A COMPARISON OF 2 NONPARAMETRIC-ESTIMATION SCHEMES - MARS AND NEURAL NETWORKS

The most popular form of artificial neural networks, feedforward networks with sigmoidal activation functions, and a new statistical technique, multivariate adaptive regression splines (MARS) are compared in terms of both their accuracy in learning different types of functions and their speed. Test problems that have been used for demonstrating the efficacy of each method are used to compare the two methods. Both methods can be classified as nonlinear, nonparametric function estimation techniques. and both show great promise for fitting general nonlinear multivariate functions. We find that MARS is in most cases both more accurate and much faster than neural networks. In addition, MARS is more interpretable due to the choice of basis functions which make up the final predictive model. This suggests that MARS could be used on many of the applications where neural networks are currently being used.