LightGBM, Light Gradient Boosting Machine
==========
[![Build Status](https://travis-ci.org/Microsoft/LightGBM.svg?branch=master)](https://travis-ci.org/Microsoft/LightGBM)

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

- Fast training efficiency
- Low memory usage
- Better accuracy
- Parallel learning supported
- Deal with large scale of data

For the details, please refer to [Features](https://github.com/Microsoft/LightGBM/wiki/Features).

The [experiments](https://github.com/Microsoft/LightGBM/wiki/Experiments#comparison-experiment) on the public data also shows that LightGBM can outperform other existing boosting tools on both learning efficiency and accuracy, with significant lower memory consumption. What's more, the [experiments](https://github.com/Microsoft/LightGBM/wiki/Experiments#parallel-experiment) shows that LightGBM can achieve linear speed-up by using multiple machines for training in specific settings. 

Get Started
------------
For a quick start,  please follow the [Installation Guide](https://github.com/Microsoft/LightGBM/wiki/Installation-Guide) and [Quick Start](https://github.com/Microsoft/LightGBM/wiki/Quick-Start).

Documents
------------
* [**Wiki**](https://github.com/Microsoft/LightGBM/wiki)
* [**Installation Guide**](https://github.com/Microsoft/LightGBM/wiki/Installation-Guide) 
* [**Quick Start**](https://github.com/Microsoft/LightGBM/wiki/Quick-Start)
* [**Examples**](https://github.com/Microsoft/LightGBM/tree/master/examples)
* [**Features**](https://github.com/Microsoft/LightGBM/wiki/Features) 
* [**Parallel Learning Guide**](https://github.com/Microsoft/LightGBM/wiki/Parallel-Learning-Guide) 
* [**Configuration**](https://github.com/Microsoft/LightGBM/wiki/Configuration) 


Microsoft Open Source Code of Conduct
------------

This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/). For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.
