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 392 articles , 1 standard article )

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  1. Aggarwal, Charu C.: Neural networks and deep learning. A textbook (2018)
  2. Aggarwal, Charu C.: Machine learning for text (2018)
  3. Alaa, Ahmed M.; van der Schaar, Mihaela: A hidden absorbing semi-Markov model for informatively censored temporal data: learning and inference (2018)
  4. Au, Timothy C.: Random forests, decision trees, and categorical predictors: the “absent levels” problem (2018)
  5. Bacry, Emmanuel; Bompaire, Martin; Deegan, Philip; Gaïffas, Stéphane; Poulsen, Søren V.: \texttttick: a Python library for statistical learning, with an emphasis on Hawkes processes and time-dependent models (2018)
  6. Brazdil, Pavel (ed.); Giraud-Carrier, Christophe (ed.): Metalearning and algorithm selection: progress, state of the art and introduction to the 2018 special issue (2018)
  7. Cerda, Patricio; Varoquaux, Gaël; Kégl, Balázs: Similarity encoding for learning with dirty categorical variables (2018)
  8. Christopher Tralie, Nathaniel Saul, Rann Bar-On: A Lean Persistent Homology Library for Python (2018) not zbMATH
  9. Daniel Emaasit: Pymc-learn: Practical Probabilistic Machine Learning in Python (2018) arXiv
  10. Das, Sanjiv R.; Mokashi, Karthik; Culkin, Robbie: Are markets truly efficient? Experiments using deep learning algorithms for market movement prediction (2018)
  11. 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)
  12. Erickson, Collin B.; Ankenman, Bruce E.; Sanchez, Susan M.: Comparison of Gaussian process modeling software (2018)
  13. Fan, Fengfeng; Li, Zhanhuai; Chen, Qun; Chen, Lei: Relational data imputation with quality guarantee (2018)
  14. Feragen, Aasa (ed.); Hotz, Thomas (ed.); Huckemann, Stephan (ed.); Miller, Ezra (ed.): Statistics for data with geometric structure. Abstracts from the workshop held January 21--27, 2018 (2018)
  15. Fiévet, L.; Sornette, D.: Decision trees unearth return sign predictability in the S&P 500 (2018)
  16. Fischer, Thomas; Krauss, Christopher: Deep learning with long short-term memory networks for financial market predictions (2018)
  17. Fredrik Eriksson, Erik Fransson, Paul Erhart: The hiphive package for the extraction of high-order force constants by machine learning (2018) arXiv
  18. Gerbeau, Jean-Frédéric; Lombardi, Damiano; Tixier, Eliott: A moment-matching method to study the variability of phenomena described by partial differential equations (2018)
  19. Giannis Siglidis, Giannis Nikolentzos, Stratis Limnios, Christos Giatsidis, Konstantinos Skianis, Michalis Vazirgianis: GraKeL: A Graph Kernel Library in Python (2018) arXiv
  20. Gudivada, Venkat N.; Arbabifard, Kamyar: Open-source libraries, application frameworks, and workflow systems for NLP (2018)

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