The area under the ROC curve (AUC-ROC) is a performance measurement for classification models, quantifying the ability of a model to distinguish between classes. AUC values range from 0 to 1, where a value of 0.5 indicates no discrimination ability, while a value of 1 signifies perfect classification. Understanding AUC-ROC is crucial for evaluating models, particularly in scenarios like few-shot and zero-shot learning where data is limited or not readily available.
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