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 | |
| Developer(s) | The XGBoost Contributors |
|---|---|
| Initial release | March 27, 2014 |
| Stable release | 2.1.4[1] 
/ 7 February 2025 |
| Repository | |
| Written in | C++ |
| Operating system | Linux, macOS, Microsoft Windows |
| Type | Machine learning |
| License | Apache License 2.0 |
| Website | xgboost |
XGBoost[2] (eXtreme Gradient Boosting) is an open-source software library which provides a regularizing gradient boosting framework for C++, Java, Python,[3] R,[4] Julia,[5] Perl,[6] and Scala. It works on Linux, Microsoft Windows,[7] and macOS.[8] From the project description, it aims to provide a "Scalable, Portable and Distributed Gradient Boosting (GBM, GBRT, GBDT) Library". It runs on a single machine, as well as the distributed processing frameworks Apache Hadoop, Apache Spark, Apache Flink, and Dask.[9][10]
XGBoost gained much popularity and attention in the mid-2010s as the algorithm of choice for many winning teams of machine learning competitions.[11]
- ^ "Release 2.1.4 Patch Release". 7 February 2025. Retrieved 11 February 2025.
- ^ "GitHub project webpage". GitHub. June 2022. Archived from the original on 2021-04-01. Retrieved 2016-04-05.
- ^ "Python Package Index PYPI: xgboost". Archived from the original on 2017-08-23. Retrieved 2016-08-01.
- ^ "CRAN package xgboost". Archived from the original on 2018-10-26. Retrieved 2016-08-01.
- ^ "Julia package listing xgboost". Archived from the original on 2016-08-18. Retrieved 2016-08-01.
- ^ "CPAN module AI::XGBoost". Archived from the original on 2020-03-28. Retrieved 2020-02-09.
- ^ "Installing XGBoost for Anaconda in Windows". IBM. Archived from the original on 2018-05-08. Retrieved 2016-08-01.
- ^ "Installing XGBoost on Mac OSX". IBM. Archived from the original on 2018-05-08. Retrieved 2016-08-01.
- ^ "Dask Homepage". Archived from the original on 2022-09-14. Retrieved 2021-07-15.
- ^ "Distributed XGBoost with Dask — xgboost 1.5.0-dev documentation". xgboost.readthedocs.io. Archived from the original on 2022-06-04. Retrieved 2021-07-15.
- ^ "XGBoost - ML winning solutions (incomplete list)". GitHub. Archived from the original on 2017-08-24. Retrieved 2016-08-01.