  When using machine learning techniques in decision-making processes, the
interpretability of the models is important. Shapley additive explanation
(SHAP) is one of the most promising interpretation methods for machine learning
models. Interaction effects occur when the effect of one variable depends on
the value of another variable. Even if each variable has little effect on the
outcome, its combination can have an unexpectedly large impact on the outcome.
Understanding interactions is important for understanding machine learning
models; however, naive SHAP analysis cannot distinguish between the main effect
and interaction effects. In this paper, we introduce the Shapley-Taylor index
as an interpretation method for machine learning models using SHAP considering
interaction effects. We apply the method to the cancer cohort data of Kyushu
University Hospital (N=29,080) to analyze what combination of factors
contributes to the risk of colon cancer.
