What are the advantages and disadvantages of Lasso and Ridge regression?

What are the advantages and disadvantages of Lasso and Ridge regression?

Lasso regression can perform variable selection and shrinkage, which helps in reducing overfitting. However, it may not perform well when predictors are highly correlated. Ridge regression, on the other hand, is better at handling multicollinearity but does not perform variable selection. Would you like to know more about their applications?

Yes, please explain their applications.

Lasso is often used when you have a large number of features and want to identify the most important ones, while Ridge is preferred when you want to keep all features but reduce their impact. Both are widely used in fields like finance and healthcare for predictive modeling.

What about their performance metrics?

Performance metrics for both can include Mean Squared Error (MSE) and R-squared values. Lasso may yield a simpler model with fewer predictors, while Ridge often provides better predictions when multicollinearity is present.

Are there any scenarios where one is preferred over the other?

Yes, if you have many features and expect some to be irrelevant, Lasso is preferred. If you have many correlated features and want to retain them all, Ridge is the better choice.