a67bc64819
* Add an option to run brute-force test for JSON round-trip * Apply reviewer's feedback * Remove unneeded objects * Parallel run. * Max. * Use signed 64-bit loop var, to support MSVC * Add exhaustive test to CI * Run JSON test in Win build worker * Revert "Run JSON test in Win build worker" This reverts commit c97b2c7dda37b3585b445d36961605b79552ca89. * Revert "Add exhaustive test to CI" This reverts commit c149c2ce9971a07a7289f9b9bc247818afd5a667. Co-authored-by: fis <jm.yuan@outlook.com>
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
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Other sponsors
The sponsors in this list are donating cloud hours in lieu of cash donation.
