The area under the receiver operating characteristic (ROC) curve, often referred to as AUC, is a metric used to evaluate the performance of a binary classification model. It measures the ability of the model to distinguish between positive and negative classes, with a value ranging from 0 to 1, where 1 indicates perfect discrimination and 0.5 represents a model with no discrimination ability, akin to random guessing. AUC provides a single scalar value that summarizes the overall performance of a classifier across all classification thresholds.
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