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Code Quality Rank: L1
Programming language: C++
License: Apache License 2.0
Tags: Machine Learning    
Latest version: v1.3.0

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README

eXtreme Gradient Boosting

Build Status Build Status XGBoost-CI Documentation Status [GitHub license](./LICENSE) CRAN Status Badge PyPI version Conda version Optuna Twitter

Community | Documentation | [Resources](demo/README.md) | [Contributors](CONTRIBUTORS.md) | [Release Notes](NEWS.md)

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 (Kubernetes, Hadoop, SGE, MPI, Dask) and can solve problems beyond billions of examples.

License

© Contributors, 2019. 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.

Reference

  • 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.

Sponsors

Become a sponsor and get a logo here. See details at Sponsoring the XGBoost Project. The funds are used to defray the cost of continuous integration and testing infrastructure (https://xgboost-ci.net).

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*Note that all licence references and agreements mentioned in the xgboost README section above are relevant to that project's source code only.