What are some common regularization techniques in Python for machine learning?

What are some common regularization techniques in Python for machine learning?

Common regularization techniques include L1 (Lasso), L2 (Ridge), and Elastic Net. They help prevent overfitting.

Can you explain how Lasso regularization works?

Lasso regularization adds a penalty equal to the absolute value of the magnitude of coefficients, which can shrink some coefficients to zero.

What about Ridge regularization? How is it different from Lasso?

Ridge regularization adds a penalty equal to the square of the magnitude of coefficients, which keeps all coefficients but reduces their size.

When should I use Elastic Net?

Elastic Net is useful when you have many correlated features. It combines L1 and L2 penalties to balance their strengths.