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
https://www.kaggle.com/burhanykiyakoglu/predicting-house-prices/comments
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?
StandardScaler
(somewhat mindlessly) is trivial to implement. $\endgroup$