SAILS: Surrogate-based Analysis of Interactions via Local Effect Smooths
Researchers have introduced SAILS, a new framework for analyzing feature interactions in machine learning models. This model-agnostic approach uses interpretable generalized additive models to understand the functional form of pairwise interactions. SAILS can detect, categorize, and visualize these interactions, offering a more detailed understanding than existing methods. AI
IMPACT Provides a novel method for understanding complex feature interactions in ML models, enhancing interpretability.