microsoft/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.

License: MIT

Language: C++

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


LightGBM, Light Gradient Boosting Machine

Azure Pipelines Build Status Appveyor Build Status Travis Build Status Documentation Status License Python Versions PyPI Version Join Gitter at https://gitter.im/Microsoft/LightGBM Slack

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 and GPU learning.
  • Capable of handling large-scale data.

For further details, please refer to Features.

Benefitting 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, parallel 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.

Some old update logs are available at Key Events page.

External (Unofficial) Repositories

Julia-package: https://github.com/Allardvm/LightGBM.jl

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

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

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

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

MMLSpark (LightGBM on Spark): https://github.com/Azure/mmlspark

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

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

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

Dask-LightGBM (distributed and parallel Python-package): https://github.com/dask/dask-lightgbm

Ruby gem: https://github.com/ankane/lightgbm

Support

How to Contribute

LightGBM has been developed and used by many active community members. Your help is very valuable to make it better for everyone.

  • Contribute to the tests to make it more reliable.
  • Contribute to the documentation to make it clearer for everyone.
  • Contribute to the examples to share your experience with other users.
  • Look for issues with tag "help wanted" and submit pull requests to address them.
  • Add your stories and experience to Awesome LightGBM. If LightGBM helped you in a machine learning competition or some research application, we want to hear about it!
  • Open an issue to report problems or recommend new features.

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

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.

Note: If you use LightGBM in your GitHub projects, please add lightgbm in the requirements.txt.

License

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

Project Statistics

Sourcerank 12
Repository Size 12.1 MB
Stars 11,212
Forks 2,959
Watchers 443
Open issues 75
Dependencies 18
Contributors 144
Tags 21
Created
Last updated
Last pushed

Top Contributors See all

Guolin Ke Nikita Titov wxchan Laurae Qiwei Ye OMOTO Tsukasa James Lamb xuehui Huan Zhang Ilya Matiach Carlos Becker kimi Allard van Mossel Yachen Yan Darío Hereñú Belinda Trotta remcob-gr olofer Preston Parry J. Mark Hou

Packages Referencing this Repo

com.microsoft.ml.lightgbm:lightgbmlib
A fast, distributed, high performance gradient boosting framework based on decision tree algorith...
Latest release 2.3.180 - Updated - 11.2K stars
LightGBM
A fast, distributed, high performance gradient boosting framework
Latest release 2.3.1 - Updated - 11.2K stars
lightgbm
LightGBM Python Package
Latest release 2.3.1 - Updated - 11.2K stars
lightgbm
A fast, distributed, high performance gradient boosting (GBDT, GBRT, GBM or MART) framework based...
Latest release 2.3.0 - Updated - 11.2K stars

Recent Tags See all

v2.3.1 November 26, 2019
v2.3.0 September 29, 2019
v2.2.3 February 05, 2019
v2.2.2 November 06, 2018
v2.2.1 October 03, 2018
v2.2.0 September 18, 2018
v2.1.2 June 22, 2018
v2.1.1 May 01, 2018
v2.1.0 January 25, 2018
v2.0.12 December 26, 2017
v2.0.11 November 25, 2017
v2.0.10 October 18, 2017
stable October 18, 2017
v2.0.8 October 14, 2017
v2.0.7 September 28, 2017

Something wrong with this page? Make a suggestion

Last synced: 2020-01-03 02:18:59 UTC

Login to resync this repository