Methoden vergelijken

	Specificiteit	Gebalanceerde nauwkeurigheid	Matthews Correlatiecoëfficiënt	Precisie
Vakgebied	Modelevaluatie	Modelevaluatie	Modelevaluatie	Modelevaluatie
Familie	MCDM	MCDM	MCDM	MCDM
Jaar van ontstaan	20th century	2010	1975	20th century
Grondlegger	Historical statistical foundations	Brodersen, Ong, Stephan, and Buhmann	Brian W. Matthews	Historical statistical foundations
Type	Evaluation metric	Evaluation metric	Evaluation metric	Evaluation metric
Oorspronkelijke bron	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.	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.	Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874.
Aliassen	True Negative Rate, TNR	Average Recall, Equal-weight Average Sensitivity	Phi Coefficient, Binary Classification Correlation	Positive Predictive Value, PPV
Verwant	5	5	5	5
Samenvatting	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 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.	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.