The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
mlr: Machine Learning in R
Open accessAbstract
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.
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Citations
Cites
The WEKA data mining software: an update
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1656274.1656278 · Cited by 17,750
Uno, H., Cai, T., Pencina, M. J., D'Agostino, R. B., & Wei, L. J. (2011) · Statistics in Medicine · doi:10.1002/sim.4154 · Cited by 1,535
Cited by
The irace package: Iterated racing for automatic algorithm configuration
López-Ibáñez, M., Dubois-Lacoste, J., Pérez Cáceres, L., Birattari, M., & Stützle, T. (2016) · Operations Research Perspectives · doi:10.1016/j.orp.2016.09.002 · Cited by 1,761
Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges
Bischl, B., Binder, M., Lang, M., Pielok, T., Richter, J., Coors, S., Thomas, J., Ullmann, T., Becker, M., Boulesteix, A., et al. (2023) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.1484 · Cited by 930
Self-citationAutomated Algorithm Selection: Survey and Perspectives
Kerschke, P., Hoos, H. H., Neumann, F., & Trautmann, H. (2019) · Evolutionary Computation · doi:10.1162/evco_a_00242 · Cited by 435
Predicting personality from patterns of behavior collected with smartphones
Stachl, C., Au, Q., Schoedel, R., Gosling, S. D., Harari, G. M., Buschek, D., Völkel, S. T., Schuwerk, T., Oldemeier, M., Ullmann, T., et al. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1920484117 · Cited by 348
Self-citationZwanenburg, A. (2019) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-019-04391-8 · Cited by 306
Metadata
- Authors
- Bischl, B., Lang, M., Kotthoff, L., Schratz, P., Schiffner, J., Richter, J., Jones, Z., Casalicchio, G., Gallo, M., & Binder, M.
- Venue
- CRAN: Contributed Packages
- Publisher
- The R Foundation
- Published
- 2013-08-30
- Volume
- 17
- Issue
- 1
- Pages
- 5938-5942
- OpenAlex
- W2544745230
- Cited by
- 277
- References
- 2
- Type
- dataset
- Language
- en
- Open access
- gold
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- Bischl, B., Lang, M., Kotthoff, L., Schratz, P., Schiffner, J., Richter, J., Jones, Z., Casalicchio, G., Gallo, M., & Binder, M. (2013). mlr: Machine Learning in R. CRAN: Contributed Packages. https://doi.org/10.32614/cran.package.mlr
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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