方法对比

	精确率-召回率曲线下面积	准确率
领域	模型评估	模型评估
方法族	MCDM	MCDM
起源年份	2006	20th century
提出者	Davis and Goadrich	Historical statistical foundations
类型	Evaluation metric	Evaluation metric
开创性文献	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.
别名	PR AUC, PR Curve	Overall Accuracy, Correct Classification Rate
相关	4	5
摘要	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.