What are the applications of ridge regression?

What are the applications of ridge regression?

Ridge regression is commonly used in scenarios where multicollinearity exists among predictors. It's applied in fields like finance for risk assessment, in biology for gene selection, and in marketing for predicting customer behavior.

Can you explain how ridge regression differs from linear regression?

Certainly! Ridge regression adds a penalty term to the loss function, which helps to reduce model complexity and prevent overfitting, especially when predictors are highly correlated. Linear regression, on the other hand, does not include this penalty.

In what situations would I prefer ridge regression over other methods?

You would prefer ridge regression when you have a large number of predictors, especially if they are correlated, and you want to improve prediction accuracy without overfitting.

Are there any limitations to using ridge regression?

Yes, ridge regression can struggle with interpretability since it doesn't perform variable selection. Additionally, it may not perform well if the true relationship is not linear.