UniODA vs. ROC Analysis: Computing the “Optimal” Cut-Point

Paul R. Yarnold

Optimal Data Analysis, LLC

Receiver operator characteristic (ROC) analysis is sometimes used to assess the classification accuracy achieved using an ordered attribute to discriminate a dichotomous class variable, and in this context to identify an “optimal” discriminant cutpoint. In ROC analysis the optimal cutpoint corresponds to the threshold value at which distance from the ROC curve to the point representing perfect classification accuracy is minimized. This note discusses the difference between an ROC-defined optimal discriminant threshold, and the optimal cutpoint identified by UniODA that maximizes ESS for the sample. ROC and UniODA methods are illustrated and compared for an application involving prediction of Cesarean delivery.

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