When should I use L1 or L2 regularization?
An example of an AI chatbot conversation about L1 and L2 regularization in machine learning.
When should I use L1 or L2 regularization in my model?
L1 regularization is great for feature selection, while L2 is better for preventing overfitting. What type of model are you using?
I'm working on a linear regression model. Should I prefer one over the other?
For linear regression, L2 regularization is often preferred as it helps to minimize the impact of multicollinearity.
What if I have a lot of irrelevant features?
In that case, L1 regularization would be beneficial as it can shrink some coefficients to zero, effectively removing those features.
Can I use both L1 and L2 together?
Yes, using both is known as Elastic Net regularization, which combines the benefits of both methods.
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