How does Lasso work?

Can you explain how Lasso regression works?

Sure! Lasso regression is a type of linear regression that includes a regularization term. This term helps to prevent overfitting by penalizing the absolute size of the coefficients.

What does the regularization term do?

The regularization term in Lasso regression shrinks some coefficients to zero, effectively selecting a simpler model by keeping only the most important features.

How is Lasso different from Ridge regression?

Lasso regression uses L1 regularization, which can reduce some coefficients to zero, while Ridge regression uses L2 regularization, which shrinks coefficients but does not set them to zero.

When should I use Lasso regression?

You should use Lasso regression when you have a large number of features and want to perform feature selection, especially if you suspect that many features are irrelevant.