Using Sequential Statistical Tests for Efficient Hyperparameter Tuning

TL;DR

This paper introduces Sequential Random Search (SQRS), a method that uses sequential statistical tests to eliminate poor hyperparameter configurations early, reducing computational costs while maintaining performance.

Contribution

The paper presents a novel hyperparameter tuning method that integrates sequential testing into random search to improve efficiency.

Findings

SQRS finds comparable hyperparameters with fewer evaluations.

Simulation shows SQRS reduces computational effort significantly.

Sequential tests effectively discard inferior configurations early.

Abstract

Hyperparameter tuning is one of the 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.