Sammenlign metoder

	Balanceret nøjagtighed	F1-score	Specificitet
Fagområde	Modelevaluering	Modelevaluering	Modelevaluering
Familie	MCDM	MCDM	MCDM
Oprindelsesår	2010	1979	20th century
Ophavsperson	Brodersen, Ong, Stephan, and Buhmann	C. J. van Rijsbergen	Historical statistical foundations
Type	Evaluation metric	Evaluation metric	Evaluation metric
Oprindelig kilde	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.
Aliasser	Average Recall, Equal-weight Average Sensitivity	F-measure, Harmonic Mean	True Negative Rate, TNR
Relaterede	5	5	5
Resumé	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.	Specificity measures the proportion of actual negative cases that were correctly identified as negative by the classifier. It answers the question: 'Of all the cases that were truly negative, how many did we correctly reject?' Specificity is complementary to recall and is essential when false positives are costly.