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 268 articles , 1 standard article )

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  1. Tobias Stål, Anya M. Reading: A Grid for Multidimensional and Multivariate Spatial Representation and Data Processing (2020) not zbMATH
  2. Uy, Wayne Isaac T.; Grigoriu, Mircea D.: Identification of input random field samples causing extreme responses (2020)
  3. van Engelen, Jesper E.; Hoos, Holger H.: A survey on semi-supervised learning (2020)
  4. Wu, Ling; Zulueta, Kepa; Major, Zoltan; Arriaga, Aitor; Noels, Ludovic: Bayesian inference of non-linear multiscale model parameters accelerated by a deep neural network (2020)
  5. Adilina, Sheikh; Farid, Dewan Md; Shatabda, Swakkhar: Effective DNA binding protein prediction by using key features via Chou’s general PseAAC (2019)
  6. Alaya, Mokhtar Z.; Bussy, Simon; Gaïffas, Stéphane; Guilloux, Agathe: Binarsity: a penalization for one-hot encoded features in linear supervised learning (2019)
  7. Alexander J. Gates; Yong-Yeol Ahn: CluSim: a python package for calculating clustering similarity (2019) not zbMATH
  8. Alex Boyd, Dennis L. Sun: salmon: A Symbolic Linear Regression Package for Python (2019) arXiv
  9. Ali, Liaqat; Khan, Shafqat Ullah; Golilarz, Noorbakhsh Amiri; Yakubu, Imrana; Qasim, Iqbal; Noor, Adeeb; Nour, Redhwan: A feature-driven decision support system for heart failure prediction based on (\chi^2) statistical model and Gaussian naive Bayes (2019)
  10. Amir M. Mir; Jalal A. Nasiri: LightTwinSVM: A Simple and Fast Implementation of Standard Twin Support Vector Machine Classifier (2019) not zbMATH
  11. Baharev, Ali; Neumaier, Arnold; Schichl, Hermann: A manifold-based approach to sparse global constraint satisfaction problems (2019)
  12. Balakrishnan, Harikrishnan Nellippallil; Kathpalia, Aditi; Saha, Snehanshu; Nagaraj, Nithin: Chaosnet: a chaos based artificial neural network architecture for classification (2019)
  13. Benjamin Bengfort; Rebecca Bilbro: Yellowbrick: Visualizing the Scikit-Learn Model Selection Process (2019) not zbMATH
  14. Biau, Gérard; Scornet, Erwan; Welbl, Johannes: Neural random forests (2019)
  15. 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)
  16. B. Perret; G. Chierchia; J. Cousty; S. J. F. Guimaraes; Y. Kenmochi; L. Najman: Higra: Hierarchical Graph Analysis (2019) not zbMATH
  17. Bruni, Renato; Bianchi, Gianpiero; Dolente, Cosimo; Leporelli, Claudio: Logical analysis of data as a tool for the analysis of probabilistic discrete choice behavior (2019)
  18. Casalicchio, Giuseppe; Bossek, Jakob; Lang, Michel; Kirchhoff, Dominik; Kerschke, Pascal; Hofner, Benjamin; Seibold, Heidi; Vanschoren, Joaquin; Bischl, Bernd: \textttOpenML: an \textttRpackage to connect to the machine learning platform openml (2019)
  19. Casenave, Fabien; Akkari, Nissrine; Charles, Alexandre; Rey, Christian: Nonintrusive approximation of parametrized limits of matrix power algorithms -- application to matrix inverses and log-determinants (2019)
  20. Castillo-Castellanos, Andrés; Sergent, Anne; Podvin, Bérengère; Rossi, Maurice: Cessation and reversals of large-scale structures in square Rayleigh-Bénard cells (2019)

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