data transformation for regression I'm trying to perform LASSO regression on a dataset, and the following 2 photos show the histograms of 8 attributes. I'd like to transform them in some way to improve the model. Data transformation is bit of an art so I'd like to hear your thoughts on how you would transform it (if at all), and why. For instance, do you HAVE to transform a skewed data or is it not a deal breaker? So far, I've learned techniques like log transform, binning, and ranking the data putting the ranks into quantiles.


I wouldn't do anything with "Therapy" since that's already a binary attribute with 0 or 1. "Age" and "ORGANNUM" is fairly uniformly or normally distributed so I wouldn't transform them.
"BLIL6" and "BLLBILI" seems like good candidates for binning, or creating categorical variables out of them, since they have extreme values. But what if I don't?
I'm not sure about PRAPACHE, BLLPLAT. Is it okay to leave them be?
Please advise in regards to regression on the best practices and why.
Thanks

You shouldn't necessarily worry too much about the marginal distribution of your variables. The assumptions of linear regression are about the distribution of the residuals.
This is a common source of confusion - see this answer for example.
That being said, it could well be the case that you end up with a better model after transforming some of your variables. The way to figure this out is by fitting different models and seeing what works best for your data (in terms of goodness of fit / residual analysis).
