Precision-Recall AUC
Precision-Recall AUC (Area Under the Curve) is a performance metric used in machine learning and statistics to evaluate binary classification models, particularly in imbalanced datasets. It measures the trade-off between precision (the proportion of true positives among predicted positives) and recall (the proportion of true positives among actual positives) across different classification thresholds, with the AUC summarizing this relationship as a single scalar value. A higher AUC indicates better model performance in distinguishing between positive and negative classes.
Developers should use Precision-Recall AUC when working with imbalanced datasets where the positive class is rare, such as fraud detection, medical diagnosis, or anomaly detection, as it provides a more informative assessment than metrics like ROC-AUC in these scenarios. It is especially valuable for evaluating models where false positives and false negatives have different costs, helping to optimize for high precision or recall based on specific application needs, such as minimizing false alarms in security systems.