Scikit-learn: machine learning in python. 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 url{}.

References in zbMATH (referenced in 278 articles , 1 standard article )

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  1. Li, Chun-Liang; Su, Yu-Chuan; Lin, Ting-Wei; Tsai, Cheng-Hao; Chang, Wei-Cheng; Huang, Kuan-Hao; Kuo, Tzu-Ming; Lin, Shan-Wei; Lin, Young-San; Lu, Yu-Chen; Yang, Chun-Pai; Chang, Cheng-Xia; Chin, Wei-Sheng; Juan, Yu-Chin; Tung, Hsiao-Yu; Wang, Jui-Pin; Wei, Cheng-Kuang; Wu, Felix; Yin, Tu-Chun; Yu, Tong; Zhuang, Yong; Lin, Shou-De; Lin, Hsuan-Tien; Lin, Chih-Jen: Combination of feature engineering and ranking models for paper-author identification in KDD cup 2013 (2015) ioport
  2. Natalia Y. Bilenko, Jack L. Gallant: Pyrcca: regularized kernel canonical correlation analysis in Python and its applications to neuroimaging (2015) arXiv
  3. Neumann, Marion; Huang, Shan; Marthaler, Daniel E.; Kersting, Kristian: pyGPs -- a Python library for Gaussian process regression and classification (2015)
  4. Swaminathan, Adith; Joachims, Thorsten: Batch learning from logged bandit feedback through counterfactual risk minimization (2015)
  5. Vejdemo-Johansson, Mikael; Pokorny, Florian T.; Skraba, Primoz; Kragic, Danica: Cohomological learning of periodic motion (2015)
  6. Zaytsev, Yury V.; Morrison, Abigail; Deger, Moritz: Reconstruction of recurrent synaptic connectivity of thousands of neurons from simulated spiking activity (2015)
  7. Barbuti, Roberto; Maggiolo-Schettini, Andrea; Milazzo, Paolo; Pardini, Giovanni: Simulation of spatial P system models (2014)
  8. Bastian Venthur, Benjamin Blankertz: Wyrm, A Pythonic Toolbox for Brain-Computer Interfacing (2014) arXiv
  9. Christophe Pouzat, Georgios Is. Detorakis: SPySort: Neuronal Spike Sorting with Python (2014) arXiv
  10. Cobo, Luis C.; Subramanian, Kaushik; Isbell, Charles L. jun.; Lanterman, Aaron D.; Thomaz, Andrea L.: Abstraction from demonstration for efficient reinforcement learning in high-dimensional domains (2014)
  11. Le Mouel, Charlotte; Harris, Kenneth D.; Yger, Pierre: Supervised learning with decision margins in pools of spiking neurons (2014)
  12. Müller, Andreas C.; Behnke, Sven: Pystruct-learning structured prediction in Python (2014)
  13. Xu, Weiping; Hancock, Edwin R.; Wilson, Richard C.: Ricci flow embedding for rectifying non-Euclidean dissimilarity data (2014)
  14. Brian P. Kent, Alessandro Rinaldo, Timothy Verstynen: DeBaCl: A Python Package for Interactive DEnsity-BAsed CLustering (2013) arXiv
  15. Coelho, L.P.: Mahotas: Open source software for scriptable computer vision (2013) not zbMATH
  16. Moewes, Christian; Kruse, Rudolf; Sabel, Bernhard A.: Analysis of dynamic brain networks using VAR models (2013) ioport
  17. Michel, Vincent; Gramfort, Alexandre; Varoquaux, Gaël; Eger, Evelyn; Keribin, Christine; Thirion, Bertrand: A supervised clustering approach for fMRI-based inference of brain states (2012)
  18. Pedregosa, Fabian; Varoquaux, Gaël; Gramfort, Alexandre; Michel, Vincent; Thirion, Bertrand; Grisel, Olivier; Blondel, Mathieu; Prettenhofer, Peter; Weiss, Ron; Dubourg, Vincent; Vanderplas, Jake; Passos, Alexandre; Cournapeau, David; Brucher, Matthieu; Perrot, Matthieu; Duchesnay, Édouard: Scikit-learn: machine learning in Python (2011)

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