High Per Parameter: A Large-Scale Study of Hyperparameter Tuning for Machine Learning Algorithms
This provides practical guidance for ML practitioners on when hyperparameter tuning is worthwhile, though it is incremental as it builds on prior smaller-scale studies.
The study conducted a large-scale investigation of hyperparameter tuning across 26 ML algorithms and 250 datasets, concluding that on average, tuning yields limited gains, but some algorithms and datasets show significant improvements, with a ranking system developed to guide practitioners.
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. But 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, this latter being truer for some algorithms than others. 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 may serve ML practitioners at large.