What is the difference between regularization and early stopping?

What is the difference between regularization and early stopping?

Regularization is a technique to prevent overfitting by adding a penalty to the loss function, while early stopping halts training when performance on a validation set starts to degrade.

Can you give examples of regularization techniques?

Sure! Common regularization techniques include L1 (Lasso) and L2 (Ridge) regularization, which add penalties based on the size of the coefficients.

How does early stopping work in practice?

In practice, early stopping involves monitoring the model's performance on a validation set during training and stopping when the performance begins to decline.

Which method is better for preventing overfitting?

It depends on the situation. Regularization is often more effective for complex models, while early stopping can be useful for simpler models or when training time is a concern.