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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.

• XGBoost GPU support with fast histogram algorithm
• XGBoost4J: Portable Distributed XGboost in Spark, Flink and Dataflow, see JVM-Package
• Story and Lessons Behind the Evolution of XGBoost
• Tutorial: Distributed XGBoost on AWS with YARN
• XGBoost brick Release

• For reporting bugs please use the xgboost/issues page.
• For generic questions or to share your experience using XGBoost please use the XGBoost User Group