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 331 articles , 1 standard article )

Showing results 1 to 20 of 331.
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  1. Bartzos, Evangelos; Emiris, Ioannis Z.; Legerský, Jan; Tsigaridas, Elias: On the maximal number of real embeddings of minimally rigid graphs in (\mathbbR^2,\mathbbR^3) and (S^2) (2021)
  2. Abrishami, Tara; Guillen, Nestor; Rule, Parker; Schutzman, Zachary; Solomon, Justin; Weighill, Thomas; Wu, Si: Geometry of graph partitions via optimal transport (2020)
  3. Alain Jungo, Olivier Scheidegger, Mauricio Reyes, Fabian Balsiger: pymia: A Python package for data handling and evaluation in deep learning-based medical image analysis (2020) arXiv
  4. Albert Steppi; Benjamin M. Gyori; John A. Bachman: Adeft: Acromine-based Disambiguation of Entities from Text with applications to the biomedical literature (2020) not zbMATH
  5. Alexandrov, Alexander; Benidis, Konstantinos; Bohlke-Schneider, Michael; Flunkert, Valentin; Gasthaus, Jan; Januschowski, Tim; Maddix, Danielle C.; Rangapuram, Syama; Salinas, David; Schulz, Jasper; Stella, Lorenzo; Türkmen, Ali Caner; Wang, Yuyang: GluonTS: probabilistic and neural time series modeling in Python (2020)
  6. Andreux, Mathieu; Angles, Tomás; Exarchakis, Georgios; Leonarduzzi, Roberto; Rochette, Gaspar; Thiry, Louis; Zarka, John; Mallat, Stéphane; Andén, Joakim; Belilovsky, Eugene; Bruna, Joan; Lostanlen, Vincent; Chaudhary, Muawiz; Hirn, Matthew J.; Oyallon, Edouard; Zhang, Sixin; Cella, Carmine; Eickenberg, Michael: Kymatio: scattering transforms in Python (2020)
  7. Anil, Robin; Capan, Gokhan; Drost-Fromm, Isabel; Dunning, Ted; Friedman, Ellen; Grant, Trevor; Quinn, Shannon; Ranjan, Paritosh; Schelter, Sebastian; Yılmazel, Özgür: Apache Mahout: machine learning on distributed dataflow systems (2020)
  8. Anthony D. Blaom, Franz Kiraly, Thibaut Lienart, Yiannis Simillides, Diego Arenas, Sebastian J. Vollmer: MLJ: A Julia package for composable Machine Learning (2020) arXiv
  9. Armin Moin, Stephan Rössler, Marouane Sayih, Stephan Günnemann: From Things’ Modeling Language (ThingML) to Things’ Machine Learning (ThingML2) (2020) arXiv
  10. Arun S. Maiya: ktrain: A Low-Code Library for Augmented Machine Learning (2020) arXiv
  11. Barbiero, Pietro; Ciravegna, Gabriele; Cirrincione, Giansalvo; Tonda, Alberto; Squillero, Giovanni: Generating neural archetypes to instruct fast and interpretable decisions (2020)
  12. Barbiero, Pietro; Tonda, Alberto: Making sense of economics datasets with evolutionary coresets (2020)
  13. Benedek Rozemberczki, Oliver Kiss, Rik Sarkar: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (2020) arXiv
  14. Benjamin H Savitzky, Lauren A Hughes, Steven E Zeltmann, Hamish G Brown, Shiteng Zhao, Philipp M Pelz, Edward S Barnard, Jennifer Donohue, Luis Rangel DaCosta, Thomas C. Pekin, Ellis Kennedy, Matthew T Janish, Matthew M Schneider, Patrick Herring, Chirranjeevi Gopal, Abraham Anapolsky, Peter Ercius, Mary Scott, Jim Ciston, Andrew M Minor, Colin Ophus: py4DSTEM: a software package for multimodal analysis of four-dimensional scanning transmission electron microscopy datasets (2020) arXiv
  15. Bhosekar, Atharv; Ierapetritou, Marianthi: A discontinuous derivative-free optimization framework for multi-enterprise supply chain (2020)
  16. Blanco, Víctor; Japón, Alberto; Puerto, Justo: Optimal arrangements of hyperplanes for SVM-based multiclass classification (2020)
  17. Blondel, Mathieu; Martins, André F. T.; Niculae, Vlad: Learning with Fenchel-Young losses (2020)
  18. Boehmke, Brad; Greenwell, Brandon M.: Hands-on machine learning with R (2020)
  19. Brian de Silva; Kathleen Champion; Markus Quade; Jean-Christophe Loiseau; J. Nathan Kutz; Steven L. Brunton: PySINDy: A Python package for the sparse identification of nonlinear dynamical systems from data (2020) not zbMATH
  20. Chiapino, Maël; Clémençon, Stephan; Feuillard, Vincent; Sabourin, Anne: A multivariate extreme value theory approach to anomaly clustering and visualization (2020)

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