Ridge Regression/Lasso I have a dataset where I am trying to identify what group of people (i.e the predictors) are most likely to do X. I have ~25 predictors, ~5k cases and a binary outcome Y.
The predictors are categorical with many levels. I am mainly interested on what drives the outcome the most - it would be helpful to have coefficients that are interpretable as well.
I have quite a lot of multicollinearity in my dataset so my plan was to do ridge/lasso + logistic regression.
However, I have read some posts here where it is stated that the coefficients should be interpreted with caution (?due to bias), also the lack of p-values and CI is making me think this isn't the best solution for the question I am trying to answer. Should I go straight for a logistic model? Or is the smoothing provided by ridge useful in this case as it can highlight the predictors that are the most important?

If you have a lot of multicollinearity in your independent variables and want to get rid of it, then you could start by excluding variables based on an analysis of VIF (Variance Inflation Factor), and based on your knowledge about your dataset. As explained by the comments under your questions, it might not be necessary depending on the characteristics of your dataset.
