How do I choose the alpha parameter in Lasso regression?

What is the best way to choose the alpha parameter in Lasso regression?

Choosing the alpha parameter in Lasso regression can significantly affect your model's performance. A common approach is to use cross-validation to find the optimal value.

How does cross-validation help in selecting alpha?

Cross-validation helps by splitting your data into training and validation sets multiple times. It allows you to evaluate how different alpha values perform on unseen data, helping you select the best one.

Are there any specific techniques for cross-validation?

Yes, techniques like k-fold cross-validation and leave-one-out cross-validation are popular. K-fold is often preferred for its balance between bias and variance.

What range of alpha values should I consider?

Typically, you can start with a range of alpha values from 0.001 to 10, and then refine based on your cross-validation results.