The area under the ROC curve (AUC) is a performance measurement for classification models, specifically in binary classification problems. It quantifies how well a model can distinguish between two classes by summarizing the trade-off between sensitivity (true positive rate) and specificity (1 - false positive rate) across various threshold settings. AUC values range from 0 to 1, where 1 indicates perfect classification and 0.5 suggests no discriminative ability, equivalent to random guessing.
congrats on reading the definition of Area Under the ROC Curve. now let's actually learn it.