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This paper introduces Interaction Aware Interpretable Machine Learning (IAIML), a novel framework designed to enhance the interpretability of classifiers for tabular data by effectively incorporating feature interactions that are often overlooked in traditional methods. By employing adaptive discretization, pairwise interaction scoring, and a partitioned explanation budget, IAIML significantly reduces the number of explanation components required while maintaining competitive predictive performance. Evaluated across 40 datasets, IAIML achieves mean AUC scores comparable to tuned gradient-boosted ensembles, particularly excelling in scenarios with strong pairwise interactions and low marginal signals.
IAIML reveals that leveraging feature interactions can dramatically enhance interpretability without sacrificing predictive power, outperforming traditional methods in complex datasets.
Inherently interpretable classifiers for tabular data typically rely on sparse features, rules, or patterns that users can inspect directly. The marginal feature-screening step common to these methods can discard variables whose predictive value emerges only through joint configurations with other variables. We present Interaction Aware Interpretable Machine Learning (IAIML), a framework that addresses this limitation through three coordinated mechanisms: adaptive per-feature discretization, finite-grid pairwise interaction scoring, and a partitioned explanation budget. Detected interactions are routed through one of two strategies: relaxing the screening filter so that interaction-supported variables enter the pattern search, or constructing explicit pair terms for a sparse downstream classifier. On a 40-dataset panel comprising 24 real-world tabular benchmarks and 16 synthetic interaction stress tests, evaluated under nested cross-validation, IAIML achieves mean AUC within 1.4 points of tuned gradient-boosted ensembles while requiring roughly 14--28 times fewer fitted explanation components. On datasets with strong pairwise interaction structure and low marginal signal, IAIML outperforms all baselines. Among compact interpretable methods, IAIML is comparable to RuleFit in AUC and component count and is less expensive to tune. EBM obtains a small but significant AUC advantage across the full panel, with a substantially larger lookup-table footprint. Performance degrades on datasets requiring higher-order interactions beyond the pairwise scope. Component-isolated ablations confirm that adaptive discretization and interaction-aware admission each contribute incrementally. These results support IAIML as a compact, interaction-aware framework appropriate for settings where bounded explanation size and controlled treatment of feature interactions are design requirements.
