The DOI matches its Crossref record; 201 of the 201 references it cites were found in a registry or an index.
Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges
Open accessAbstract
Most machine learning algorithms are configured by a set of hyperparameters whose values must be carefully chosen and which often considerably impact performance. To avoid a time‐consuming and irreproducible manual process of trial‐and‐error to find well‐performing hyperparameter configurations, various automatic hyperparameter optimization (HPO) methods—for example, based on resampling error estimation for supervised machine learning—can be employed. After introducing HPO from a general perspective, this paper reviews important HPO methods, from simple techniques such as grid or random search to more advanced methods like evolution strategies, Bayesian optimization, Hyperband, and racing. This work gives practical recommendations regarding important choices to be made when conducting HPO, including the HPO algorithms themselves, performance evaluation, how to combine HPO with machine learning pipelines, runtime improvements, and parallelization. This article is categorized under: Algorithmic Development > Statistics Technologies > Machine Learning Technologies > Prediction
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Cites
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Cited by
General Pitfalls of Model-Agnostic Interpretation Methods for Machine Learning Models
Molnar, C., König, G., Herbinger, J., Freiesleben, T., Dandl, S., Scholbeck, C. A., Casalicchio, G., Grosse-Wentrup, M., & Bischl, B. (2022) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-04083-2_4 · Cited by 211
Self-citationMirzaei, S., Mao, H., Al-Nima, R. R. O., & Woo, W. L. (2023) · Information · doi:10.3390/info15010004 · Cited by 25
Mehraban, M. H., Mirzabeigi, S., Faraji, S., Soltanian-Zadeh, S., & Sepasgozar, S. M. E. (2025) · Buildings · doi:10.3390/buildings15213950 · Cited by 11
Metadata
- Authors
- Bischl, B., Binder, M., Lang, M., Pielok, T., Richter, J., Coors, S., Thomas, J., Ullmann, T., Becker, M., Boulesteix, A., Deng, D., & Lindauer, M.
- Venue
- WIREs Data Mining and Knowledge Discovery
- Publisher
- Wiley
- Published
- 2023-01-16
- Volume
- 13
- Issue
- 2
- ISSN
- 1942-4787 (ISSN-L)
- 1942-4795
- OpenAlex
- W3179950556
- Cited by
- 930
- References
- 201
- FWCI
- 80.56
- Type
- journal article
- Language
- en
- Open access
- hybrid
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