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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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R-package Deprecate `reg:linear' in favor of `reg:squarederror'. (#4267) Mar 17, 2019
cmake support cuda 10.1 (#4223) Mar 7, 2019
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demo Deprecate `reg:linear' in favor of `reg:squarederror'. (#4267) Mar 17, 2019
dmlc-core @ ac98309
include/xgboost Remove deprecated C APIs. (#4266) Mar 17, 2019
jvm-packages [jvm-packages] Allow supression of Rabit output in Booster::train in … Mar 21, 2019
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tests Optimisations for gpu_hist. (#4248) Mar 20, 2019
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eXtreme Gradient Boosting

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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.


© 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


  • 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.


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