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eXtreme Gradient Boosting

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An optimized general purpose gradient boosting library. The library is parallelized, and also provides an optimized distributed version.

It implements machine learning algorithms under the Gradient Boosting framework, including Generalized Linear Model (GLM) and Gradient Boosted Decision Trees (GBDT). XGBoost can also be distributed and scale to Terascale data

XGBoost is part of Distributed Machine Learning Common projects


What's New


  • Current version xgboost-0.4
    • Change log
    • This version is compatible with 0.3x versions


  • Easily accessible through CLI, python, R, Julia
  • Its fast! Benchmark numbers comparing xgboost, H20, Spark, R - benchm-ml numbers
  • Memory efficient - Handles sparse matrices, supports external memory
  • Accurate prediction, and used extensively by data scientists and kagglers - highlight links
  • Distributed version runs on Hadoop (YARN), MPI, SGE etc., scales to billions of examples.

Bug Reporting

Contributing to XGBoost

XGBoost has been developed and used by a group of active community members. Everyone is more than welcome to contribute. It is a way to make the project better and more accessible to more users.


© Contributors, 2015. Licensed under an Apache-2 license.

XGBoost in Graphlab Create

  • XGBoost is adopted as part of boosted tree toolkit in Graphlab Create (GLC). Graphlab Create is a powerful python toolkit that allows you to do data manipulation, graph processing, hyper-parameter search, and visualization of TeraBytes scale data in one framework. Try the Graphlab Create
  • Nice blogpost by Jay Gu about using GLC boosted tree to solve kaggle bike sharing challenge:
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