विधियों की तुलना करें

	सटीकता	कन्फ्यूजन मैट्रिक्स	सटीकता (Precision)
क्षेत्र	मॉडल मूल्यांकन	मॉडल मूल्यांकन	मॉडल मूल्यांकन
परिवार	MCDM	MCDM	MCDM
उद्भव वर्ष	20th century	20th century	20th century
प्रवर्तक	Historical statistical foundations	Statistical foundations	Historical statistical foundations
प्रकार	Evaluation metric	Evaluation visualization	Evaluation metric
मौलिक स्रोत	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.	Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874.
उपनाम	Overall Accuracy, Correct Classification Rate	Error Matrix, Contingency Table	Positive Predictive Value, PPV
संबंधित	5	5	5
सारांश	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.	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.