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eXtreme Gradient Boosting ========================== |Build Status| |Documentation Status| |CRAN Status Badge| |PyPI version| |Gitter chat for developers at https://gitter.im/dmlc/xgboost| An optimized general purpose gradient boosting library. The library is parallelized, and also provides an optimized distributed version. It implements machine learning algorithms under the `Gradient Boosting <https://en.wikipedia.org/wiki/Gradient_boosting>`__ framework, including `Generalized Linear Model <https://en.wikipedia.org/wiki/Generalized_linear_model>`__ (GLM) and `Gradient Boosted Decision Trees <https://en.wikipedia.org/wiki/Gradient_boosting#Gradient_tree_boosting>`__ (GBDT). XGBoost can also be `distributed <#features>`__ and scale to Terascale data XGBoost is part of `Distributed Machine Learning Common <http://dmlc.github.io/>`__ <img src=https://avatars2.githubusercontent.com/u/11508361?v=3&s=20> projects Contents -------- - `What's New <#whats-new>`__ - `Version <#version>`__ - `Documentation <doc/index.md>`__ - `Build Instruction <doc/build.md>`__ - `Features <#features>`__ - `Distributed XGBoost <multi-node>`__ - `Usecases <doc/index.md#highlight-links>`__ - `Bug Reporting <#bug-reporting>`__ - `Contributing to XGBoost <#contributing-to-xgboost>`__ - `Committers and Contributors <CONTRIBUTORS.md>`__ - `License <#license>`__ - `XGBoost in Graphlab Create <#xgboost-in-graphlab-create>`__ What's New ---------- - XGBoost helps Owen Zhang to win the `Avito Context Ad Click competition <https://www.kaggle.com/c/avito-context-ad-clicks>`__. Check out the `interview from Kaggle <http://blog.kaggle.com/2015/08/26/avito-winners-interview-1st-place-owen-zhang/>`__. - XGBoost helps Chenglong Chen to win `Kaggle CrowdFlower Competition <https://www.kaggle.com/c/crowdflower-search-relevance>`__ Check out the `winning solution <https://github.com/ChenglongChen/Kaggle_CrowdFlower>`__ - XGBoost-0.4 release, see `CHANGES.md <CHANGES.md#xgboost-04>`__ - XGBoost helps three champion teams to win `WWW2015 Microsoft Malware Classification Challenge (BIG 2015) <http://www.kaggle.com/c/malware-classification/forums/t/13490/say-no-to-overfitting-approaches-sharing>`__ Check out the `winning solution <doc/README.md#highlight-links>`__ - `External Memory Version <doc/external_memory.md>`__ Version ------- - Current version xgboost-0.4 - `Change log <CHANGES.md>`__ - This version is compatible with 0.3x versions 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 ------------- - 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/>`__ 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. \* 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>`__ after your patch has been merged. License ------- © Contributors, 2015. Licensed under an `Apache-2 <https://github.com/dmlc/xgboost/blob/master/LICENSE>`__ license. XGBoost in Graphlab Create -------------------------- - XGBoost is adopted as part of boosted tree toolkit in Graphlab Create (GLC). Graphlab Create is a powerful python toolkit that allows you to do data manipulation, graph processing, hyper-parameter search, and visualization of TeraBytes scale data in one framework. Try the `Graphlab Create <http://graphlab.com/products/create/quick-start-guide.html>`__ - Nice `blogpost <http://blog.graphlab.com/using-gradient-boosted-trees-to-predict-bike-sharing-demand>`__ by Jay Gu about using GLC boosted tree to solve kaggle bike sharing challenge: .. |Build Status| image:: https://travis-ci.org/dmlc/xgboost.svg?branch=master :target: https://travis-ci.org/dmlc/xgboost .. |Documentation Status| image:: https://readthedocs.org/projects/xgboost/badge/?version=latest :target: https://xgboost.readthedocs.org .. |CRAN Status Badge| image:: http://www.r-pkg.org/badges/version/xgboost :target: http://cran.r-project.org/web/packages/xgboost .. |PyPI version| image:: https://badge.fury.io/py/xgboost.svg :target: https://pypi.python.org/pypi/xgboost/ .. |Gitter chat for developers at https://gitter.im/dmlc/xgboost| image:: https://badges.gitter.im/Join%20Chat.svg :target: https://gitter.im/dmlc/xgboost?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge

