I've seen this post as well as this one regarding the difference between the
glmnet solution paths for fitting the lasso. From my understanding,
glmnet uses coordinate descent optimisation to identify its coefficient path. If that is the case, what does the
lars implementation use to identify its path? Is it the same as coordinate descent?
While the first post provides a simple example, I also wondered how they determine their "best" coefficients from those paths. Further, it doesn't seem particularly efficient to me to have to use a lambda value from the
lars function with
glmnet, what if you want to move on from the
lars package and solely use
glmnet? How can you trust that you are identifying the right coefficients using the right lambdas?
On more complex examples, I've found that
lars is able to identify the true regression model using the lasso whereas the
glmnet method has not. Theoretically,
glmnet should work in identifying the model correctly, so I'm trying to understand the disconnect.