Neural Networks Demystified

Data Mining & Neural Networks (narrow topic or advanced); This paper will introduce the neural network technique of analyzing data as a generalization of more familiar linear models such as linear regression. The reader is introduced to the traditional explanation of neural networks as being modeled on the functioning of neurons in the brain. Then a comparison is made of the structure and function of neural networks to that of linear models that the reader is more familiar with. The paper will then show that back propagation neural networks with a single hidden layer are universal function approximators. The paper will also compare neural networks to procedures such as Factor Analysis which perform dimension reduction. The application of both the neural network method and classical statistical procedures to insurance problems such as the prediction of frequencies and severities is illustrated. One key criticism of neural networks is that they are a "black box". Data goes into the "black box" and a prediction comes out of it, but the nature of the relationship between independent and dependent variables is usually not revealed. Several methods for interpreting the results of a neural network analysis, including a procedure for visualizing the form of the fitted function will be presented.