scikit-learn 0,15,63,53,0,30,0,42 python

scikit-learn: machine learning in Python

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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 <AUTHORS.rst>_ file for a complete list of contributors.

It is currently maintained by a team of volunteers.



Dependencies ~~~~~~~~~~~~

scikit-learn requires:

  • Python (>= 2.7 or >= 3.3)
  • NumPy (>= 1.6.1)
  • SciPy (>= 0.9)

For running the examples Matplotlib >= 1.1.1 is required.

scikit-learn also uses CBLAS, the C interface to the Basic Linear Algebra Subprograms library. scikit-learn comes with a reference implementation, but the system CBLAS will be detected by the build system and used if present. CBLAS exists in many implementations; see Linear algebra libraries <>_ for known issues.

User installation ~~~~~~~~~~~~~~~~~

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

pip install -U scikit-learn

or conda::

conda install scikit-learn

The documentation includes more detailed installation instructions <>_.


We welcome new contributors of all experience levels. The scikit-learn community goals are to be helpful, welcoming, and effective. The Development Guide <>_ has detailed information about contributing code, documentation, tests, and more. We’ve included some basic information in this README.

Important links ~~~~~~~~~~~~~~~

Source code ~~~~~~~~~~~

You can check the latest sources with the command::

git clone

Setting up a development environment ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Quick tutorial on how to go about setting up your environment to contribute to scikit-learn:

Testing ~~~~~~~

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

nosetests -v sklearn

Under Windows, it is recommended to use the following command (adjust the path to the python.exe program) as using the nosetests.exe program can badly interact with tests that use multiprocessing::

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

See the web page for more information.

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

Submitting a Pull Request ~~~~~~~~~~~~~~~~~~~~~~~~~

Before opening a Pull Request, have a look at the full Contributing page to make sure your code complies with our guidelines:

Project History

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 <AUTHORS.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.

Help and Support


- HTML documentation (stable release):
- HTML documentation (development version):
- FAQ:


Citation ~~~~~~~~

If you use scikit-learn in a scientific publication, we would appreciate citations:

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Top Contributors

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