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question marks in linear regression output

AD2019AD2019 Member Posts: 7 Newbie
I ran a linear regression model with 18 independent variables and feature selection turned off.  For some of the independent variables there were question marks for the standard error of the estimate, and therefore for the t-statistic and p-value for the coefficient.  I ran the mode again with feature selection turned on and got the same question marks.  What do these question marks mean?  Thay cannot have anything to do with missing values as the regression would not have run to completion in that case.  I am baffled about what these "?" symbols might mean.  Help..... 

Answers

  • Telcontar120Telcontar120 Moderator, RapidMiner Certified Analyst, RapidMiner Certified Expert, Member Posts: 1,267   Unicorn
    Can you post your process xml?  Do you have the bias parameter checked in the LR operator or the exclude collinear features?  There are several options that can affect the output.

    Brian T.
    Lindon Ventures 
    Data Science Consulting from Certified RapidMiner Experts
  • AD2019AD2019 Member Posts: 7 Newbie
    Hi, I have attached my process rmp file.  the 'exclude collinear features' is unchecked.  and you are correct about the bias thing.  if 'use bias' is checked, i do not get question marks.  if it is unchecked, i do get question marks.  I did all this with 'feature selection' turned off.  Something else is also strange.  I then turned on feature selection and used T_Test as the selection method with alpha set to 0.05.  I got a solution that included Independent variables with p-value much much higher than 0.05.  I am confused why these IVs were not trimmed from the output. thanks in advance for your help.
  • AD2019AD2019 Member Posts: 7 Newbie
    by the way, regardless of the cause, I would like to know what the question mark in the regression output is trying to communicate to the user.  does it mean a computational underflow or overflow or a computational error or what?
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