Methoden vergleichen

	Matthews-Korrelationskoeffizient	Balanced Accuracy	Präzision
Fachgebiet	Modellevaluation	Modellevaluation	Modellevaluation
Familie	MCDM	MCDM	MCDM
Entstehungsjahr	1975	2010	20th century
Urheber	Brian W. Matthews	Brodersen, Ong, Stephan, and Buhmann	Historical statistical foundations
Typ	Evaluation metric	Evaluation metric	Evaluation metric
Wegweisende Quelle	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.	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.	Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874.
Aliasnamen	Phi Coefficient, Binary Classification Correlation	Average Recall, Equal-weight Average Sensitivity	Positive Predictive Value, PPV
Verwandt	5	5	5
Zusammenfassung	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.	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.	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.