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L1-L2 REG - Which is better ?

Hey,

in the lecture notes 3d) we are asked

  • when and why is sparsity in the model (L1) better than least squares ?

I can picture some intuitive examples but can't get a complete "true" answer....

Thanks in advance ! :)

I suppose that, since L1 regularisation comes up with a sparse solution (some of the weights will be 0), it would be useful when you have to do feature selection. I'd appreciate if someone can confirm this :)

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