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Using sequential statistical tests for efficient hyperparameter tuning

Author

Listed:
  • Philip Buczak

    (TU Dortmund University)

  • Andreas Groll

    (TU Dortmund University)

  • Markus Pauly

    (TU Dortmund University
    UA Ruhr)

  • Jakob Rehof

    (TU Dortmund University)

  • Daniel Horn

    (TU Dortmund University
    UA Ruhr)

Abstract

Hyperparameter tuning is one of the most time-consuming parts in machine learning. Despite the existence of modern optimization algorithms that minimize the number of evaluations needed, evaluations of a single setting may still be expensive. Usually a resampling technique is used, where the machine learning method has to be fitted a fixed number of k times on different training datasets. The respective mean performance of the k fits is then used as performance estimator. Many hyperparameter settings could be discarded after less than k resampling iterations if they are clearly inferior to high-performing settings. However, resampling is often performed until the very end, wasting a lot of computational effort. To this end, we propose the sequential random search (SQRS) which extends the regular random search algorithm by a sequential testing procedure aimed at detecting and eliminating inferior parameter configurations early. We compared our SQRS with regular random search using multiple publicly available regression and classification datasets. Our simulation study showed that the SQRS is able to find similarly well-performing parameter settings while requiring noticeably fewer evaluations. Our results underscore the potential for integrating sequential tests into hyperparameter tuning.

Suggested Citation

  • Philip Buczak & Andreas Groll & Markus Pauly & Jakob Rehof & Daniel Horn, 2024. "Using sequential statistical tests for efficient hyperparameter tuning," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 108(2), pages 441-460, June.
  • Handle: RePEc:spr:alstar:v:108:y:2024:i:2:d:10.1007_s10182-024-00495-1
    DOI: 10.1007/s10182-024-00495-1
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    References listed on IDEAS

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    1. Aderemi O. Adewumi & Andronicus A. Akinyelu, 2017. "A survey of machine-learning and nature-inspired based credit card fraud detection techniques," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 8(2), pages 937-953, November.
    2. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    3. López-Ibáñez, Manuel & Dubois-Lacoste, Jérémie & Pérez Cáceres, Leslie & Birattari, Mauro & Stützle, Thomas, 2016. "The irace package: Iterated racing for automatic algorithm configuration," Operations Research Perspectives, Elsevier, vol. 3(C), pages 43-58.
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    Cited by:

    1. Benjamin Säfken & David Rügamer, 2024. "Editorial special issue: Bridging the gap between AI and Statistics," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 108(2), pages 225-229, June.

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