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Imagine a linear model with 100 input features:

10 are highly informative.

90 are non-informative.

Assume that all features have values between -1 and 1. Which of the following statements are true?

L₂ regularization will encourage many of the non-informative weights to be nearly (but not exactly) 0.0.

Yes, L₂ regularization encourages weights to be near 0.0, but not exactly 0.0.

L₂ regularization will encourage most of the non-informative weights to be exactly 0.0.

L₂ regularization does not tend to force weights to exactly 0.0. L₂ regularization penalizes larger weights more than smaller weights. As a weight gets close to 0.0, L₂ "pushes" less forcefully toward 0.0.

L₂ regularization may cause the model to learn a moderate weight for some non-informative features.

Surprisingly, this can happen when a non-informative feature happens to be correlated with the label. In this case, the model incorrectly gives such non-informative features some of the "credit" that should have gone to informative features.