השוואת שיטות

	דיוק מאוזן	דיוק	עֶרְכָּה (רגישות)
תחום	הערכת מודלים	הערכת מודלים	הערכת מודלים
משפחה	MCDM	MCDM	MCDM
שנת המקור	2010	20th century	20th century
הוגה השיטה	Brodersen, Ong, Stephan, and Buhmann	Historical statistical foundations	Historical statistical foundations
סוג	Evaluation metric	Evaluation metric	Evaluation metric
מקור מכונן	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.	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.
כינויים	Average Recall, Equal-weight Average Sensitivity	Overall Accuracy, Correct Classification Rate	Sensitivity, True Positive Rate, TPR
קשורות	5	5	5
תקציר	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.	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.