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.
Keywords for this software
References in zbMATH (referenced in 6 articles )
Showing results 1 to 6 of 6.
- 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)
- Bischl, Bernd; Kühn, Tobias; Szepannek, Gero: On class imbalance correction for classification algorithms in credit scoring (2016)
- 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)
- Krey, Sebastian; Ligges, Uwe; Leisch, Friedrich: Music and timbre segmentation by recursive constrained $K$-means clustering (2014)
- Bischl, Bernd; Schiffner, Julia; Weihs, Claus: Benchmarking local classification methods (2013)
- Ligges, Uwe; Krey, Sebastian: Feature clustering for instrument classification (2011)