# When should you use different scaling methods?

There are already some good answers on when feature scaling is desired and when to center vs. scale. However, they don't explain which scaling method to use in which situation. For context, assume that you have some numerical data that you've already decided needs to be scaled, and that both model predictive power and interpretability are important. Some reasons for scaling could be:

• Preparing the data to create interaction variables
• Ensuring that regularization affects all variables equally
• Making the coefficients easier to interpret

Jeff Hale has a sensible-looking blog post on different scaling methods. Here are his conclusions:

Do his conclusions make sense, and if not, what's the correct methodology for choosing a scaling method? Ideally there'd be some scientific evidence as well.

I'll also add Sebastian Raschka's recommendation to use standardization for PCA, because "we are interested in the components that maximize the variance".

• Could you provide more information? What kind of data is being scaled here? Apr 18, 2020 at 1:10
• Added some context. Let me know if it's still unclear. Apr 18, 2020 at 17:19
• The Hale post asserts that linear scaling can yield distributions closer to normal, which is always wrong; that (value $-$ median) / IQR can moderate outliers, which is often wrong; and that it is a goal to reduce homoscedasticity, which gets homo- and hetero- the wrong way round. I can't recommend it. Apr 18, 2020 at 19:33
• I found the Raschka post less clear than posts here on CV. A sharper consistency on covariance matrix as compared with correlation matrix would help. We are always interested in principal components with large fractions of the total variance, and that's true regardless of whether that is the sum of variances of standardized or of unstandardized variables. More generally, blogs run by individuals can vary to excellent to the opposite: peer review has many faults, but lack of peer review has even more. Apr 18, 2020 at 19:36
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