Empirical Logit vs Partial Residual Plots for Logistic Regression

[pvareschi]

Re: Predictive Modeling Using Logistic Regression

Alongside Empirical Logit Plots (page 3.54 of course text), would it make sense to also use Partial Residual Plots as done for Neural Networks (see page 4-55 of Neural Network Modelling)?

The point is that Empirical Logit Plots are based simply on the univariate relationship between an input and the target, without taking account of other inputs:Partial Residual Plots, on the other hand, should provide a clearer picture.

Perhaps Empirical Logit Plots could be used as a first step to get an initial idea of what inputs may require a transformation and Partial Residual Plots should be used to refine the transformation as part of the final model fitting step.

[gcjfernandez · Accepted solution]

Yes I agree with your statement. Empirical Logit Plots are useful for pre-screening (reducing redundant variables) and to assess whether you have a non-linear association exists between target and the input.Depending on the outcome you can use optimal binning, or quadratic terms etc.
Partial residual plots are useful in fine tuning in the presence of other input.