README.md

    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.

    License

    © 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.

    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.

    Sponsors

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

    Open Source Collective sponsors

    Backers on Open Collective Sponsors on Open Collective

    Sponsors

    [Become a sponsor]

    NVIDIA

    Backers

    [Become a backer]

    Other sponsors

    The sponsors in this list are donating cloud hours in lieu of cash donation.

    Amazon Web Services

    项目简介

    🚀 Github 镜像仓库 🚀

    源项目地址

    https://github.com/dmlc/xgboost

    发行版本 36

    Release candidate of version 1.5.0

    全部发行版

    贡献者 354

    全部贡献者

    开发语言

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