What are the benefits of Lasso regularization in machine learning?

What are the benefits of Lasso regularization in machine learning?

Lasso regularization helps prevent overfitting by adding a penalty for larger coefficients, promoting simpler models.

Can you explain how it differs from Ridge regularization?

Sure! Lasso can shrink some coefficients to zero, effectively performing variable selection, while Ridge shrinks all coefficients but doesn't set any to zero.

When should I use Lasso over Ridge?

Use Lasso when you suspect that many features are irrelevant and want a simpler model. Ridge is better when you believe all features contribute to the outcome.

Are there any drawbacks to using Lasso?

Yes, Lasso can be sensitive to the choice of the regularization parameter and may perform poorly if features are highly correlated.

How do I choose the regularization parameter for Lasso?

You can use techniques like cross-validation to find the optimal regularization parameter that minimizes prediction error.