Some days ago, I learned in a lecture that the intersection of Sensitivity and Specificity provides an optimal compromise for choosing a classification threshold for logit or probit models. However, no one told me in which sense it is optimal. Is there some criterion which is minimized or maximized by doing it? I think there are several different approaches for choosing a threshold. So, I doubt the intersection of Sensitivity and Specificity is the only one.
This overall approach is inconsistent with the theory of optimum decision making. The goal of a logit or probit model is to accurately estimate the probability of an event - nothing more, nothing less. Risk estimation nicely avoids the multitude of problems that arise from seeking thresholds, and avoids the use of improper accuracy scoring rules such as sensitivity and specificity.