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Cutoff Values for Two-Group Classification Using Neural Networks

Statistical classification is to assign an object to an appropriate class. The assignment is made by comparing a score based on a set of attributes describing the object and a cutoff value. Bayesian methods, which are the bases for most classical statistical methods including linear discriminant analysis, use the a priori (or the prior) probability distribution to determine the cutoff value. The prior probabilities are equal to the proportions of the classes in a finite population. The predicted values of neural networks approximate the posterior probabilities when the network architecture and sample size are large. Thus, 0.5 will be the appropriate cutoff value in a two-group classification problem. Yet for a practitioner with a relatively small dataset, 0.5 may not be appropriate. This study illustrates with a data set obtained from AT&T that 0.5 is still the appropriate cutoff value to use when the sample size is relatively small.