Practitioner Motives to Use Different Hyperparameter Optimization Methods

Understand

Programmatic hyperparameter optimization (HPO) methods, such as Bayesian optimization and evolutionary algorithms, are highly sample-efficient in identifying optimal hyperparameter configurations for machine learning (ML) models.

• However, practitioners frequently use less efficient methods, such as grid search, which can lead to under-optimized models.
• We suspect this behavior is driven by a range of practitioner-specific motives.
• Practitioner motives, however, still need to be clarified to enhance user-centered development of HPO tools.