What are the best practices for Lasso regression?

What are the best practices for Lasso regression?

Lasso regression is great for feature selection and regularization. Key practices include standardizing your data, tuning the regularization parameter, and using cross-validation to avoid overfitting.

How do I choose the regularization parameter?

You can use techniques like cross-validation to find the optimal regularization parameter. Grid search is also a popular method.

Should I standardize my features before applying Lasso?

Yes, standardizing your features is crucial for Lasso regression, as it ensures that all features contribute equally to the penalty term.

What are some common pitfalls to avoid?

Common pitfalls include not standardizing your data, using too many features without proper selection, and neglecting to validate your model.