Encyclopedia of Machine Learning and Data Mining

2017 Edition
| Editors: Claude Sammut, Geoffrey I. Webb


Reference work entry
DOI: https://doi.org/10.1007/978-1-4899-7687-1_3


Accuracy refers to a measure of the degree to which the predictions of a model matches the reality being modeled. The term accuracy is often applied in the context of  classification models. In this context, accuracy = P(λ(X) = Y ), where XY is a joint distribution and the classification model λ is a function XY. Sometimes, this quantity is expressed as a percentage rather than a value between 0.0 and 1.0.

The accuracy of a model is often assessed or estimated by applying it to test data for which the  labels (Y values) are known. The accuracy of a classifier on test data may be calculated as number of correctly classified objects/total number of objects. Alternatively, a smoothing function may be applied, such as a  Laplace estimate or an m-estimate.

Accuracy is directly related to  error rate, such that accuracy = 1. 0 – error rate (or when expressed as a percentage, accuracy = 100 – error rate).


This is a preview of subscription content, log in to check access.

Copyright information

© Springer Science+Business Media New York 2017