Comparar métodos

	AUC de Precisão-Revocação	Sensibilidade
Área	Avaliação de modelos	Avaliação de modelos
Família	MCDM	MCDM
Ano de origem	2006	20th century
Autor original	Davis and Goadrich	Historical statistical foundations
Tipo	Evaluation metric	Evaluation metric
Fonte 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.
Outros nomes	PR AUC, PR Curve	Sensitivity, True Positive Rate, TPR
Relacionados	4	5
Resumo	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.