Skip to content
Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Flink and DataFlow
Branch: master
Clone or download
Type Name Latest commit message Commit time
Failed to load latest commit information.
.github Enable auto-locking of issues closed long ago (#3821) Oct 24, 2018
R-package Deprecate `reg:linear' in favor of `reg:squarederror'. (#4267) Mar 17, 2019
amalgamation Refactor fast-hist, add tests for some updaters. (#3836) Nov 7, 2018
cmake support cuda 10.1 (#4223) Mar 7, 2019
cub @ b20808b Update cub submodule again (fixes GPU build) (#2599) Aug 13, 2017
demo Deprecate `reg:linear' in favor of `reg:squarederror'. (#4267) Mar 17, 2019
dmlc-core @ ac98309 Update dmlc-core submodule (#3907) Nov 16, 2018
include/xgboost Remove deprecated C APIs. (#4266) Mar 17, 2019
jvm-packages [jvm-packages] add configuration flag to control whether to cache tra… Mar 18, 2019
python-package Mark Scikit-Learn RF interface as experimental in doc. (#4258) Mar 15, 2019
rabit @ 1cc34f0
src Use Monitor in quantile hist. (#4273) Mar 20, 2019
.clang-tidy Fix clang-tidy warnings. (#4149) Mar 12, 2019
.editorconfig Added configuration for python into .editorconfig (#3494) Jul 23, 2018
.gitignore Performance optimizations for Intel CPUs (#3957) Jan 9, 2019
.travis.yml Fix travis R tests (#4277) Mar 19, 2019
CMakeLists.txt Remove various synchronisations from cuda API calls, instrument monit… Mar 10, 2019 [REVIEW] Enable Multi-Node Multi-GPU functionality (#4095) Mar 1, 2019
Jenkinsfile Broken link for NCCL: cannot use CUDA 10.1 (#4232) Mar 8, 2019
Jenkinsfile-restricted Disable retries in Jenkins CI, since we're now using On-Demand instan… Nov 28, 2018
Makefile Fix CRAN check warnings/notes (#3988) Dec 12, 2018 Simplify README page (#4254) Mar 12, 2019
appveyor.yml Fix test_gpu_coordinate. (#3974) Feb 19, 2019 Suggest git submodule update instead of delete + reclone (#3214) May 9, 2018

eXtreme Gradient Boosting

Build Status Build Status Build Status Documentation Status GitHub license CRAN Status Badge PyPI version

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 (Hadoop, SGE, MPI) and can solve problems beyond billions of examples.


© Contributors, 2016. 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


  • 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.


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 (

Open Source Collective sponsors

Backers on Open Collective Sponsors on Open Collective


[Become a sponsor]



[Become a backer]

Other sponsors

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

Amazon Web Services

You can’t perform that action at this time.