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 78 articles , 1 standard article )

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  1. Au, Timothy C.: Random forests, decision trees, and categorical predictors: the “absent levels” problem (2018)
  2. 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)
  3. Brazdil, Pavel (ed.); Giraud-Carrier, Christophe (ed.): Metalearning and algorithm selection: progress, state of the art and introduction to the 2018 special issue (2018)
  4. 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)
  5. Erickson, Collin B.; Ankenman, Bruce E.; Sanchez, Susan M.: Comparison of Gaussian process modeling software (2018)
  6. Fischer, Thomas; Krauss, Christopher: Deep learning with long short-term memory networks for financial market predictions (2018)
  7. Gerbeau, Jean-Frédéric; Lombardi, Damiano; Tixier, Eliott: A moment-matching method to study the variability of phenomena described by partial differential equations (2018)
  8. Hokanson, Jeffrey M.; Constantine, Paul G.: Data-driven polynomial ridge approximation using variable projection (2018)
  9. Ignatiev, Alexey; Morgado, Antonio; Marques-Silva, Joao: PySAT: A Python toolkit for prototyping with SAT oracles (2018)
  10. Jin Zhu, Wenliang Pan, Wei Zheng, Xueqin Wang: Ball: An R package for detecting distribution difference and association in metric spaces (2018) arXiv
  11. Lorena, Ana C.; Maciel, Aron I.; de Miranda, Péricles B. C.; Costa, Ivan G.; Prud^encio, Ricardo B. C.: Data complexity meta-features for regression problems (2018)
  12. Mania, Horia; Ramdas, Aaditya; Wainwright, Martin J.; Jordan, Michael I.; Recht, Benjamin: On kernel methods for covariates that are rankings (2018)
  13. Marins, Matheus A.; Ribeiro, Felipe M. L.; Netto, Sergio L.; da Silva, Eduardo A. B.: Improved similarity-based modeling for the classification of rotating-machine failures (2018)
  14. Olier, Ivan; Sadawi, Noureddin; Bickerton, G. Richard; Vanschoren, Joaquin; Grosan, Crina; Soldatova, Larisa; King, Ross D.: Meta-QSAR: a large-scale application of meta-learning to drug design and discovery (2018)
  15. Rafael M. O. Cruz, Luiz G. Hafemann, Robert Sabourin, George D. C. Cavalcanti: DESlib: A Dynamic ensemble selection library in Python (2018) arXiv
  16. Ronan, Tom; Anastasio, Shawn; Qi, Zhijie; Tavares, Pedro Henrique S. Vieira; Sloutsky, Roman; Naegle, Kristen M.: OpenEnsembles: a Python resource for ensemble clustering (2018)
  17. Sen, Deborshee; Thiery, Alexandre H.; Jasra, Ajay: On coupling particle filter trajectories (2018)
  18. Sharma, Manali; Bilgic, Mustafa: Learning with rationales for document classification (2018)
  19. Sung, Chih-Li; Gramacy, Robert B.; Haaland, Benjamin: Exploiting variance reduction potential in local Gaussian process search (2018)
  20. Tahmassebi, Amirhessam; Gandomi, Amir H.; Schulte, Mieke H. J.; Goudriaan, Anna E.; Foo, Simon Y.; Meyer-Baese, Anke: Optimized naive-Bayes and decision tree approaches for fMRI smoking cessation classification (2018)

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