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 31 articles , 1 standard article )

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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. Fitzpatrick, Trevor; Mues, Christophe: How can lenders prosper? Comparing machine learning approaches to identify profitable peer-to-peer loan investments (2021)
  3. Jakob A. Dambon, Fabio Sigrist, Reinhard Furrer: varycoef: An R Package for Gaussian Process-based Spatially Varying Coefficient Models (2021) arXiv
  4. Miron B. Kursa: Praznik: High performance information-based feature selection (2021) not zbMATH
  5. Anthony D. Blaom, Franz Kiraly, Thibaut Lienart, Yiannis Simillides, Diego Arenas, Sebastian J. Vollmer: MLJ: A Julia package for composable Machine Learning (2020) arXiv
  6. Bommert, Andrea; Sun, Xudong; Bischl, Bernd; Rahnenführer, Jörg; Lang, Michel: Benchmark for filter methods for feature selection in high-dimensional classification data (2020)
  7. F. Aragón-Royón, A. Jiménez-Vílchez, A. Arauzo-Azofra, J. M. Benítez: FSinR: an exhaustive package for feature selection (2020) arXiv
  8. Gero Szepannek: An Overview on the Landscape of R Packages for Credit Scoring (2020) arXiv
  9. Sayan Putatunda, Dayananda Ubrangala, Kiran Rama, Ravi Kondapalli: DriveML: An R Package for Driverless Machine Learning (2020) arXiv
  10. Szymon Maksymiuk, Alicja Gosiewska, Przemyslaw Biecek: Landscape of R packages for eXplainable Artificial Intelligence (2020) arXiv
  11. 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)
  12. 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
  13. Viktor Kazakov, Franz J. Király: Machine Learning Automation Toolbox (MLaut) (2019) arXiv
  14. Adriano Rivolli; Andre C. P. L. F. de Carvalho: The utiml Package: Multi-label Classification in R (2018) not zbMATH
  15. Bojan Mihaljević, Concha Bielza, Pedro Larrañaga: bnclassify: Learning Bayesian Network Classifiers (2018) not zbMATH
  16. Probst, Philipp; Boulesteix, Anne-Laure: To tune or not to tune the number of trees in random forest (2018)
  17. 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
  18. Bommert, Andrea; Rahnenführer, Jörg; Lang, Michel: A multicriteria approach to find predictive and sparse models with stable feature selection for high-dimensional data (2017)
  19. 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
  20. Michel Lang: checkmate: Fast Argument Checks for Defensive R Programming (2017) arXiv

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