Constrained Generalized Additive 2 Model with Consideration of High-Order Interactions

TL;DR

This paper introduces CGA2M+, a model that enhances interpretability and prediction accuracy by incorporating monotonicity and higher-order interactions into the Generalized Additive 2 Model, validated through numerical experiments.

Contribution

The paper proposes CGA2M+, a novel extension of GA2M that includes monotonicity constraints and higher-order interactions to improve interpretability and predictive performance.

Findings

CGA2M+ achieves high predictive accuracy.

Incorporating monotonicity improves generalization.

Model maintains interpretability with added complexity.

Abstract

In recent years, machine learning and AI have been introduced in many industrial fields. In fields such as finance, medicine, and autonomous driving, where the inference results of a model may have serious consequences, high interpretability as well as prediction accuracy is required. In this study, we propose CGA2M+, which is based on the Generalized Additive 2 Model (GA2M) and differs from it in two major ways. The first is the introduction of monotonicity. Imposing monotonicity on some functions based on an analyst's knowledge is expected to improve not only interpretability but also generalization performance. The second is the introduction of a higher-order term: given that GA2M considers only second-order interactions, we aim to balance interpretability and prediction accuracy by introducing a higher-order term that can capture higher-order interactions. In this way, we can improve prediction performance without compromising interpretability by applying learning innovation. Numerical experiments showed that the proposed model has high predictive performance and interpretability. Furthermore, we confirmed that generalization performance is improved by introducing monotonicity.