方法对比

	准确率	平衡准确率	精确率
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	2010	20th century
提出者	Historical statistical foundations	Brodersen, Ong, Stephan, and Buhmann	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.	Brodersen, K. H., Ong, C. S., Stephan, K. E., & Buhmann, J. M. (2010). The balanced accuracy and its posterior distribution. 20th International Conference on Pattern Recognition (ICPR), 3121-3124.	Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874.
别名	Overall Accuracy, Correct Classification Rate	Average Recall, Equal-weight Average Sensitivity	Positive Predictive Value, PPV
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.	Balanced accuracy is the average of recall values computed for each class separately. It corrects for class imbalance by giving equal weight to the performance on each class, regardless of class frequency in the dataset.	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.