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 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.

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For reporting bugs please use the xgboost/issues page.
For generic questions for to share your experience using xgboost please use the XGBoost User Group

Help to Make XGBoost Better

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.

Check out call for contributions and Roadmap to see what can be improved, or open an issue if you want something.
Contribute to the documents and examples to share your experience with other users.
Add your stories and experience to Awesome XGBoost.
Please add your name to CONTRIBUTORS.md and after your patch has been merged.
- Please also update NEWS.md on changes and improvements in API and docs.

License

Reference

Tianqi Chen and Carlos Guestrin. XGBoost: A Scalable Tree Boosting System. In 22nd SIGKDD Conference on Knowledge Discovery and Data Mining, 2016
XGBoost originates from research project at University of Washington, see also the Project Page at UW.

Languages

C++ 45.5%

Python 20.3%

Cuda 15.2%

R 6.8%

Scala 6.4%

Other 5.6%