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 217 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. Boehmke, Brad; Greenwell, Brandon M.: Hands-on machine learning with R (2020)
  3. Cope, Robert C.; Ross, Joshua V.: Identification of the relative timing of infectiousness and symptom onset for outbreak control (2020)
  4. Dantas, Augusto; Pozo, Aurora: On the use of fitness landscape features in meta-learning based algorithm selection for the quadratic assignment problem (2020)
  5. 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
  6. 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)
  7. Kharrat, Tarak; McHale, Ian G.; Peña, Javier López: Plus-minus player ratings for soccer (2020)
  8. 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
  9. Tobias Stål, Anya M. Reading: A Grid for Multidimensional and Multivariate Spatial Representation and Data Processing (2020) not zbMATH
  10. Adilina, Sheikh; Farid, Dewan Md; Shatabda, Swakkhar: Effective DNA binding protein prediction by using key features via Chou’s general PseAAC (2019)
  11. Alaya, Mokhtar Z.; Bussy, Simon; Gaïffas, Stéphane; Guilloux, Agathe: Binarsity: a penalization for one-hot encoded features in linear supervised learning (2019)
  12. Alexander J. Gates; Yong-Yeol Ahn: CluSim: a python package for calculating clustering similarity (2019) not zbMATH
  13. Alex Boyd, Dennis L. Sun: salmon: A Symbolic Linear Regression Package for Python (2019) arXiv
  14. Amir M. Mir; Jalal A. Nasiri: LightTwinSVM: A Simple and Fast Implementation of Standard Twin Support Vector Machine Classifier (2019) not zbMATH
  15. Baharev, Ali; Neumaier, Arnold; Schichl, Hermann: A manifold-based approach to sparse global constraint satisfaction problems (2019)
  16. Balakrishnan, Harikrishnan Nellippallil; Kathpalia, Aditi; Saha, Snehanshu; Nagaraj, Nithin: Chaosnet: a chaos based artificial neural network architecture for classification (2019)
  17. Benjamin Bengfort; Rebecca Bilbro: Yellowbrick: Visualizing the Scikit-Learn Model Selection Process (2019) not zbMATH
  18. Bir-Jmel, Ahmed; Douiri, Sidi Mohamed; Elbernoussi, Souad: Gene selection via a new hybrid ant colony optimization algorithm for cancer classification in high-dimensional data (2019)
  19. Bravo-Marquez, Felipe; Frank, Eibe; Pfahringer, Bernhard; Mohammad, Saif M.: AffectiveTweets: a Weka package for analyzing affect in tweets (2019)
  20. Bruni, Renato; Bianchi, Gianpiero; Dolente, Cosimo; Leporelli, Claudio: Logical analysis of data as a tool for the analysis of probabilistic discrete choice behavior (2019)

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