Comparar métodos

	Coeficiente de Correlación de Matthews	Precisión equilibrada	Sensibilidad
Campo	Evaluación de modelos	Evaluación de modelos	Evaluación de modelos
Familia	MCDM	MCDM	MCDM
Año de origen	1975	2010	20th century
Autor original	Brian W. Matthews	Brodersen, Ong, Stephan, and Buhmann	Historical statistical foundations
Tipo	Evaluation metric	Evaluation metric	Evaluation metric
Fuente seminal	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.
Alias	Phi Coefficient, Binary Classification Correlation	Average Recall, Equal-weight Average Sensitivity	Sensitivity, True Positive Rate, TPR
Relacionados	5	5	5
Resumen	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.