방법 비교

	혼동 행렬	정확도	재현율 (Recall, 민감도)
분야	모델 평가	모델 평가	모델 평가
계열	MCDM	MCDM	MCDM
기원 연도	20th century	20th century	20th century
창시자	Statistical foundations	Historical statistical foundations	Historical statistical foundations
유형	Evaluation visualization	Evaluation metric	Evaluation metric
원전	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.	Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874.
별칭	Error Matrix, Contingency Table	Overall Accuracy, Correct Classification Rate	Sensitivity, True Positive Rate, TPR
관련	5	5	5
요약	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.	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.