What is regularization in machine learning algorithms?

What is regularization in machine learning algorithms?

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

Can you explain the different types of regularization?

Sure! The two most common types are L1 regularization (Lasso) and L2 regularization (Ridge). L1 adds absolute value penalties, while L2 adds squared penalties.

When should I use L1 over L2 regularization?

Use L1 regularization when you want feature selection, as it can shrink some coefficients to zero. L2 is better for cases where you want to keep all features.

How does regularization affect model performance?

Regularization can improve model performance on unseen data by reducing overfitting, but it may slightly increase bias.

Are there any algorithms that don't require regularization?

Yes, some algorithms like decision trees and random forests are less prone to overfitting and may not require regularization.