The document discusses ROC-AUC curves and how they are used to evaluate classification models. It provides the following key points: - ROC-AUC curves plot the true positive rate against the false positive rate for a classification model at various threshold settings. This shows its ability to distinguish classes. - The area under the ROC curve (AUC) represents the degree of separability between classes, with a higher AUC indicating better performance. An AUC of 1 means perfect classification and 0.5 indicates performance no better than random. - An example is provided to demonstrate how to calculate true positive rate, false positive rate, and plot the ROC curve using a sample classification task with different threshold settings. The curve and AUC are computed