@article{pedregosa:hal-00650905, hal_id = {hal-00650905}, url = {http://hal.inria.fr/hal-00650905}, title = {{Scikit-learn: Machine Learning in Python}}, author = {Pedregosa, Fabian and Varoquaux, Ga{\"e}l and Gramfort, Alexandre and Michel, Vincent and Thirion, Bertrand and Grisel, Olivier and Blondel, Mathieu and Prettenhofer, Peter and Weiss, Ron and Dubourg, Vincent and Vanderplas, Jake and Passos, Alexandre and Cournapeau, David and Brucher, Matthieu and Perrot, Matthieu and Duchesnay, {\'E}douard}, 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}, language = {English}, affiliation = {PARIETAL - INRIA Saclay - Ile de France , Laboratoire de Neuroimagerie Assist{\'e}e par Ordinateur - LNAO , Nuxeo , Kobe University , Bauhaus-Universit{\"a}t Weimar , Google Inc , Laboratoire de M{\'e}canique et Ing{\'e}nieries - LAMI , University of Washington , Department of Mechanical and Industrial Engineering [UMass] , Enthought Inc , TOTAL}, publisher = {MIT Press}, journal = {Journal of Machine Learning Research}, audience = {international }, year = {2011}, month = Oct, pdf = {http://hal.inria.fr/hal-00650905/PDF/pedregosa11a.pdf}, }