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. Binary classification is the task of classifying the members of a given set of objects into two groups on the basis of whether they have some property or not. ... Flowcharts are often used to represent algorithms. ...

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. In binary testing, e. ...

F-measure can be used as a single measure of performance of the test. The F-measure is the harmonic mean of sensitivity and specificity: In mathematics, the harmonic mean is one of several methods of calculating an average. ...

F = 2 * precision * recall / (precision + recall).

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. One may be faced with the problem of making a definite decision with respect to an uncertain hypothesis which is known only through its observable consequences. ... The power of a statistical test is the probability that the test will reject a false null hypothesis, or in other words that it will not make a Type II error. ... In statistical hypothesis testing, a Type II error consists of failing to reject an invalid null hypothesis (i. ...

In information retrieval, sensitivity is called recall. Information retrieval (IR) is the art and science of searching for information in documents, searching for documents themselves, searching for metadata which describes documents, or searching within databases, whether relational stand alone databases or hypertext networked databases such as the Internet or intranets, for text, sound, images or data. ...

See also

• specificity
• binary classification