Bias-Variance Tradeoff: A Property-Casualty Modeler’s Perspective

By Joshua Brady, Donald R. Brockmeier

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The concept of bias-variance tradeoff provides a mathematical basis for understanding the common modeling problem of under-fitting 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 simula-tion, 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.


Brady, Joshua, and Donald R. Brockmeier, "Bias-Variance Tradeoff: A Property-Casualty Modeler’s Perspective," Variance 13:2, 2021, pp. 207-232.

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Mission Statement

Variance (ISSN 1940-6452) is a peer-reviewed journal published by the Casualty Actuarial Society to disseminate work of interest to casualty actuaries worldwide. The focus of Variance is original practical and theoretical research in casualty actuarial science. Significant survey or similar articles are also considered for publication. Membership in the Casualty Actuarial Society is not a prerequisite for submitting papers to the journal and submissions by non-CAS members is encouraged.