Сравнение на методи

	Балансирана точност	F1-резултат	Покритие (Чувствителност)
Област	Оценка на модели	Оценка на модели	Оценка на модели
Семейство	MCDM	MCDM	MCDM
Година на възникване	2010	1979	20th century
Създател	Brodersen, Ong, Stephan, and Buhmann	C. J. van Rijsbergen	Historical statistical foundations
Тип	Evaluation metric	Evaluation metric	Evaluation metric
Основополагащ източник	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.
Други названия	Average Recall, Equal-weight Average Sensitivity	F-measure, Harmonic Mean	Sensitivity, True Positive Rate, TPR
Свързани	5	5	5
Резюме	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.	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.