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 41 articles , 1 standard article )

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  1. Andrew C. Heusser, Kirsten Ziman, Lucy L. W. Owen, Jeremy R. Manning: HyperTools: A Python toolbox for visualizing and manipulating high-dimensional data (2017) arXiv
  2. Bacciu, Davide; Carta, Antonio; Gnesi, Stefania; Semini, Laura: An experience in using machine learning for short-term predictions in smart transportation systems (2017)
  3. Benjamin Guedj, Bhargav Srinivasa Desikan: Pycobra: A Python Toolbox for Ensemble Learning and Visualisation (2017) arXiv
  4. Chiang, Alvin; David, Esther; Lee, Yuh-Jye; Leshem, Guy; Yeh, Yi-Ren: A study on anomaly detection ensembles (2017)
  5. Di Iura, Andrea; Meloni, Davide: Probability densities of the effective neutrino masses $m_\beta$ and $m_\beta\beta$ (2017)
  6. Igual, Laura; Seguí, Santi: Introduction to data science. A Python approach to concepts, techniques and applications. With contributions from Jordi Vitrià, Eloi Puertas Petia Radeva, Oriol Pujol, Sergio Escalera, Francesc Dantíand Lluís Garrido (2017)
  7. Kotthoff, Lars; Thornton, Chris; Hoos, Holger H.; Hutter, Frank; Leyton-Brown, Kevin: Auto-WEKA 2.0: automatic model selection and hyperparameter optimization in WEKA (2017)
  8. Kruber, Markus; Lübbecke, Marco E.; Parmentier, Axel: Learning when to use a decomposition (2017)
  9. Lema^ıtre, Guillaume; Nogueira, Fernando; Aridas, Christos K.: Imbalanced-learn: a python toolbox to tackle the curse of imbalanced datasets in machine learning (2017)
  10. Lodi, Andrea; Zarpellon, Giulia: On learning and branching: a survey (2017)
  11. Louveaux, Quentin: Comments on: “On learning and branching: a survey” (2017)
  12. Mısır, Mustafa; Sebag, Michèle: Alors: an algorithm recommender system (2017)
  13. Pierre Fernique, Christophe Pradal: AutoWIG: Automatic Generation of Python Bindings for C++ Libraries (2017) arXiv
  14. Robin Scheibler, Eric Bezzam, Ivan Dokmanic: Pyroomacoustics: A Python package for audio room simulations and array processing algorithms (2017) arXiv
  15. Verwer, Sicco; Zhang, Yingqian; Ye, Qing Chuan: Auction optimization using regression trees and linear models as integer programs (2017)
  16. Xie, Jiang; Xu, Junfu; Nie, Celine; Nie, Qing: Machine learning of swimming data via wisdom of crowd and regression analysis (2017) ioport
  17. Yao-Yuan Yang, Shao-Chuan Lee, Yu-An Chung, Tung-En Wu, Si-An Chen, Hsuan-Tien Lin: libact: Pool-based Active Learning in Python (2017) arXiv
  18. Bischl, Bernd; Lang, Michel; Kotthoff, Lars; Schiffner, Julia; Richter, Jakob; Studerus, Erich; Casalicchio, Giuseppe; Jones, Zachary M.: Mlr: machine learning in $\bold R$ (2016)
  19. Eiter, Thomas; Kaminski, Tobias: Exploiting contextual knowledge for hybrid classification of visual objects (2016)
  20. Frandi, Emanuele; Ñanculef, Ricardo; Lodi, Stefano; Sartori, Claudio; Suykens, Johan A.K.: Fast and scalable Lasso via stochastic Frank-Wolfe methods with a convergence guarantee (2016)

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