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 264 articles , 1 standard article )

Showing results 1 to 20 of 264.
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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. Arun S. Maiya: ktrain: A Low-Code Library for Augmented Machine Learning (2020) arXiv
  3. Benedek Rozemberczki, Oliver Kiss, Rik Sarkar: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (2020) arXiv
  4. Bhosekar, Atharv; Ierapetritou, Marianthi: A discontinuous derivative-free optimization framework for multi-enterprise supply chain (2020)
  5. Blanco, Víctor; Japón, Alberto; Puerto, Justo: Optimal arrangements of hyperplanes for SVM-based multiclass classification (2020)
  6. Boehmke, Brad; Greenwell, Brandon M.: Hands-on machine learning with R (2020)
  7. 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
  8. Chinot, Geoffrey; Lecué, Guillaume; Lerasle, Matthieu: Robust statistical learning with Lipschitz and convex loss functions (2020)
  9. Cope, Robert C.; Ross, Joshua V.: Identification of the relative timing of infectiousness and symptom onset for outbreak control (2020)
  10. Dantas, Augusto; Pozo, Aurora: On the use of fitness landscape features in meta-learning based algorithm selection for the quadratic assignment problem (2020)
  11. de Zordo-Banliat, M.; Merle, X.; Dergham, G.; Cinnella, P.: Bayesian model-scenario averaged predictions of compressor cascade flows under uncertain turbulence models (2020)
  12. 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
  13. 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
  14. Emmanuel Jordy Menvouta, Sven Serneels, Tim Verdonck: direpack: A Python 3 package for state-of-the-art statistical dimension reduction methods (2020) arXiv
  15. Freitas, Pedro Garcia; da Eira, Luísa Peixoto; Santos, Samuel Soares; Farias, Mylène C. Q.: Image quality assessment using BSIF, CLBP, LCP, and LPQ operators (2020)
  16. Gryak, Jonathan; Haralick, Robert M.; Kahrobaei, Delaram: Solving the conjugacy decision problem via machine learning (2020)
  17. Hajij, Mustafa; Jonoska, Nataša; Kukushkin, Denys; Saito, Masahico: Graph based analysis for gene segment organization in a scrambled genome (2020)
  18. Heider, Yousef; Wang, Kun; Sun, WaiChing: SO(3)-invariance of informed-graph-based deep neural network for anisotropic elastoplastic materials (2020)
  19. 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
  20. Kadziński, Miłosz; Ghaderi, Mohammad; Dąbrowski, Maciej: Contingent preference disaggregation model for multiple criteria sorting problem (2020)

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