Ridge/lasso logistic regression in r

Machine Learning and Modeling
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Hi all,

I am currently struggling with understanding how ridge/lasso logistic regressions work in R. I have some questions below and would be very grateful for all the feedback:

  1. Can we compute the odds ratio from ridge/lasso logistic regression just like we did in logistic regression? If so, can someone pls share the code?
  2. Can we also construct the AUC ROC curve from the penalized model? If so, can someone pls share the code?
  3. I saw a post sharing the below output. Can someone pls help me understand what this means? (In the post, they said only "triceps" has zero coefficient, which I am not sure I understand..)
    Reference: Penalized Logistic Regression Essentials in R: Ridge, Lasso and Elastic Net - Articles - STHDA 
coef(cv.lasso, cv.lasso$lambda.min)

## 9 x 1 sparse Matrix of class "dgCMatrix"
##                     1
## (Intercept) -8.615615
## pregnant     0.035076
## glucose      0.036916
## pressure     .       
## triceps      0.016484
## insulin     -0.000392
## mass         0.030485
## pedigree     0.785506
## age          0.036265
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