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 159 articles , 1 standard article )

Showing results 1 to 20 of 159.
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  1. Adilina, Sheikh; Farid, Dewan Md; Shatabda, Swakkhar: Effective DNA binding protein prediction by using key features via Chou’s general PseAAC (2019)
  2. Alexander J. Gates; Yong-Yeol Ahn: CluSim: a python package for calculating clustering similarity (2019) not zbMATH
  3. Amir M. Mir; Jalal A. Nasiri: LightTwinSVM: A Simple and Fast Implementation of Standard Twin Support Vector Machine Classifier (2019) not zbMATH
  4. Benjamin Bengfort; Rebecca Bilbro: Yellowbrick: Visualizing the Scikit-Learn Model Selection Process (2019) not zbMATH
  5. Bruni, Renato; Bianchi, Gianpiero; Dolente, Cosimo; Leporelli, Claudio: Logical analysis of data as a tool for the analysis of probabilistic discrete choice behavior (2019)
  6. Cherubin, Giovanni: Majority vote ensembles of conformal predictors (2019)
  7. Daniel Smilkov, Nikhil Thorat, Yannick Assogba, Ann Yuan, Nick Kreeger, Ping Yu, Kangyi Zhang, Shanqing Cai, Eric Nielsen, David Soergel, Stan Bileschi, Michael Terry, Charles Nicholson, Sandeep N. Gupta, Sarah Sirajuddin, D. Sculley, Rajat Monga, Greg Corrado, Fernanda B. Viegas, Martin Wattenberg: TensorFlow.js: Machine Learning for the Web and Beyond (2019) arXiv
  8. Davide Micieli, Triestino Minniti, Giuseppe Gorini: NeuTomPy toolbox, a Python package for tomographic data processing and reconstruction (2019) not zbMATH
  9. Eric W. Koch, Ryan D. Boyden, Blakesley Burkhart, Adam Ginsburg, Jason L. Loeppky, Stella S.R. Offner: TurbuStat: Turbulence Statistics in Python (2019) arXiv
  10. Fischetti, Martina; Fraccaro, Marco: Machine learning meets mathematical optimization to predict the optimal production of offshore wind parks (2019)
  11. Gostick J, Khan ZA, Tranter TG, Kok MDR, Agnaou M, Sadeghi MA, Jervis R.: PoreSpy: A Python Toolkit for Quantitative Analysis of Porous Media Images (2019) not zbMATH
  12. Jaewon Chung, Benjamin D. Pedigo, Eric W. Bridgeford, Bijan K. Varjavand, Joshua T. Vogelstein: GraSPy: Graph Statistics in Python (2019) arXiv
  13. Johansson, Robert: Numerical Python. Scientific computing and data science applications with Numpy, SciPy and Matplotlib (2019)
  14. Mercadier, Mathieu; Lardy, Jean-Pierre: Credit spread approximation and improvement using random forest regression (2019)
  15. Michael E.Rose; John R.Kitchin: pybliometrics: Scriptable bibliometrics using a Python interface to Scopus (2019) not zbMATH
  16. Michael Hippke, Trevor J. David, Gijs D. Mulders, René Heller: Wotan: Comprehensive time-series de-trending in Python (2019) arXiv
  17. Oliver Tomic; Thomas Gra; Kristian Hovde Liland; Tormod Næs: hoggorm: a python library for explorative multivariate statistics (2019) not zbMATH
  18. Rayhan, Farshid; Ahmed, Sajid; Md Farid, Dewan; Dehzangi, Abdollah; Shatabda, Swakkhar: CFSBoost: cumulative feature subspace boosting for drug-target interaction prediction (2019)
  19. Szymański, Piotr; Kajdanowicz, Tomasz: scikit-multilearn: a scikit-based Python environment for performing multi-label classification (2019)
  20. Tabourier, Lionel; Bernardes, Daniel F.; Libert, Anne-Sophie; Lambiotte, Renaud: RankMerging: a supervised learning-to-rank framework to predict links in large social networks (2019)

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