mlbench

R package mlbench: Machine Learning Benchmark Problems. A collection of artificial and real-world machine learning benchmark problems, including, e.g., several data sets from the UCI repository.

Keywords for this software

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References in zbMATH (referenced in 40 articles )

Showing results 1 to 20 of 40.
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  1. Calhoun, Peter; Hallett, Melodie J.; Su, Xiaogang; Cafri, Guy; Levine, Richard A.; Fan, Juanjuan: Random forest with acceptance-rejection trees (2020)
  2. Chakraborty, Saptarshi; Paul, Debolina; Das, Swagatam: Hierarchical clustering with optimal transport (2020)
  3. Genuer, Robin; Poggi, Jean-Michel: Random forests with R (2020)
  4. Khan, Zardad; Gul, Asma; Perperoglou, Aris; Miftahuddin, Miftahuddin; Mahmoud, Osama; Adler, Werner; Lausen, Berthold: Ensemble of optimal trees, random forest and random projection ensemble classification (2020)
  5. Cipolli, William III; Hanson, Timothy: Supervised learning via smoothed Polya trees (2019)
  6. Dvořák, Jakub: Classification trees with soft splits optimized for ranking (2019)
  7. Nengsih, Titin Agustin; Bertrand, Frédéric; Maumy-Bertrand, Myriam; Meyer, Nicolas: Determining the number of components in PLS regression on incomplete data set (2019)
  8. Ramasubramanian, Karthik; Singh, Abhishek: Machine learning using R. With time series and industry-based use cases in R (2019)
  9. Gilles Kratzer, Reinhard Furrer: varrank: an R package for variable ranking based on mutual information with applications to observed systemic datasets (2018) arXiv
  10. Gul, Asma; Perperoglou, Aris; Khan, Zardad; Mahmoud, Osama; Miftahuddin, Miftahuddin; Adler, Werner; Lausen, Berthold: Ensemble of a subset of (k)NN classifiers (2018)
  11. Benjamin R. Fitzpatrick, Kerrie Mengersen: A network flow approach to visualising the roles of covariates in random forests (2017) arXiv
  12. Conversano, Claudio; Dusseldorp, Elise: Modeling threshold interaction effects through the logistic classification trunk (2017)
  13. Marjolein Fokkema: pre: An R Package for Fitting Prediction Rule Ensembles (2017) arXiv
  14. Michael Hahsler and Matthew Bolaños and John Forrest: Introduction to stream: An Extensible Framework for Data Stream Clustering Research with R (2017) not zbMATH
  15. Reto Bürgin; Gilbert Ritschard: Coefficient-Wise Tree-Based Varying Coefficient Regression with vcrpart (2017) not zbMATH
  16. Alquier, Pierre; Ridgway, James; Chopin, Nicolas: On the properties of variational approximations of Gibbs posteriors (2016)
  17. Tutz, Gerhard; Koch, Dominik: Improved nearest neighbor classifiers by weighting and selection of predictors (2016)
  18. Cichosz, Paweł: Data mining algorithms. Explained using R (2015)
  19. David Conde; Miguel Fernández; Bonifacio Salvador; Cristina Rueda: dawai: An R Package for Discriminant Analysis with Additional Information (2015) not zbMATH
  20. Ishwaran, Hemant: The effect of splitting on random forests (2015)

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