Methoden vergleichen

	Konfusionsmatrix	Genauigkeit	Matthews-Korrelationskoeffizient	Präzision	Recall (Sensitivität)
Fachgebiet	Modellevaluation	Modellevaluation	Modellevaluation	Modellevaluation	Modellevaluation
Familie	MCDM	MCDM	MCDM	MCDM	MCDM
Entstehungsjahr	20th century	20th century	1975	20th century	20th century
Urheber	Statistical foundations	Historical statistical foundations	Brian W. Matthews	Historical statistical foundations	Historical statistical foundations
Typ	Evaluation visualization	Evaluation metric	Evaluation metric	Evaluation metric	Evaluation metric
Wegweisende Quelle	Everitt, B. S., & Hothorn, T. (2005). A Handbook of Statistical Analyses Using R. Chapman and Hall/CRC.	Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874.	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.	Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874.
Aliasnamen	Error Matrix, Contingency Table	Overall Accuracy, Correct Classification Rate	Phi Coefficient, Binary Classification Correlation	Positive Predictive Value, PPV	Sensitivity, True Positive Rate, TPR
Verwandt	5	5	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.	Accuracy is the proportion of correct predictions among the total number of predictions made by a classification model. It is the most intuitive performance metric and measures how often the classifier makes correct predictions overall, regardless of class.	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.	Recall measures the proportion of actual positive cases that were correctly identified by the classifier. It answers the question: 'Of all the cases that were truly positive, how many did we find?' Recall is critical in scenarios where missing positive cases is costly.