Log transformations in Ridge and lasso

Do we need to do a log transformation if we are running a ridge or lasso? I was told if I was doing regularisation I wouldn't need to. All the predictors as well as the response variable would undergo transformation.


Less is more. Stay pure. Stay poor.
Well in these models the inputs are usually all converted to standard normal. I felt by the user. I have always had concerns that how do categorical variables get converted (which you can do this, but if they are >2 groups, seems very weird and confusing, right).

I have historically only used them in the logistic settings and I have moved away from them, preferring Bayesian models where the regularization comes from the user provided priors.


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Converting to a standardized version of a variable really makes no difference at all and typically the algorithm will take care of that part for you. But a log transform is a different beast and would need to be done manually.

Maybe OP is confusing ridge/lasso with tree/forest based methods? Monotonic transformations really don't make a difference there (at least for main effects) in terms of prediction but they still very much have an impact on any linear model based algorithm which ridge and lasso are a part of.


Less is more. Stay pure. Stay poor.
I suppose another question is why transform data - e.g., skewness, interpretatibility (scale), etc. And if it is necessary.