I have a data set with 50 predictors of categorical and numerical variables and 1 dichotomous outcome. I'd like to perform logistic regression, model it and k-fold cross validate it.
However, I have stumbled upon deciding which predictors to include in my model. I have started with the initial hypothesis making, where I try to find some reasonable physical entity. However, my model doesn't produce any good AUC (0.74).
Then I tried stepwise (backward and backward/forward) regression combining both AIC and BIC to let the computer guess which variables better for the outcome. I still can't achieve a better AUC score of 0.75.
Therefore, I would like to enquire if there is gold standard method in such occasion to help me get a grasp of which predictors are best in order to optimize my predictive power of the model.
I use R for my modeling.