方法对比

	准确率	混淆矩阵	F1分数
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	20th century	1979
提出者	Historical statistical foundations	Statistical foundations	C. J. van Rijsbergen
类型	Evaluation metric	Evaluation visualization	Evaluation metric
开创性文献	Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874.	Everitt, B. S., & Hothorn, T. (2005). A Handbook of Statistical Analyses Using R. Chapman and Hall/CRC.	van Rijsbergen, C. J. (1979). Information Retrieval (2nd ed.). Butterworth-Heinemann.
别名	Overall Accuracy, Correct Classification Rate	Error Matrix, Contingency Table	F-measure, Harmonic Mean
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 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.	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.