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Are There Specific Cases Where L2 Regularization Outperforms L1 Regularization?

In my experience, L2 regularization often works better than L1 in some cases.

Here are a few reasons why:

  • Feature Connections: When different features are linked or related, L2 can share the weights more evenly. This usually helps the model perform better.

  • Avoiding Overfitting with Many Features: If you have many features, L2 is a good option. Unlike L1, which can completely cancel out some weights, L2 just makes them smaller. This helps keep all of the features working together.

  • Smoother Results: L2 makes the loss surface smoother. This means that when the model is learning, it tends to find solutions more steadily and reliably.

So, even though both L1 and L2 have their uses, L2 really shines in these situations!

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Are There Specific Cases Where L2 Regularization Outperforms L1 Regularization?

In my experience, L2 regularization often works better than L1 in some cases.

Here are a few reasons why:

  • Feature Connections: When different features are linked or related, L2 can share the weights more evenly. This usually helps the model perform better.

  • Avoiding Overfitting with Many Features: If you have many features, L2 is a good option. Unlike L1, which can completely cancel out some weights, L2 just makes them smaller. This helps keep all of the features working together.

  • Smoother Results: L2 makes the loss surface smoother. This means that when the model is learning, it tends to find solutions more steadily and reliably.

So, even though both L1 and L2 have their uses, L2 really shines in these situations!

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