Skip to Main Content
In an optimum pattern-recognition system the error rate is determined by the reject function. This correspondence describes how this property may be exploited to provide quantitative tests of model validity using unclassified test samples. These tests are basically goodness-of-fit tests for a function of the observations. One of these tests is shown to provide an improved estimate of error in Monte Carlo studies of complex systems. Results are given for normal distributions when parameters are estimated. In this case error estimates obtained from the empirical reject rate underestimate the actual error and performance depends on the ratio of design samples to dimension.