# How to decide which penalty measure to use ? any general guidelines or thumb rules out of textbook

A number of regularization measures are available in literatures, which is kind of confusing to beginners. The classical penalty is ridge by Hoerl & Kennard (1970,Technometrics 12, 55–67).

Another modification to this is lasso by Tibshirani (1996, Journal of the Royal Statis- tical Society B 58, 267–288), defined as:

Another penalty is the elastic net penalty (Zou and Hastie 2005, Journal of the Royal Statistical Society B 67, 301–320) , which is a linear combination of the lasso penalty and the ridge penalty. Therefore the penalty covers these both as extreme cases.

The another penalty that I could find is bridge penalty introduced in Frank & Friedman (1993, Technometrics 35, 109–148). where λ ̃ = (λ, γ). It features an additional tuning parameter γ that controls the degree of preference for the estimated coefficient vector to align with the original, hence standardized, data axis direc- tions in the regressor space. It comprises the lasso penalty (γ = 1) and the ridge penalty (γ = 2) as special cases.

My question is : are there any preferences on type of penalty to use - something from or out of statistical text books ? Or this is just trial and error ? Please explain to layman language.

• The no free lunch theorem might apply here? At least in terms of predictive power. The lasso penalty has the benefit of inducing sparseness if you're into that. – user44764 Jul 21 '14 at 19:59

2. Sparsity: If you desire a model with only a few predictors (say, for speed of prediction, for interpretability, ...) then you will want $l_1$ regularization.