  The importance of tuning hyperparameters in Machine Learning (ML) and Deep
Learning (DL) is established through empirical research and applications,
evident from the increase in new hyperparameter optimization (HPO) algorithms
and benchmarks steadily added by the community. However, current benchmarking
practices using averaged performance across many datasets may obscure key
differences between HPO methods, especially for pairwise comparisons. In this
work, we apply Linear Mixed-Effect Models-based (LMEMs) significance testing
for post-hoc analysis of HPO benchmarking runs. LMEMs allow flexible and
expressive modeling on the entire experiment data, including information such
as benchmark meta-features, offering deeper insights than current analysis
practices. We demonstrate this through a case study on the PriorBand paper's
experiment data to find insights not reported in the original work.
