Comparer des méthodes

	Aire sous la courbe Précision-Rappel	Score F1	Précision
Domaine	Évaluation de modèles	Évaluation de modèles	Évaluation de modèles
Famille	MCDM	MCDM	MCDM
Année d'origine	2006	1979	20th century
Auteur d'origine	Davis and Goadrich	C. J. van Rijsbergen	Historical statistical foundations
Type	Evaluation metric	Evaluation metric	Evaluation metric
Source fondatrice	Davis, J., & Goadrich, M. (2006). The relationship between precision-recall and ROC curves. Proceedings of the 23rd International Conference on Machine Learning, 233-240.	van Rijsbergen, C. J. (1979). Information Retrieval (2nd ed.). Butterworth-Heinemann.	Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874.
Alias	PR AUC, PR Curve	F-measure, Harmonic Mean	Positive Predictive Value, PPV
Apparentées	4	5	5
Résumé	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.	The F1-score is the harmonic mean of precision and recall, providing a single metric that balances both concerns. It was introduced by van Rijsbergen in information retrieval and has become a standard metric for evaluating classification models where both precision and recall are important.	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.