Módszerek összehasonlítása

	Pontosság (Accuracy)	Konfúziós mátrix	F1-pontszám
Tudományterület	Modellértékelés	Modellértékelés	Modellértékelés
Módszercsalád	MCDM	MCDM	MCDM
Keletkezés éve	20th century	20th century	1979
Megalkotó	Historical statistical foundations	Statistical foundations	C. J. van Rijsbergen
Típus	Evaluation metric	Evaluation visualization	Evaluation metric
Alapmű	Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874.	Everitt, B. S., & Hothorn, T. (2005). A Handbook of Statistical Analyses Using R. Chapman and Hall/CRC.	van Rijsbergen, C. J. (1979). Information Retrieval (2nd ed.). Butterworth-Heinemann.
Alternatív nevek	Overall Accuracy, Correct Classification Rate	Error Matrix, Contingency Table	F-measure, Harmonic Mean
Kapcsolódó	5	5	5
Összefoglaló	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 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 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.