Exact Functional ANOVA Decomposition for Categorical Inputs Models
Functional ANOVA offers a principled framework for interpretability by decomposing a model's prediction into main effects and higher-order interactions. For independent features, this decomposition is well-defined, strongly linked with SHAP values, and serves as a cornerstone of additive explainability. However, the lack of an explicit closed-form expression for general dependent distributions has forced practitioners to rely on costly sampling-based approximations. We completely resolve this limitation for categorical inputs. By bridging functional analysis with the extension of discrete Fourier analysis, we derive a closed-form decomposition without any assumption. Our formulation is co...
| Authors | Baptiste Ferrere, Nicolas Bousquet, Fabrice Gamboa et al. |
|---|---|
| Primary category | stat.ML |
| Published | 3 March 2026 |
| Last updated at source | 9 June 2026 |
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