Methoden vergelijken

	Nauwkeurigheid	F1-score	Precisie	Gevoeligheid (Recall)
Vakgebied	Modelevaluatie	Modelevaluatie	Modelevaluatie	Modelevaluatie
Familie	MCDM	MCDM	MCDM	MCDM
Jaar van ontstaan	20th century	1979	20th century	20th century
Grondlegger	Historical statistical foundations	C. J. van Rijsbergen	Historical statistical foundations	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.	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.	Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874.
Aliassen	Overall Accuracy, Correct Classification Rate	F-measure, Harmonic Mean	Positive Predictive Value, PPV	Sensitivity, True Positive Rate, TPR
Verwant	5	5	5	5
Samenvatting	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 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.	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.