Krahasoni metodat

	Saktësi e Balancuar	Saktësi	Koeficienti i Korrelacionit Matthews	Kujtesë (ndjeshmëri)
Fusha	Vlerësimi i modeleve	Vlerësimi i modeleve	Vlerësimi i modeleve	Vlerësimi i modeleve
Familja	MCDM	MCDM	MCDM	MCDM
Viti i origjinës	2010	20th century	1975	20th century
Krijuesi	Brodersen, Ong, Stephan, and Buhmann	Historical statistical foundations	Brian W. Matthews	Historical statistical foundations
Lloji	Evaluation metric	Evaluation metric	Evaluation metric	Evaluation metric
Burimi themelues	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.	Matthews, B. W. (1975). Comparison of predicted and observed secondary structure of T4 phage lysozyme. Biochimica et Biophysica Acta (BBA)-Protein Structure, 405(2), 442-451.	Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874.
Emërtime të tjera	Average Recall, Equal-weight Average Sensitivity	Overall Accuracy, Correct Classification Rate	Phi Coefficient, Binary Classification Correlation	Sensitivity, True Positive Rate, TPR
Të lidhura	5	5	5	5
Përmbledhja	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.	The Matthews Correlation Coefficient (MCC) is a correlation measure between predicted and actual binary classifications. It ranges from -1 to 1 and is considered one of the most reliable single-score metrics for evaluating binary classifiers, especially on imbalanced datasets.	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.