Dependencies

scikit-learn requires:

• Python (>= 3.5)

• NumPy (>= 1.11.0)

• SciPy (>= 0.17.0)

• joblib (>= 0.11)

Scikit-learn 0.20 was the last version to support Python2.7. Scikit-learn 0.21 and later require Python 3.5 or newer.

For running the examples Matplotlib >= 1.5.1 is required. A few examples require scikit-image >= 0.12.3, a few examples require pandas >= 0.18.0.

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.

Important links

• Official source code repo: https://github.com/scikit-learn/scikit-learn

• Download releases: https://pypi.org/project/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 pytest >= 3.3.0 installed):

pytest sklearn

See the web page http://scikit-learn.org/dev/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

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

Communication

• Mailing list: https://mail.python.org/mailman/listinfo/scikit-learn

• IRC channel: #scikit-learn at webchat.freenode.net

• Stack Overflow: https://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