Compară metode

	Precizie	Acuratețe	Rechemare (Sensibilitate)
Domeniu	Evaluarea modelelor	Evaluarea modelelor	Evaluarea modelelor
Familie	MCDM	MCDM	MCDM
Anul apariției	20th century	20th century	20th century
Autorul original	Historical statistical foundations	Historical statistical foundations	Historical statistical foundations
Tip	Evaluation metric	Evaluation metric	Evaluation metric
Sursa seminală	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.
Denumiri alternative	Positive Predictive Value, PPV	Overall Accuracy, Correct Classification Rate	Sensitivity, True Positive Rate, TPR
Înrudite	5	5	5
Rezumat	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.