eXtreme Gradient Boosting =========== [](https://travis-ci.org/dmlc/xgboost) [](https://ci.appveyor.com/project/tqchen/xgboost) [](https://xgboost.readthedocs.org) [](./LICENSE) [](http://cran.r-project.org/web/packages/xgboost) [](https://pypi.python.org/pypi/xgboost/) [Community](https://xgboost.ai/community) | [Documentation](https://xgboost.readthedocs.org) | [Resources](demo/README.md) | [Contributors](CONTRIBUTORS.md) | [Release Notes](NEWS.md) XGBoost is an optimized distributed gradient boosting library designed to be highly ***efficient***, ***flexible*** and ***portable***. It implements machine learning algorithms under the [Gradient Boosting](https://en.wikipedia.org/wiki/Gradient_boosting) framework. XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many data science problems in a fast and accurate way. The same code runs on major distributed environment (Hadoop, SGE, MPI) and can solve problems beyond billions of examples. License ------- © Contributors, 2016. Licensed under an [Apache-2](https://github.com/dmlc/xgboost/blob/master/LICENSE) license. Contribute to XGBoost --------------------- XGBoost has been developed and used by a group of active community members. Your help is very valuable to make the package better for everyone. Checkout the [Community Page](https://xgboost.ai/community) Reference --------- - Tianqi Chen and Carlos Guestrin. [XGBoost: A Scalable Tree Boosting System](http://arxiv.org/abs/1603.02754). In 22nd SIGKDD Conference on Knowledge Discovery and Data Mining, 2016 - XGBoost originates from research project at University of Washington.