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 136 articles , 1 standard article )

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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. Bruni, Renato; Bianchi, Gianpiero; Dolente, Cosimo; Leporelli, Claudio: Logical analysis of data as a tool for the analysis of probabilistic discrete choice behavior (2019)
  3. Cherubin, Giovanni: Majority vote ensembles of conformal predictors (2019)
  4. 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
  5. Eric W. Koch, Ryan D. Boyden, Blakesley Burkhart, Adam Ginsburg, Jason L. Loeppky, Stella S.R. Offner: TurbuStat: Turbulence Statistics in Python (2019) arXiv
  6. Fischetti, Martina; Fraccaro, Marco: Machine learning meets mathematical optimization to predict the optimal production of offshore wind parks (2019)
  7. Jaewon Chung, Benjamin D. Pedigo, Eric W. Bridgeford, Bijan K. Varjavand, Joshua T. Vogelstein: GraSPy: Graph Statistics in Python (2019) arXiv
  8. Mercadier, Mathieu; Lardy, Jean-Pierre: Credit spread approximation and improvement using random forest regression (2019)
  9. Rayhan, Farshid; Ahmed, Sajid; Md Farid, Dewan; Dehzangi, Abdollah; Shatabda, Swakkhar: CFSBoost: cumulative feature subspace boosting for drug-target interaction prediction (2019)
  10. Szymański, Piotr; Kajdanowicz, Tomasz: scikit-multilearn: a scikit-based Python environment for performing multi-label classification (2019)
  11. Toccaceli, Paolo; Gammerman, Alexander: Combination of inductive Mondrian conformal predictors (2019)
  12. Viktor Kazakov, Franz J. Király: Machine Learning Automation Toolbox (MLaut) (2019) arXiv
  13. Xiao, D.; Heaney, C. E.; Fang, F.; Mottet, L.; Hu, R.; Bistrian, D. A.; Aristodemou, E.; Navon, I. M.; Pain, C. C.: A domain decomposition non-intrusive reduced order model for turbulent flows (2019)
  14. Yue Zhao, Zain Nasrullah, Zheng Li: PyOD: A Python Toolbox for Scalable Outlier Detection (2019) arXiv
  15. Aggarwal, Charu C.: Machine learning for text (2018)
  16. Aggarwal, Charu C.: Neural networks and deep learning. A textbook (2018)
  17. Alaa, Ahmed M.; van der Schaar, Mihaela: A hidden absorbing semi-Markov model for informatively censored temporal data: learning and inference (2018)
  18. Au, Timothy C.: Random forests, decision trees, and categorical predictors: the “absent levels” problem (2018)
  19. Bacry, Emmanuel; Bompaire, Martin; Deegan, Philip; Gaïffas, Stéphane; Poulsen, Søren V.: \texttttick: a Python library for statistical learning, with an emphasis on Hawkes processes and time-dependent models (2018)
  20. Brazdil, Pavel (ed.); Giraud-Carrier, Christophe (ed.): Metalearning and algorithm selection: progress, state of the art and introduction to the 2018 special issue (2018)

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