
A
Hybrid StatisticsMachine Learning
Paradigm for Database Response Modeling Bruce Ratner, Ph.D.
The regnant statistical
paradigm for database 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 wellestablished 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 process acquires knowledge of the form without being explicitly programmed. The strengths of the ML paradigm are its flexibility within a nonparametric, assumptionfree 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 a hybrid statisticsML paradigm  integrating the
best features of two paradigms  to yield a utile alternative for
database response modeling. The proposed paradigm is: The data analyst
fits the data to the LRM with the LRM variable selection among the
original variables and the constructed variables, which are the
subroutines of the computer program. Thus, the hybrid LRMML database
response model includes a) the redoubtable regression coefficients,
which provide the necessary comfort level for model acceptance, and b)
the probably inclusion of powerful MLpredictor variables. I use the
machine learning GenIQ Model and LRM to build a hybrid LRMGenIQ database
response model, which predicts the rankorder likelihood of response,
to illustrate the promise of the proposed hybrid approach.

