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Hyperparameter tuning plays a crucial role in optimizing the performance ofpredictive learners. Cross--validation (CV) is a widely adopted technique forestimating the error of different hyperparameter settings. Repeatedcross-validation (RCV) has been commonly employed to reduce the variability ofCV errors. In this paper, we introduce a novel approach called blockedcross-validation (BCV), where the repetitions are blocked with respect to bothCV partition and the random behavior of the learner. Theoretical analysis andempirical experiments demonstrate that BCV provides more precise errorestimates compared to RCV, even with a significantly reduced number of runs. Wepresent extensive examples using real--world data sets to showcase theeffectiveness and efficiency of BCV in hyperparameter tuning. Our resultsindicate that BCV outperforms RCV in hyperparameter tuning, achieving greaterprecision with fewer computations.

Blocked Cross-Validation: A Precise and Efficient Method for Hyperparameter Tuning