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

Logistic Regression versus Machine Learning Regression
 Bruce Ratner, Ph.D.

The statistical paradigm for response modeling is: The data analyst fits the data to the presumedly true logistic regression model (LRM), whose form (equation) is the sum of weighted predictor variables. The weights (better known as regression coefficients) are the main appeal of the statistical paradigm, as they provide the key to interpreting what the equation means. The well-established LRM variable selection methodology, which identifies the predictor variables for the LRM, is the inherent weakness in the statistical paradigm. The variable selection is exclusive of the data analyst's will and ability for constructing new variables with potential predictive power (data mining).

The antithetical machine learning (ML) paradigm is: The data suggests the "true" model form (a computer program), as the ML automatically data mines for new variables, performs variable selection, and then specifies the model equation without being explicitly programmed. The strengths of the ML paradigm are its flexibility within a nonparametric, assumption-free openwork that accommodates big data, and its serviceability as a data mining tool. The weakness in the ML paradigm is the difficulty in interpreting the abstruse computer program; this surely has accounted for the limited use of ML methods.

The purpose of this article is to present the most compelling illustration of Logistic Regression versus Machine Learning Regression, using the GenIQ Model© as the latter. For the most compelling illustration, click here.

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