What is the impact of regularization on overfitting in machine learning?

What is the impact of regularization on overfitting in machine learning?

Regularization helps to prevent overfitting by adding a penalty for larger coefficients in the model, which encourages simpler models.

Can you explain how L1 and L2 regularization differ?

L1 regularization adds the absolute value of the coefficients as a penalty, promoting sparsity, while L2 regularization adds the square of the coefficients, which tends to distribute the weights more evenly.

When should I use L1 over L2 regularization?

Use L1 regularization when you suspect that many features are irrelevant, as it can reduce the number of features. Use L2 when you want to keep all features but reduce their impact.

What are some common techniques to evaluate overfitting?

Common techniques include using a validation set, cross-validation, and monitoring the training vs. validation loss during training.