Sensitivity and Specificity
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Sensitivity and specificity are statistical measures of the performance of a binary classification classification rule|test, also known in statistics as Statistical classification|classification function. Sensitivity (also called Precision and recall#Definition (classification context)|recall rate in some fields) measures the proportion of actual positives which are correctly identified as such (e.g. the percentage of sick people who are correctly identified as having the condition). Specificity measures the proportion of negatives which are correctly identified (e.g. the percentage of healthy people who are correctly identified as not having the condition). These two measures are closely related to the concepts of type I and type II errors. A perfect predictor would be described as 100% sensitive (i.e. predicting all people from the sick group as sick) and 100% specific (i.e. not predicting anyone from the healthy group as sick); however, theoretically any predictor will possess a...
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