mlr

mlr: Machine Learning in R. Interface to a large number of classification and regression techniques, including machine-readable parameter descriptions. There is also an experimental extension for survival analysis, clustering and general, example-specific cost-sensitive learning. Generic resampling, including cross-validation, bootstrapping and subsampling. Hyperparameter tuning with modern optimization techniques, for single- and multi-objective problems. Filter and wrapper methods for feature selection. Extension of basic learners with additional operations common in machine learning, also allowing for easy nested resampling. Most operations can be parallelized.


References in zbMATH (referenced in 19 articles , 1 standard article )

Showing results 1 to 19 of 19.
Sorted by year (citations)

  1. Casalicchio, Giuseppe; Bossek, Jakob; Lang, Michel; Kirchhoff, Dominik; Kerschke, Pascal; Hofner, Benjamin; Seibold, Heidi; Vanschoren, Joaquin; Bischl, Bernd: \textttOpenML: an \textttRpackage to connect to the machine learning platform openml (2019)
  2. Viktor Kazakov, Franz J. Király: Machine Learning Automation Toolbox (MLaut) (2019) arXiv
  3. Adriano Rivolli; Andre C. P. L. F. de Carvalho: The utiml Package: Multi-label Classification in R (2018) not zbMATH
  4. Bojan Mihaljević, Concha Bielza, Pedro Larrañaga: bnclassify: Learning Bayesian Network Classifiers (2018) not zbMATH
  5. Probst, Philipp; Boulesteix, Anne-Laure: To tune or not to tune the number of trees in random forest (2018)
  6. Bernd Bischl, Jakob Richter, Jakob Bossek, Daniel Horn, Janek Thomas, Michel Lang: mlrMBO: A Modular Framework for Model-Based Optimization of Expensive Black-Box Functions (2017) arXiv
  7. Bommert, Andrea; Rahnenführer, Jörg; Lang, Michel: A multicriteria approach to find predictive and sparse models with stable feature selection for high-dimensional data (2017)
  8. Giuseppe Casalicchio, Jakob Bossek, Michel Lang, Dominik Kirchhoff, Pascal Kerschke, Benjamin Hofner, Heidi Seibold, Joaquin Vanschoren, Bernd Bischl: OpenML: An R Package to Connect to the Networked Machine Learning Platform OpenML (2017) arXiv
  9. Michel Lang: checkmate: Fast Argument Checks for Defensive R Programming (2017) arXiv
  10. Pascal Kerschke: Comprehensive Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems Using the R-Package flacco (2017) arXiv
  11. Bischl, Bernd; Kerschke, Pascal; Kotthoff, Lars; Lindauer, Marius; Malitsky, Yuri; Fréchette, Alexandre; Hoos, Holger; Hutter, Frank; Leyton-Brown, Kevin; Tierney, Kevin; Vanschoren, Joaquin: ASlib: a benchmark library for algorithm selection (2016)
  12. Bischl, Bernd; Kühn, Tobias; Szepannek, Gero: On class imbalance correction for classification algorithms in credit scoring (2016)
  13. Bischl, Bernd; Lang, Michel; Kotthoff, Lars; Schiffner, Julia; Richter, Jakob; Studerus, Erich; Casalicchio, Giuseppe; Jones, Zachary M.: mlr: machine learning in (\mathbfR) (2016)
  14. Steponavičė, Ingrida; Shirazi-Manesh, Mojdeh; Hyndman, Rob J.; Smith-Miles, Kate; Villanova, Laura: On sampling methods for costly multi-objective black-box optimization (2016)
  15. Eugster, Manuel J. A.; Leisch, Friedrich; Strobl, Carolin: (Psycho-)analysis of benchmark experiments: a formal framework for investigating the relationship between data sets and learning algorithms (2014)
  16. Kerschke, Pascal; Preuss, Mike; Hernández, Carlos; Schütze, Oliver; Sun, Jian-Qiao; Grimme, Christian; Rudolph, Günter; Bischl, Bernd; Trautmann, Heike: Cell mapping techniques for exploratory landscape analysis (2014)
  17. Krey, Sebastian; Ligges, Uwe; Leisch, Friedrich: Music and timbre segmentation by recursive constrained (K)-means clustering (2014)
  18. Bischl, Bernd; Schiffner, Julia; Weihs, Claus: Benchmarking local classification methods (2013)
  19. Ligges, Uwe; Krey, Sebastian: Feature clustering for instrument classification (2011)