方法对比

	平衡准确率	混淆矩阵	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	2010	20th century	20th century
提出者	Brodersen, Ong, Stephan, and Buhmann	Statistical foundations	Historical statistical foundations
类型	Evaluation metric	Evaluation visualization	Evaluation metric
开创性文献	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.	Everitt, B. S., & Hothorn, T. (2005). A Handbook of Statistical Analyses Using R. Chapman and Hall/CRC.	Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874.
别名	Average Recall, Equal-weight Average Sensitivity	Error Matrix, Contingency Table	Positive Predictive Value, PPV
相关	5	5	5
摘要	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.	The confusion matrix is a table that displays the counts of true positives, true negatives, false positives, and false negatives. It provides a complete picture of where a classifier makes correct and incorrect predictions, enabling calculation of all other classification metrics.	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.