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 114 articles , 1 standard article )

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  1. Daniel Smilkov, Nikhil Thorat, Yannick Assogba, Ann Yuan, Nick Kreeger, Ping Yu, Kangyi Zhang, Shanqing Cai, Eric Nielsen, David Soergel, Stan Bileschi, Michael Terry, Charles Nicholson, Sandeep N. Gupta, Sarah Sirajuddin, D. Sculley, Rajat Monga, Greg Corrado, Fernanda B. Viegas, Martin Wattenberg: TensorFlow.js: Machine Learning for the Web and Beyond (2019) arXiv
  2. Viktor Kazakov, Franz J. Király: Machine Learning Automation Toolbox (MLaut) (2019) arXiv
  3. Yue Zhao, Zain Nasrullah, Zheng Li: PyOD: A Python Toolbox for Scalable Outlier Detection (2019) arXiv
  4. Aggarwal, Charu C.: Neural networks and deep learning. A textbook (2018)
  5. Aggarwal, Charu C.: Machine learning for text (2018)
  6. Alaa, Ahmed M.; van der Schaar, Mihaela: A hidden absorbing semi-Markov model for informatively censored temporal data: learning and inference (2018)
  7. Au, Timothy C.: Random forests, decision trees, and categorical predictors: the “absent levels” problem (2018)
  8. Bacry, Emmanuel; Bompaire, Martin; Deegan, Philip; Gaïffas, Stéphane; Poulsen, Søren V.: tick: a Python library for statistical learning, with an emphasis on Hawkes processes and time-dependent models (2018)
  9. Brazdil, Pavel (ed.); Giraud-Carrier, Christophe (ed.): Metalearning and algorithm selection: progress, state of the art and introduction to the 2018 special issue (2018)
  10. Cerda, Patricio; Varoquaux, Gaël; Kégl, Balázs: Similarity encoding for learning with dirty categorical variables (2018)
  11. Daniel Emaasit: Pymc-learn: Practical Probabilistic Machine Learning in Python (2018) arXiv
  12. de La Fuente Canto, C.; Kalogiros, D. I.; Ptashnyk, M.; George, T. S.; Waugh, R.; Bengough, A. G.; Russell, J.; Dupuy, Lionel X.: Morphological and genetic characterisation of the root system architecture of selected barley recombinant chromosome substitution lines using an integrated phenotyping approach (2018)
  13. Erickson, Collin B.; Ankenman, Bruce E.; Sanchez, Susan M.: Comparison of Gaussian process modeling software (2018)
  14. Fischer, Thomas; Krauss, Christopher: Deep learning with long short-term memory networks for financial market predictions (2018)
  15. Gerbeau, Jean-Frédéric; Lombardi, Damiano; Tixier, Eliott: A moment-matching method to study the variability of phenomena described by partial differential equations (2018)
  16. Giannis Siglidis, Giannis Nikolentzos, Stratis Limnios, Christos Giatsidis, Konstantinos Skianis, Michalis Vazirgianis: GraKeL: A Graph Kernel Library in Python (2018) arXiv
  17. Gudivada, Venkat N.; Arbabifard, Kamyar: Open-source libraries, application frameworks, and workflow systems for NLP (2018)
  18. Guedj, Benjamin; Desikan, Bhargav Srinivasa: Pycobra: a Python toolbox for ensemble learning and visualisation (2018)
  19. Heiberg, Thomas; Kriener, Birgit; Tetzlaff, Tom; Einevoll, Gaute T.; Plesser, Hans E.: Firing-rate models for neurons with a broad repertoire of spiking behaviors (2018)
  20. Heo, Kihong; Oh, Hakjoo; Yang, Hongseok: Learning analysis strategies for octagon and context sensitivity from labeled data generated by static analyses (2018)

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