Scikit

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{http://scikit-learn.sourceforge.net}.


References in zbMATH (referenced in 67 articles , 1 standard article )

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  1. Brazdil, Pavel (ed.); Giraud-Carrier, Christophe (ed.): Metalearning and algorithm selection: progress, state of the art and introduction to the 2018 special issue (2018)
  2. 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)
  3. Erickson, Collin B.; Ankenman, Bruce E.; Sanchez, Susan M.: Comparison of Gaussian process modeling software (2018)
  4. Fischer, Thomas; Krauss, Christopher: Deep learning with long short-term memory networks for financial market predictions (2018)
  5. Gerbeau, Jean-Frédéric; Lombardi, Damiano; Tixier, Eliott: A moment-matching method to study the variability of phenomena described by partial differential equations (2018)
  6. Hokanson, Jeffrey M.; Constantine, Paul G.: Data-driven polynomial ridge approximation using variable projection (2018)
  7. Ignatiev, Alexey; Morgado, Antonio; Marques-Silva, Joao: PySAT: A Python toolkit for prototyping with SAT oracles (2018)
  8. 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)
  9. 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)
  10. Rafael M. O. Cruz, Luiz G. Hafemann, Robert Sabourin, George D. C. Cavalcanti: DESlib: A Dynamic ensemble selection library in Python (2018) arXiv
  11. Sen, Deborshee; Thiery, Alexandre H.; Jasra, Ajay: On coupling particle filter trajectories (2018)
  12. Sharma, Manali; Bilgic, Mustafa: Learning with rationales for document classification (2018)
  13. Sung, Chih-Li; Gramacy, Robert B.; Haaland, Benjamin: Exploiting variance reduction potential in local Gaussian process search (2018)
  14. 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)
  15. Andersen, Michael Riis; Vehtari, Aki; Winther, Ole; Hansen, Lars Kai: Bayesian inference for spatio-temporal spike-and-slab priors (2017)
  16. Andrew C. Heusser, Kirsten Ziman, Lucy L. W. Owen, Jeremy R. Manning: HyperTools: A Python toolbox for visualizing and manipulating high-dimensional data (2017) arXiv
  17. Bacciu, Davide; Carta, Antonio; Gnesi, Stefania; Semini, Laura: An experience in using machine learning for short-term predictions in smart transportation systems (2017)
  18. Benjamin Guedj, Bhargav Srinivasa Desikan: Pycobra: A Python Toolbox for Ensemble Learning and Visualisation (2017) arXiv
  19. Chiang, Alvin; David, Esther; Lee, Yuh-Jye; Leshem, Guy; Yeh, Yi-Ren: A study on anomaly detection ensembles (2017)
  20. Di Iura, Andrea; Meloni, Davide: Probability densities of the effective neutrino masses $m_\beta$ and $m_\beta\beta$ (2017)

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