How do I choose a regularization parameter?

What is a regularization parameter and why is it important?

A regularization parameter helps prevent overfitting by adding a penalty for larger coefficients in your model.

How do I determine the best value for the regularization parameter?

You can use techniques like cross-validation to evaluate different values and choose the one that minimizes validation error.

Are there specific methods or algorithms to help with this?

Yes, methods like Lasso and Ridge regression have built-in regularization parameters that you can tune.

What happens if I choose a value that is too high or too low?

A value too high may lead to underfitting, while too low can result in overfitting, so finding a balance is key.