Bandingkan metode

	Akurasi Seimbang	Akurasi	Koefisien Korelasi Matthews
Bidang	Evaluasi Model	Evaluasi Model	Evaluasi Model
Keluarga	MCDM	MCDM	MCDM
Tahun asal	2010	20th century	1975
Pencetus	Brodersen, Ong, Stephan, and Buhmann	Historical statistical foundations	Brian W. Matthews
Tipe	Evaluation metric	Evaluation metric	Evaluation metric
Sumber perintis	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.
Alias	Average Recall, Equal-weight Average Sensitivity	Overall Accuracy, Correct Classification Rate	Phi Coefficient, Binary Classification Correlation
Terkait	5	5	5
Ringkasan	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.