34 lines
2.1 KiB
Markdown
34 lines
2.1 KiB
Markdown
<img src=https://raw.githubusercontent.com/dmlc/dmlc.github.io/master/img/logo-m/xgboost.png width=135/> eXtreme Gradient Boosting
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===========
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[](https://travis-ci.org/dmlc/xgboost)
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[](https://ci.appveyor.com/project/tqchen/xgboost)
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[](https://xgboost.readthedocs.org)
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[](./LICENSE)
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[](http://cran.r-project.org/web/packages/xgboost)
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[](https://pypi.python.org/pypi/xgboost/)
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[Community](https://xgboost.ai/community) |
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[Documentation](https://xgboost.readthedocs.org) |
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[Resources](demo/README.md) |
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[Contributors](CONTRIBUTORS.md) |
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[Release Notes](NEWS.md)
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XGBoost is an optimized distributed gradient boosting library designed to be highly ***efficient***, ***flexible*** and ***portable***.
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It implements machine learning algorithms under the [Gradient Boosting](https://en.wikipedia.org/wiki/Gradient_boosting) framework.
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XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many data science problems in a fast and accurate way.
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The same code runs on major distributed environment (Hadoop, SGE, MPI) and can solve problems beyond billions of examples.
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License
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-------
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© Contributors, 2016. Licensed under an [Apache-2](https://github.com/dmlc/xgboost/blob/master/LICENSE) license.
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Contribute to XGBoost
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---------------------
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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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Checkout the [Community Page](https://xgboost.ai/community)
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Reference
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---------
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- Tianqi Chen and Carlos Guestrin. [XGBoost: A Scalable Tree Boosting System](http://arxiv.org/abs/1603.02754). In 22nd SIGKDD Conference on Knowledge Discovery and Data Mining, 2016
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- XGBoost originates from research project at University of Washington.
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