dmlc/xgboost
dmlc/xgboost has 28,957 GitHub stars and 8,966 forks. GitHot measured +4 stars in 24 hours. Last checked 2026-11-08 21:23 UTC. Last push 2026-11-02.
可扩展、可移植的分布式梯度提升（GBDT、GBRT 或 GBM）库，支持 Python、R、Java、Scala、C 等语言。可运行于单机以及 Hadoop、Spark、Dask、Flink 和 DataFlow。
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README.md
原文
eXtreme Gradient Boosting
Blog | 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, Dask, Spark, PySpark) 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. Check out the contributor documentation.
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
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