Сравнение на методи

	Точност	Покритие (Чувствителност)
Област	Оценка на модели	Оценка на модели
Семейство	MCDM	MCDM
Година на възникване	20th century	20th century
Създател	Historical statistical foundations	Historical statistical foundations
Тип	Evaluation metric	Evaluation metric
Основополагащ източник	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.
Други названия	Overall Accuracy, Correct Classification Rate	Sensitivity, True Positive Rate, TPR
Свързани	5	5
Резюме	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.