53 lines
3.6 KiB
Markdown
53 lines
3.6 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://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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[](https://gitter.im/dmlc/xgboost?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
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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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XGBoost is part of [DMLC](http://dmlc.github.io/) projects.
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Contents
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--------
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* [Documentation and Tutorials](https://xgboost.readthedocs.org)
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* [Code Examples](demo)
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* [Installation](doc/build.md)
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* [Contribute to XGBoost](http://xgboost.readthedocs.org/en/latest/dev-guide/contribute.html)
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What's New
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----------
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* [XGBoost brick](NEWS.md) Release
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Features
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--------
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* Easily accessible through CLI, [python](https://github.com/dmlc/xgboost/blob/master/demo/guide-python/basic_walkthrough.py),
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[R](https://github.com/dmlc/xgboost/blob/master/R-package/demo/basic_walkthrough.R),
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[Julia](https://github.com/antinucleon/XGBoost.jl/blob/master/demo/basic_walkthrough.jl)
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* Its fast! Benchmark numbers comparing xgboost, H20, Spark, R - [benchm-ml numbers](https://github.com/szilard/benchm-ml)
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* Memory efficient - Handles sparse matrices, supports external memory
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* Accurate prediction, and used extensively by data scientists and kagglers - [highlight links](https://github.com/dmlc/xgboost/blob/master/doc/README.md#highlight-links)
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* Distributed version runs on Hadoop (YARN), MPI, SGE etc., scales to billions of examples.
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Bug Reporting
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-------------
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* For reporting bugs please use the [xgboost/issues](https://github.com/dmlc/xgboost/issues) page.
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* For generic questions or to share your experience using xgboost please use the [XGBoost User Group](https://groups.google.com/forum/#!forum/xgboost-user/)
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Contributing to XGBoost
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-----------------------
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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.
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* Check out [Feature Wish List](https://github.com/dmlc/xgboost/labels/Wish-List) to see what can be improved, or open an issue if you want something.
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* Contribute to the [documents and examples](https://github.com/dmlc/xgboost/blob/master/doc/) to share your experience with other users.
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* Please add your name to [CONTRIBUTORS.md](CONTRIBUTORS.md) and after your patch has been merged.
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- Please also update [NEWS.md](NEWS.md) on changes and improvements in API and docs.
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License
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-------
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© Contributors, 2015. Licensed under an [Apache-2](https://github.com/dmlc/xgboost/blob/master/LICENSE) license.
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