เปรียบเทียบวิธี

	Precision-Recall AUC	ความแม่นยำ	ความแม่นยำ (Precision)	การเรียกคืน (ความไว)
สาขาวิชา	การประเมินแบบจำลอง	การประเมินแบบจำลอง	การประเมินแบบจำลอง	การประเมินแบบจำลอง
ตระกูล	MCDM	MCDM	MCDM	MCDM
ปีกำเนิด	2006	20th century	20th century	20th century
ผู้ริเริ่ม	Davis and Goadrich	Historical statistical foundations	Historical statistical foundations	Historical statistical foundations
ประเภท	Evaluation metric	Evaluation metric	Evaluation metric	Evaluation metric
แหล่งต้นตำรับ	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.
ชื่อเรียกอื่น	PR AUC, PR Curve	Overall Accuracy, Correct Classification Rate	Positive Predictive Value, PPV	Sensitivity, True Positive Rate, TPR
ที่เกี่ยวข้อง	4	5	5	5
สรุป	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.