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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--
421
Node ClassificationPhoto (test)
Mean Accuracy93
241
Credit Card Fraud DetectionBankSim
Precision@K77.8
200
Out-of-sample random-X regressionSpiked covariance model d/N = 0.9, noise = 1 synthetic (out-of-sample)
Median MSE1
196
ClassificationSVHN (test)--
182
Link PredictionCiteseer
AUC99.8
174
Credit Card Fraud DetectionBankSim
Expected Cost88.1
160
ClusteringRandom datasets Uniform distribution on the unit interval in R^d (test)
Runtime (s)0.00e+0
117
Credit Card Fraud DetectionBankSim
Partial PR AUC40.7
110
ClassificationLung
ACC96.47
96
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