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Data defines the model by dint of genetic programming, producing the best decile table.
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Webcast for Demonstrating the GenIQ Model
Bruce Ratner, Ph.D. |
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Subject: WEBCAST for Statistical Modelers and Data Miners: Demonstration of the GenIQ Model© - no charge!
Date: Mutually Convenient Date & Time
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. GenIQ requires no programming, produces models that outdo statistical models, and is a different model: unsuspected equation, ungainly interpretation, and easy implementation.
Prerequisites for the GenIQ Webcast
1. Entry-level/well-schooled modeler of statistical ordinary least squares and logistic regression models, AND who can think out of the box.
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2. Mid-level or Advanced modeler of statistical ordinary least squares and logistic regression models, AND who can think out of the box.
3. Business emails are required.
GenIQ Webcasts are one-on-one, you, your invited colleagues, and me.This allows for me to give you my full attention to answer all your questions, without feeling that you are taking-up other attendees' time. Please do not be put off by the webcast. It has proven to be the most effective way of my explaining the GenIQ Model.
After the webcast, I email you the GenIQ demo download link, and the GenIQ-webcast pdf-file for your reference.
To Register one of the webcast versions (described below), please anwser the multipart Question, referenced (*) lower on this page:
Version 1: The GenIQ Model Software Demonstration (30 minutes; must have an understanding of genetic programming (GP)).
Version 2: The GenIQ Model Presentation (90 minutes: GP theory-lite tutorial and demonstration of the GenIQ software).
Verision3: If a 90-minute webcast is not practical due to your workload, then I can split the webcast into two sessions:
The first webcast is be a 60-minute GP theory-lite tutorial, and the second one is be a 30-minute demonstration of the GenIQ software.
The GenIQ Model Webcast is a perfect place to start if you want to perform better predictive modeling and data mining!
(*) Question: Please answer the items in this link. The purpose of the items ARE NOT to judge your statistical modeling ability. "Judge not lest you be judged." - Bible
They are to assess if your modeling approach is compatible with the GenIQ Model. I DO NOT want to waste your time - if your approach is not in sync with GenIQ modeling.
Once we agree on a date & time, you will receive a "GoToMeeting© Invitation" email with instructions on how to join the GenIQ Webcast. I discuss:
- Data basics: What kind of data is required for genetic modeling and data mining; in what format must the data be; what preprocessing steps are necessary (There are none! And, I explain why).
- What kinds of questions can be answered with GenIQ data mining.
- How GenIQ models work: The inputs, the outputs, and the nature of its predictive mechanism of GP.
- Evaluation criteria: How GenIQ predictive models can be assessed and their worthiness measured.
I guarantee:
- To show that 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/cum lift.
Please do not hesitate to contact me if you have any questions.
Bruce Ratner, Ph.D.
Sneak Peek of Webcast
Genetic vs. Statistic Regression - A Comparison
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| For more information about this article, call Bruce Ratner at 516.791.3544 or 1 800 DM STAT-1; or e-mail at br@dmstat1.com. |
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br@dmstat1.com