Methoden vergleichen

	Precision-Recall AUC	Präzision
Fachgebiet	Modellevaluation	Modellevaluation
Familie	MCDM	MCDM
Entstehungsjahr	2006	20th century
Urheber	Davis and Goadrich	Historical statistical foundations
Typ	Evaluation metric	Evaluation metric
Wegweisende Quelle	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.
Aliasnamen	PR AUC, PR Curve	Positive Predictive Value, PPV
Verwandt	4	5
Zusammenfassung	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.	Precision measures the proportion of positive predictions that were actually correct. It answers the question: 'Of all the cases we predicted as positive, how many were truly positive?' Precision is critical in scenarios where false positives are costly.