提出新方法精准评估条件搜索空间中超参重要性
Conditional PED-ANOVA: Hyperparameter Importance in Hierarchical & Dynamic Search Spaces
- 基于条件结构设计新的超参重要性估计框架
- 在真实条件搜索空间中准确反映超参激活与取值变化
- 适合做超参优化与自动化机器学习的研究者
我们提出条件PED-ANOVA（condPED-ANOVA），一种在条件搜索空间中估算超参数重要性（HPI）的合理框架。传统PED-ANOVA虽能高效估计高性能区域内的超参重要性，但假设搜索空间固定不变，无法处理依赖其他超参存在或取值域的条件超参。为此，我们定义了高性能区域内的条件超参重要性，并推导出闭式估计器，可准确反映条件激活与域变化。实验表明，现有方法的简单扩展在条件设置下会产生误导性或不可解释的重要性结果，而condPED-ANOVA始终提供符合实际条件结构的有意义评估。代码已公开于 https://github.com/kAIto47802/condPED-ANOVA。
原文摘要 · Abstract (English)
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.
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