Discrimination Between Alternative
Binary Response Models
Bruce Ratner, Ph.D.
Logistic regression is a popular technique for classifying individuals into two mutually exclusive and exhaustive categories, for example: buy-not buy or responder-non-responder. It is the workhorse of response modeling as its results are considered the gold standard. Moreover, it is used as the benchmark for assessing the superiority of newer techniques, such as a the GenIQ Model©. In database marketing, response to a prior solicitation is the binary class variable (defined by responder and non-responder), and the logistic regression model is built to classify an individual as either most likely or least likely to respond to a future solicitation. The purpose of this article is to present a discrimination between the statistical logistic regression model and the non-statistical GenIQ Model. GenIQ is as an assumption-free, nonparametric methodology based the machine learning genetic programming paradigm. The genetic-based logistic regression alternative offers a clear advantage over the statistical logistic regression method, whose performance is dependent on theoretical assumptions and data restrictions. The GenIQ Model determines the best set of predictors based on a simultaneous and virtually unbiased assessment of all variables, an achievement not possible with current statistical logistic regression. For an eye-opening preview of the 9-step modeling process of GenIQ, click here.
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