Which metric is generally most appropriate for comparing and evaluating classification models against each other?

AUC (Area Under the ROC Curve) is generally the most appropriate metric for comparing and evaluating classification models because it provides a single scalar value that summarizes the model's performance across all possible classification thresholds. It represents the probability that a randomly chosen positive instance will be ranked higher than a randomly chosen negative instance. Recall (sensitivity) measures the proportion of actual positives correctly identified, but doesn't consider false positives. Misclassification rate (accuracy) can be misleading with imbalanced datasets. MAPE is a metric used for regression problems, not classification.