Methoden vergleichen

	Konfusionsmatrix	Matthews-Korrelationskoeffizient	Präzision
Fachgebiet	Modellevaluation	Modellevaluation	Modellevaluation
Familie	MCDM	MCDM	MCDM
Entstehungsjahr	20th century	1975	20th century
Urheber	Statistical foundations	Brian W. Matthews	Historical statistical foundations
Typ	Evaluation visualization	Evaluation metric	Evaluation metric
Wegweisende Quelle	Everitt, B. S., & Hothorn, T. (2005). A Handbook of Statistical Analyses Using R. Chapman and Hall/CRC.	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.
Aliasnamen	Error Matrix, Contingency Table	Phi Coefficient, Binary Classification Correlation	Positive Predictive Value, PPV
Verwandt	5	5	5
Zusammenfassung	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 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.