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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.

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Not sure about gold standard, but have you looked at regularizarion methods such as LASSO? They are used when one is trying to fit a regression with a large number of predictors - LASSO in particular can double as a variable selection tool. The R packages gamlr and glmnet both should allow you to easily run a cross-validated LASSO with logistic regression.

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  • $\begingroup$ Yes I would agree with RA334.I think LASSO is the gold standard. glmnet can be used to fit ridge regression. See the following references for more detailed info: $\endgroup$ – Alejandro Ochoa Jan 13 '17 at 16:22
  • $\begingroup$ 1) Regularization and variable selection via the elastic net Zou, Hui, and Trevor Hastie. "Regularization and variable selection via the elastic net." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 67.2 (2005): 301-320. 2) The Elements of Statistical Learning (Hastie, Tibshirani, Friedman) Link: onlinelibrary.wiley.com/doi/10.1111/j.1467-9868.2005.00503.x/… $\endgroup$ – Alejandro Ochoa Jan 13 '17 at 17:27

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