README.md

LightGBM, Light Gradient Boosting Machine
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LightGBM is a gradient boosting framework that using tree based learning algorithms. It is designed to be distributed and high performance, and has following advantages:

- Fast training speed
- Low memory consumption
- Better accuracy
- Efficient parallel learning
- Can learn from very big data

For the details about these advantages, please refer to [Feature Highlight](https://github.com/Microsoft/LightGBM/wiki/Feature-Highlight).

The [experiments](https://github.com/Microsoft/LightGBM/wiki/Experiments#comparison-experiment) on real data also show LightGBM can outperform other existing boosting tools on both learning efficiency and accuracy, with much lower memory consumption. And LightGBM can achieve linear speed-up for parallel learning in our [experiments](https://github.com/Microsoft/LightGBM/wiki/Experiments#parallel-experiment).

Get Started
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To quick start,  please refer to [Installation](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**](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)
* [**Feature Highlight**](https://github.com/Microsoft/LightGBM/wiki/Feature-Highlight) 
* [**Parallel Learning Guide**](https://github.com/Microsoft/LightGBM/wiki/Parallel-Learning-Guide) 
* [**Parameters**](https://github.com/Microsoft/LightGBM/wiki/Parameters) 


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.