Model selection for neural network classification

Classification rates on out-of-sample predictions can often be improved thr ough the use of model selection when fitting a model on the training data. Using correlated predictors or fitting a model of too high a dimensionality can lead to overfitting, which in turn leads to poor out-of-sample perform ance. I will discuss methodology using the Bayesian Information Criterion ( BIC) of Schwarz (1978) that can search over large model spaces and find app ropriate models that reduce the danger of overfitting. The methodology can be interpreted as either a frequentist method with a Bayesian inspiration o r as a Bayesian method based on noninformative priors.