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README.md

eXtreme Gradient Boosting

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Community | Documentation | Resources | Contributors | Release Notes

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, 2019. 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

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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 33

Release 1.4.0 stable

全部发行版

贡献者 399

全部贡献者

开发语言

  • C++ 42.4 %
  • Python 18.2 %
  • Cuda 16.6 %
  • Scala 8.8 %
  • R 7.5 %