References in zbMATH (referenced in 73 articles , 1 standard article )

Showing results 1 to 20 of 73.
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  1. Binder, Martin; Pfisterer, Florian; Lang, Michel; Schneider, Lennart; Kotthoff, Lars; Bischl, Bernd: mlr3pipelines -- flexible machine learning pipelines in R (2021)
  2. David Ardia, Keven Bluteau, Samuel Borms, Kris Boudt: The R package sentometrics to compute, aggregate and predict with textual sentiment (2021) arXiv
  3. Ferri-García, Ramón; Castro-Martín, Luis; del Mar Rueda, María: Evaluating machine learning methods for estimation in online surveys with superpopulation modeling (2021)
  4. Itsaso Rodriguez, Itziar Irigoien, Basilio Sierra, Concepcion Arenas: dbcsp: User-friendly R package for Distance-Based Common Spacial Patterns (2021) arXiv
  5. Krzysztof Gajowniczek, Tomasz Ząbkowski: ImbTreeEntropy: An R package for building entropy-based classification trees on imbalanced datasets (2021) not zbMATH
  6. Krzysztof Gajowniczek; Tomasz Ząbkowski: ImbTreeAUC: An R package for building classification trees using the area under the ROC curve (AUC) on imbalanced datasets (2021) not zbMATH
  7. Murray, Jared S.: Log-linear Bayesian additive regression trees for multinomial logistic and count regression models (2021)
  8. Nikolopoulos, Konstantinos; Punia, Sushil; Schäfers, Andreas; Tsinopoulos, Christos; Vasilakis, Chrysovalantis: Forecasting and planning during a pandemic: COVID-19 growth rates, supply chain disruptions, and governmental decisions (2021)
  9. Van Belle, Jente; Guns, Tias; Verbeke, Wouter: Using shared sell-through data to forecast wholesaler demand in multi-echelon supply chains (2021)
  10. Anthony D. Blaom, Franz Kiraly, Thibaut Lienart, Yiannis Simillides, Diego Arenas, Sebastian J. Vollmer: MLJ: A Julia package for composable Machine Learning (2020) arXiv
  11. Barinder Thind, Sidi Wu, Richard Groenewald, Jiguo Cao: FuncNN: An R Package to Fit Deep Neural Networks Using Generalized Input Spaces (2020) arXiv
  12. Begüm D. Topçuoğlu; Zena Lapp; Kelly L. Sovacool; Evan Snitkin; Jenna Wiens; Patrick D. Schloss: mikropml: User-Friendly R Package for Supervised Machine Learning Pipelines (2020) not zbMATH
  13. Chambaz, Antoine; Benkeser, David: A ride in targeted learning territory (2020)
  14. F. Aragón-Royón, A. Jiménez-Vílchez, A. Arauzo-Azofra, J. M. Benítez: FSinR: an exhaustive package for feature selection (2020) arXiv
  15. Gero Szepannek: An Overview on the Landscape of R Packages for Credit Scoring (2020) arXiv
  16. Kim, Sun Hye; Boukouvala, Fani: Machine learning-based surrogate modeling for data-driven optimization: a comparison of subset selection for regression techniques (2020)
  17. Larkin, Taylor; Mcmanus, Denise: An analytical toast to wine: using stacked generalization to predict wine preference (2020)
  18. Neeraj Dhanraj Bokde; Gorm Bruun Andersen: ForecastTB - An R Package as a Test-bench for Forecasting Methods Comparison (2020) arXiv
  19. Roustant, Olivier; Padonou, Espéran; Deville, Yves; Clément, Aloïs; Perrin, Guillaume; Giorla, Jean; Wynn, Henry: Group kernels for Gaussian process metamodels with categorical inputs (2020)
  20. Sage, Andrew J.; Genschel, Ulrike; Nettleton, Dan: Tree aggregation for random forest class probability estimation (2020)

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