Can You Predict the Future?
While there clearly are many differences that distinguish regression from neural "nets," an unambiguous distinction is the number of weights and associated independent variables contained in the model architectures. Because regression modeling typically contains five to 15 weights, the analyst may have to place added emphasis on assuring that the variables he or she selects are the right ones.
Of course, these "right" ones may include appropriate recodes or transformations. If these transformations are not properly constructed, a best set of final-model variables may not be available. The regression tool will not discern these recodes automatically.
Neural networks, however, may sense intricate interactions among the predictors without having to worry about transforming the inputs or independent variables. If we refer back to our binning example, where we categorized age, a neural network may not need this binning transformation to take place. Rather, it may be able to use actual age as the predictor. It may be able to discover the most appropriate binning classification for each particular variable. While regression modeling may also use actual age, for example, without binning, due diligence requires the analyst to evaluate whether or not binning or some other transformation is required. In regression, the modeler may have to determine how to create these "bins." In neural networks, as part of the nonlinear processing, the hidden layer may take care of that for the analyst.
While the neural network approach is adept at identifying relationships, this may not always be a plus. Because these relationships are uncovered as a result of the black box part of the process, it may not be practical to secure a lucid clarification as to how and why the predictors are being incorporated into the model. Yet, many marketers demand to know what is going on. It may not be possible to explain the inner workings of the neural net model to marketing managers.