Skip to content
/ xgboost Public

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, Dask, Flink and DataFlow

License

Notifications You must be signed in to change notification settings

dmlc/xgboost

This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.

Folders and files

NameName
Last commit message
Last commit date

Latest commit

author
Nan Zhu
Jul 8, 2019
12f78c7 · Jul 8, 2019
Oct 24, 2018
Jul 3, 2019
Nov 7, 2018
May 14, 2019
Aug 13, 2017
Jun 26, 2019
May 20, 2019
May 16, 2019
Jul 8, 2019
Jul 4, 2019
Jul 7, 2019
Jul 7, 2017
Aug 30, 2018
Jul 7, 2019
Jun 27, 2019
Jul 4, 2019
Jul 7, 2019
Jul 4, 2019
Jul 23, 2018
May 27, 2019
Jul 10, 2018
Apr 27, 2019
Dec 1, 2017
Jun 19, 2019
May 30, 2019
Jun 19, 2019
May 16, 2019
Sep 13, 2018
Apr 27, 2019
May 20, 2019
Apr 9, 2019
Apr 26, 2019

Repository files navigation

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.

License

© 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

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

[Become a backer]

Other sponsors

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

Amazon Web Services

About

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, Dask, Flink and DataFlow

Topics

Resources

License

Security policy

Citation

Stars

Watchers

Forks

Packages

No packages published