What are the best practices for using regularization in machine learning?

What are the best practices for using regularization in machine learning?

Regularization helps prevent overfitting. Key practices include choosing the right type (L1, L2), tuning hyperparameters, and using cross-validation.

How do I decide between L1 and L2 regularization?

L1 regularization can lead to sparse models, while L2 regularization tends to distribute weights more evenly. It depends on your feature set and goals.

Can you explain how to tune the hyperparameters for regularization?

You can use techniques like grid search or random search with cross-validation to find the optimal regularization strength.

What are some common pitfalls to avoid when using regularization?

Common pitfalls include over-regularizing, not validating your model properly, and ignoring the impact of regularization on interpretability.