Which model performance metric is used for SVR model by sklearn?

Question

I noticed the math for SVR states that SVR uses L1 penalty or epsilon insensitive loss function. But sklearn SVR model documentation mentions L2 penalty. I don't have much experience with SVR thought the community who has experience could shed some light on this.

Here is the snippet from the documentation :

C: float, default=1.0

Regularization parameter. The strength of the regularization is inversely proportional to C. Must be strictly positive. The penalty is a squared l2 penalty .

Answer 1

Check out this link:https://scikit-learn.org/stable/modules/svm.html#svm-regression . quote - Here, we are penalizing samples whose prediction is at least away from their true target