What is the difference between L1 and L2 regularization?
An example of an AI chatbot conversation about the differences between L1 and L2 regularization techniques.
Can you explain the difference between L1 and L2 regularization?
Sure! L1 regularization adds the absolute value of the coefficients as a penalty term to the loss function, while L2 regularization adds the square of the coefficients.
What are the effects of using L1 regularization?
L1 regularization can lead to sparse models, meaning it can effectively reduce the number of features by setting some coefficients to zero.
And what about L2 regularization?
L2 regularization tends to shrink the coefficients evenly, which helps in reducing overfitting but usually keeps all features in the model.
When should I use L1 over L2?
Use L1 when you suspect that many features are irrelevant and want a simpler model. Use L2 when you want to keep all features but reduce overfitting.
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