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Latest commit 413f119 @tqchen Update dmlc-core

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. XGBoost is part of DMLC projects.


What's New


  • 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.

  • Check out Feature Wish List to see what can be improved, or open an issue if you want something.
  • Contribute to the documents and examples to share your experience with other users.
  • Please add your name to and after your patch has been merged.
    • Please also update on changes and improvements in API and docs.


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

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