Comparar métodos

	Precisión equilibrada	Matriz de confusión	Puntuación F1
Campo	Evaluación de modelos	Evaluación de modelos	Evaluación de modelos
Familia	MCDM	MCDM	MCDM
Año de origen	2010	20th century	1979
Autor original	Brodersen, Ong, Stephan, and Buhmann	Statistical foundations	C. J. van Rijsbergen
Tipo	Evaluation metric	Evaluation visualization	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.	Everitt, B. S., & Hothorn, T. (2005). A Handbook of Statistical Analyses Using R. Chapman and Hall/CRC.	van Rijsbergen, C. J. (1979). Information Retrieval (2nd ed.). Butterworth-Heinemann.
Alias	Average Recall, Equal-weight Average Sensitivity	Error Matrix, Contingency Table	F-measure, Harmonic Mean
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.	The confusion matrix is a table that displays the counts of true positives, true negatives, false positives, and false negatives. It provides a complete picture of where a classifier makes correct and incorrect predictions, enabling calculation of all other classification metrics.	The F1-score is the harmonic mean of precision and recall, providing a single metric that balances both concerns. It was introduced by van Rijsbergen in information retrieval and has become a standard metric for evaluating classification models where both precision and recall are important.