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[![PyPI version](https://badge.fury.io/py/xgboost.svg)](https://pypi.python.org/pypi/xgboost/)
[![Gitter chat for developers at https://gitter.im/dmlc/xgboost](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/dmlc/xgboost?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
|[Documentation](https://xgboost.readthedocs.org)| [Resources](demo/README.md) | [Installation](https://xgboost.readthedocs.org/en/latest/build.html)|
[Documentation](https://xgboost.readthedocs.org) |
[Resources](demo/README.md) |
[Installation](https://xgboost.readthedocs.org/en/latest/build.html) |
[Release Notes](NEWS.md)|
[RoadMap](https://github.com/dmlc/xgboost/issues/873)
XGBoost is an optimized distributed gradient boosting library designed to be highly *efficient*, *flexible* and *portable*.
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](https://en.wikipedia.org/wiki/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](http://dmlc.github.io/) projects.
What's New
----------
* [XGBoost brick](NEWS.md) Release
Features
--------
* Easily accessible through CLI, [python](https://github.com/dmlc/xgboost/blob/master/demo/guide-python/basic_walkthrough.py),
[R](https://github.com/dmlc/xgboost/blob/master/R-package/demo/basic_walkthrough.R),
[Julia](https://github.com/antinucleon/XGBoost.jl/blob/master/demo/basic_walkthrough.jl)
* Its fast! Benchmark numbers comparing xgboost, H20, Spark, R - [benchm-ml numbers](https://github.com/szilard/benchm-ml)
* Memory efficient - Handles sparse matrices, supports external memory
* Accurate prediction, and used extensively by data scientists and kagglers - [highlight links](https://github.com/dmlc/xgboost/blob/master/doc/README.md#highlight-links)
* Distributed version runs on Hadoop (YARN), MPI, SGE etc., scales to billions of examples.
Bug Reporting
-------------
Ask a Question
--------------
* For reporting bugs please use the [xgboost/issues](https://github.com/dmlc/xgboost/issues) page.
* For generic questions or to share your experience using xgboost please use the [XGBoost User Group](https://groups.google.com/forum/#!forum/xgboost-user/)
* For generic questions for to share your experience using xgboost please use the [XGBoost User Group](https://groups.google.com/forum/#!forum/xgboost-user/)
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](https://github.com/dmlc/xgboost/labels/Wish-List) to see what can be improved, or open an issue if you want something.
* Check out [call for contributions](https://github.com/dmlc/xgboost/issues?q=is%3Aissue+is%3Aclosed+label%3Acall-for-contribution) and [Roadmap](https://github.com/dmlc/xgboost/issues/873) to see what can be improved, or open an issue if you want something.
* Contribute to the [documents and examples](https://github.com/dmlc/xgboost/blob/master/doc/) to share your experience with other users.
* Please add your name to [CONTRIBUTORS.md](CONTRIBUTORS.md) and after your patch has been merged.
- Please also update [NEWS.md](NEWS.md) on changes and improvements in API and docs.