قارن الطرق

	مساحة تحت منحنى الدقة-الاستدعاء (PR AUC)	الدقة	الدقة	الاستدعاء (الحساسية)
المجال	تقييم النماذج	تقييم النماذج	تقييم النماذج	تقييم النماذج
العائلة	MCDM	MCDM	MCDM	MCDM
سنة النشأة	2006	20th century	20th century	20th century
صاحب الطريقة	Davis and Goadrich	Historical statistical foundations	Historical statistical foundations	Historical statistical foundations
النوع	Evaluation metric	Evaluation metric	Evaluation metric	Evaluation metric
المصدر التأسيسي	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.	Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874.
الأسماء البديلة	PR AUC, PR Curve	Overall Accuracy, Correct Classification Rate	Positive Predictive Value, PPV	Sensitivity, True Positive Rate, TPR
ذات صلة	4	5	5	5
الملخص	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.	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.