When should I use ridge regularization in my model?

When should I use ridge regularization in my model?

Ridge regularization is useful when you have multicollinearity in your data or when you want to prevent overfitting.

Can you explain what multicollinearity is?

Multicollinearity occurs when two or more predictor variables in a regression model are highly correlated, making it hard to determine their individual effects.

How does ridge regularization help with multicollinearity?

Ridge regularization adds a penalty to the loss function, which shrinks the coefficients of correlated predictors, stabilizing the estimates.

Are there any situations where I shouldn't use ridge regularization?

Yes, if your model is already simple and you have enough data, ridge may not be necessary and could complicate interpretation.