How do I tune parameters in Lasso regression?

How do I tune parameters in Lasso regression?

Tuning parameters in Lasso regression typically involves adjusting the regularization parameter, often denoted as lambda (λ). This controls the strength of the penalty applied to the coefficients.

What methods can I use to find the best lambda value?

You can use techniques like cross-validation, grid search, or randomized search to find the optimal lambda value for your model.

Can you explain how cross-validation works in this context?

Sure! Cross-validation involves splitting your dataset into training and validation sets multiple times. You train the model on the training set and evaluate it on the validation set to ensure that the chosen lambda generalizes well.

What are the consequences of choosing a lambda that is too high or too low?

If lambda is too high, you may underfit the model, leading to poor performance. If it's too low, you risk overfitting, capturing noise in the data.