let say I have M subsets of independent variables and I want to use stack learner to predict dependent variable y.
for each subset I use lasso method to get meta features (predictions).
I have 2 options and Im not sure which one is better:
- try alot of $\lambda s$ for each subset and stack all the models so I get (number of $\lambda$s tested * num. of subsets) models for stacking.
- find the best $\lambda$ for each subset and stack only M models.
I suspect option number one will lead to overfit but maybe can do better performances.
which of the options is better for generalize?