  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.
