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 LightGBM, Light Gradient Boosting Machine
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 [![Build Status](https://travis-ci.org/Microsoft/LightGBM.svg?branch=master)](https://travis-ci.org/Microsoft/LightGBM)
 
 LightGBM is a gradient boosting framework that uses tree based learning algorithms. It is designed to be distributed and efficient with the following advantages:
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 [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.
 
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+12/02/2012 : Release [python-package](https://github.com/Microsoft/LightGBM/tree/master/python-package) beta version, welcome to have a try and provide issues and feedback.
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 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).