# Is there any justification for not standardizing predictors on disparate scales when using Lasso/Ridge?

I've looked at some Kaggle notebooks lately of people using Lasso/Ridge for linear regression. The majority that I've seen don't seem to standardize the predictors before they fit Lasso/Ridge even though the variables are on disparate scales (e.g., multiple orders of magnitude in difference)

Here are a couple of Jupyter notebooks that I've seen that uses no standardization:

https://www.kaggle.com/mohaiminul101/car-price-prediction

Most of the notebooks I've seen actually lack this standardization, and I only look at the top rated notebooks for popular datasets, so I was thinking there their methodology may be more reputable. So now I'm wondering if there's something I'm missing, or if people are indeed being negligent/incorrect by not standardizing when using regularization.

Is there any theoretical justification or practical advantage to not performing standardization on regressors when they exist on disparate scales?

• I assume these are just beginner notebooks rather than competition entries May 8 at 16:13
• All serious regularised regression (LASSO/Ridge) I have read standardised their variables. Especially within Python, where we might often use pipeline, dropping a StandardScaler(somewhat mindlessly) is trivial to implement. May 11 at 0:11
• @usεr11852 Can you suggest a resource where I may be able to find more reputable linear regression data analyses? Not advanced analysis per se, just something that shows reputable beginning to end procedure May 11 at 14:38
• Kuhn & Johnson (2013) Applied Predictive Modeling is really readable and to the point. May 11 at 21:23
• @usεr11852 I'll check that out. Is it more focused on prediction rather than inference? May 11 at 21:47