I am using LassoLarsCV from sklearn on a dataset with around 100 variables. After fitting the model around 80 of them have a coefficient of 0. I want to remove those variables from my dataset because it requires unnecessary load to the DB and network to request them every time during prediction.
After refitting LassoLarsCV with the same settings on the reduced dataset I get different coefficients and R2 score on my test dataset.
Is this behavior expected? What would be a better approach to not change the actual model, can i remove manually the 0-coefficients from the model object or would it have side effects on the internal model structure?