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Journal articles
Scikit-learn: Machine Learning in Python
Fabian Pedregosa
1, 2
Gaël Varoquaux
1, 2
Alexandre Gramfort
1, 2
Vincent Michel
1, 2
Bertrand Thirion
1, 2
Olivier Grisel
3
Mathieu Blondel
4
Peter Prettenhofer
5
Ron Weiss
6
Vincent Dubourg
7
Jake Vanderplas
8
Alexandre Passos
9
David Cournapeau
10
Matthieu Brucher
11
Matthieu Perrot
12
Édouard Duchesnay
2
1
PARIETAL - Modelling brain structure, function and variability based on high-field MRI data
Inria Saclay - Ile de France, NEUROSPIN - Service NEUROSPIN
3
Nuxeo
11
TOTAL S.A.
Fabian Pedregosa 1, 2
Author
Gaël Varoquaux 1, 2
Author IdHAL : gael-varoquaux ORCID : https://orcid.org/0000-0003-1076-5122
Alexandre Gramfort 1, 2
Author IdHAL : agramfort
Vincent Michel 1, 2
Author
Bertrand Thirion 1, 2
Author
Olivier Grisel 3
Author
Mathieu Blondel 4
Author
Peter Prettenhofer 5
Author
David Cournapeau 10
Author
Matthieu Brucher 11
Author
Matthieu Perrot 12
Author
Édouard Duchesnay 2
Author
1
PARIETAL - Modelling brain structure, function and variability based on high-field MRI data (Neurospin, CEA Saclay, Bâtiment 145, 91191 Gif-sur-Yvette Cedex - France)
- Inria Saclay - Ile de France (1 rue Honoré d'Estienne d'Orves
Bâtiment Alan Turing
Campus de l'École Polytechnique
91120 Palaiseau - France)
- Inria - Institut National de Recherche en Informatique et en Automatique (Domaine de Voluceau Rocquencourt - BP 105 78153 Le Chesnay Cedex - France)
- NEUROSPIN - Service NEUROSPIN (France)
- DRF (CEA) - Direction de Recherche Fondamentale (CEA) : DRF/JOLIOT (Direction de Recherche Fondamentale
Fundamental Research Division
CEA Saclay
91191 Gif-sur-Yvette
- France)
- CEA - Commissariat à l'énergie atomique et aux énergies alternatives (Centre de Saclay Centre de Grenoble Centre de Cadarache etc - France)
- Université Paris-Saclay (Espace Technologique, Bat. Discovery - RD 128 - 2e ét., 91190 Saint-Aubin - France)
- DRF (CEA) - Direction de Recherche Fondamentale (CEA) : DRF/JOLIOT (Direction de Recherche Fondamentale
Fundamental Research Division
CEA Saclay
91191 Gif-sur-Yvette
- France)
2
LNAO - Laboratoire de Neuroimagerie Assistée par Ordinateur (France)
- CEA - Commissariat à l'énergie atomique et aux énergies alternatives : DSV/I2BM/NEUROSPIN (Centre de Saclay Centre de Grenoble Centre de Cadarache etc - France)
4
Kobe University (Japan)
5
Bauhaus-Universität Weimar (Geschwister-Scholl-Straße 8 99423 Weimar - Germany)
7
LAMI - Laboratoire de Mécanique et Ingénieries (IFMA. Campus des Cézeaux BP 265 63175 Aubière Cedex - France)
- IFMA - Institut Français de Mécanique Avancée (Clermont Université, Institut Français de Mécanique Avancée (IFMA) / Laboratoire de Mécanique et Ingénieries (LaMI), BP 10448 - F-63000 Clermont Ferrand - France)
- UBP - Université Blaise Pascal - Clermont-Ferrand 2 : EA3867 (34, avenue Carnot - BP 185 / 63006 Clermont-Ferrand cedex - France)
8
University of Washington [Seattle] (Seattle, Washington 98105 - United States)
9
Department of Mechanical and Industrial Engineering [UMass] (UMass Amherst College of Engineering Department of Mechanical and Industrial Engineering, 220 ELAB, University of Massachusetts Amherst, MA 01003-2210 - United States)
- UMass Amherst - University of Massachusetts [Amherst] (300 Massachusetts Ave, Amherst, MA 01003 - United States)
- UMASS - University of Massachusetts System (United States)
10
Enthought Inc (515 Congress Avenue Suite 2100 Austin, TX 78701 - United States)
- Enthought Inc (France)
12
LNAO (France)
- LNAO - Laboratoire de Neuroimagerie Assistée par Ordinateur (France)
- CEA - Commissariat à l'énergie atomique et aux énergies alternatives : DSV/I2BM/NEUROSPIN (Centre de Saclay Centre de Grenoble Centre de Cadarache etc - France)
Abstract : Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation, and API consistency. It has minimal dependencies and is distributed under the simplified BSD license, encouraging its use in both academic and commercial settings. Source code, binaries, and documentation can be downloaded from http://scikit-learn.sourceforge.net.
Document type :
Journal articles
Complete list of metadatas
https://hal.inria.fr/hal-00650905
Contributor : Fabian Pedregosa <>
Submitted on : Saturday, March 2, 2013 - 9:58:17 PM
Last modification on : Monday, February 10, 2020 - 6:13:43 PM
Long-term archiving on: : Friday, May 31, 2013 - 3:54:50 AM
Contributor : Fabian Pedregosa <>
Submitted on : Saturday, March 2, 2013 - 9:58:17 PM
Last modification on : Monday, February 10, 2020 - 6:13:43 PM
Long-term archiving on: : Friday, May 31, 2013 - 3:54:50 AM
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pedregosa11a.pdf
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Identifiers
- HAL Id : hal-00650905, version 2
- ARXIV : 1201.0490
Collections
CEA | INRIA | INSTITUT_PASCAL | PRES_CLERMONT | SIGMA-CLERMONT | CEA-DRF | JOLIOT | NEUROSPIN
Citation
Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, et al.. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, Microtome Publishing, 2011. ⟨hal-00650905v2⟩
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