I am performing Lasso Regularization on my linear model.
When I steadily increase my
lambda in cross-validation. I see the residual sum of squares will increasing steadily in my training set. Since Lasso adding more and more penalty which minimizes the weights/parameters of the linear model.
What about for my test set?
What kind of RSS change would I expect to see on my test data if I increase my
0? and Why?