What is the difference between Lasso and Ridge regression?

Can you explain the difference between Lasso and Ridge regression?

Sure! Lasso regression adds a penalty equal to the absolute value of the magnitude of coefficients, while Ridge regression adds a penalty equal to the square of the magnitude of coefficients.

What are the main advantages of using Lasso over Ridge?

Lasso can perform variable selection by shrinking some coefficients to zero, effectively selecting a simpler model, while Ridge tends to keep all variables.

In what scenarios would you prefer Ridge regression?

Ridge regression is preferred when you have many features that are correlated, as it can handle multicollinearity better than Lasso.

Can both methods be used together?

Yes, they can! Elastic Net combines both Lasso and Ridge penalties, allowing for a balance between variable selection and coefficient shrinkage.