方法对比

	平衡准确率	F1分数	Matthews Correlation Coefficient	精确率
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	1975	20th century
提出者	Brodersen, Ong, Stephan, and Buhmann	C. J. van Rijsbergen	Brian W. Matthews	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.	Matthews, B. W. (1975). Comparison of predicted and observed secondary structure of T4 phage lysozyme. Biochimica et Biophysica Acta (BBA)-Protein Structure, 405(2), 442-451.	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	Phi Coefficient, Binary Classification Correlation	Positive Predictive Value, PPV
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.	The Matthews Correlation Coefficient (MCC) is a correlation measure between predicted and actual binary classifications. It ranges from -1 to 1 and is considered one of the most reliable single-score metrics for evaluating binary classifiers, especially on imbalanced datasets.	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.