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scikit-learn: machine learning in Python
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scikit-learn is a Python module for machine learning built on top ofSciPy and is distributed under the 3-Clause BSD license.
The project was started in 2007 by David Cournapeau as a Google Summerof Code project, and since then many volunteers have contributed. SeetheAbout us pagefor a list of core contributors.
It is currently maintained by a team of volunteers.
Website:https://scikit-learn.org
scikit-learn requires:
- Python (>= 3.10)
- NumPy (>= 1.22.0)
- SciPy (>= 1.8.0)
- joblib (>= 1.2.0)
- threadpoolctl (>= 3.1.0)
Scikit-learn plotting capabilities (i.e., functions start withplot_
andclasses end withDisplay
) require Matplotlib (>= 3.5.0).For running the examples Matplotlib >= 3.5.0 is required.A few examples require scikit-image >= 0.19.0, a few examplesrequire pandas >= 1.4.0, some examples require seaborn >=0.9.0 and plotly >= 5.14.0.
If you already have a working installation of NumPy and SciPy,the easiest way to install scikit-learn is usingpip
:
pip install -U scikit-learn
orconda
:
conda install -c conda-forge scikit-learn
The documentation includes more detailedinstallation instructions.
See thechangelogfor a history of notable changes to scikit-learn.
We welcome new contributors of all experience levels. The scikit-learncommunity goals are to be helpful, welcoming, and effective. TheDevelopment Guidehas detailed information about contributing code, documentation, tests, andmore. 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
To learn more about making a contribution to scikit-learn, please see ourContributing guide.
After installation, you can launch the test suite from outside the sourcedirectory (you will need to havepytest
>= 7.1.2 installed):
pytest sklearn
See the web pagehttps://scikit-learn.org/dev/developers/contributing.html#testing-and-improving-test-coveragefor more information.
Random number generation can be controlled during testing by settingtheSKLEARN_SEED
environment variable.
Before opening a Pull Request, have a look at thefull Contributing page to make sure your code complieswith our guidelines:https://scikit-learn.org/stable/developers/index.html
The project was started in 2007 by David Cournapeau as a Google Summerof Code project, and since then many volunteers have contributed. SeetheAbout us pagefor 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):https://scikit-learn.org
- HTML documentation (development version):https://scikit-learn.org/dev/
- FAQ:https://scikit-learn.org/stable/faq.html
- Mailing list:https://mail.python.org/mailman/listinfo/scikit-learn
- Logos & Branding:https://github.com/scikit-learn/scikit-learn/tree/main/doc/logos
- Blog:https://blog.scikit-learn.org
- Calendar:https://blog.scikit-learn.org/calendar/
- Stack Overflow:https://stackoverflow.com/questions/tagged/scikit-learn
- GitHub Discussions:https://github.com/scikit-learn/scikit-learn/discussions
- Website:https://scikit-learn.org
- LinkedIn:https://www.linkedin.com/company/scikit-learn
- Bluesky:https://bsky.app/profile/scikit-learn.org
- Mastodon:https://mastodon.social/@sklearn@fosstodon.org
- YouTube:https://www.youtube.com/channel/UCJosFjYm0ZYVUARxuOZqnnw/playlists
- Facebook:https://www.facebook.com/scikitlearnofficial/
- Instagram:https://www.instagram.com/scikitlearnofficial/
- TikTok:https://www.tiktok.com/@scikit.learn
- Discord:https://discord.gg/h9qyrK8Jc8
If you use scikit-learn in a scientific publication, we would appreciate citations:https://scikit-learn.org/stable/about.html#citing-scikit-learn
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scikit-learn: machine learning in Python