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Logistic Regression Losing 1 Polynomial attribute consistently

frederickfrederick Member Posts: 3 Newbie
edited October 19 in Help
I have two polynomnials data columns, one being an age group that is has 5 attributes (e.g. 16-20, 21-30....) and another polynomial with 4 attributes (Tariff plan 1, 2, 3, 4). When applying the logistic regression model, one attribute does not show in the model. For example I lose Tariff Plan 2 and Age Group 31-40, every time I run the model.

In the cross validation ExampleSet there is no loss of data, but the logistic regression model does not show it.





If I try to change them from polynomial to numerical I see all the attributes in the Logistic Regression, but one shows no analysis. Since it removes collinear columns.


Answers

  • mschmitzmschmitz Administrator, Moderator, Employee, RapidMiner Certified Analyst, RapidMiner Certified Expert, University Professor Posts: 3,123  RM Data Scientist
    Hi,
    the model uses an one-hot encoding removing one column. Actually you only need classes-1 columns to contain all the information. If you do not want this: Please use nominal to numerical before hand.

    Best,
    Martin
    - Head of Data Science Services at RapidMiner -
    Dortmund, Germany
  • frederickfrederick Member Posts: 3 Newbie
    I have tried to use nominal to numerical, but as I stated above, the Logistic Regression Model removes the co-linear columns (Tariff Plan 4 and Age Group 16=20).


  • mschmitzmschmitz Administrator, Moderator, Employee, RapidMiner Certified Analyst, RapidMiner Certified Expert, University Professor Posts: 3,123  RM Data Scientist
    Did you try to remove the remove colinear option?


    - Head of Data Science Services at RapidMiner -
    Dortmund, Germany
  • frederickfrederick Member Posts: 3 Newbie
    Yes, but I wish to use the removed collinear columns for analysis? Sorry, I'm a noob, but It is removing 1 of each of my nominal to numerical columns such as Complaints = True, Tariff Plan = 4, International Plan = No...

    I want to use all of these removed attributes for analysis.


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