方法对比

	准确率	Matthews Correlation Coefficient	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	1975	20th century
提出者	Historical statistical foundations	Brian W. Matthews	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.	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	Overall Accuracy, Correct Classification Rate	Phi Coefficient, Binary Classification Correlation	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.	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.