AUC-ROC

AUC-ROC (Area Under the Curve - Receiver Operating Characteristic) is a metric used to evaluate how well a model can distinguish between different outcomes, such as predicting whether an email is spam or not. The ROC curve plots the true positive rate (how many correct positives) against the false positive rate (incorrect positives) at various threshold settings. The AUC measures the overall ability of the model to rank positive cases higher than negative ones. A value closer to 1 indicates excellent discrimination, while 0.5 suggests no better than random guessing. It helps assess the effectiveness of classification models across different thresholds.