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scikit-learn: machine learning in Python

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.. -*- mode: rst -*-


scikits.learn is a python module for machine learning built on top of

The project was started in 2007 by David Cournapeu as a Google Summer
of Code project, and since then many volunteers have contributed. See
the AUTHORS file for a complete list of contributors.

It is currently maintained by a team of volunteers.


You can download source code and Windows binaries from SourceForge:


The required dependencies to build the software are python >= 2.5,
NumPy >= 1.1, SciPy, the Boost libraries and a working C++ compiler.

Optional dependencies are scikits.optimization for module

To run the tests you will also need nosetests and python-dap


This packages uses distutils, which is the default way of installing
python modules. The install command is::

  python install

Mailing list

There's a general and development mailing list, visit to
subscribe to the mailing list.




To check out the sources from the main repository, which is under
subversion control, run the command::

   svn co scikit-learn

You can also browse the code online at

GIT Mirror

There is also an automatically updated git mirror of scikit-learn
available from github (  So
you could setup your development using GIT and commit results back
to SVN using ``git svn``.  To get started using git mirror and link it
to original SVN, use::

  git clone --origin=svn git:// scikit-learn
  cd scikit-learn
  git svn init --prefix=svn/ -s
  git svn rebase -l -n			         # should be quick
  git config svn.authorsfile .svnauthors

Such setup was inspired by GIT Mirror for NumPy and SciPy projects,
which is well described (including some of ``git svn`` caveats) at .

If you are not a part of the team and/or have no commit rights to SVN,
but want to share code/documentation changes (bug fixes, enhancements,
etc.), you can either make use of ``git format-patch`` commands to
prepare a bundle of patches to be posted with bugreport (see Bugs), or
simply create your own fork of git repository on github ("Fork" button
in github web UI) and point to corresponding commits within your


Please submit bugs you might encounter, as well as patches and feature
requests to the tracker located at the address


To execute the test suite, run from the project's top directory (you
will need to have nosetest installed)::

    python test

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