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 About us page for a list of core contributors.
It is currently maintained by a team of volunteers.
Website: http://scikit-learn.org
scikit-learn requires:
- Python (>= 3.5)
- NumPy (>= 1.11.0)
- SciPy (>= 0.17.0)
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 and a few example require joblib >= 0.11.
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.
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.
See the changelog for a history of notable changes to scikit-learn.
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.
- 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
You can check the latest sources with the command:
git clone https://github.com/scikit-learn/scikit-learn.git
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
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.
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
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 About us page for a list of core contributors.
The project is currently maintained by a team of volunteers.
Note: scikit-learn was previously referred to as scikits.learn.
- 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-learnatwebchat.freenode.net - Stack Overflow: https://stackoverflow.com/questions/tagged/scikit-learn
- Website: http://scikit-learn.org
If you use scikit-learn in a scientific publication, we would appreciate citations: http://scikit-learn.org/stable/about.html#citing-scikit-learn