Reading through Prof. Ben Etzkorn's piece on Data Normalization and Standardization (1), he writes:
"To normalize data, traditionally this means to fit the data within unity (1), so all data values will take on a value of 0 to 1. Since some models collapse at the value of zero, sometimes an arbitrary range of say 0.1 to 0.9 is chosen instead, but for this post I will assume a unity-based normalization."
He goes on to say:
"If you desire to have a more centralized set of normalized data, with zero being the central point, then normalize your data (-1 to +1)."
The question is, when is it best to use the 'Unity Method' and when does it make sense to use the 'Centralized Set'?
Thanks in advance.
Barnet Sherman
(1) https://themodelmill.com/data-normalization-and-standardization/
Maybe there is no generic answer for that question, but I think it is more useful to adopt [-1, 1] when the original data include both positive and negative numbers and adopt [0, 1] otherwise. In the first case, you can normalize data dividing all the values by the max absolute value.
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