z-score VS min-max normalization Working with data that use different dimensions, you do not want that one dimension dominate.
This means feature scaling!
A very intuitive way is to use min-max scaling so you scale everything between 0 to 1.
What I do not understand and what is not intuitive for me at all is to use z-score for feature scaling.
Why is z-score used?
What is the motivation to not use min-max and to use z-score?
Why is it a good idea to scale your data in standard deviations from the mean?
What was the motivation to use z-score for scaling?
Why is min-max not used all the time?
What problem does z-score solve what min-max does not solve?
hope someone can help me and make it somehow clear.

The answer to your specific question about why z-score normalisation handles outliers better is largely to do with how standard deviations are calculated in the first place.
If there are outliers, then the effect that the deviation from the mean related to those outliers will have on the final statistic (i.e, the standard deviation; the same value that will be used to normalise the feature) will be mitigated by the rest of the deviations within that same feature. In short, standard deviation is an aggregated calculation so individual values will carry less weight with the more observations there are.
Conversely with min-max scaling where the values used to normalise the data will literally be the outliers themselves (assuming there are outliers of course). No aggregating, no averaging, just take the minimum value, take the maximum value and normalise all the observations in the feature relative to those values. If those minimum and maximum values happen to be outliers then you can see how they will impact the resulting normalisation.
As far as I can see, how important this difference is will probably depend on the model that the data is being preprocessed for, and the question of "why those outliers would be kept in the data in the first place" is also valid, but maybe that's another discussion entirely. Anyway, Hope this helps.
