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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.
Sensitivity alone does not tell us all about the test, because a 100% sensitivity can be trivially achieved by labeling all test cases positive.
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.
Sensitivity analysis (SA), broadly defined, is the investigation of these potential changes and errors and their impacts on conclusions to be drawn from the model.
Sensitivity analysis (SA), broadly defined, is the investigation of these potential changes and errors and their impacts on conclusions to be drawn from the model (e.g.
A sensitivity index is a number calculated by a defined procedure which gives information about the relative sensitivity of results to different parameters of the model.
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