What are the benefits of using regularization techniques in machine learning?

What are the benefits of using regularization techniques in machine learning?

Regularization techniques help prevent overfitting, improve model generalization, and enhance predictive performance.

Can you explain how regularization works?

Sure! Regularization adds a penalty to the loss function, discouraging complex models and promoting simpler ones.

What are some common regularization methods?

Common methods include L1 regularization (Lasso), L2 regularization (Ridge), and Elastic Net, which combines both.

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

It depends on your data and model. L1 is great for feature selection, while L2 is better for multicollinearity.

Are there any downsides to using regularization?

Yes, too much regularization can lead to underfitting, where the model is too simple to capture the underlying patterns.