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) , Edouard Duchesnay (2)

1 PARIETAL - Modelling brain structure, function and variability based on high-field MRI data
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

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.

Mots clés: Python; supervised learning; unsupervised learning; model selection

Domaines: Apprentissage [cs.LG]; Logiciel mathématique [cs.MS]

Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, et al.. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, 2011. ⟨hal-00650905v2⟩