31d1baba3d
* add back train method but mark as deprecated * add back train method but mark as deprecated * fix scalastyle error * fix scalastyle error * add new * update doc * finish Gang Scheduling * more * intro * Add sections: Prediction, Model persistence and ML pipeline. * Add XGBoost4j-Spark MLlib pipeline example * partial finished version * finish the doc * adjust code * fix the doc * use rst * Convert XGBoost4J-Spark tutorial to reST * Bring XGBoost4J up to date * add note about using hdfs * remove duplicate file * fix descriptions * update doc * Wrap HDFS/S3 export support as a note * update * wrap indexing_mode example in code block
eXtreme Gradient Boosting
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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.