I have a dataset of 1931 observations and I intend to predict a binary outcome out of that. There is a list of 128 predictors (both binary and continuous). First I ran logistic regression modeling using all predictors and got a highly significant model (AUC = 0.84). Assuming that the high value of AUC was due to overfitting the model by using too many predictors, I did stepwise modeling to find the effective predictors:
mylogit <- glm(outcome ~ . , data = temp,family="binomial") step <- step(mylogit, direction="both")
Now, I am not sure whether should I have done cross validation before or after stepwise modeling.