A bias-variance analysis of ensemble learning for classification (original) (raw)

2016

Abstract

A decomposition of the expected prediction error into bias and variance components is useful when investigating the accuracy of a predictor. However, in classification such a decomposition is not as straightforward as in the case of squared-error loss in regression. As a result various definitions of bias and variance for classification can be found in the literature. In this paper these definitions are reviewed and an empirical study of a particular bias-variance decomposition is presented for ensemble classifiers.

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