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 a ~ 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.

Authors#

The following people are currently core contributors to 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

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

Roman Yurchak

Meekail Zain

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.

Documentation Team#

The following people help with documenting the project:

Arturo Amor

Lucy Liu

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

francoisgoupil

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The following people have been active contributors in the past, but are no longer active in the project: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

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.

Past Sponsors#

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

2007 - David Cournapeau
2011 - Vlad Niculae
2012 - Vlad Niculae, Immanuel Bayer.
2013 - Kemal Eren, Nicolas Trésegnie
2014 - Hamzeh Alsalhi, Issam Laradji, Maheshakya Wijewardena, Manoj Kumar.
2015 - Raghav RV, Wei Xue
2016 - Nelson Liu, YenChen Lin

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:

Sprints#

The International 2019 Paris sprint was kindly hosted by AXA. Also some participants could attend thanks to the support of the Alfred P. Sloan Foundation, the Python Software Foundation (PSF) and the DATAIA Institute.

The 2013 International Paris Sprint was made possible thanks to the support of Télécom Paristech, tinyclues, the French Python Association and the Fonds de la Recherche Scientifique.

The 2011 International Granada sprint was made possible thanks to the support of the PSF and tinyclues.

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.