AUC
A single scalar metric representing the complete two-dimensional area underneath the Receiver Operating Characteristic curve of a classification model.
Think of It Like This
Like grading a metal detector not just on how many coins it finds, but how well it performs across every possible sensitivity dial setting you could use.
AUC measures how well a model can distinguish between positive and negative classes across all possible probability thresholds, unlike accuracy which only evaluates a single cutoff. An AUC of 1.0 means perfect separation, while 0.5 indicates random guessing. This makes it an incredibly robust evaluation metric for datasets with severe class imbalances.