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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
AUTHORS.rst <AUTHORS.rst>_ file for a complete list of contributors.
It is currently maintained by a team of volunteers.
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 <http://scikit-learn.org/stable/modules/computational_performance.html#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 install -U scikit-learn
conda install scikit-learn
The documentation includes more detailed
installation instructions <http://scikit-learn.org/stable/install.html>_.
We welcome new contributors of all experience levels. The scikit-learn
community goals are to be helpful, welcoming, and effective. The
Development Guide <http://scikit-learn.org/stable/developers/index.html>_
has detailed information about contributing code, documentation, tests, and
more. We've included some basic information in this README.
- Official source code repo: https://github.com/scikit-learn/scikit-learn - Download releases: https://pypi.python.org/pypi/scikit-learn - Issue tracker: https://github.com/scikit-learn/scikit-learn/issues Source code
You can check the latest sources with the command::
git clone https://github.com/scikit-learn/scikit-learn.git
Setting up a development environment
Quick tutorial on how to go about setting up your environment to contribute to scikit-learn: https://github.com/scikit-learn/scikit-learn/blob/master/CONTRIBUTING.md 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
python.exe program) as using the
nosetests.exe program can badly
interact with tests that use
C:\Python34\python.exe -c "import nose; nose.main()" -v sklearn
See the web page http://scikit-learn.org/stable/developers/advanced_installation.html#testing 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: http://scikit-learn.org/stable/developers/index.html 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 ---------------- Documentation
- HTML documentation (stable release): http://scikit-learn.org
- HTML documentation (development version): http://scikit-learn.org/dev/
- FAQ: http://scikit-learn.org/stable/faq.html
- Mailing list: https://mail.python.org/mailman/listinfo/scikit-learn - IRC channel: ``#scikit-learn`` at ``webchat.freenode.net`` - Stack Overflow: http://stackoverflow.com/questions/tagged/scikit-learn - Website: http://scikit-learn.org Citation
If you use scikit-learn in a scientific publication, we would appreciate citations: http://scikit-learn.org/stable/about.html#citing-scikit-learn