How can CART complement other data mining packages and/or suites?
A13. CART is an excellent pre-processing complement to data mining packages, such as SAS®. In the first stage of a data mining project, CART can extract the most important variables from a very large list of potential predictors. Focusing on the top variables from the CART model can significantly speed up neural networks and other data mining techniques. For neural nets in particular, CART bypasses “noise” and irrelevant variables, quickly and effectively selecting the best variables for input. The result is significant reductions in neural-net training speeds and more accurate and robust neural networks. In addition, the CART outputs, or “predicted values,” can be used as inputs to the neural net. CART can also be used to: • establish performance benchmarks; • detect important interactions that should be included in statistical models; and • impute values for variables with missing values.
