השוואת שיטות

	דיוק מאוזן	מדד F1	סגוליות (Specificity)
תחום	הערכת מודלים	הערכת מודלים	הערכת מודלים
משפחה	MCDM	MCDM	MCDM
שנת המקור	2010	1979	20th century
הוגה השיטה	Brodersen, Ong, Stephan, and Buhmann	C. J. van Rijsbergen	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.	van Rijsbergen, C. J. (1979). Information Retrieval (2nd ed.). Butterworth-Heinemann.	Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874.
כינויים	Average Recall, Equal-weight Average Sensitivity	F-measure, Harmonic Mean	True Negative Rate, TNR
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.	The F1-score is the harmonic mean of precision and recall, providing a single metric that balances both concerns. It was introduced by van Rijsbergen in information retrieval and has become a standard metric for evaluating classification models where both precision and recall are important.	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.