The purpose of this article is to present a new method – the GenIQ Model© – as an alternative technique for modeling a response variable. For ease of presentation, I use two categorical and two continuous variables, and a binary (yes-no) target variable. The GenIQ Model, which is based on the assumption-free, nonparametric genetic paradigm inspired by Darwin’s Principle of Survival of the Fittest, offers theoretical and ease-of-use advantages over the standard logistic regression model (LRM) and the log-linear model (LLM). It automatically and simultaneously “evolves” the response model structure, and the variable selection among categorical predictor variables. The open-worked GenIQ Model and its wordbook are both generally regarded as not demanding on newcomers of genetic modeling. A novel case study using the “Let’s Play” dataset is illustrated to encourage the use of the new method.

I use the machine learning GenIQ Model to build a database response model, which predicts the rank-order likelihood of response, to illustrate the advantages, and to highlight the singular weakness of the machine learning paradigm. Specifically, the GenIQ Model shows the superiority of the machine learning paradigm over the statistical paradigm, as it not only specifies the true model form (a computer program), but simultaneously performs variable selection, data mining and build the model – it’s like a Genetic Jackknife 3-in-1 Method. The difficulty in interpreting the computer program often accounts for the limited use of the machine learning paradigm.

When my daughter Amanda was in grade school, she could not understand the decision-making process of her principal Dr. Katz. On some rainy days, Dr. Katz would permit the class to go outside for recess to play. On other days when it was sunny, Dr. Katz would said, “no play.” As a statistician’s daughter, Amanda collected some weather information, and asked me to build a model to predict what Dr. Katz will do in the days to come. Amanda created a “Let’s Play” database, in Table 1 (also in Quinlan’s C4.5, page 18!), which included the weather conditions for two weeks:

I built the easy-to-interpret LRM, and the not-so-easy-to-interpret GenIQ Model for the target variable Play (yes). This creates a counterpoint where the data analyst now can choose between a good interpretable model and a potentially better unexplainable model.

GenIQ variable selection provides a rank-ordering of variable importance for a predictor variable with respect to other predictor variables considered jointly. This is in stark contrast to the well-known, always-used statistical correlation coefficient, which only provides a simple correlation between a predictor variable and the target variable - independent of the other predictor variables under consideration.

GenIQ data mining is directly apparent from the GenIQ tree itself: Each branch is a newly constructed variable, which has power to increase the rank-order predictions.

The machine learning paradigm (MLP) “let the data suggest the model” is a practical alternative to the statistical paradigm “fit the data to the equation,” which has its roots when data were only “small.” It was – and still is – reasonable to fit small data in a rigid parametric, assumption-filled model. However, the current information (big data) in, say, cyberspace requires a paradigm shift. MLP is a utile approach for database response modeling when dealing with big data, as big data can be difficult to fit in a specified model. Thus, MLP can function alongside the regnant statistical approach when the data – big or small – simply do not “fit.” As demonstrated with the “Let’s Play” data, MLP works well within small data settings.