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.
08/15/2017 : Optimal split for categorical features.
07/13/2017 : Gitter is available.
06/20/2017 : Python-package is on PyPI now.
06/09/2017 : LightGBM Slack team is available.
05/03/2017 : LightGBM v2 stable release.
04/10/2017 : LightGBM supports GPU-accelerated tree learning now. Please read our GPU Tutorial and Performance Comparison.
02/20/2017 : Update to LightGBM v2.
02/12/2017 : LightGBM v1 stable release.
01/08/2017 : Release R-package beta version, welcome to have a try and provide feedback.
12/05/2016 : Categorical Features as input directly (without one-hot coding).
12/02/2016 : Release Python-package beta version, welcome to have a try and provide feedback.
More detailed update logs : Key Events.
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
ONNXMLTools (ONNX converter): https://github.com/onnx/onnxmltools
SHAP (model output explainer): https://github.com/slundberg/shap
MMLSpark (Spark-package): https://github.com/Azure/mmlspark
ML.NET (.NET/C#-package): https://github.com/dotnet/machinelearning
Dask-LightGBM (distributed and parallel Python-package): https://github.com/dask/dask-lightgbm
Install by following guide for the command line program, Python-package or R-package. Then please see the Quick Start guide.
Our primary documentation is at https://lightgbm.readthedocs.io/ and is generated from this repository.
Next you may want to read:
- Examples showing command line usage of common tasks.
- Features and algorithms supported by LightGBM.
- Parameters is an exhaustive list of customization you can make.
- Parallel Learning and GPU Learning can speed up computation.
- Laurae++ interactive documentation is a detailed guide for hyperparameters.
Documentation for contributors:
- How we update readthedocs.io.
- Check out the Development Guide.
- Ask a question on Stack Overflow with the
lightgbmtag, we monitor this for new questions. - Discuss on the LightGBM Gitter.
- Discuss on the LightGBM Slack team.
- Use this invite link to join the team.
- Open bug reports and feature requests (not questions) on GitHub issues.
LightGBM has been developed and used by many active community members. Your help is very valuable to make it better for everyone.
- Check out call for contributions to see what can be improved, or open an issue if you want something.
- Contribute to the tests to make it more reliable.
- Contribute to the documents to make it clearer for everyone.
- Contribute to the examples to share your experience with other users.
- Add your stories and experience to Awesome LightGBM.
- Open issue if you met problems during development.
This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [email protected] with any additional questions or comments.
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.
This project is licensed under the terms of the MIT license. See LICENSE for additional details.