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Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Flink and DataFlow

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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.

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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.

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© Contributors, 2016. Licensed under an Apache-2 license.

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Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Flink and DataFlow

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  • C++ 41.2%
  • Scala 15.5%
  • R 13.2%
  • Python 11.6%
  • Cuda 7.8%
  • Java 6.3%
  • Other 4.4%