Compară metode

	Acuratețe Echilibrată	Matrice de confuzie	Scorul F1
Domeniu	Evaluarea modelelor	Evaluarea modelelor	Evaluarea modelelor
Familie	MCDM	MCDM	MCDM
Anul apariției	2010	20th century	1979
Autorul original	Brodersen, Ong, Stephan, and Buhmann	Statistical foundations	C. J. van Rijsbergen
Tip	Evaluation metric	Evaluation visualization	Evaluation metric
Sursa seminală	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.	Everitt, B. S., & Hothorn, T. (2005). A Handbook of Statistical Analyses Using R. Chapman and Hall/CRC.	van Rijsbergen, C. J. (1979). Information Retrieval (2nd ed.). Butterworth-Heinemann.
Denumiri alternative	Average Recall, Equal-weight Average Sensitivity	Error Matrix, Contingency Table	F-measure, Harmonic Mean
Înrudite	5	5	5
Rezumat	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 confusion matrix is a table that displays the counts of true positives, true negatives, false positives, and false negatives. It provides a complete picture of where a classifier makes correct and incorrect predictions, enabling calculation of all other classification metrics.	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.