Compara mètodes

	Precisió	Exactitud	Recordació (Sensibilitat)
Camp	Avaluació de models	Avaluació de models	Avaluació de models
Família	MCDM	MCDM	MCDM
Any d'origen	20th century	20th century	20th century
Autor original	Historical statistical foundations	Historical statistical foundations	Historical statistical foundations
Tipus	Evaluation metric	Evaluation metric	Evaluation metric
Font seminal	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.	Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874.
Àlies	Positive Predictive Value, PPV	Overall Accuracy, Correct Classification Rate	Sensitivity, True Positive Rate, TPR
Relacionats	5	5	5
Resum	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.	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.	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.