مقایسهٔ روش‌ها

	مساحت زیر منحنی دقت-فراخوانی	دقت	بازیابی (حساسیت)
حوزه	ارزیابی مدل	ارزیابی مدل	ارزیابی مدل
خانواده	MCDM	MCDM	MCDM
سال پیدایش	2006	20th century	20th century
پدیدآور	Davis and Goadrich	Historical statistical foundations	Historical statistical foundations
نوع	Evaluation metric	Evaluation metric	Evaluation metric
منبع بنیادین	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.
نام‌های دیگر	PR AUC, PR Curve	Overall Accuracy, Correct Classification Rate	Sensitivity, True Positive Rate, TPR
مرتبط	4	5	5
خلاصه	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.	Accuracy is the proportion of correct predictions among the total number of predictions made by a classification model. It is the most intuitive performance metric and measures how often the classifier makes correct predictions overall, regardless of class.	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.