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 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. ...
For a test to determine who has a certain disease, a specificity of 100% means that all healthy people are labeled as healthy. 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. 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 tests performance. ...
Sensitivity is not the same as the positive predictive value defined as which is as much a statement about the proportion of actual positives in the population being tested as it is about the test. In information retrieval, positive predictive value is called precision, and sensitivity is known as recall. Information retrieval (IR) is the art and science of searching for information in documents, searching for documents themselves, searching for metadata which describe 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. ...
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 tests performance. ...
Fmeasure can be used as a single measure of performance of the test. The Fmeasure is the harmonic mean of precision and recall: In mathematics, the harmonic mean is one of several methods of calculating an average. ...
A test with a high specificity has a low Type I error. In statistical hypothesis testing, a Type I error consists of rejecting a null hypothesis that is true, in other words finding a result to have statistical significance when this has in fact happened by chance. ...
See also
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. ...
In signal detection theory, a receiver operating characteristic (ROC), also receiver operating curve, is a graphical plot of the sensitivity vs. ...
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 tests performance. ...
In statistics, a result is significant if it is unlikely to have occurred by chance, given that a presumed null hypothesis is true. ...
A false positive, also called false alarm, exists when a test reports, incorrectly, that it has found a signal where none exists in reality. ...
A false negative, also called a miss, exists when a test reports, incorrectly, that a signal was not detected when, in fact, was present. ...
External link  Sensitivity and Specificity  Medical University of South Carolina
