Compară metode

	Acuratețe	Acuratețe Echilibrată	Matrice de confuzie	Precizie
Domeniu	Evaluarea modelelor	Evaluarea modelelor	Evaluarea modelelor	Evaluarea modelelor
Familie	MCDM	MCDM	MCDM	MCDM
Anul apariției	20th century	2010	20th century	20th century
Autorul original	Historical statistical foundations	Brodersen, Ong, Stephan, and Buhmann	Statistical foundations	Historical statistical foundations
Tip	Evaluation metric	Evaluation metric	Evaluation visualization	Evaluation metric
Sursa seminală	Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874.	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.
Denumiri alternative	Overall Accuracy, Correct Classification Rate	Average Recall, Equal-weight Average Sensitivity	Error Matrix, Contingency Table	Positive Predictive Value, PPV
Înrudite	5	5	5	5
Rezumat	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.	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.