Neural networks and logistic regression: Part I

A comparative investigation of both logistic regression models and feed-forward neural networks including some extensions is presented and the theoretical features and properties are reviewed and illustrated in two examples.

Abstract

Feed-forward neural networks have recently been applied in situations where an analysis based on the logistic regression model would have been a standard statistical approach; direct comparisons of results, however, are seldomly attempted. We therefore present a comparative investigation of both logistic regression models and feed-forward neural networks including some extensions. The theoretical features and properties are reviewed and illustrated in two examples, also discussing practical problems with their application. In Part II of the paper some further important aspects of approximation, overfitting and model selection are investigated in more detail both analytically and by means of simulation studies.