AUC-ROC

AUC-ROC (Area Under the Receiver Operating Characteristic Curve) is a performance metric used to evaluate the quality of a binary classification model. It measures the ability of the model to distinguish between positive and negative classes by plotting the True Positive Rate (TPR) against the False Positive Rate (FPR) at different classification thresholds. The AUC-ROC score ranges from 0 to 1, where a score of 0.5 indicates a random classifier and a score of 1 indicates a perfect classifier. AUC-ROC is a popular metric because it is insensitive to class imbalance and threshold selection, making it useful for evaluating models on imbalanced datasets or when the optimal threshold is unknown.
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