The tag feature-scaling seems to convey that one of the scaling methods is Standard Normal Distribution. Further, I read an Answer on this site saying that Mean Normalization is a form of feature scaling.
What is the difference between two approaches to scaling?
Note: I think that statistics and mathematics of normalization do differ.
The terms standardization and normalization are often used interchangeably. However, strictly speaking they do refer to distinct feature transformations.
Normalization, also called feature scaling usually means scaling the data between 0 and 1. There are many approaches that can be used to achieve this. One common way is by
$x' = \frac{x - x_{min}}{x_{max} - x_{min}}$
Standardization transforms the feature to have a mean 0 and a standard deviation of 1. This is also called z-scoring and can be achieved by
$x_i' = \frac{x_i - \bar{x}}{s}$
where $\bar{x}$ is the mean of the feature and $s$ is the standard deviation of the feature.
Scaling means quantifying or estimating weights to be assigned to different categories of response to a question item after data had been generated in terms of responses, e.g. Strongly agree,agree,somewhat agree -if standard scale (s) are not available for data-processing and analysis. The tag feature-scaling seems to convey that one of the scaling methods is Standard Normal Distribution.Given the meaning and purpose of scaling it is incorrect to say that standard normal distribution serves as a basis of scaling. Similarly, "normalization" of raw data does not invoke scaling of data generated by an experiment or a survey.(Also, the method of normalization cited by authors is incorrect). In view of observations above, the tag feature scaling should be corrected.
