Optimal thresholding for binary classification applied in credit scoring
Optimal thresholding for binary classification applied in credit scoring

Summary/Abstract: The paper concerns a new method of classifying individuals into two subpopulations and demonstrates the application of this method in credit scoring. Individuals are classified into two subpopulations depending on the duration of a certain phenomenon (e.g., default). The duration may be shorter or longer than a certain fixed value . It is assumed that the variable is not known at the time of classification, so the explanatory continuous predictive marker is used instead. The optimal acceptance threshold for a predictive marker is determined by a time-dependent receiver operating curve (ROC) estimated from a random sample. A typical complexity of time-to-event data is that observations in the sample can be right-censored. Therefore, the estimation is based on a sequential random sampling and the Kaplan-Meier estimator.

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