Area Under the Curve (AUC) is a performance measurement for classification models, particularly in binary classification, that evaluates the trade-off between sensitivity and specificity across different threshold settings. AUC quantifies how well a model distinguishes between positive and negative classes, with a higher AUC indicating better model performance. This metric is crucial for understanding the effectiveness of predictive models, especially in contexts where precision and recall are vital for decision-making.
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