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LightGBM, Light Gradient Boosting Machine

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
• Parallel and GPU learning supported
• Capable of handling large-scale data

For more details, please refer to Features.

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, the experiments show that LightGBM can achieve a linear speed-up by using multiple machines for training in specific settings.

News

05/03/2017: LightGBM v2 stable release.

04/10/2017 : LightGBM now supports GPU-accelerated tree learning. 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). Experiment on Expo data shows about 8x speed-up with same accuracy compared with one-hot coding.

12/02/2016 : Release python-package beta version, welcome to have a try and provide feedback.

External (unofficial) Repositories

Julia Package: https://github.com/Allardvm/LightGBM.jl

JPMML: https://github.com/jpmml/jpmml-lightgbm

Get Started And Documents

To get started, please follow the Installation Guide and Quick Start.

• Wiki
• Installation Guide
• Quick Start
• Examples
• Features
• Parallel Learning Guide
• GPU Learning Tutorial
• Configuration
• Document Indexer

External Links

Useful if you are looking for details:

• Read The Docs for an all in one documentation from this repository in a browsable fashion
• Laurae++ interactive documentation for an interactive and detailed documentation on hyperparameters

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.

• 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.
• Check out Development Guide.
• Open issue if you met problems during development.

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.