Comparar métodos

	AUC de Precisión-Recall	Exactitud
Campo	Evaluación de modelos	Evaluación de modelos
Familia	MCDM	MCDM
Año de origen	2006	20th century
Autor original	Davis and Goadrich	Historical statistical foundations
Tipo	Evaluation metric	Evaluation metric
Fuente seminal	Davis, J., & Goadrich, M. (2006). The relationship between precision-recall and ROC curves. Proceedings of the 23rd International Conference on Machine Learning, 233-240.	Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874.
Alias	PR AUC, PR Curve	Overall Accuracy, Correct Classification Rate
Relacionados	4	5
Resumen	The Precision-Recall Area Under the Curve (PR AUC) is the area under the curve formed by plotting recall on the x-axis and precision on the y-axis. It is particularly useful for evaluating classifiers on imbalanced datasets, where it is often more informative than ROC AUC.	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.