Repository files navigation

scikit-learn is a Python module for machine learning built on top ofSciPy and distributed under the 3-Clause BSD license.

The project was started in 2007 by David Cournapeau as a Google Summerof Code project, and since then many volunteers have contributed. SeetheAUTHORS.rst file for a complete list of contributors.

It is currently maintained by a team of volunteers.

Website:http://scikit-learn.org

scikit-learn requires:

• Python (>= 2.7 or >= 3.3)
• NumPy (>= 1.8.2)
• SciPy (>= 0.13.3)

For running the examples Matplotlib >= 1.1.1 is required.

scikit-learn also uses CBLAS, the C interface to the Basic Linear AlgebraSubprograms library. scikit-learn comes with a reference implementation, butthe system CBLAS will be detected by the build system and used if present.CBLAS exists in many implementations; seeLinear algebra librariesfor known issues.

If you already have a working installation of numpy and scipy,the easiest way to install scikit-learn is usingpip

pip install -U scikit-learn

orconda:

conda install scikit-learn

The documentation includes more detailedinstallation instructions.

We welcome new contributors of all experience levels. The scikit-learncommunity goals are to be helpful, welcoming, and effective. TheDevelopment Guidehas detailed information about contributing code, documentation, tests, andmore. We've included some basic information in this README.

• Official source code repo:https://github.com/scikit-learn/scikit-learn
• Download releases:https://pypi.python.org/pypi/scikit-learn
• Issue tracker:https://github.com/scikit-learn/scikit-learn/issues

You can check the latest sources with the command:

git clone https://github.com/scikit-learn/scikit-learn.git

Quick tutorial on how to go about setting up your environment tocontribute to scikit-learn:https://github.com/scikit-learn/scikit-learn/blob/master/CONTRIBUTING.md

After installation, you can launch the test suite from outside thesource directory (you will need to have thenose package installed):

nosetests -v sklearn

Under Windows, it is recommended to use the following command (adjust the pathto thepython.exe program) as using thenosetests.exe program can badlyinteract with tests that usemultiprocessing:

C:\Python34\python.exe -c "import nose; nose.main()" -v sklearn

See the web pagehttp://scikit-learn.org/stable/developers/advanced_installation.html#testingfor more information.

Random number generation can be controlled during testing by settingtheSKLEARN_SEED environment variable.

Before opening a Pull Request, have a look at thefull Contributing page to make sure your code complieswith our guidelines:http://scikit-learn.org/stable/developers/index.html

The project was started in 2007 by David Cournapeau as a Google Summerof Code project, and since then many volunteers have contributed. SeetheAUTHORS.rst file for a complete list of contributors.

The project is currently maintained by a team of volunteers.

Note: scikit-learn was previously referred to as scikits.learn.

• HTML documentation (stable release):http://scikit-learn.org
• HTML documentation (development version):http://scikit-learn.org/dev/
• FAQ:http://scikit-learn.org/stable/faq.html

• Mailing list:https://mail.python.org/mailman/listinfo/scikit-learn
• IRC channel:#scikit-learn atwebchat.freenode.net
• Stack Overflow:http://stackoverflow.com/questions/tagged/scikit-learn
• Website:http://scikit-learn.org

If you use scikit-learn in a scientific publication, we would appreciate citations:http://scikit-learn.org/stable/about.html#citing-scikit-learn