# 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. Can anyone explain that please?

## marked as duplicate by kjetil b halvorsen, mdewey, jbowman, Community♦Dec 11 '17 at 10:41

The L1 norm tends to overfit the most contributory $X_i$ variable. With ridge regression a bias is added that can reduce the propagated error of a parameter of interest, for example, see this. Alternatively, ridge regression can be used to reduce covariance if one uses a smoothing factor appropriate to that task.