I'm currently working on building a predictive model for a binary outcome on a dataset with ~300 variables and 800 observations. I've read much on this site about the problems associated with stepwise regression and why not to use it.
I've been reading into LASSO regression and its ability for feature selection and have been successful in implementing it with the use of the "caret" package and "glmnet".
I am able to extract the coefficient of the model with the optimal
alpha from "caret"; however, I'm unfamiliar with how to interpret the coefficients.
- Are the LASSO coefficients interpreted in the same method as logistic regression?
- Would it be appropriate to use the features selected from LASSO in logistic regression?
Interpretation of the coefficients, as in the exponentiated coefficients from the LASSO regression as the log odds for a 1 unit change in the coefficient while holding all other coefficients constant.