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Scikit-learn: Machine Learning in Python

About

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.org.

Fabian Pedregosa, Ga\"el Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Andreas M\"uller, Joel Nothman, Gilles Louppe, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, Jake Vanderplas, Alexandre Passos, David Cournapeau, Matthieu Brucher, Matthieu Perrot, \'Edouard Duchesnay• 2012

Related benchmarks

TaskDatasetResultRank
Semantic segmentationCOCO Stuff--
195
Text ClassificationPubmed
micro-F160.77
50
Document Classification20 Newsgroups (test)
Accuracy80.7
43
ClassificationAdult
Accuracy84.7
33
ClusteringFMNIST
NMI51
31
Veracity PredictionRAWFC (test)
Precision32.33
28
Unsupervised image segmentationCoco-Stuff (test)
Accuracy14.1
26
Regularized Linear RegressionYearPredictionMSD (train)
Average Cost90.45
25
Semantic segmentationPotsdam-3
Pixel Accuracy45.7
25
Knowledge TracingPOJ
AUC77.9
24
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