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 278 articles , 1 standard article )

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  1. Gryak, Jonathan; Haralick, Robert M.; Kahrobaei, Delaram: Solving the conjugacy decision problem via machine learning (2020)
  2. Hajij, Mustafa; Jonoska, Nataša; Kukushkin, Denys; Saito, Masahico: Graph based analysis for gene segment organization in a scrambled genome (2020)
  3. Heider, Yousef; Wang, Kun; Sun, WaiChing: (\mathrmSO(3))-invariance of informed-graph-based deep neural network for anisotropic elastoplastic materials (2020)
  4. Kacper Sokol; Alexander Hepburn; Rafael Poyiadzi; Matthew Clifford; Raul Santos-Rodriguez; Peter Flach: FAT Forensics: A Python Toolbox for Implementing and Deploying Fairness, Accountability and Transparency Algorithms in Predictive Systems (2020) not zbMATH
  5. Kadziński, Miłosz; Ghaderi, Mohammad; Dąbrowski, Maciej: Contingent preference disaggregation model for multiple criteria sorting problem (2020)
  6. Kharrat, Tarak; McHale, Ian G.; Peña, Javier López: Plus-minus player ratings for soccer (2020)
  7. Kisung You, Changhee Suh: Rdimtools: An R package for Dimension Reduction and Intrinsic Dimension Estimation (2020) arXiv
  8. Kolb, Samuel; Teso, Stefano; Dries, Anton; De Raedt, Luc: Predictive spreadsheet autocompletion with constraints (2020)
  9. Kwon, Yongchan; Kim, Wonyoung; Sugiyama, Masashi; Paik, Myunghee Cho: Principled analytic classifier for positive-unlabeled learning via weighted integral probability metric (2020)
  10. Leonardo Uieda; Santiago Rubén Soler; Rémi Rampin; Hugo van Kemenade; Matthew Turk; Daniel Shapero; Anderson Banihirwe; John Leeman: Pooch: A friend to fetch your data files (2020) not zbMATH
  11. Liberti, Leo: Distance geometry and data science (2020)
  12. Mahajan, Pravar Dilip; Maurya, Abhinav; Megahed, Aly; Elwany, Alaa; Strong, Ray; Blomberg, Jeanette: Optimizing predictive precision in imbalanced datasets for actionable revenue change prediction (2020)
  13. Mainak Jas; Titipat Achakulvisut; Aid Idrizović; Daniel E. Acuna; Matthew Antalek; Vinicius Marques; Tommy Odland; Ravi Prakash Garg; Mayank Agrawal; Yu Umegaki; Peter Foley; Hugo L Fernandes; Drew Harris; Beibin Li; Olivier Pieters; Scott Otterson; Giovanni De Toni; Chris Rodgers; Eva Dyer; Matti Hamalainen; Konrad Kording; Pavan Ramkumar: Pyglmnet: Python implementation of elastic-net regularized generalized linear models (2020) not zbMATH
  14. Malyscheff, Alexander M.; Trafalis, Theodore B.: Kernel classification using a linear programming approach (2020)
  15. Martí Bosch: DetecTree: Tree detection from aerial imagery in Python (2020) not zbMATH
  16. Morgan J. Williams, Louise Schoneveld, Yajing Mao, Jens Klump, Justin Gosses, Hayden Dalton, Adam Bath, Steve Barnes: pyrolite: Python for geochemistry (2020) not zbMATH
  17. Muammar El Khatib, Wibe A de Jong: ML4Chem: A Machine Learning Package for Chemistry and Materials Science (2020) arXiv
  18. Otsuka, Hajime; Takemoto, Kenta: Deep learning and k-means clustering in heterotic string vacua with line bundles (2020)
  19. Panahi, Ashkan; Chehreghani, Morteza Haghir; Dubhashi, Devdatt: Accelerated proximal incremental algorithm schemes for non-strongly convex functions (2020)
  20. Parish, Eric J.; Carlberg, Kevin T.: Time-series machine-learning error models for approximate solutions to parameterized dynamical systems (2020)

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