  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.

Machine Learning;Machine Learning