AI RESEARCH
Conditional PED-ANOVA: Hyperparameter Importance in Hierarchical & Dynamic Search Spaces
arXiv CS.AI
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ArXi:2601.20800v3 Announce Type: replace-cross We propose conditional PED-ANOVA (condPED-ANOVA), a principled framework for estimating hyperparameter importance (HPI) in conditional search spaces, where the presence or domain of a hyperparameter can depend on other hyperparameters. Although the original PED-ANOVA provides a fast and efficient way to estimate HPI within the top-performing regions of the search space, it assumes a fixed, unconditional search space and. therefore. cannot properly handle conditional hyperparameters. To address this, we.