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version 0.14.1

A set of python modules for machine learning and data mining

People: Andreas Mueller




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.


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).

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 install --user

To install for all users on Unix/Linux:

python build
sudo python install




You can check the latest sources with the command:

git clone git://

or if you have write privileges:

git clone


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 for more information.

Random number generation can be controlled during testing by setting the SKLEARN_SEED environment variable.



You can download the latest distribution from PyPI here:

Easy Install

Install the Easy Install tools. Afterwards you can install scikit-learn from the terminal by executing:

sudo easy_install scikit-learn

If you prefer to do a local installation, specify an installation prefix:

easy_install --prefix=${HOME} scikit-learn
and ensure that your PYTHONPATH is up to date, e.g.:
export PYTHONPATH=$PYTHONPATH:${HOME}/lib/python2.5/site-packages

This package was discovered in PyPI.