dmlc/xgboost

dmlc/xgboost has 28,947 GitHub stars and 8,965 forks. GitHot measured +3 stars in 24 hours. Last checked 2026-11-06 01:23 UTC. Last push 2026-11-02.

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Bibliothèque évolutive, portable et distribuée de gradient boosting (GBDT, GBRT ou GBM) pour Python, R, Java, Scala, C et bien d’autres langages. Fonctionne sur une seule machine ainsi que sur Hadoop, Spark, Dask, Flink et DataFlow.

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README.md

Original

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

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.