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XGBoost is an optimized distributed gradient boosting library designed to be highlyefficient,flexible andportable.It implements machine learning algorithms under theGradient 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 (Kubernetes, Hadoop, SGE, Dask, Spark, PySpark) and can solve problems beyond billions of examples.

© Contributors, 2021. Licensed under anApache-2 license.

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 theCommunity Page.

• 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.

Become a sponsor and get a logo here. See details atSponsoring the XGBoost Project. The funds are used to defray the cost of continuous integration and testing infrastructure (https://xgboost-ci.net).

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