What is the difference between L1 and L2 regularization?
An example of an AI chatbot conversation about L1 vs L2 regularization in machine learning.
What is the difference between L1 and L2 regularization?
L1 regularization adds the absolute value of the coefficients as a penalty term, while L2 regularization adds the square of the coefficients.
Can you explain how each affects model performance?
L1 can lead to sparse models by driving some coefficients to zero, while L2 tends to distribute the error across all coefficients.
When should I use L1 over L2 regularization?
Use L1 when you suspect that many features are irrelevant, and L2 when you want to keep all features but reduce their impact.
Are there situations where I should use both?
Yes, using both L1 and L2 together is known as Elastic Net, which can combine the benefits of both methods.
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