What is regularization in machine learning?

What is regularization in machine learning?

Regularization is a technique used to prevent overfitting in machine learning models by adding a penalty for larger coefficients.

Why is overfitting a problem?

Overfitting occurs when a model learns the noise in the training data instead of the actual pattern, leading to poor performance on unseen data.

What are some common types of regularization?

Common types include L1 regularization (Lasso) and L2 regularization (Ridge), each adding different penalties to the loss function.

How do I choose the right regularization technique?

Choosing the right technique depends on your data and model. L1 can lead to sparse models, while L2 tends to distribute weights more evenly.

Can regularization be used in deep learning?

Yes, regularization techniques like dropout and weight decay are commonly used in deep learning to improve generalization.