The concept of bias-variance tradeoff provides a mathematical basis for understanding the common modeling problem of underfitting vs. overfitting. While bias-variance tradeoff is a standard topic in machine learning discussions, the terminology and application differ from that of actuarial literature. In this paper we demystify the bias-variance decomposition by providing a detailed foundation for the theory. Basic examples, a simulation, and a connection to credibility theory are provided to help the reader gain an appreciation for the connections between the actuarial and machine learning perspectives for balancing model complexity. In addition, we extend the traditional bias-variance decomposition to the GLM deviance measure. Address for Correspondence: bradymath@gmail.com
Brady et al. (Fri,) studied this question.