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The GenIQ Model is a machine learning alternative model to the statistical ordinary least squares and logistic regression models. GenIQ lets the data define the model – automatically data mines for new variables, performs variable selection, and then specifies the model equation – so as to "optimize the decile table," to fill the upper deciles with as much profit/many responses as possible. Put differently, GenIQ seeks to maximize cum lift, a measure of model predictiveness of identifying the upper performing individuals often displayed in a decile table. GenIQ produces models that outdo statistical models, and is a different model: unsuspected equation, ungainly interpretation, and easy implementation.
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To put GenIQ to your test complete the form
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Database Marketing (DM) regression models seek to maximize cum lift, a measure
of model predictiveness of identifying the upper performing individuals often displayed in a decile table. DM regression models built on today's big data – consisting of a multitude of variables, an army of observations – using statistical regression models, conceived and testing within the small-data setting of the day, 205 years ago, is problematic: Fitting big data to a pre-specified small-framed model produces a skewed model with doubtful interpretability and questionable results. The GenIQ Model is a machine-learning alternative regression model to the statistical models. It is an assumption-free, free-form model that maximizes cum lift, equivalently, the decile table. Sign-up for a free GenIQ webcast: Click here.
Genetic Modeling & Data Mining Software is an implementation of the GenIQ Model©, which explicitly maximizes expected response or profit – as displayed in a decile table – from direct/database marketing solicitations, telemarketing programs, CRM campaigns, web broadcasts, and the like. The GenIQ Model is a flexible assumption-free, nonparametric model based on the machine learning paradigm of genetic programming. The GenIQ-Response and GenIQ-Profit modules offer a clear advantage over "inflexible" logistic and ordinary regression methods, whose performance is dependent on theoretical assumptions, a pre-specified parametric model, and data restrictions.
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