New Interpretable Model Handles Feature Interactions in Tabular Data.

Interpretable machine learning models for tabular data often struggle to account for interactions between features, as they typically rely on sparse, individually screened variables. This can lead to overlooking predictive power that only emerges when features are considered together. Researchers have developed IAIML (Interaction Aware Interpretable Machine Learning) to overcome this challenge. IAIML employs three coordinated mechanisms: adaptive per-feature discretization, a finite-grid method for scoring pairwise interactions, and a partitioned budget for explanations. When interactions are detected, the framework either relaxes screening filters to include interaction-supported variables in the pattern search or constructs explicit pair terms for a sparse classifier. Benchmarking across 40 datasets showed IAIML achieving accuracy comparable to gradient-boosted ensembles with significantly fewer explanation components, especially excelling on datasets with strong pairwise interactions.