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Can someone please explain how regularization helps to shrink the " less important " features to zero ? As far as I know , Regularization only penalizes the weights of ALL the features to get them closer to 0 , so that they don't overfit to the training data . But how does it help in feature selection or deal with multicollinearity ?

Can someone please explain this in detail in a more intuitive manner ?

Bharathi
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  • Does this answer your question? https://stats.stackexchange.com/q/74542/276972 – hbadger19042 Jul 23 '20 at 07:14
  • If you haven't take your time to go through these [videos](https://www.youtube.com/watch?v=ctmNq7FgbvI), helped me a lot and they are described in detail and very intuitive. – Thomas Jul 23 '20 at 08:04

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