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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--
379
ClusteringRandom datasets Uniform distribution on the unit interval in R^d (test)
Runtime (s)0.00e+0
117
Binary ClassificationHaberman
Accuracy0.7357
59
Classificationpima
Accuracy76.9
52
Text ClassificationPubmed
micro-F160.77
50
ClusteringPla85900
Ek5.65
48
ClusteringMFCCs for Speech Emotion Recognition
Ek (Clustering Score)5.53
48
ClusteringCovertype
Ek5.15
48
ClusteringSkin Segmentation (full)
Ek0.08
48
ClusteringGas Sensor Array Drift
Ek Relative Error0.74
48
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