方法对比

	平衡准确率	F1分数	精确率	召回率（灵敏度）
领域	模型评估	模型评估	模型评估	模型评估
方法族	MCDM	MCDM	MCDM	MCDM
起源年份	2010	1979	20th century	20th century
提出者	Brodersen, Ong, Stephan, and Buhmann	C. J. van Rijsbergen	Historical statistical foundations	Historical statistical foundations
类型	Evaluation metric	Evaluation metric	Evaluation metric	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.	van Rijsbergen, C. J. (1979). Information Retrieval (2nd ed.). Butterworth-Heinemann.	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	Average Recall, Equal-weight Average Sensitivity	F-measure, Harmonic Mean	Positive Predictive Value, PPV	Sensitivity, True Positive Rate, TPR
相关	5	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 F1-score is the harmonic mean of precision and recall, providing a single metric that balances both concerns. It was introduced by van Rijsbergen in information retrieval and has become a standard metric for evaluating classification models where both precision and recall are important.	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.