方法对比

	混淆矩阵	准确率	Matthews Correlation Coefficient	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	20th century	20th century	1975	20th century
提出者	Statistical foundations	Historical statistical foundations	Brian W. Matthews	Historical statistical foundations
类型	Evaluation visualization	Evaluation metric	Evaluation metric	Evaluation metric
开创性文献	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.	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	Error Matrix, Contingency Table	Overall Accuracy, Correct Classification Rate	Phi Coefficient, Binary Classification Correlation	Sensitivity, True Positive Rate, TPR
相关	5	5	5	5
摘要	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.	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.	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.	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.