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 479 articles , 1 standard article )

Showing results 1 to 20 of 479.
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  1. Chou, Ping; Chuang, Howard Hao-Chun; Chou, Yen-Chun; Liang, Ting-Peng: Predictive analytics for customer repurchase: interdisciplinary integration of buy till you die modeling and machine learning (2022)
  2. Gahm, Christian; Uzunoglu, Aykut; Wahl, Stefan; Ganschinietz, Chantal; Tuma, Axel: Applying machine learning for the anticipation of complex nesting solutions in hierarchical production planning (2022)
  3. Adam Pocock: Tribuo: Machine Learning with Provenance in Java (2021) arXiv
  4. Adam Richie-Halford, Manjari Narayan, Noah Simon, Jason Yeatman, Ariel Rokem: Groupyr: Sparse Group Lasso in Python (2021) not zbMATH
  5. Alexander Fabisch: gmr: Gaussian Mixture Regression (2021) not zbMATH
  6. Alfredo Mejia-Narvaez, Gustavo Bruzual, Sebastian F. Sanchez, Leticia Carigi, Jorge Barrera-Ballesteros, Mabel Valerdi, Renbin Yan, Niv Drory: CoSHA: Code for Stellar properties Heuristic Assignment - for the MaStar stellar library (2021) arXiv
  7. Ali Haidar, Matthew Field, Jonathan Sykes, Martin Carolan, Lois Holloway: PSPSO: A package for parameters selection using particle swarm optimization (2021) not zbMATH
  8. Ali, Mehdi; Berrendorf, Max; Hoyt, Charles Tapley; Vermue, Laurent; Sharifzadeh, Sahand; Tresp, Volker; Lehmann, Jens: PyKEEN 1.0: a Python library for training and evaluating knowledge graph embeddings (2021)
  9. Andrea Bommert, Michel Lang: stabm: Stability Measures for Feature Selection (2021) not zbMATH
  10. Anirudhan Badrinath, Frederic Wang, Zachary Pardos: pyBKT: An Accessible Python Library of Bayesian Knowledge Tracing Models (2021) arXiv
  11. Anna Jenul, Stefan Schrunner, Bao Ngoc Huynh, Oliver Tomic: RENT: A Python Package for Repeated Elastic Net Feature Selection (2021) not zbMATH
  12. Antoine de Mathelin, François Deheeger, Guillaume Richard, Mathilde Mougeot, Nicolas Vayatis: ADAPT : Awesome Domain Adaptation Python Toolbox (2021) arXiv
  13. Antoine Prouvost, Justin Dumouchelle, Maxime Gasse, Didier Chételat, Andrea Lodi: Ecole: A Library for Learning Inside MILP Solvers (2021) arXiv
  14. Arnout M.P. Boelens, Hamdi A. Tchelepi: QuantImPy: Minkowski functionals and functions with Python (2021) not zbMATH
  15. Askari, Armin; Rebjock, Quentin; d’Aspremont, Alexandre; El Ghaoui, Laurent: FANOK: knockoffs in linear time (2021)
  16. Barber, Rina Foygel; Candès, Emmanuel J.; Ramdas, Aaditya; Tibshirani, Ryan J.: Predictive inference with the jackknife+ (2021)
  17. Bartzos, Evangelos; Emiris, Ioannis Z.; Legerský, Jan; Tsigaridas, Elias: On the maximal number of real embeddings of minimally rigid graphs in (\mathbbR^2,\mathbbR^3) and (S^2) (2021)
  18. Benjamin Paaßen, Jessica McBroom, Bryn Jeffries, Irena Koprinska, Kalina Yacef: ast2vec: Utilizing Recursive Neural Encodings of Python Programs (2021) arXiv
  19. Binder, Martin; Pfisterer, Florian; Lang, Michel; Schneider, Lennart; Kotthoff, Lars; Bischl, Bernd: mlr3pipelines -- flexible machine learning pipelines in R (2021)
  20. Burov, Dmitry; Giannakis, Dimitrios; Manohar, Krithika; Stuart, Andrew: Kernel analog forecasting: multiscale test problems (2021)

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