Сравнение методов

	Precision-Recall AUC	Точность
Область	Оценка моделей	Оценка моделей
Семейство	MCDM	MCDM
Год появления	2006	20th century
Автор метода	Davis and Goadrich	Historical statistical foundations
Тип	Evaluation metric	Evaluation metric
Основополагающий источник	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.
Другие названия	PR AUC, PR Curve	Positive Predictive Value, PPV
Связанные	4	5
Сводка	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.	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.