Blocked Cross-Validation: A Precise and Efficient Method for Hyperparameter Tuning
This is an incremental improvement for machine learning practitioners needing efficient hyperparameter tuning methods.
The paper tackles the problem of hyperparameter tuning by introducing blocked cross-validation (BCV), which provides more precise error estimates than repeated cross-validation (RCV) with significantly fewer computational runs.
Hyperparameter tuning plays a crucial role in optimizing the performance of predictive learners. Cross--validation (CV) is a widely adopted technique for estimating the error of different hyperparameter settings. Repeated cross-validation (RCV) has been commonly employed to reduce the variability of CV errors. In this paper, we introduce a novel approach called blocked cross-validation (BCV), where the repetitions are blocked with respect to both CV partition and the random behavior of the learner. Theoretical analysis and empirical experiments demonstrate that BCV provides more precise error estimates compared to RCV, even with a significantly reduced number of runs. We present extensive examples using real--world data sets to showcase the effectiveness and efficiency of BCV in hyperparameter tuning. Our results indicate that BCV outperforms RCV in hyperparameter tuning, achieving greater precision with fewer computations.