What is regularization in machine learning models?

What is regularization in machine learning models?

Regularization is a technique used to prevent overfitting in machine learning models by adding a penalty to the loss function.

Can you explain how L1 and L2 regularization differ?

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

In which models is regularization commonly used?

Regularization is commonly used in linear regression, logistic regression, and neural networks to improve generalization.

How does regularization affect model performance?

Regularization can improve model performance on unseen data by reducing overfitting, but it may also lead to underfitting if too strong.