Vertaile menetelmiä

	Precision-Recall AUC	Tarkkuus
Tieteenala	Mallien arviointi	Mallien arviointi
Menetelmäperhe	MCDM	MCDM
Syntyvuosi	2006	20th century
Kehittäjä	Davis and Goadrich	Historical statistical foundations
Tyyppi	Evaluation metric	Evaluation metric
Alkuperäislähde	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.
Rinnakkaisnimet	PR AUC, PR Curve	Positive Predictive Value, PPV
Liittyvät	4	5
Tiivistelmä	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.