Paper Info
Title
Scikit-learn: Machine Learning in Python
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.
Year
Venue
Keywords
2011
The Journal of Machine Learning Research
api consistency,minimal dependency,general-purpose high-level language,bsd license,machine learning,source code,state-of-the-art machine,python module,wide range,unsupervised problem,commercial setting,unsupervised learning,python,supervised learning,model selection
DocType
Volume
ISSN
Journal
abs/1201.0490
Journal of Machine Learning Research (2011)
Citations 
PageRank 
References 
3842
157.21
11
Authors
16
Search Limit
1001000
Name
Order
Citations
PageRank
Fabian Pedregosa14164179.32
Gael Varoquaux25309285.24
Alexandre Gramfort34791234.87
Vincent Michel44056181.15
Bertrand Thirion55047270.40
Olivier Grisel63954163.84
Mathieu Blondel74055174.33
Peter Prettenhofer84032166.10
Ron Weiss93842157.21
Vincent Dubourg103842157.21
Jake Vanderplas113979167.73
Passos, Alexandre124083167.18
David Cournapeau133910161.81
Matthieu Brucher143844157.58
Matthieu Perrot153975166.21
Edouard Duchesnay164093178.47