Gaining Insights from Your Data: A Neoteric Machine Learning Method

Statistically trained data analysts know that their data are not being fully realized in terms of providing their organizations with strategic and tactical decision making, such as estimating risk, detecting fraud, predicting customer behavior, and analyzing business strategies. Models created with the proposed neoteric machine learning GenIQ Model© discover (data mine) relationships that data analysts simply cannot find using the traditional linear statistical techniques (i.e., ordinary regression and logistic regression). With GenIQ data analysts do not have to do any programming; but they still have control of the process. The purpose of this article is to introduce the nonstatistical machine learning GenIQ Model as a 3-in-1 tool for automatically and simultaneously performing the trinity of techniques: selecting important original variables, finding patterns within the data by constructing new important variables from the original variables, and formulating a mathematical equation based on the best set of original and constructed variables. GenIQ is based on the assumption-free, nonparametric genetic paradigm inspired by Darwin’s Principle of Survival of the Fittest and the biological operations of reproduction, sexual recombination and mutation. GenIQ offers a clear advantage over traditional statistical methods, whose performance is dependent upon theoretical assumptions, predefined model formulations, and data-type restrictions.