Intrinsic Meaning of Shapley Values in Regression

Abstract

SHAP is a measurement based on Shapley values and has been used widely in machine-learning regressions. In the paper, I describe the intrinsic meaning of SHAP values and I propose that the SHAP was a better measurement for the performance evaluation of a company in the same industry, compared with a raw variable value such as ROE. In my regression analysis of company performance, I found that a linear relationship appeared between the target values and the SHAP values of the predictor variables, even when there was no linear relationship between the target values and the raw predictor values. This visualization of the relationships made us notice the intrinsic meaning and potential of SHAP values. In the SHAP calculation process, through each company's characteristics, how effective a predictor value works to increase the target value within the company is evaluated. The utility of the predictor depends on the individual company's characteristics. Because the individual company's characteristics are used as the characteristic function, the linear relationship could be extracted.