Why is another term for StandardScaler is Z-Score Normalization if it uses Standardization?

Question

I found this online: "StandardScaler or Z-Score Normalization is one of the feature scaling techniques, here the transformation of features is done by subtracting from the mean and dividing by standard deviation. This is often called Z-score normalization. The resulting data will have the mean as 0 and the standard deviation as 1."

To sum it up: why is it called Z-Score Normalization if it uses a Standardization technique?

What I'm thinking is that if it is called Z-Score Normalization shouldn't it use a Normalization technique rather than a Standardization one?

Answer 1

According to sklearn StandardScaler documentation:

StandardScaler standardize features by removing the mean and scaling to unit variance.

The standard score of a sample x is calculated as:

z = (x - u) / s (The formula for calculating a z-score)

So, both of StandardScaler (standard normalization) and Z-Score Normalization use the same formula and they are equivalent.