XGBoost Python Package ====================== |PyPI version| |PyPI downloads| Installation ------------ We are on `PyPI <https://pypi.python.org/pypi/xgboost>`__ now. For stable version, please install using pip: - ``pip install xgboost`` - Note for windows users: this pip installation may not work on some windows environment, and it may cause unexpected errors. pip installation on windows is currently disabled for further invesigation, please install from github. For up-to-date version, please install from github. - To make the python module, type ``./build.sh`` in the root directory of project - Make sure you have `setuptools <https://pypi.python.org/pypi/setuptools>`__ - Install with ``python setup.py install`` from this directory. - For windows users, please use the Visual Studio project file under `windows folder <../windows/>`__. See also the `installation tutorial <https://www.kaggle.com/c/otto-group-product-classification-challenge/forums/t/13043/run-xgboost-from-windows-and-python>`__ from Kaggle Otto Forum. Examples -------- - Refer also to the walk through example in `demo folder <../demo/guide-python>`__ - See also the `example scripts <../demo/kaggle-higgs>`__ for Kaggle Higgs Challenge, including `speedtest script <../demo/kaggle-higgs/speedtest.py>`__ on this dataset. Note ---- - If you want to build xgboost on Mac OS X with multiprocessing support where clang in XCode by default doesn't support, please install gcc 4.9 or higher using `homebrew <http://brew.sh/>`__ ``brew tap homebrew/versions; brew install gcc49`` - If you want to run XGBoost process in parallel using the fork backend for joblib/multiprocessing, you must build XGBoost without support for OpenMP by ``make no_omp=1``. Otherwise, use the forkserver (in Python 3.4) or spawn backend. See the `sklearn\_parallel.py <../demo/guide-python/sklearn_parallel.py>`__ demo. .. |PyPI version| image:: https://badge.fury.io/py/xgboost.svg :target: http://badge.fury.io/py/xgboost .. |PyPI downloads| image:: https://img.shields.io/pypi/dm/xgboost.svg :target: https://pypi.python.org/pypi/xgboost/
[![CRAN Status Badge](http://www.r-pkg.org/badges/version/xgboost)](http://cran.r-project.org/web/packages/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)
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
* [What's New](#whats-new)
* [Build Instruction](doc/build.md)
* [Distributed XGBoost](multi-node)
* [Bug Reporting](#bug-reporting)
* [Contributing to XGBoost](#contributing-to-xgboost)
* [Committers and Contributors](CONTRIBUTORS.md)
* [XGBoost in Graphlab Create](#xgboost-in-graphlab-create)
* 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)
* Current version xgboost-0.4
- [Change log](CHANGES.md)
- This version is compatible with 0.3x versions
* Easily accessible through CLI, [python](https://github.com/dmlc/xgboost/blob/master/demo/guide-python/basic_walkthrough.py),
* 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.
* 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.
© 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: