Compara mètodes

	Superfície sota la corba Precision-Recall	Recordació (Sensibilitat)
Camp	Avaluació de models	Avaluació de models
Família	MCDM	MCDM
Any d'origen	2006	20th century
Autor original	Davis and Goadrich	Historical statistical foundations
Tipus	Evaluation metric	Evaluation metric
Font seminal	Davis, J., & Goadrich, M. (2006). The relationship between precision-recall and ROC curves. Proceedings of the 23rd International Conference on Machine Learning, 233-240.	Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874.
Àlies	PR AUC, PR Curve	Sensitivity, True Positive Rate, TPR
Relacionats	4	5
Resum	The Precision-Recall Area Under the Curve (PR AUC) is the area under the curve formed by plotting recall on the x-axis and precision on the y-axis. It is particularly useful for evaluating classifiers on imbalanced datasets, where it is often more informative than ROC AUC.	Recall measures the proportion of actual positive cases that were correctly identified by the classifier. It answers the question: 'Of all the cases that were truly positive, how many did we find?' Recall is critical in scenarios where missing positive cases is costly.