What is regularization in machine learning?

Can you explain what regularization is in machine learning?

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

How does regularization help with overfitting?

It discourages the model from fitting too closely to the training data by penalizing large coefficients, promoting simpler models.

What are the common types of regularization?

The most common types are L1 regularization (Lasso) and L2 regularization (Ridge), each applying different penalties.

When should I use regularization?

You should use regularization when you have a complex model or a small dataset to avoid overfitting.