Conditional PED-ANOVA: Hyperparameter Importance in Hierarchical & Dynamic Search Spaces
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866917258907877376 |
|---|---|
| author | Baba, Kaito Ozaki, Yoshihiko Watanabe, Shuhei |
| author_facet | Baba, Kaito Ozaki, Yoshihiko Watanabe, Shuhei |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_20800 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Conditional PED-ANOVA: Hyperparameter Importance in Hierarchical & Dynamic Search Spaces Baba, Kaito Ozaki, Yoshihiko Watanabe, Shuhei Machine Learning Artificial Intelligence 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. |
| title | Conditional PED-ANOVA: Hyperparameter Importance in Hierarchical & Dynamic Search Spaces |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2601.20800 |