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Error rate refers to a measure of the degree of prediction error of a model made with respect to the true model.
The term error rate is often applied in the context of classification models. In this context, error rate = P(λ(X)≠Y ), where XY is a joint distribution and the classification model λ is a function X → Y. Sometimes this quantity is expressed as a percentage rather than a value between 0.0 and 1.0.
Two common measures of error rate for regression models are mean squared errorand mean absolute error.
The error rate of a model is often assessed or estimated by applying it to test data for which the class labels (Y values) are known. The error rate of a classifier on test data may be calculated as number of incorrectly classified objects/total number of objects. Alternatively, a smoothing function may be applied, such as a Laplace estimate or an m-estimate.
Error rate is directly related to accuracy, such that error rate = 1.0 − accuracy(or when...
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Ting, K.M. (2011). Error Rate. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-30164-8_262
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DOI: https://doi.org/10.1007/978-0-387-30164-8_262
Publisher Name: Springer, Boston, MA
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