What is the importance of regularization in machine learning?

What is the importance of regularization in machine learning?

Regularization helps prevent overfitting by adding a penalty for larger coefficients in the model.

Can you explain how L1 and L2 regularization differ?

L1 regularization adds the absolute value of coefficients as a penalty, while L2 adds the square of coefficients.

When should I use L1 over L2 regularization?

Use L1 when you want feature selection, as it can shrink some coefficients to zero. L2 is better for generalization.

What are some common techniques to implement regularization?

Common techniques include Ridge regression for L2 and Lasso regression for L1 regularization.