About us#

History#

This project was started in 2007 as a Google Summer of Code project by David Cournapeau. Later that year, Matthieu Brucher started work 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.

Governance#

The decision making process and governance structure of scikit-learn 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 teams, listed below, have central roles, however a more complete list of contributors can be found on github.

Maintainers Team#

The following people are currently maintainers, in charge of consolidating scikit-learn’s development and maintenance:


Jérémie du Boisberranger


Joris Van den Bossche


Loïc Estève


Thomas J. Fan


Alexandre Gramfort


Olivier Grisel


Yaroslav Halchenko


Tim Head


Nicolas Hug


Adrin Jalali


Julien Jerphanion


Guillaume Lemaitre


Adam Li


Christian Lorentzen


Jan Hendrik Metzen


Andreas Mueller


Vlad Niculae


Joel Nothman


Hanmin Qin


Omar Salman


Bertrand Thirion


Tom Dupré la Tour


Gael Varoquaux


Nelle Varoquaux


Yao Xiao


Roman Yurchak


Meekail Zain

Note

Please do not email the authors directly to ask for assistance or report issues. Instead, please see What’s the best way to ask questions about scikit-learn in the FAQ.

See also

How you can contribute to the project.

documentation Team#

The following people help with documenting the project:


Arturo Amor


Lucy Liu


Yao Xiao

Contributor Experience Team#

The following people are active contributors who also help with triaging issues, PRs, and general maintenance:


Juan Carlos Alfaro Jiménez


Lucy Liu


Maxwell Liu


Juan Martin Loyola


Sylvain Marié


Norbert Preining


Reshama Shaikh


Albert Thomas


Maren Westermann

Communication Team#

The following people help with communication around scikit-learn.


Lauren Burke-McCarthy


François Goupil

Emeritus Core Developers#

The following people have been active contributors in the past, but are no longer active in the project:

  • Mathieu Blondel

  • 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

  • Brian Holt

  • Arnaud Joly

  • Thouis (Ray) Jones

  • Kyle Kastner

  • manoj kumar

  • Robert Layton

  • Wei Li

  • Paolo Losi

  • Gilles Louppe

  • Vincent Michel

  • Jarrod Millman

  • Alexandre Passos

  • Fabian Pedregosa

  • Peter Prettenhofer

  • (Venkat) Raghav, Rajagopalan

  • Jacob Schreiber

  • 杜世橋 Du Shiqiao

  • Jake Vanderplas

  • David Warde-Farley

  • Ron Weiss

Emeritus Communication Team#

The following people have been active in the communication team in the past, but no longer have communication responsibilities:

  • Reshama Shaikh

Emeritus Contributor Experience Team#

The following people have been active in the contributor experience team in the past:

  • Chiara Marmo

Citing scikit-learn#

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},
}

Artwork#

High quality PNG and SVG logos are available in the doc/logos/ source directory.

_images/scikit-learn-logo-notext.png

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.

_images/probabl.png

The Members of the Scikit-learn Consortium at Inria Foundation help at maintaining and improving the project through their financial support.

chanel

axa

bnp

nvidia

dataiku

inria

NVidia funds Tim Head since 2022 and is part of the scikit-learn consortium at Inria.

_images/nvidia.png

Microsoft funds Andreas Müller since 2020.

_images/microsoft.png

Quansight Labs funds Lucy Liu since 2022.

_images/quansight-labs.png

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.

czi

wellcome

Tidelift supports the project via their service agreement.

_images/Tidelift-logo-on-light.svg

Past Sponsors#

Quansight Labs funded Meekail Zain in 2022 and 2023, and funded Thomas J. Fan from 2021 to 2023.

_images/quansight-labs.png

Columbia University funded Andreas Müller (2016-2020).

_images/columbia.png

The University of Sydney funded Joel Nothman (2017-2021).

_images/sydney-primary.jpeg

Andreas Müller received a grant to improve scikit-learn from the Alfred P. Sloan Foundation . This grant supported the position of Nicolas Hug and Thomas J. Fan.

_images/sloan_banner.png

INRIA actively supports this project. It has provided funding for Fabian Pedregosa (2010-2012), Jaques Grobler (2012-2013) and Olivier Grisel (2013-2017) to work on this project full-time. It also hosts coding sprints and other events.

_images/inria-logo.jpg

Paris-Saclay Center for Data Science funded one year for a developer to work on the project full-time (2014-2015), 50% of the time of Guillaume Lemaitre (2016-2017) and 50% of the time of Joris van den Bossche (2017-2018).

_images/cds-logo.png

NYU Moore-Sloan Data Science Environment funded Andreas Mueller (2014-2016) to work on this project. The Moore-Sloan Data Science Environment also funds several students to work on the project part-time.

_images/nyu_short_color.png

Télécom Paristech funded Manoj Kumar (2014), Tom Dupré la Tour (2015), Raghav RV (2015-2017), Thierry Guillemot (2016-2017) and Albert Thomas (2017) to work on scikit-learn.

_images/telecom.png

The Labex DigiCosme funded Nicolas Goix (2015-2016), Tom Dupré la Tour (2015-2016 and 2017-2018), Mathurin Massias (2018-2019) to work part time on scikit-learn during their PhDs. It also funded a scikit-learn coding sprint in 2015.

_images/digicosme.png

The Chan-Zuckerberg Initiative funded Nicolas Hug to work full-time on scikit-learn in 2020.

_images/czi.png

The following students were sponsored by Google to work on scikit-learn through the Google Summer of Code program.

The NeuroDebian project providing Debian packaging and contributions is supported by Dr. James V. Haxby (Dartmouth College).

The following organizations funded the scikit-learn consortium at Inria in the past:

msn

bcg

fujitsu

aphp

hf

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 are interested in donating to the project or to one of our code-sprints, please donate via the NumFOCUS Donations Page.

Help us, donate!

All donations will be handled by NumFOCUS, a non-profit organization which is managed by a board of Scipy community members. NumFOCUS’s mission is to foster scientific computing software, in particular in Python. As a fiscal home of scikit-learn, it ensures that money is available when needed to keep the project funded and available while in compliance with tax regulations.

The received donations for the scikit-learn project mostly will go towards covering travel-expenses for code sprints, as well as towards the organization budget of the project [1].

Notes

Infrastructure support#

We would also like to thank Microsoft Azure, Cirrus Cl, CircleCl for free CPU time on their Continuous Integration servers, and Anaconda Inc. for the storage they provide for our staging and nightly builds.

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