b29b8c2f34
* [phase 1] expose sets of rabit configurations to spark layer * add back mutable import * disable ring_mincount till https://github.com/dmlc/rabit/pull/106d * Revert "disable ring_mincount till https://github.com/dmlc/rabit/pull/106d" This reverts commit 65e95a98e24f5eb53c6ba9ef9b2379524258984d. * apply latest rabit * fix build error * apply https://github.com/dmlc/xgboost/pull/4880 * downgrade cmake in rabit * point to rabit with DMLC_ROOT fix * relative path of rabit install prefix * split rabit parameters to another trait * misc * misc * Delete .classpath * Delete .classpath * Delete .classpath * Update XGBoostClassifier.scala * Update XGBoostRegressor.scala * Update GeneralParams.scala * Update GeneralParams.scala * Update GeneralParams.scala * Update GeneralParams.scala * Delete .classpath * Update RabitParams.scala * Update .gitignore * Update .gitignore * apply rabitParams to training * use string as rabit parameter value type * cleanup * add rabitEnv check * point to dmlc/rabit * per feedback * update private scope * misc * update rabit * add rabit_timtout, fix failing test. * split tests * allow build jvm with rabit mock * pass mock failures to rabit with test * add mock error and graceful handle rabit assertion error test * split mvn test * remove sign for test * update rabit * build jvm_packages with rabit mock * point back to dmlc/rabit * per feedback, update scala header * cleanup pom * per feedback * try fix lint * fix lint * per feedback, remove bootstrap_cache * per feedback 2 * try replace dev profile with passing mvn property * fix build error * remove mvn property and replace with env setting to build test jar * per feedback * revert copyright headlines, point to dmlc/rabit * revert python lint * remove multiple failure test case as retry is not enabled in spark * Update core.py * Update core.py * per feedback, style 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).
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The sponsors in this list are donating cloud hours in lieu of cash donation.
