Sensitivity and Specificity

Definition

Sensitivity and specificity are two measures used together in some domains to measure the predictive performance of a classification model or a diagnostic test. For example, to measure the effectiveness of a diagnostic test in the medical domain, sensitivity measures the fraction of people with disease (i.e., positive examples) who have a positive test result; and specificity measures the fraction of people without disease (i.e., negative examples) who have a negative test result. They are defined with reference to a special case of the confusion matrix, with two classes, one designated the positive class, and the other the negative class, as indicated in Table 1.

Sensitivity and Specificity. Table 1 The outcomes of classification into positive and negative classes