scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
The project was started in 2007 by David Cournapeau as a Google Summer of Code project, and since then many volunteers have contributed. See the AUTHORS.rst file for a complete list of contributors.
It is currently maintained by a team of volunteers.
Note scikit-learn was previously referred to as scikits.learn.
- Official source code repo: https://github.com/scikit-learn/scikit-learn
- HTML documentation (stable release): http://scikit-learn.org
- HTML documentation (development version): http://scikit-learn.org/dev/
- Download releases: http://sourceforge.net/projects/scikit-learn/files/
- Issue tracker: https://github.com/scikit-learn/scikit-learn/issues
- Mailing list: https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
- IRC channel: #scikit-learn at irc.freenode.net
scikit-learn is tested to work under Python 2.6+ and Python 3.3+ (using the same codebase thanks to an embedded copy of [six]( http://pythonhosted.org/six/)).
The required dependencies to build the software Numpy >= 1.3, SciPy >= 0.7 and a working C/C++ compiler.
For running the examples Matplotlib >= 0.99.1 is required and for running the tests you need nose >= 0.10.
This configuration matches the Ubuntu 10.04 LTS release from April 2010.
This package uses distutils, which is the default way of installing python modules. To install in your home directory, use:
python setup.py install --user
To install for all users on Unix/Linux:
python setup.py build sudo python setup.py install
You can check the latest sources with the command:
git clone git://github.com/scikit-learn/scikit-learn.git
or if you have write privileges:
git clone email@example.com:scikit-learn/scikit-learn.git
After installation, you can launch the test suite from outside the source directory (you will need to have nosetests installed):
$ nosetests --exe sklearn
See the web page http://scikit-learn.org/stable/install.html#testing for more information.
Random number generation can be controlled during testing by setting the SKLEARN_SEED environment variable.