Vertaile menetelmiä

	Tarkkuus	Tunnistus (herkkyys)
Tieteenala	Mallien arviointi	Mallien arviointi
Menetelmäperhe	MCDM	MCDM
Syntyvuosi	20th century	20th century
Kehittäjä	Historical statistical foundations	Historical statistical foundations
Tyyppi	Evaluation metric	Evaluation metric
Alkuperäislähde	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.
Rinnakkaisnimet	Overall Accuracy, Correct Classification Rate	Sensitivity, True Positive Rate, TPR
Liittyvät	5	5
Tiivistelmä	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.