I am constructing a 2-class classifier and using cross validation to tune certain parameters in my model.
The predictor variables are both continuous and one is ordinal. Based on looking at the response variable and the ordinal variable it seems like it might be a good idea to collapase/combine certain levels of the ordinal variable.
Am I allowed to make this decision based on the full data or will it mess up the cross validation procedure?
If I am not allowed to do so, would it then make sense to treat the value I am using as a threshold for collapsing the levels in the ordinal variable as a tuning parameter?