방법 비교

	정밀도-재현율 AUC	정밀도(Precision)	재현율 (Recall, 민감도)
분야	모델 평가	모델 평가	모델 평가
계열	MCDM	MCDM	MCDM
기원 연도	2006	20th century	20th century
창시자	Davis and Goadrich	Historical statistical foundations	Historical statistical foundations
유형	Evaluation metric	Evaluation metric	Evaluation metric
원전	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.	Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874.
별칭	PR AUC, PR Curve	Positive Predictive Value, PPV	Sensitivity, True Positive Rate, TPR
관련	4	5	5
요약	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.	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.