Porównaj metody

	Specyficzność	Zrównoważona dokładność	Wynik F1	Współczynnik korelacji Matthews’a
Dziedzina	Ocena modeli	Ocena modeli	Ocena modeli	Ocena modeli
Rodzina	MCDM	MCDM	MCDM	MCDM
Rok powstania	20th century	2010	1979	1975
Twórca	Historical statistical foundations	Brodersen, Ong, Stephan, and Buhmann	C. J. van Rijsbergen	Brian W. Matthews
Typ	Evaluation metric	Evaluation metric	Evaluation metric	Evaluation metric
Źródło pierwotne	Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874.	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.	Matthews, B. W. (1975). Comparison of predicted and observed secondary structure of T4 phage lysozyme. Biochimica et Biophysica Acta (BBA)-Protein Structure, 405(2), 442-451.
Inne nazwy	True Negative Rate, TNR	Average Recall, Equal-weight Average Sensitivity	F-measure, Harmonic Mean	Phi Coefficient, Binary Classification Correlation
Pokrewne	5	5	5	5
Podsumowanie	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.	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.	The Matthews Correlation Coefficient (MCC) is a correlation measure between predicted and actual binary classifications. It ranges from -1 to 1 and is considered one of the most reliable single-score metrics for evaluating binary classifiers, especially on imbalanced datasets.