When should ridge regression be preferred over lasso?

In machine learning interviews, if you’re asked when ridge regression should be preferred over lasso, you can provide the following explanation:

Ridge regression and Lasso are both regularization techniques used to address multicollinearity and prevent overfitting in linear regression models by adding a penalty term to the cost function. The key difference lies in the type of penalty they apply.

Ridge regression adds a penalty term equivalent to the squared magnitude of the coefficients (L2 regularization), while Lasso adds a penalty term equivalent to the absolute magnitude of the coefficients (L1 regularization).

Ridge regression should be preferred over Lasso in the following situations:

1. When there are many small to medium-sized coefficients: Ridge regression tends to shrink the coefficients toward zero but not to absolute zero. If you have a large number of predictors with small to medium-sized coefficients, ridge regression may be more appropriate as it won’t completely eliminate any of the variables.
2. When there is multicollinearity: Ridge regression performs well in the presence of multicollinearity, where independent variables are highly correlated. It can handle correlated predictors by distributing the effect of correlated variables among them.
3. When interpretability of coefficients is less critical: Ridge regression tends to shrink coefficients uniformly, making interpretation of individual coefficients less straightforward. If interpretability is not a primary concern and the focus is on predictive performance, ridge regression may be a suitable choice.

In summary, ridge regression is preferred when dealing with multicollinearity and situations where a set of predictors all contribute somewhat to the response, making it suitable for maintaining stability in the presence of correlated features.