方法对比

	准确率	精确率	召回率（灵敏度）
领域	模型评估	模型评估	模型评估
方法族	MCDM	MCDM	MCDM
起源年份	20th century	20th century	20th century
提出者	Historical statistical foundations	Historical statistical foundations	Historical statistical foundations
类型	Evaluation metric	Evaluation metric	Evaluation metric
开创性文献	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.
别名	Overall Accuracy, Correct Classification Rate	Positive Predictive Value, PPV	Sensitivity, True Positive Rate, TPR
相关	5	5	5
摘要	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.