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

scikit-learn: machine learning in Python

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scikit-learn

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.

Website: http://scikit-learn.org

Installation

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
<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 ::

pip install -U scikit-learn

or conda::

conda install scikit-learn

The documentation includes more detailed installation instructions <http://scikit-learn.org/stable/install.html>_.

Development

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.

Important links


- 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 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 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

Communication


- 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


Top Contributors

ogrisel amueller fabianp larsmans agramfort glouppe pprett mblondel vene arjoly jnothman jakevdp MechCoder NelleV robertlayton ndawe ahojnnes raghavrv dengemann weilinear clayw kemaleren oddskool AlexanderFabisch alextp VirgileFritsch bthirion dsullivan7 hamsal maheshakya

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