An error rate comparison of classification methods with continuous explanatory variables

In the fields of statistics and computer science a wide variety of methodologies exist for solving the traditional classification problem. This study will compare the error rates for various methods under a variety of conditions when the explanatory variables are continuous. The methods under considerations are neural networks, classical discriminant analysis, and two different approaches to decision trees. Training and testing sets are utilized to estimate the error rates of these methods for different numbers of sample sizes, number of explanatory variables, and the number of classes in the dependent variable. These error rates will be used to draw generalized conclusions about the relative efficiencies of the techniques.