96f8843694
* Relax shotgun test. (#6900) It's non-deterministic algorithm, the test is flaky. * Disable pylint error. (#6911) * [CI] Skip external memory gtest on osx. (#6901) * [CI] Fix custom metric test with empty dataset. (#6917) * Reduce Travis environment setup time. (#6912) * Remove unused r from travis. * Don't update homebrew. * Don't install indirect/unused dependencies like libgit2, wget, openssl. * Move graphviz installation to conda. * Relax shotgun test. (#6918) * Relax test for decision stump in distributed environment. (#6919) * Backport cupy fix.
eXtreme Gradient Boosting
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 (Kubernetes, Hadoop, SGE, MPI, Dask) and can solve problems beyond billions of examples.
License
© Contributors, 2019. 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
Sponsors
Backers
Other sponsors
The sponsors in this list are donating cloud hours in lieu of cash donation.
