Abstract
In this paper, we explain Shapley value that is widely used to increase explainability of AI-based analysis result, using a concrete example of a prefectural comparison of the birthrate in Japan. Suppose that we conduct the regression with birthrate as the target variable and several predictor variables such as the number of marriages. We did not find that the relationship between the target and the raw predictor variable values. On the other hand, when we use the Shapley value is used instead of the raw predictor value, a stronger correlation can be obtained. This is because the Shapley values are calculated based on the characteristic functions of the individual data (in this case, each prefecture). The structural characteristics of the prefecture vary from prefecture to prefecture, and the effect is different even in the same number of marriages they have. Similarly, in the medical field, the incidence rate is considered to be different even under the same conditions, depending on the characteristics of individual person. The advantage of interpretation by Shapley values is that multiple factors can be examined using characteristic functions and the importance of the factors can be determined based on the structural characteristics of the individual. The intrinsic meaning of Shapley values is explained in this paper in an easy-to-understand way by visualization.