Project description

<img src=https://raw.githubusercontent.com/dmlc/dmlc.github.io/master/img/logo-m/xgboost.png width=135/> eXtreme Gradient Boosting
===========
[![Build Status](https://travis-ci.org/dmlc/xgboost.svg?branch=master)](https://travis-ci.org/dmlc/xgboost)
[![Documentation Status](https://readthedocs.org/projects/xgboost/badge/?version=latest)](https://xgboost.readthedocs.org)
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[![PyPI version](https://badge.fury.io/py/xgboost.svg)](https://pypi.python.org/pypi/xgboost/)
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An optimized general purpose gradient boosting library. The library is parallelized, and also provides an optimized distributed version.

It implements machine learning algorithms under the [Gradient Boosting](https://en.wikipedia.org/wiki/Gradient_boosting) framework, including [Generalized Linear Model](https://en.wikipedia.org/wiki/Generalized_linear_model) (GLM) and [Gradient Boosted Decision Trees](https://en.wikipedia.org/wiki/Gradient_boosting#Gradient_tree_boosting) (GBDT). XGBoost can also be [distributed](#features) and scale to Terascale data

XGBoost is part of [Distributed Machine Learning Common](http://dmlc.github.io/) <img src=https://avatars2.githubusercontent.com/u/11508361?v=3&s=20> projects

Contents
--------
* [What's New](#whats-new)
* [Version](#version)
* [Documentation](doc/index.md)
* [Build Instruction](doc/build.md)
* [Features](#features)
* [Distributed XGBoost](multi-node)
* [Usecases](doc/index.md#highlight-links)
* [Bug Reporting](#bug-reporting)
* [Contributing to XGBoost](#contributing-to-xgboost)
* [Committers and Contributors](CONTRIBUTORS.md)
* [License](#license)
* [XGBoost in Graphlab Create](#xgboost-in-graphlab-create)

What's New
----------

* XGBoost helps Owen Zhang to win the [Avito Context Ad Click competition](https://www.kaggle.com/c/avito-context-ad-clicks). Check out the [interview from Kaggle](http://blog.kaggle.com/2015/08/26/avito-winners-interview-1st-place-owen-zhang/).
* XGBoost helps Chenglong Chen to win [Kaggle CrowdFlower Competition](https://www.kaggle.com/c/crowdflower-search-relevance)
Check out the [winning solution](https://github.com/ChenglongChen/Kaggle_CrowdFlower)
* XGBoost-0.4 release, see [CHANGES.md](CHANGES.md#xgboost-04)
* XGBoost helps three champion teams to win [WWW2015 Microsoft Malware Classification Challenge (BIG 2015)](http://www.kaggle.com/c/malware-classification/forums/t/13490/say-no-to-overfitting-approaches-sharing)
Check out the [winning solution](doc/README.md#highlight-links)
* [External Memory Version](doc/external_memory.md)

Version
-------

* Current version xgboost-0.4
- [Change log](CHANGES.md)
- This version is compatible with 0.3x versions

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
-------------

* 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/)


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.
* 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) after your patch has been merged.

License
-------
© Contributors, 2015. Licensed under an [Apache-2](https://github.com/dmlc/xgboost/blob/master/LICENSE) license.

XGBoost in Graphlab Create
--------------------------
* XGBoost is adopted as part of boosted tree toolkit in Graphlab Create (GLC). Graphlab Create is a powerful python toolkit that allows you to do data manipulation, graph processing, hyper-parameter search, and visualization of TeraBytes scale data in one framework. Try the [Graphlab Create](http://graphlab.com/products/create/quick-start-guide.html)
* Nice [blogpost](http://blog.graphlab.com/using-gradient-boosted-trees-to-predict-bike-sharing-demand) by Jay Gu about using GLC boosted tree to solve kaggle bike sharing challenge:

Project details


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