Data defines the model by dint of genetic programming, producing the best decile table.

Multinomial Logistic Regression: A Model-free Approach
Bruce Ratner, Ph.D.

The binary logistic regression is a popular technique for classifying individuals into two mutually exclusive and exhaustive categories, namely, when the target variable is binary: for example, buy-not buy, or responder-non-responder. The multinomial logistic regression is the appropriate method when the target variable assumes K (greater than 2) unordered categorical values: for example, read, white, and blue. The multinomial logistic regression model is an assumption-full, parametric model in which the model structure (equation form) is pre-specified. The purpose of this article is to present the Genetic Multinomial Logistic Regression (GOMR) as an assumption-free, nonparametric model – model-free where the data defines the predictor variables and the K-1 model equations – based on the machine learning paradigm of genetic programming. Pointedly, the multinomial logistic regression’s untenable restriction of model equations having only one set of coefficients across the K-1 equations is a nonissue in GMLR. Moreover, the GMLR determines the best set of predictor variables based on a simultaneous and virtually unbiased assessment of all variables, an achievement not possible with the statistical multinomial logistic regression.  GMLR is a straightforward extension of the GenIQ Model©, which serves as the model-free alternative to the binary logistic regression.

See companion article A Genetic Logistic Regression Model: A Model-free Approach to Identifying Responders to a CRM Solicitation

For more information about this article, call Bruce Ratner at 516.791.3544 or 1 800 DM STAT-1; or e-mail at
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