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 24 articles , 1 standard article )

Showing results 1 to 20 of 24.
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  1. Bommert, Andrea; Sun, Xudong; Bischl, Bernd; Rahnenführer, Jörg; Lang, Michel: Benchmark for filter methods for feature selection in high-dimensional classification data (2020)
  2. F. Aragón-Royón, A. Jiménez-Vílchez, A. Arauzo-Azofra, J. M. Benítez: FSinR: an exhaustive package for feature selection (2020) arXiv
  3. Sayan Putatunda, Dayananda Ubrangala, Kiran Rama, Ravi Kondapalli: DriveML: An R Package for Driverless Machine Learning (2020) arXiv
  4. 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)
  5. 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
  6. Viktor Kazakov, Franz J. Király: Machine Learning Automation Toolbox (MLaut) (2019) arXiv
  7. Adriano Rivolli; Andre C. P. L. F. de Carvalho: The utiml Package: Multi-label Classification in R (2018) not zbMATH
  8. Bojan Mihaljević, Concha Bielza, Pedro Larrañaga: bnclassify: Learning Bayesian Network Classifiers (2018) not zbMATH
  9. Probst, Philipp; Boulesteix, Anne-Laure: To tune or not to tune the number of trees in random forest (2018)
  10. 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
  11. Bommert, Andrea; Rahnenführer, Jörg; Lang, Michel: A multicriteria approach to find predictive and sparse models with stable feature selection for high-dimensional data (2017)
  12. 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
  13. Michel Lang: checkmate: Fast Argument Checks for Defensive R Programming (2017) arXiv
  14. Pascal Kerschke: Comprehensive Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems Using the R-Package flacco (2017) arXiv
  15. 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)
  16. Bischl, Bernd; Kühn, Tobias; Szepannek, Gero: On class imbalance correction for classification algorithms in credit scoring (2016)
  17. Bischl, Bernd; Lang, Michel; Kotthoff, Lars; Schiffner, Julia; Richter, Jakob; Studerus, Erich; Casalicchio, Giuseppe; Jones, Zachary M.: mlr: machine learning in (\mathbfR) (2016)
  18. Steponavičė, Ingrida; Shirazi-Manesh, Mojdeh; Hyndman, Rob J.; Smith-Miles, Kate; Villanova, Laura: On sampling methods for costly multi-objective black-box optimization (2016)
  19. Kotthaus, Helena; Korb, Ingo; Lang, Michel; Bischl, Bernd; Rahnenführer, Jörg; Marwedel, Peter: Runtime and memory consumption analyses for machine learning R programs (2015)
  20. 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)

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