Tree-like Pairwise Interaction Networks
This work addresses the problem of interpretable predictive modeling in domains like insurance, though it is incremental as it builds on existing interaction models.
The paper tackles the challenge of modeling feature interactions in tabular data, such as for insurance pricing, by proposing the Tree-like Pairwise Interaction Network (PIN), which outperforms benchmarks in predictive accuracy on the French motor insurance dataset while offering interpretability.
Modeling feature interactions in tabular data remains a key challenge in predictive modeling, for example, as used for insurance pricing. This paper proposes the Tree-like Pairwise Interaction Network (PIN), a novel neural network architecture that explicitly captures pairwise feature interactions through a shared feed-forward neural network architecture that mimics the structure of decision trees. PIN enables intrinsic interpretability by design, allowing for direct inspection of interaction effects. Moreover, it allows for efficient SHapley's Additive exPlanation (SHAP) computations because it only involves pairwise interactions. We highlight connections between PIN and established models such as GA2Ms, gradient boosting machines, and graph neural networks. Empirical results on the popular French motor insurance dataset show that PIN outperforms both traditional and modern neural networks benchmarks in predictive accuracy, while also providing insight into how features interact with each another and how they contribute to the predictions.