I know this has been asked before, and I have read through the responses to the earlier queries related to binning continuous variables. I do understand that generally we should avoid binning, given that it potentially results in throwing away useful information (among other issues). However, I am trying to build a logistic regression model, and one of my significant predictors is a continuous variable. I have tried 2 different models. In the first, I input the variable as-is (continuous), whereas in the second, I fed it as a categorical variable (categorized as per quartiles).
The second model (with the binned variable) had lower AIC score and cross validated error. Could this be considered sufficient justification for binning in this particular case?