Area Under the Curve (AUC)

The Area Under the Curve (AUC) is a metric commonly used in statistics and machine learning to evaluate the performance of a binary classification model. It represents the ability of the model to distinguish between positive and negative classes by measuring the quality of the model’s predicted probabilities.

The AUC calculates the area under the Receiver Operating Characteristic (ROC) curve, which plots the True Positive Rate (Sensitivity) against the False Positive Rate (1 – Specificity) at various classification thresholds. The ROC curve summarizes the model’s performance across all possible thresholds and provides a visual representation of the trade-off between sensitivity and specificity.

The AUC ranges between 0 and 1, where a value of 0.5 indicates the model’s performance is equivalent to random guessing, and a value of 1 represents a perfect classification model. In general, a higher AUC suggests a better model performance and a better ability to correctly discriminate between positive and negative instances. Therefore, the AUC serves as a reliable measure to compare and select different models or evaluate the overall performance of a single model.