A unifying approach on bias and variance analysis for classification
share
Standard bias and variance (B V) terminologies were originally defined for the regression setting and their extensions to classification have led to several different models / definitions in the literature. In this paper, we aim to provide the link between the commonly used frameworks of Tumer Ghosh (T G) and James. By unifying the two approaches, we relate the B V defined for the 0/1 loss to the standard B V of the boundary distributions given for the squared error loss. The closed form relationships provide a deeper understanding of classification performance, and their use is demonstrated in two case studies.
READ FULL TEXT