Specificity
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In binary testing, e.g. a medical diagnostic test for a certain disease, specificity is the proportion of true negatives of all the negative samples tested, that is
- <math>{\rm specificity}=\frac{\rm number\ of\ true\ negatives}{{\rm number\ of\ true\ negatives}+{\rm number\ of\ false\ positives}}<math>
In information retrieval, specificity is called precision.
For a test to determine who has a certain disease, a specificity of 100% means that all people labeled as sick are actually sick.
Specificity alone does not tell us all about the test, because a 100% specificity can be trivially achieved by labeling all test cases negative. Therefore, we also need to know the sensitivity of the test.
F-measure can be used as a single measure of performance of the test. The F-measure is the geometric mean of sensitivity and specificity:
- <math>F = 2 * precision * recall / (precision + recall)<math>.
A test with a high specificity has a low Type I error.
Sensitivity is not the same as the positive predictive value defined as
- <math>\frac{\rm number\ of\ true\ positives}{{\rm number\ of\ true\ positives}+{\rm number\ of\ false\ positives}}<math>
which is as much a statement about the proportion of actual positives in the population being tested as it is about the test.
See also
- binary classification
- receiver operating characteristic
- sensitivity (tests)
- statistical significance
External link
- Sensitivity and Specificity - Medical University of South Carolina (http://www.musc.edu/dc/icrebm/sensitivity.html)