Linganisha mbinu

	Usawa wa Usahihi (Balanced Accuracy)	Usahihi	Kumbukumbu (Usikivu)
Nyanja	Tathmini ya Modeli	Tathmini ya Modeli	Tathmini ya Modeli
Familia	MCDM	MCDM	MCDM
Mwaka wa asili	2010	20th century	20th century
Mwanzilishi	Brodersen, Ong, Stephan, and Buhmann	Historical statistical foundations	Historical statistical foundations
Aina	Evaluation metric	Evaluation metric	Evaluation metric
Chanzo asilia	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.
Majina mbadala	Average Recall, Equal-weight Average Sensitivity	Overall Accuracy, Correct Classification Rate	Sensitivity, True Positive Rate, TPR
Zinazohusiana	5	5	5
Muhtasari	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.