Jämför metoder

	Noggrannhet	Förväxlingsmatris	F1-poäng	Precision
Ämnesområde	Modellutvärdering	Modellutvärdering	Modellutvärdering	Modellutvärdering
Familj	MCDM	MCDM	MCDM	MCDM
Ursprungsår	20th century	20th century	1979	20th century
Upphovsperson	Historical statistical foundations	Statistical foundations	C. J. van Rijsbergen	Historical statistical foundations
Typ	Evaluation metric	Evaluation visualization	Evaluation metric	Evaluation metric
Ursprungskälla	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.	Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874.
Alias	Overall Accuracy, Correct Classification Rate	Error Matrix, Contingency Table	F-measure, Harmonic Mean	Positive Predictive Value, PPV
Närliggande	5	5	5	5
Sammanfattning	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.	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.