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In statistical classification, the Bayes error rate is the lowest possible error rate for a given class of classifier.

A number of approaches to the estimation of the Bayes error rate exist. One method seeks to obtain analytical bounds which are inherently dependent on distribution parameters, and hence difficult to estimate. Another approach focuses on class densities, while yet another method combines and compares various classifiers.

The Bayes error rate finds important use in the study of patterns and machine learning techniques.

Error Determination

In terms of machine learning and pattern classification, the data set can be discretely divided into 2 or more classes. Each element of the dataset is called an instance and the class it belongs to is called the lable. The bayes error rate of the dataset classifier is probability of the classifier to incorrectly classify an instance. For an n-multiclass classifier, the bayes error rate may be calculated as follows :

$\sum _{i=1}^{i=n}\textstyle \int \limits _{x\in H_{i}}P(C_{i}|x)p(x)\,dx$

Where x is an instance, C is the class into which an instance is classified, H$_{i}$ is the area/region that the classifier function h classifies as C$_{i}$
A bayes error is non-zero if the distributions of the instances overlap, i.e. a certain instance x can wear more than one lable.

See also

• Naive Bayes classifier

References

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