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. Guedj, Benjamin; Desikan, Bhargav Srinivasa: Pycobra: a Python toolbox for ensemble learning and visualisation (2018)
  2. Haifeng Jin, Qingquan Song, Xia Hu: Auto-Keras: An Efficient Neural Architecture Search System (2018) arXiv
  3. Hasimu, Maimaitiyiming; Silamu, Wushour: On hierarchical text language-identification algorithms (2018)
  4. 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)
  5. Heo, Kihong; Oh, Hakjoo; Yang, Hongseok: Learning analysis strategies for Octagon and context sensitivity from labeled data generated by static analyses (2018)
  6. Heusser, Andrew C.; Ziman, Kirsten; Owen, Lucy L. W.; Manning, Jeremy R.: \textttHypertools: a Python toolbox for gaining geometric insights into high-dimensional data (2018)
  7. Hokanson, Jeffrey M.; Constantine, Paul G.: Data-driven polynomial ridge approximation using variable projection (2018)
  8. Huang, Lingxiao; Jin, Yifei; Li, Jian: SVM via saddle point optimization: new bounds and distributed algorithms (2018)
  9. Ignatiev, Alexey; Morgado, Antonio; Marques-Silva, Joao: PySAT: A Python toolkit for prototyping with SAT oracles (2018)
  10. Ignatiev, Alexey; Pereira, Filipe; Narodytska, Nina; Marques-Silva, Joao: A SAT-based approach to learn explainable decision sets (2018)
  11. Jin Zhu, Wenliang Pan, Wei Zheng, Xueqin Wang: Ball: An R package for detecting distribution difference and association in metric spaces (2018) arXiv
  12. Kamishima, Toshihiro; Akaho, Shotaro; Asoh, Hideki; Sakuma, Jun: Model-based and actual independence for fairness-aware classification (2018)
  13. Kumar, Vaibhaw; Bass, Gideon; Tomlin, Casey; Dulny, Joseph III: Quantum annealing for combinatorial clustering (2018)
  14. Leland McInnes, John Healy, James Melville: UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction (2018) arXiv
  15. Leonardo Uieda: Verde: Processing and gridding spatial data using Green’s functions (2018) not zbMATH
  16. Lipponen, A.; Huttunen, J. M. J.; Romakkaniemi, S.; Kokkola, H.; Kolehmainen, V.: Correction of model reduction errors in simulations (2018)
  17. Lorena, Ana C.; Maciel, Aron I.; de Miranda, Péricles B. C.; Costa, Ivan G.; Prudêncio, Ricardo B. C.: Data complexity meta-features for regression problems (2018)
  18. Mania, Horia; Ramdas, Aaditya; Wainwright, Martin J.; Jordan, Michael I.; Recht, Benjamin: On kernel methods for covariates that are rankings (2018)
  19. 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)
  20. Mathieu Andreux, Tomás Angles, Georgios Exarchakis, Roberto Leonarduzzi, Gaspar Rochette, Louis Thiry, John Zarka, Stéphane Mallat, Joakim Andén, Eugene Belilovsky, Joan Bruna, Vincent Lostanlen, Matthew J. Hirn, Edouard Oyallon, Sixhin Zhang, Carmine Cella, Michael Eickenberg: Kymatio: Scattering Transforms in Python (2018) arXiv

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