Salīdzināt metodes

	Balansētā precizitāte	Kļūdu matrica	F1-novērtējums	Precizitāte
Nozare	Modeļu novērtēšana	Modeļu novērtēšana	Modeļu novērtēšana	Modeļu novērtēšana
Saime	MCDM	MCDM	MCDM	MCDM
Izcelsmes gads	2010	20th century	1979	20th century
Autors	Brodersen, Ong, Stephan, and Buhmann	Statistical foundations	C. J. van Rijsbergen	Historical statistical foundations
Tips	Evaluation metric	Evaluation visualization	Evaluation metric	Evaluation metric
Pirmavots	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.	Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874.
Citi nosaukumi	Average Recall, Equal-weight Average Sensitivity	Error Matrix, Contingency Table	F-measure, Harmonic Mean	Positive Predictive Value, PPV
Saistītās	5	5	5	5
Kopsavilkums	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.	Precision measures the proportion of positive predictions that were actually correct. It answers the question: 'Of all the cases we predicted as positive, how many were truly positive?' Precision is critical in scenarios where false positives are costly.