I am looking for a variable selection method for linear regression. I have 25 correlated independent variables and one dependent variable that is an aggregated score of a Likert scale. I also have 90 samples. I tried LASSO with AIC and BIC but the suggested alpha from a 5-fold cross validation doesn't reduce the regressors to a number that is not overfitting my model. Any suggestions? PS I did the analysis in python but I am not proficient with it.
Create a validation set, separate from your test set etc. Add variables, one at a time, eg using LARS path, which lets you do this easily, and quickly, until validation shows that validation loss is not going down and/or is increasing.