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scikit-learn: machine learning in Python
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Latest commit f63ac6e @GaelVaroquaux GaelVaroquaux Merge pull request #5936 from olologin/MultiLabelBinarizer_check_is_f…

[MRG+1] MultiLabelBinarizer check_is_fitted validation for transform() and inverse_transform()


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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 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.

Important links


scikit-learn is tested to work under Python 2.6, Python 2.7, and Python 3.4. (using the same codebase thanks to an embedded copy of six). It should also work with Python 3.3.

The required dependencies to build the software are NumPy >= 1.6.1, SciPy >= 0.9 and a working C/C++ compiler. For the development version, you will also require Cython >=0.23.

For running the examples Matplotlib >= 1.1.1 is required and for running the tests you need nose >= 1.1.2.

This configuration matches the Ubuntu Precise 12.04 LTS release from April 2012.

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.


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

For more detailed installation instructions, see the web page




You can check the latest sources with the command:

git clone

or if you have write privileges:

git clone


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

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


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.
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