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Variable Selection in Regression: Few Statisticians Know They Produce Poor Models

Replied Oct 28

Variable Selection in Regression: Few Statisticians Know They Produce Poor Models

Replied Oct 27

How Does Spearman's Coefficient Relate to Pearson's Coefficient?

Started Oct 26

Variable Selection in Regression: Few Statisticians Know They Produce Poor Models
4 Replies

Started this discussion. Last reply by Bruce Ratner Oct 28.

A Database Marketing Regression Model that Maximizes Cum Lift

Started Sep 30

What If There Were No Significance Testing?

Started Aug 19

Pop Quiz on Pi

Started Jul 30

Linear Probability, Logit, and Probit Models: How Do They Differ?

Started Jul 30

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Optimization

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Data Mining

165 members

Analytical Techniques

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Bhaswati Sengupta replied to Bruce Ratner's discussion 'Variable Selection in Regression: Few Statisticians Know They Produce Poor Models'

Is there any difference in Variable selection for a linear regression vis-a-vis a logistics regression model?

October 28

Steffen Springer replied to Bruce Ratner's discussion 'Variable Selection in Regression: Few Statisticians Know They Produce Poor Models'

Hm. In my opinion this statement is too general to be true. I recently used Logistic Regression in combination with genetic attribute construction and selection. It indeed improved the results significantly (yes, variables were removed). Despite...

October 27

Bruce Ratner added 2 discussions

October 26

Bruce Ratner was featured

Congratulate them!

August 18

Bruce Ratner added a discussion to the group Data Mining

Confusion Matrix: Perhaps Confusing, but Definitely Biased

August 13

Bruce Ratner updated their profile

May 20

Bruce Ratner added a photo

BruceRatner, Ph.D.

Bruce Ratner, Ph.D., founder and President of DM STAT-1 Consulting, has made the company the ensample for Statistical Modeling & Analysis and Data Mining in Direct & Database Marketing, Customer Relationship Management, Business Intelligence, and ...

May 20

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Profile Information

Short Bio:: Bruce Ratner, Ph.D. is the inventor of the GenIQ Model. 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. GenIQ requires no programming (though there is optional control of process), produces models that outdo statistical models, and is for modelers who daringly consider a different model: unsuspected equation, ungainly interpretation, and easy implementation. 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.

My Website:: http://www.GenIQModel.com

Field of Expertise:: Data Mining, Marketing Databases, Operations Research, SAS, Web Analytics, Statistical Consulting, Teaching, Medical Statistics, Artificial Intelligence