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

What is regularization and why is it important in machine learning?

Regularization is a technique used to prevent overfitting by adding a penalty to the loss function. It's important because it helps improve the model's generalization to unseen data.

How do I know which regularization technique to use?

The choice of regularization technique often depends on the model and the data. L1 regularization (Lasso) can be useful for feature selection, while L2 regularization (Ridge) is better for handling multicollinearity.

Can you explain the difference between L1 and L2 regularization?

Sure! L1 regularization adds the absolute value of the coefficients to the loss function, promoting sparsity. L2 regularization adds the square of the coefficients, which tends to distribute the error more evenly.

What are some common scenarios where I should use regularization?

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