विधियों की तुलना करें

	प्रिसिजन-रिकॉल एयूसी	सटीकता (Precision)
क्षेत्र	मॉडल मूल्यांकन	मॉडल मूल्यांकन
परिवार	MCDM	MCDM
उद्भव वर्ष	2006	20th century
प्रवर्तक	Davis and Goadrich	Historical statistical foundations
प्रकार	Evaluation metric	Evaluation metric
मौलिक स्रोत	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.
उपनाम	PR AUC, PR Curve	Positive Predictive Value, PPV
संबंधित	4	5
सारांश	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.