Encyclopedia of Machine Learning

2010 Edition
| Editors: Claude Sammut, Geoffrey I. Webb

Sensitivity and Specificity

  • Kai Ming Ting
Reference work entry
DOI: https://doi.org/10.1007/978-0-387-30164-8_752


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


Assigned Class






True Positive (TP)

False Negative (FN)


Actual Class


False Positive (FP)

True Negative (TN)

This is a preview of subscription content, log in to check access.

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Kai Ming Ting

There are no affiliations available