A unifying approach on bias and variance analysis for classification

The analysis is borrowed from the regression setting and aims to decompose the prediction error of a given classifier into the terms of B&V to evaluate their effects on the performance. Therefore, it can help answer questions such as "How can we compare the accuracy of two different types of classifiers?", "What is it that makes stronger classifiers perform well? Is it the reduction in the bias they bring about, or in variance, or both?". Other than being theoretically interesting, the answers to these questions are also meant to provide better classifier design strategies which bring about improved prediction performance. After the initial decomposition of the prediction error into the standard B&V terms in the regression setting by [1], different studies have attempted to carry over this analysis into the classification setting while preserving the meanings of the terms and the additive property of the decomposition.