For a presented case, a Bayesian network classifier in essence
posterior probability distribution over its class variable. Based
distribution, the classifier's classification function returns a single, determinate class value and thereby hides the uncertainty involved. To provide
reliable decision support, however, the classifier should be able to convey indecisiveness if the posterior distribution computed for the case does not
clearly favour one class value over another. In this paper we present an approach for this purpose, and introduce new measures to capture the performance and practicability of such classifiers.