เปรียบเทียบวิธี

	Precision-Recall AUC	คะแนน F1	ความแม่นยำ (Precision)	การเรียกคืน (ความไว)
สาขาวิชา	การประเมินแบบจำลอง	การประเมินแบบจำลอง	การประเมินแบบจำลอง	การประเมินแบบจำลอง
ตระกูล	MCDM	MCDM	MCDM	MCDM
ปีกำเนิด	2006	1979	20th century	20th century
ผู้ริเริ่ม	Davis and Goadrich	C. J. van Rijsbergen	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.	van Rijsbergen, C. J. (1979). Information Retrieval (2nd ed.). Butterworth-Heinemann.	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	F-measure, Harmonic Mean	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.	The F1-score is the harmonic mean of precision and recall, providing a single metric that balances both concerns. It was introduced by van Rijsbergen in information retrieval and has become a standard metric for evaluating classification models where both precision and recall are important.	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.