Porovnat metody

	AUC přesnosti a úplnosti (Precision-Recall AUC)	Přesnost	F1-skóre	Citlivost (senzitivita)
Obor	Hodnocení modelů	Hodnocení modelů	Hodnocení modelů	Hodnocení modelů
Rodina	MCDM	MCDM	MCDM	MCDM
Rok vzniku	2006	20th century	1979	20th century
Tvůrce	Davis and Goadrich	Historical statistical foundations	C. J. van Rijsbergen	Historical statistical foundations
Typ	Evaluation metric	Evaluation metric	Evaluation metric	Evaluation metric
Původní zdroj	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.	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.
Další názvy	PR AUC, PR Curve	Overall Accuracy, Correct Classification Rate	F-measure, Harmonic Mean	Sensitivity, True Positive Rate, TPR
Příbuzné	4	5	5	5
Shrnutí	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.	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.	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.