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(In a past job interview I failed at explaining how tocalculate and interprete ROC curves– so here goes my attempt to fill this knowledge gap.) Think of a regression modelmapping a number of features onto a real number (potentially a probability). The resulting real number can then be mapped on one of two classes, depending on whether this predicted number is greater or lower than some choosable threshold. Let’s take for example a logistic regression and data on the survivorship of the Titanic accident to introduce the relevant concepts which will lead naturally to the ROC (Receiver Operating Characteristic) and its AUC or AUROC (Area Under ROC Curve).