Timeline for Selected variables varies depending on whether or not standardization is in lasso regression (glmnet)
Current License: CC BY-SA 4.0
4 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Sep 9, 2019 at 15:29 | comment | added | runr |
Main point is that for whatever data set, glmnet estimates a single hyperparameter $\lambda$ value. Therefore, usually standartization is expected and is enabled by default, since we want that one value of $\lambda$ to give an appropriate penalty for all variables simultaneously. On the other hand, Adaptive LASSO solves this problem differently -- here re-weighting the $\hat\lambda_j := \hat w_j \lambda_j, \forall j$ allows for standardized = F since the weights now also account for the scale, among other important qualities.
|
|
| Sep 9, 2019 at 14:46 | answer | added | Edgar | timeline score: 2 | |
| Sep 6, 2019 at 7:42 | comment | added | user2974951 | If standardize=T then glmnet will standardize the values during optimization, but it will return the results on the original scale. Post some data with an example. | |
| Sep 6, 2019 at 3:02 | history | asked | Tae Lee | CC BY-SA 4.0 |