Inequalities for Optimization of Classification Algorithms: A Perspective Motivated by Diagnostic Testing

Motivated by canonical problems in medical diagnostics, we propose and study properties of an objective function that uniformly bounds uncertainties in quantities of interest extracted from classifiers and related data analysis tools. We begin by adopting a set-theoretic perspective to show how two main tasks in diagnostics -- classification and prevalence estimation -- can be recast in terms of a variation on the confusion (or error) matrix boldsymbol rm P typically considered in supervised learning. We then combine arguments from conditional probability with the Gershgorin circle theorem to demonstrate that the largest Gershgorin radius boldsymbol ρm of the matrix mathbb I-boldsymbol rm P (where mathbb I is the identity) yields uniform error bounds for both classification and prevalence estimation. In a two-class setting, boldsymbol ρm is minimized via a measure-theoretic ``water-leveling'' argument that optimizes an appropriately defined partition U generating the matrix boldsymbol rm P. We also consider an example that illustrates the difficulty of generalizing the binary solution to a multi-class setting and deduce relevant properties of the confusion matrix.