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LightGBM, Light Gradient Boosting Machine
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==========
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[![Build Status](https://travis-ci.org/Microsoft/LightGBM.svg?branch=master)](https://travis-ci.org/Microsoft/LightGBM)
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LightGBM is a gradient boosting framework that is using tree based learning algorithms. It is designed to be distributed and efficient with following advantages:
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- Fast training efficiency
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- Low memory usage
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- Better accuracy
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- Parallel learning supported
- Deal with large scale of data
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For more details, please refer to [Features](https://github.com/Microsoft/LightGBM/wiki/Features).
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The [experiments](https://github.com/Microsoft/LightGBM/wiki/Experiments#comparison-experiment) on public dataset show that LightGBM outperform other existing boosting tools 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 linear speed-up by using multiple machines for training in specific settings. 
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Get Started
------------
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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).
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Documents
------------
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* [**Wiki**](https://github.com/Microsoft/LightGBM/wiki)
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* [**Installation Guide**](https://github.com/Microsoft/LightGBM/wiki/Installation-Guide) 
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* [**Quick Start**](https://github.com/Microsoft/LightGBM/wiki/Quick-Start)
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* [**Examples**](https://github.com/Microsoft/LightGBM/tree/master/examples)
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* [**Features**](https://github.com/Microsoft/LightGBM/wiki/Features) 
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* [**Parallel Learning Guide**](https://github.com/Microsoft/LightGBM/wiki/Parallel-Learning-Guide) 
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* [**Configuration**](https://github.com/Microsoft/LightGBM/wiki/Configuration) 
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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.