# LASSO with cross validation doesn't reduce the regressors to a number that is not overfitting my model. What can I do?

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.

• How do you know you are overfitting? AIC and BIC should not overfit. Or if you do not like the answer by AIC and BIC, try optimizing with respect to some measure of forecast error, e.g. mean squared error, when you cross validate your model. Mar 21, 2017 at 7:34

• (maybe max_iter is what you need? But, you can just get back all the coefficients/features, and then walk along it, until the validation loss stops decreasing?) Mar 22, 2017 at 13:57