LightGBM, Light Gradient Boosting Machine
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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 learning supported
- Capable of handling large-scale data

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

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

News
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12/02/2016 : Release [python-package](https://github.com/Microsoft/LightGBM/tree/master/python-package) beta version, welcome to have a try and provide issues and feedback.

Get Started
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To get started, 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
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* [**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
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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.
