aa4ee6a0e4
* Adding Java/Scala doc build to Jenkins CI * Deploy built doc to S3 bucket * Build doc only for branches * Build doc first, to get doc faster for branch updates * Have ReadTheDocs download doc tarball from S3 * Update JVM doc links * Put doc build commands in a script * Specify Spark 2.3+ requirement for XGBoost4J-Spark * Build GPU wheel without NCCL, to reduce binary size
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 (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.