What is regularization in machine learning?

What is regularization in machine learning?

Regularization is a technique used to prevent overfitting by adding a penalty to the loss function.

How does L1 regularization differ from L2 regularization?

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

When should I use regularization?

You should use regularization when your model is complex and you notice overfitting on the training data.

Can you show me an example of regularization in Python using scikit-learn?

Sure! You can use Ridge for L2 regularization and Lasso for L1 regularization in scikit-learn.