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 284 articles , 1 standard article )

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  1. 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
  2. Anthony D. Blaom, Franz Kiraly, Thibaut Lienart, Yiannis Simillides, Diego Arenas, Sebastian J. Vollmer: MLJ: A Julia package for composable Machine Learning (2020) arXiv
  3. Arun S. Maiya: ktrain: A Low-Code Library for Augmented Machine Learning (2020) arXiv
  4. Barbiero, Pietro; Ciravegna, Gabriele; Cirrincione, Giansalvo; Tonda, Alberto; Squillero, Giovanni: Generating neural archetypes to instruct fast and interpretable decisions (2020)
  5. Barbiero, Pietro; Tonda, Alberto: Making sense of economics datasets with evolutionary coresets (2020)
  6. Benedek Rozemberczki, Oliver Kiss, Rik Sarkar: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (2020) arXiv
  7. 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
  8. Bhosekar, Atharv; Ierapetritou, Marianthi: A discontinuous derivative-free optimization framework for multi-enterprise supply chain (2020)
  9. Blanco, Víctor; Japón, Alberto; Puerto, Justo: Optimal arrangements of hyperplanes for SVM-based multiclass classification (2020)
  10. Boehmke, Brad; Greenwell, Brandon M.: Hands-on machine learning with R (2020)
  11. 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
  12. Chiapino, Maël; Clémençon, Stephan; Feuillard, Vincent; Sabourin, Anne: A multivariate extreme value theory approach to anomaly clustering and visualization (2020)
  13. Chinot, Geoffrey; Lecué, Guillaume; Lerasle, Matthieu: Robust statistical learning with Lipschitz and convex loss functions (2020)
  14. Cope, Robert C.; Ross, Joshua V.: Identification of the relative timing of infectiousness and symptom onset for outbreak control (2020)
  15. Dantas, Augusto; Pozo, Aurora: On the use of fitness landscape features in meta-learning based algorithm selection for the quadratic assignment problem (2020)
  16. de Zordo-Banliat, M.; Merle, X.; Dergham, G.; Cinnella, P.: Bayesian model-scenario averaged predictions of compressor cascade flows under uncertain turbulence models (2020)
  17. Dhindsa, Kiret; Cook, Oliver; Mudway, Thomas; Khawaja, Areeb; Harwood, Ron; Sonnadara, Ranil: LFSpy: A Python Implementation of Local Feature Selection for Data Classification with scikit-learn Compatibility (2020) not zbMATH
  18. Duc, Nguyen Dinh; Lam, Pham Tien; Quan, Tran Quoc; Quang, Pham Minh; Van Quyen, Nguyen: Nonlinear post-buckling and vibration of 2D penta-graphene composite plates (2020)
  19. Duncan N. Johnstone, Ben H. Martineau, Phillip Crout, Paul A. Midgley, Alexander S. Eggeman: Density-based clustering of crystal orientations and misorientations and the orix python library (2020) arXiv
  20. Emmanuel Jordy Menvouta, Sven Serneels, Tim Verdonck: direpack: A Python 3 package for state-of-the-art statistical dimension reduction methods (2020) arXiv

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