  The convergence of simultaneous and marginal predictive classifiers under
partition exchangeability in supervised classification is obtained. The result
shows the asymptotic convergence of these classifiers under infinite amount of
training or test data, such that after observing umpteen amount of data, the
differences between these classifiers would be negligible. This is an important
result from the practical perspective as under the presence of sufficiently
large amount of data, one can replace the simpler marginal classifier with
computationally more expensive simultaneous one.
