  A great many tools have been developed for supervised classification, ranging
from early methods such as linear discriminant analysis through to modern
developments such as neural networks and support vector machines. A large
number of comparative studies have been conducted in attempts to establish the
relative superiority of these methods. This paper argues that these comparisons
often fail to take into account important aspects of real problems, so that the
apparent superiority of more sophisticated methods may be something of an
illusion. In particular, simple methods typically yield performance almost as
good as more sophisticated methods, to the extent that the difference in
performance may be swamped by other sources of uncertainty that generally are
not considered in the classical supervised classification paradigm.
