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Joshua Weiner
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  • Woodcliff Lake, NJ
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Michael S. Pukish left a comment for Joshua Weiner
"Hi Josh, In reference to your question, "What is the answer to question 37? What is wrong with mean square error? As long as you are looking at the MSE on the test set... and using it compare models, then I think it is a perfectly fine…"
Apr 2, 2015
Joshua Weiner commented on Vincent Granville's blog post 66 job interview questions for data scientists
"What is the answer to question 37? What is wrong with mean square error? As long as you are looking at the MSE on the test set... and using it compare models, then I think it is a perfectly fine measure."
Apr 12, 2014
Joshua Weiner is now a member of Data Science Central
Feb 14, 2013

Profile Information

Short Bio
A senior consultant in IBM's Advanced Analytics and Optimization Consulting Practice. Specalizing in advanced marketing analytics. M.S Predictive Analytics Northwestern University. B.S Finance, Chemistry Carnegie Mellon University.
My Web Site Or LinkedIn Profile
http://www.linkedin.com/in/joshuaweineranalyticsconsultan/
Field of Expertise
Analytics, Big Data
Professional Status
Consultant
Years of Experience:
5+
Your Company:
IBM
Industry:
Professional Services, Consultanting
How did you find out about DataScienceCentral?
Linkedin
Interests:
Networking

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At 8:38am on April 2, 2015, Michael S. Pukish said…

Hi Josh,

In reference to your question, "What is the answer to question 37? What is wrong with mean square error? As long as you are looking at the MSE on the test set... and using it compare models, then I think it is a perfectly fine measure."

The key is, (and the question is not structured in the best way...) the question asks why MSE is bad to compare _models_.  It is actually not, used as part of the criteria.  But by itself, MSE does not indicate any penalty scoring for the *complexity* of the model.  It's trying to get you to say something about methods like the Aikake (AICC or AIC..) modification to the standard MSE cost function, which add a penalty factor for the complexity of the model.

 
 
 

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