AUC stands for “Area Under the Receiver Operating Characteristic Curve.” AUC is a commonly used metric in machine learning and statistics to evaluate the performance of binary classification models, especially when dealing with imbalanced datasets or situations where the cost of false positives and false negatives is not equal.
The Receiver Operating Characteristic (ROC) curve is a graphical representation of a model’s performance across different discrimination thresholds. It plots the true positive rate (sensitivity) against the false positive rate (1 – specificity) for various threshold values. The AUC is the area under this ROC curve, and it quantifies the overall performance of a classification model regardless of the threshold chosen.
Here’s how AUC is interpreted:
- A model with an AUC of 0.5 has performance equivalent to random chance. It’s not providing meaningful discrimination between classes.
- A model with an AUC between 0.5 and 1 indicates that the model has some degree of predictive power. Higher AUC values indicate better performance.
- A perfect model would have an AUC of 1, meaning it perfectly separates the two classes without any misclassifications.
AUC is a useful metric because it considers the entire range of possible thresholds and provides a single value that summarizes a model’s discriminatory power. It’s particularly valuable when comparing multiple models or evaluating model performance across different levels of class distribution imbalance.