SAILS: Surrogate-based Analysis of Interactions via Local Effect Smooths
Feature interactions drive much of the predictive power of machine learning models, yet existing explanation methods only detect and quantify interactions without revealing their functional form, or visualize only restricted interaction types. We propose Surrogate-based Analysis of Interactions via Local Effect Smooths (SAILS), a model-agnostic framework that analyzes pairwise interactions through generalized additive model (GAM) surrogates fitted to the local effects of a black-box model. For each interval of a feature of interest, the surrogate smooth terms isolate the interaction components on derivative level, enabling (i) interaction detection through a heuristic derived from signifi...
| Authors | Timo Heiß, Julia Herbinger, Bernd Bischl et al. |
|---|---|
| Primary category | stat.ML |
| Published | 8 June 2026 |
| Last updated at source | 13 September 2026 |
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