eXtreme Gradient Boosting

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    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 (Kubernetes, Hadoop, SGE, MPI, Dask) and can solve problems beyond billions of examples.


    © Contributors, 2021. Licensed under an Apache-2 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.


    • 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.


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    发行版本 36

    Release candidate of version 1.5.0


    贡献者 354



    • C++ 41.8 %
    • Python 18.5 %
    • Cuda 16.8 %
    • Scala 8.5 %
    • R 7.4 %