Explain the contrast between L1 and L2 regularization methods in regression analysis, and the reason for selecting one approach over the other.

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Anonymous · Accepted Answer

L1 uses the sum of absolute weights, promoting sparse models with zeroed-out
features. L2 uses the sum of squared weights, evenly distributing coefficient
shrinkage without eliminating them. L1 would be preferable when variable redu
tion needs to be done while L2 is better where variable weightage towards final
prediction needs to be reduced but not completely eliminated.

Explain the contrast between L1 and L2 regularization methods in regression analysis, and the reason for selecting one approach over the other.

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