Comparar métodos

	Sensibilidad	Precisión equilibrada	Puntuación F1	Precisión
Campo	Evaluación de modelos	Evaluación de modelos	Evaluación de modelos	Evaluación de modelos
Familia	MCDM	MCDM	MCDM	MCDM
Año de origen	20th century	2010	1979	20th century
Autor original	Historical statistical foundations	Brodersen, Ong, Stephan, and Buhmann	C. J. van Rijsbergen	Historical statistical foundations
Tipo	Evaluation metric	Evaluation metric	Evaluation metric	Evaluation metric
Fuente seminal	Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874.	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.	van Rijsbergen, C. J. (1979). Information Retrieval (2nd ed.). Butterworth-Heinemann.	Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874.
Alias	Sensitivity, True Positive Rate, TPR	Average Recall, Equal-weight Average Sensitivity	F-measure, Harmonic Mean	Positive Predictive Value, PPV
Relacionados	5	5	5	5
Resumen	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.	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 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.	Precision measures the proportion of positive predictions that were actually correct. It answers the question: 'Of all the cases we predicted as positive, how many were truly positive?' Precision is critical in scenarios where false positives are costly.