2024/08/05 by Anton Geburek, Geburek, Anton, Neeratyoy Mallik +7 · 1 citation
Computer Science · Decision Sciences · Engineering · #Advanced Adaptive Filtering Techniques #FOS: Computer and information sciences #Machine Learning (cs.LG) #Simulation Techniques and Applications #Wireless Sensor Networks for Data Analysis
paper · pdf · doi:10.48550/arxiv.2408.02533
openalex publication_date 2024/08/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/30
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.