In the context of machine learning, what does AUC stand for?

AUC stands for Area Under the Curve. It is a performance measurement for classification models at various thresholds. Specifically, AUC refers to the area under the Receiver Operating Characteristic (ROC) curve, which plots the true positive rate against the false positive rate as the threshold for classification changes.

The AUC provides a single scalar value that summarizes the model's ability to discriminate between positive and negative classes across all classification thresholds. AUC values range from 0 to 1, where a value of 0.5 indicates a model with no discrimination power (similar to random guessing), while a value of 1 indicates perfect discrimination between the classes.

Understanding AUC is crucial for evaluating model performance, especially in binary classification tasks, as it helps in comparing the trade-offs between sensitivity and specificity across different models or configurations.