions: 62
  • Dependent Packages: 2
  • Dependent Repositories: 0
  • Downloads: 2,625,483 Total
Rankings
Downloads: 1.0%
Average: 12.1%
Dependent repos count: 15.0%
Dependent packages count: 20.3%
Maintainers (3)
insulator jameslamb StrikerRUS
Last synced: 6 months ago
formulae.brew.sh: lightgbm

Fast, distributed, high performance gradient boosting framework

  • Versions: 11
  • Dependent Packages: 0
  • Dependent Repositories: 1
  • Downloads: 168 Last month
Rankings
Forks count: 0.5%
Stargazers count: 1.2%
Average: 13.8%
Dependent packages count: 18.4%
Downloads: 19.5%
Dependent repos count: 29.2%
Last synced: 6 months ago
conda-forge.org: r-lightgbm
  • Versions: 11
  • Dependent Packages: 0
  • Dependent Repositories: 0
Rankings
Forks count: 1.6%
Stargazers count: 2.0%
Average: 22.2%
Dependent repos count: 34.0%
Dependent packages count: 51.2%
Last synced: 6 months ago
pypi.org: lightgbm-no-openmp

LightGBM Python Package

  • Versions: 9
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 66 Last month
Rankings
Dependent packages count: 9.2%
Average: 30.4%
Dependent repos count: 51.6%
Maintainers (1)
tlyservices
Last synced: 6 months ago

Dependencies

R-package/DESCRIPTION cran
  • R >= 3.5 depends
  • Matrix >= 1.1 imports
  • R6 >= 2.0 imports
  • data.table >= 1.9.6 imports
  • graphics * imports
  • jsonlite >= 1.0 imports
  • methods * imports
  • parallel * imports
  • utils * imports
  • RhpcBLASctl * suggests
  • knitr * suggests
  • processx * suggests
  • rmarkdown * suggests
  • testthat * suggests
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python-package/pyproject.toml pypi
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  • numpy *
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