There are a growing number of new statistical methods, referred to as “algorithmic,” which primarily come from the fields of statistics and computer science. Algorithmic methods are nominally statistical models, or more aptly non-statistical models, in that no effort is made to represent how the data were generated. They are nonparametric, assumption-free procedures that let the data define the form of the model itself. Typically, the data analyst approaches a problem directly with a procedure designed specifically for that purpose. For example, the everyday statistical problems of classification (i.e., assigning class membership), and prediction of a continuous target variable are solved by the binary or polynomial logistic regression models, and ordinary least-squares regression model, respectively. The most notable algorithmic methods for the everyday statistical problems are the decision trees (sets of if-then rules), such as CART, CHAID, and C5.0. There is growing evidence that the number of algorithmic methods perform better than conventional statistical procedures for the tasks that algorithmic methods are designed to undertake. The purpose of this article is to introduce the GenIQ Model©  as an algorithmic method, as it is a non-statistical method solving the statistical problems of classification and prediction by uniquely unfolding the task of optimizing the decile table, or equivalently, the ROC curve, and the K-S statistic. For an eye-opening preview of the 9-step modeling process of GenIQ, click here.
For FAQs about GenIQ, click here.