Importance of Tuning Hyperparameters of Machine Learning Algorithms
This addresses the problem of reducing computational costs for practitioners by identifying when hyperparameter tuning is unnecessary, though it is incremental as it builds on existing tuning methodologies.
The study investigated whether hyperparameters in machine learning algorithms need tuning or can be set to defaults, using a non-inferiority test and tuning risk on 59 datasets from OpenML, finding that defaults were non-inferior to tuning and sometimes outperformed limited tuning.
The performance of many machine learning algorithms depends on their hyperparameter settings. The goal of this study is to determine whether it is important to tune a hyperparameter or whether it can be safely set to a default value. We present a methodology to determine the importance of tuning a hyperparameter based on a non-inferiority test and tuning risk: the performance loss that is incurred when a hyperparameter is not tuned, but set to a default value. Because our methods require the notion of a default parameter, we present a simple procedure that can be used to determine reasonable default parameters. We apply our methods in a benchmark study using 59 datasets from OpenML. Our results show that leaving particular hyperparameters at their default value is non-inferior to tuning these hyperparameters. In some cases, leaving the hyperparameter at its default value even outperforms tuning it using a search procedure with a limited number of iterations.