विधियों की तुलना करें

	संतुलित सटीकता (Balanced Accuracy)	कन्फ्यूजन मैट्रिक्स	सटीकता (Precision)
क्षेत्र	मॉडल मूल्यांकन	मॉडल मूल्यांकन	मॉडल मूल्यांकन
परिवार	MCDM	MCDM	MCDM
उद्भव वर्ष	2010	20th century	20th century
प्रवर्तक	Brodersen, Ong, Stephan, and Buhmann	Statistical foundations	Historical statistical foundations
प्रकार	Evaluation metric	Evaluation visualization	Evaluation metric
मौलिक स्रोत	Brodersen, K. H., Ong, C. S., Stephan, K. E., & Buhmann, J. M. (2010). The balanced accuracy and its posterior distribution. 20th International Conference on Pattern Recognition (ICPR), 3121-3124.	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	Average Recall, Equal-weight Average Sensitivity	Error Matrix, Contingency Table	Positive Predictive Value, PPV
संबंधित	5	5	5
सारांश	Balanced accuracy is the average of recall values computed for each class separately. It corrects for class imbalance by giving equal weight to the performance on each class, regardless of class frequency in the dataset.	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.