What are the effects of regularization on model accuracy?

What are the effects of regularization on model accuracy?

Regularization helps prevent overfitting by adding a penalty for larger coefficients, which can improve model accuracy on unseen data.

Can you explain how L1 and L2 regularization differ?

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

When should I use regularization in my models?

Use regularization when you have a complex model with many features, especially if your training data is limited or noisy.

Does regularization always improve accuracy?

Not always. While it can improve generalization, it may also lead to underfitting if the regularization strength is too high.

What are some common regularization techniques?

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