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Sensitivity (tests)

From Biocrawler, the free encyclopedia.

The sensitivity of a binary classification test or algorithm, such as a blood test to determine if a person has a certain disease, or an automated system to detect faulty products in a factory, is a parameter that expresses something about the test's performance. The sensitivity of such a test is the proportion of those cases having a positive test result of all positive cases (e.g., people with the disease, faulty products) tested.

${\rm sensitivity}=\frac{\rm number\ of\ true\ positives}{{\rm number\ of\ true\ positives}+{\rm number\ of\ false\ negatives}}.$

A sensitivity of 100% means that all sick people or faulty products were recognized as such.

Sensitivity alone does not tell us all about the test, because a 100% sensitivity can be trivially achieved by labeling all test cases positive. Therefore, we also need to know the specificity 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:

F = 2 * p r e c i s i o n * r e c a l l / (p r e c i s i o n + r e c a l l)

In the traditional language of statistical hypothesis testing, the sensitivity of a test is called the statistical power of the test, although the word power in that context has a more general usage that is not applicable in the present context. A sensitive test will have fewer Type II errors.

In information retrieval, sensitivity is called recall.

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