High Per Parameter: A Large-Scale Study of Hyperparameter Tuning for Machine Learning Algorithms

• Published in: Algorithms Vol. 15; no. 9; p. 315
• Main Author: Sipper, Moshe
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.09.2022
• Subjects: Accuracy; Algorithms; Analysis; Automation; Classification; Data mining; Datasets; Decision trees; Default; Experiments; hyperparemeters; Machine learning; Optimization; Software packages; Support vector machines; Tuning
• ISSN: 1999-4893; 1999-4893
• DOI: 10.3390/a15090315

Hyperparameters in machine learning (ML) have received a fair amount of attention, and hyperparameter tuning has come to be regarded as an important step in the ML pipeline. However, just how useful is said tuning? While smaller-scale experiments have been previously conducted, herein we carry out a large-scale investigation, specifically one involving 26 ML algorithms, 250 datasets (regression and both binary and multinomial classification), 6 score metrics, and 28,857,600 algorithm runs. Analyzing the results we conclude that for many ML algorithms, we should not expect considerable gains from hyperparameter tuning on average; however, there may be some datasets for which default hyperparameters perform poorly, especially for some algorithms. By defining a single hp_score value, which combines an algorithm’s accumulated statistics, we are able to rank the 26 ML algorithms from those expected to gain the most from hyperparameter tuning to those expected to gain the least. We believe such a study shall serve ML practitioners at large.