Search Results
| Search type | Search syntax |
|---|---|
| Tags | [tag] |
| Exact | "words here" |
| Author |
user:1234 user:me (yours) |
| Score |
score:3 (3+) score:0 (none) |
| Answers |
answers:3 (3+) answers:0 (none) isaccepted:yes hasaccepted:no inquestion:1234 |
| Views | views:250 |
| Code | code:"if (foo != bar)" |
| Sections |
title:apples body:"apples oranges" |
| URL | url:"*.example.com" |
| Saves | in:saves |
| Status |
closed:yes duplicate:no migrated:no wiki:no |
| Types |
is:question is:answer |
| Exclude |
-[tag] -apples |
| For more details on advanced search visit our help page | |
Results tagged with lasso
Search options not deleted
user 128939
A regularization method for regression models that shrinks coefficients towards zero, making some of them equal to zero. Thus lasso performs feature selection.
5
votes
Accepted
L1-regularization enforces sparsity for convex functions
Regarding your question about general convex functions, you will get a sparse solution given that you apply a sparsity-inducing norm (which l1 is one such norm). For further information, read up to se …
- 847
3
votes
Report coefficients in elastic net regression after cross validation
Regarding your question about post-selection confidence intervals for the LASSO look for a paper by Tibshirani et al. titled "Exact Post-Selection Inference for Sequential Regression Procedures". …
- 847
1
vote
1
answer
58
views
Preparation for modeling: how to deal with a huge number of variables
I read here that some people suggested LASSO or PCA. … It might be that x_1 alone gets dropped out by LASSO, but there is important information at the intersection between x_1 and x_2 that I miss. …
- 847
1
vote
1
answer
1k
views
Why Ridge regularization has the grouping effect [duplicate]
I want to use elastic net (lasso + ridge) method for feature selection process. I can't understand why does the ridge method gives me the grouping effect for correlated variables. …
- 847