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XGBoost is an optimized distributed gradient boosting system 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. The most recent version integrates naturally with DataFlow frameworks(e.g. Flink and Spark).

Reference Paper

Technical Highlights



XGBoost open source project is actively developed by amazing contributors from DMLC/XGBoost community.

This work was supported in part by ONR (PECASE) N000141010672, NSF IIS 1258741 and the TerraSwarm Research Center sponsored by MARCO and DARPA.



Tianqi Chen
Assistant Professor - CMU
Carlos Guestrin
Professor - Stanford