This project was started in 2007 as a Google Summer of Code project by David Cournapeau. Later that year, Matthieu Brucher started working on this project as part of his thesis.
In 2010 Fabian Pedregosa, Gael Varoquaux, Alexandre Gramfort and Vincent Michel of INRIA took leadership of the project and made the first public release, February the 1st 2010. Since then, several releases have appeared following an approximately 3-month cycle, and a thriving international community has been leading the development. As a result, INRIA holds the copyright over the work done by people who were employed by INRIA at the time of the contribution.
Governance#The decision making process and governance structure of scikit-learn, like roles and responsibilities, is laid out in the governance document.
The people behind scikit-learn#scikit-learn is a community project, developed by a large group of people, all across the world. A few core contributor teams, listed below, have central roles, however a more complete list of contributors can be found on GitHub.
Active Core Contributors# Maintainers Team#The following people are currently maintainers, in charge of consolidating scikit-learn’s development and maintenance:
Jérémie du Boisberranger
Loïc Estève
Thomas J. Fan
Alexandre Gramfort
Olivier Grisel
Tim Head
Nicolas Hug
Adrin Jalali
Julien Jerphanion
Guillaume Lemaitre
Adam Li
Lucy Liu
Christian Lorentzen
Andreas Mueller
Joel Nothman
Omar Salman
Gael Varoquaux
Yao Xiao
Meekail Zain
Documentation Team#The following people help with documenting the project:
Contributor Experience Team#The following people are active contributors who also help with triaging issues, PRs, and general maintenance:
Virgil Chan
Juan Carlos Alfaro Jiménez
Lucy Liu
Maxwell Liu
Juan Martin Loyola
Sylvain Marié
Norbert Preining
Stefanie Senger
Reshama Shaikh
Albert Thomas
Maren Westermann
Communication Team#The following people help with communication around scikit-learn.
Lauren Burke-McCarthy
François Goupil
Emeritus Core Contributors# Emeritus Maintainers Team#The following people have been active contributors in the past, but are no longer active in the project:
Mathieu Blondel
Joris Van den Bossche
Matthieu Brucher
Lars Buitinck
David Cournapeau
Noel Dawe
Vincent Dubourg
Edouard Duchesnay
Alexander Fabisch
Virgile Fritsch
Satrajit Ghosh
Angel Soler Gollonet
Chris Gorgolewski
Jaques Grobler
Yaroslav Halchenko
Brian Holt
Arnaud Joly
Thouis (Ray) Jones
Kyle Kastner
Manoj Kumar
Robert Layton
Wei Li
Paolo Losi
Gilles Louppe
Jan Hendrik Metzen
Vincent Michel
Jarrod Millman
Vlad Niculae
Alexandre Passos
Fabian Pedregosa
Peter Prettenhofer
Hanmin Qin
(Venkat) Raghav, Rajagopalan
Jacob Schreiber
杜世橋 Du Shiqiao
Bertrand Thirion
Tom Dupré la Tour
Jake Vanderplas
Nelle Varoquaux
David Warde-Farley
Ron Weiss
Roman Yurchak
The following people have been active in the communication team in the past, but no longer have communication responsibilities:
Reshama Shaikh
The following people have been active in the contributor experience team in the past:
Chiara Marmo
If you use scikit-learn in a scientific publication, we would appreciate citations to the following paper:
Scikit-learn: Machine Learning in Python, Pedregosa et al., JMLR 12, pp. 2825-2830, 2011.
Bibtex entry:
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
journal={Journal of Machine Learning Research},
volume={12},
pages={2825--2830},
year={2011}
}
If you want to cite scikit-learn for its API or design, you may also want to consider the following paper:
API design for machine learning software: experiences from the scikit-learn project, Buitinck et al., 2013.
Bibtex entry:
@inproceedings{sklearn_api,
author = {Lars Buitinck and Gilles Louppe and Mathieu Blondel and
Fabian Pedregosa and Andreas Mueller and Olivier Grisel and
Vlad Niculae and Peter Prettenhofer and Alexandre Gramfort
and Jaques Grobler and Robert Layton and Jake VanderPlas and
Arnaud Joly and Brian Holt and Ga{\"{e}}l Varoquaux},
title = {{API} design for machine learning software: experiences from the scikit-learn
project},
booktitle = {ECML PKDD Workshop: Languages for Data Mining and Machine Learning},
year = {2013},
pages = {108--122},
}
Branding & Logos#
High quality PNG and SVG logos are available in the doc/logos source directory. The color palette is available in the Branding Guide.
Funding#Scikit-learn is a community driven project, however institutional and private grants help to assure its sustainability.
The project would like to thank the following funders.
:probabl. employs Adrin Jalali, Arturo Amor, François Goupil, Guillaume Lemaitre, Jérémie du Boisberranger, Loïc Estève, Olivier Grisel, and Stefanie Senger.
The Members of the Scikit-learn Consortium at Inria Foundation help at maintaining and improving the project through their financial support.
NVidia funds Tim Head since 2022 and is part of the scikit-learn consortium at Inria.
Microsoft funds Andreas Müller since 2020.
Quansight Labs funds Lucy Liu since 2022.
The Chan-Zuckerberg Initiative and Wellcome Trust fund scikit-learn through the Essential Open Source Software for Science (EOSS) cycle 6.
It supports Lucy Liu and diversity & inclusion initiatives that will be announced in the future.
Tidelift supports the project via their service agreement.
Donations in Kind#The following organizations provide non-financial contributions to the scikit-learn project.
Company Contribution Anaconda Inc Storage for our staging and nightly builds CircleCI CPU time on their Continuous Integration servers GitHub Teams account Microsoft Azure CPU time on their Continuous Integration servers Coding Sprints#The scikit-learn project has a long history of open source coding sprints with over 50 sprint events from 2010 to present day. There are scores of sponsors who contributed to costs which include venue, food, travel, developer time and more. See scikit-learn sprints for a full list of events.
Donating to the project#If you have found scikit-learn to be useful in your work, research, or company, please consider making a donation to the project commensurate with your resources. There are several options for making donations:
Donate via NumFOCUS Donate via GitHub Sponsors Donate via Benevity
Donation Options:
NumFOCUS: Donate via the NumFOCUS Donations Page, scikit-learn’s fiscal sponsor.
GitHub Sponsors: Support the project directly through GitHub Sponsors.
Benevity: If your company uses scikit-learn, you can also support the project through Benevity, a platform to manage employee donations. It is widely used by hundreds of Fortune 1000 companies to streamline and scale their social impact initiatives. If your company uses Benevity, you are able to make a donation with a company match as high as 100%. Our project ID is 433725.
All donations are managed by NumFOCUS, a 501(c)(3) non-profit organization based in Austin, Texas, USA. The NumFOCUS board consists of SciPy community members. Contributions are tax-deductible to the extent allowed by law.
Notes
Contributions support the maintenance of the project, including development, documentation, infrastructure and coding sprints.
scikit-learn Swag#Official scikit-learn swag is available for purchase at the NumFOCUS online store. A portion of the proceeds from each sale goes to support the scikit-learn project.
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