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 introduce a conditional HPI for top-performing regions and derive a closed-form estimator that accurately reflects conditional activation and domain changes. Experiments show that naive adaptations of existing HPI estimators yield misleading or uninterpretable importances in conditional settings, whereas condPED-ANOVA consistently provides meaningful importances that reflect the underlying conditional structure. Our code is publicly available at https://github.com/kAIto47802/condPED-ANOVA.

翻译：本文提出条件PED-ANOVA（condPED-ANOVA），这是一个用于估计条件搜索空间中超参数重要性（HPI）的原则性框架。在条件搜索空间中，某个超参数的存在与否或其定义域可能依赖于其他超参数。尽管原始PED-ANOVA提供了一种快速有效的方法来估计搜索空间最优性能区域内的HPI，但它假设了一个固定的、无条件的搜索空间，因此无法正确处理条件超参数。为解决这一问题，我们针对最优性能区域引入了条件HPI，并推导出一个闭式估计器，该估计器能准确反映条件激活与定义域变化。实验表明，在条件设置下，对现有HPI估计器的简单适配会产生误导性或难以解释的重要性评估，而condPED-ANOVA始终能提供反映底层条件结构的有意义的重要性评估。我们的代码公开于 https://github.com/kAIto47802/condPED-ANOVA。