Inherently interpretable classifiers for tabular data typically rely on sparse features, rules, or patterns that users can inspect directly. However, 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: Dynamically adjusting discretization strategies based on feature distributions.
- Finite-grid pairwise interaction scoring: Evaluating the interaction effects between pairs of features.
- Partitioned explanation budget: Providing a controlled size of explanations for the model.
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, 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.
Blogger's Review: The IAIML framework demonstrates how introducing interaction awareness can enhance interpretability while maintaining model compactness. This work not only addresses the shortcomings of traditional methods but also provides new insights for future research in interpretable machine learning, especially in applications dealing with complex data structures. Its excellent performance across multiple benchmark datasets indicates significant practical value and applicability.