Comparar métodos

	Precisión equilibrada	Exactitud	Coeficiente de Correlación de Matthews
Campo	Evaluación de modelos	Evaluación de modelos	Evaluación de modelos
Familia	MCDM	MCDM	MCDM
Año de origen	2010	20th century	1975
Autor original	Brodersen, Ong, Stephan, and Buhmann	Historical statistical foundations	Brian W. Matthews
Tipo	Evaluation metric	Evaluation metric	Evaluation metric
Fuente seminal	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
Relacionados	5	5	5
Resumen	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.