What are the effects of regularization on model performance?

What are the effects of regularization on model performance?

Regularization helps prevent overfitting by adding a penalty for larger coefficients in the model. This can improve generalization.

Can you explain the different types of regularization techniques?

Sure! The most common types are L1 regularization (Lasso), which can shrink some coefficients to zero, and L2 regularization (Ridge), which penalizes the size of coefficients but doesn't set them to zero.

How do I choose the right regularization technique for my model?

It depends on your data and the problem. If you suspect many features are irrelevant, L1 might be better. For multicollinearity, L2 is often preferred.

What impact does regularization have on bias and variance?

Regularization typically increases bias but decreases variance, leading to a better trade-off and improved model performance on unseen data.

Can regularization be used in deep learning models?

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