<img src=https://raw.githubusercontent.com/dmlc/dmlc.github.io/master/img/logo-m/xgboost.png width=135/> eXtreme Gradient Boosting

Documentation | Resources | Installation | Release Notes | RoadMap

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.

What's New

• XGBoost GPU support with fast histogram algorithm
• XGBoost4J: Portable Distributed XGboost in Spark, Flink and Dataflow, see JVM-Package
• Story and Lessons Behind the Evolution of XGBoost
• Tutorial: Distributed XGBoost on AWS with YARN
• XGBoost brick Release

Ask a Question

• For reporting bugs please use the xgboost/issues page.
• For generic questions or 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

© Contributors, 2016. Licensed under an Apache-2 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.