When should I use ridge regression?

When should I use ridge regression instead of linear regression?

You should consider using ridge regression when you have multicollinearity in your data, which can inflate the variance of the coefficient estimates in linear regression.

What is multicollinearity?

Multicollinearity occurs when two or more independent variables in a regression model are highly correlated, making it difficult to determine the individual effect of each variable.

How does ridge regression handle multicollinearity?

Ridge regression adds a penalty to the loss function, which helps to stabilize the estimates of the coefficients and reduces their variance, effectively addressing multicollinearity.

Are there any downsides to using ridge regression?

Yes, one downside is that ridge regression can introduce bias into the estimates, which may not be ideal if interpretability is crucial.