Linganisha mbinu

	Kiwango cha Uwiano cha Matthews	Usawa wa Usahihi (Balanced Accuracy)	Kumbukumbu (Usikivu)
Nyanja	Tathmini ya Modeli	Tathmini ya Modeli	Tathmini ya Modeli
Familia	MCDM	MCDM	MCDM
Mwaka wa asili	1975	2010	20th century
Mwanzilishi	Brian W. Matthews	Brodersen, Ong, Stephan, and Buhmann	Historical statistical foundations
Aina	Evaluation metric	Evaluation metric	Evaluation metric
Chanzo asilia	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.	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.
Majina mbadala	Phi Coefficient, Binary Classification Correlation	Average Recall, Equal-weight Average Sensitivity	Sensitivity, True Positive Rate, TPR
Zinazohusiana	5	5	5
Muhtasari	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.	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.	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.