/* ---- Google Analytics Code Below */

Wednesday, January 31, 2018

No Exceptions

Nicely done piece by William Vorhies of DSC.  Worth always keeping in mind.  He also provides a favorite quote from James Thurber.   I like it.   Good detail at the link:

When Variable Reduction Doesn’t Work
Posted by William Vorhies     

Summary:  Exceptions sometimes make the best rules.  Here’s an example of well accepted variable reduction techniques resulting in an inferior model and a case for dramatically expanding the number of variables we start with.

One of the things that keeps us data scientists on our toes is that the well-established rules-of-thumb don’t always work.  Certainly one of the most well-worn of these rules is the parsimonious model; always seek to create the best model with the fewest variables.  And woe to you who violate this rule.  Your model will over fit, include false random correlations, or at very least will just be judged to be slow and clunky.

Certainly this is a rule I embrace when building models so I was surprised and then delighted to find a well conducted study by Lexis/Nexis that lays out a case where this clearly isn’t true.  ... " 

No comments: