References in zbMATH (referenced in 49 articles , 1 standard article )

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  1. F. Aragón-Royón, A. Jiménez-Vílchez, A. Arauzo-Azofra, J. M. Benítez: FSinR: an exhaustive package for feature selection (2020) arXiv
  2. Kim, Sun Hye; Boukouvala, Fani: Machine learning-based surrogate modeling for data-driven optimization: a comparison of subset selection for regression techniques (2020)
  3. Neeraj Dhanraj Bokde; Gorm Bruun Andersen: ForecastTB - An R Package as a Test-bench for Forecasting Methods Comparison (2020) arXiv
  4. Sayan Putatunda, Dayananda Ubrangala, Kiran Rama, Ravi Kondapalli: DriveML: An R Package for Driverless Machine Learning (2020) arXiv
  5. Ahsen, Mehmet Eren; Vogel, Robert M.; Stolovitzky, Gustavo A.: Unsupervised evaluation and weighted aggregation of ranked classification predictions (2019)
  6. Baíllo, Amparo; Cárcamo, Javier; Getman, Konstantin: New distance measures for classifying X-ray astronomy data into stellar classes (2019)
  7. Berrendero, José R.; Cárcamo, Javier: Linear components of quadratic classifiers (2019)
  8. Dena J. Clink, Holger Klinck: GIBBONR: An R package for the detection and classification of acoustic signals using machine learning (2019) arXiv
  9. Feuerriegel, Stefan; Gordon, Julius: News-based forecasts of macroeconomic indicators: a semantic path model for interpretable predictions (2019)
  10. Haziq Jamil, Wicher Bergsma: iprior: An R Package for Regression Modelling using I-priors (2019) arXiv
  11. Michel Lang, Martin Binder, Jakob Richter, Patrick Schratz, Florian Pfisterer, Stefan Coors, Quay Au, Giuseppe Casalicchio, Lars Kotthoff, Bernd Bischl: mlr3: A modern object-oriented machine learning framework in R (2019) not zbMATH
  12. N. Benjamin Erichson, Sergey Voronin, Steven L. Brunton, J. Nathan Kutz: Randomized Matrix Decompositions Using R (2019) not zbMATH
  13. Quach, Anna; Symanzik, Jürgen; Forsgren, Nicole: Soul of the community: an attempt to assess attachment to a community (2019)
  14. Ramasubramanian, Karthik; Singh, Abhishek: Machine learning using R. With time series and industry-based use cases in R (2019)
  15. Victor Maus and Gilberto Câmara and Marius Appel and Edzer Pebesma: dtwSat: Time-Weighted Dynamic Time Warping for Satellite Image Time Series Analysis in R (2019) not zbMATH
  16. Viktor Kazakov, Franz J. Király: Machine Learning Automation Toolbox (MLaut) (2019) arXiv
  17. Aggarwal, Charu C.: Machine learning for text (2018)
  18. Alfonso Iodice D’Enza, Angelos Markos, Davide Buttarazzi: The idm Package: Incremental Decomposition Methods in R (2018) not zbMATH
  19. Biecek, Przemysław: DALEX: explainers for complex predictive models in \textttR (2018)
  20. Bojan Mihaljević, Concha Bielza, Pedro Larrañaga: bnclassify: Learning Bayesian Network Classifiers (2018) not zbMATH

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