विधियों की तुलना करें

	कन्फ्यूजन मैट्रिक्स	विशिष्टता (Specificity)
क्षेत्र	मॉडल मूल्यांकन	मॉडल मूल्यांकन
परिवार	MCDM	MCDM
उद्भव वर्ष	20th century	20th century
प्रवर्तक	Statistical foundations	Historical statistical foundations
प्रकार	Evaluation visualization	Evaluation metric
मौलिक स्रोत	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.
उपनाम	Error Matrix, Contingency Table	True Negative Rate, TNR
संबंधित	5	5
सारांश	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.	Specificity measures the proportion of actual negative cases that were correctly identified as negative by the classifier. It answers the question: 'Of all the cases that were truly negative, how many did we correctly reject?' Specificity is complementary to recall and is essential when false positives are costly.