  Advanced programmatic hyperparameter optimization (HPO) methods, such as
Bayesian optimization, have high sample efficiency in reproducibly finding
optimal hyperparameter values of machine learning (ML) models. Yet, ML
practitioners often apply less sample-efficient HPO methods, such as grid
search, which often results in under-optimized ML models. As a reason for this
behavior, we suspect practitioners choose HPO methods based on individual
motives, consisting of contextual factors and individual goals. However,
practitioners' motives still need to be clarified, hindering the evaluation of
HPO methods for achieving specific goals and the user-centered development of
HPO tools. To understand practitioners' motives for using specific HPO methods,
we used a mixed-methods approach involving 20 semi-structured interviews and a
survey study with 71 ML experts to gather evidence of the external validity of
the interview results. By presenting six main goals (e.g., improving model
understanding) and 14 contextual factors affecting practitioners' selection of
HPO methods (e.g., available computer resources), our study explains why
practitioners use HPO methods that seem inappropriate at first glance. This
study lays a foundation for designing user-centered and context-adaptive HPO
tools and, thus, linking social and technical research on HPO.
