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I am building a logistic regression model with a binary rating (High and Low) as the dependent variable and 40+ independent variables. One of the independent variable (Age) has a non-linear relationship (bimodal shape) with the dependent.

What is the best approach / transformation to deal with this? Are splines the best method? I feel splines make the model too complicated and hard to interpret especially if interactions are also included.

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  • $\begingroup$ There is no one size that fits all. Could you try splitting the independent variable into bins and see the relationship with the dependent variable? $\endgroup$ – karthikbharadwaj May 9 '16 at 23:34
  • $\begingroup$ Because the response in a logistic regression is binary, could you explain what you mean by "non-linear relationship" or "bimodal shape"? $\endgroup$ – whuber May 13 '16 at 15:56
  • $\begingroup$ @karthikbharadwaj The independent variable Age is already bucketed into 5-year bins. I do not want to bucket further to avoid losing more "information". $\endgroup$ – user112920 May 14 '16 at 4:10
  • $\begingroup$ @whuber The bimodal shape is seen when I smooth the Rating vs. Age plot (e.g. using loess) $\endgroup$ – user112920 May 14 '16 at 4:11
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IMO,Two approaches can be followed: 1)You can visualize how the target varies with the different age buckets 2) The most popular transformations used are log transformations or quadratic transformation. For each transformation, you can use cross-validation to check which one performs better

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