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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
2 LNAO - Laboratoire de Neuroimagerie Assistée par Ordinateur
3 Nuxeo
4 Kobe University
5 Bauhaus-Universität Weimar
6 Google Inc
7 LAMI - Laboratoire de Mécanique et Ingénieries
8 University of Washington [Seattle]
9 Department of Mechanical and Industrial Engineering [UMass]
10 Enthought Inc
11 TOTAL S.A.
12 LNAO
LNAO - Laboratoire de Neuroimagerie Assistée par Ordinateur
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.
Keywords : Python supervised learning unsupervised learning model selection
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Submitted on : Saturday, March 2, 2013 - 9:58:17 PM
Last modification on : Monday, December 13, 2021 - 9:16:03 AM
Long-term archiving on: : Friday, May 31, 2013 - 3:54:50 AM

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  • HAL Id : hal-00650905, version 2
  • ARXIV : 1201.0490

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CEA | INRIA | INSTITUT_PASCAL | PRES_CLERMONT | CEA-DRF | JOLIOT | NEUROSPIN

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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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