In the left column, I have an ordinal integer field. In the right column, I have a scaled float feature.

Should I scale the ordinal field since it is getting so much bigger than the other feature?

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  • $\begingroup$ scaled float feature. Would you elaborate what could be understood from it? $\endgroup$ Jul 9, 2020 at 13:12
  • $\begingroup$ Left column does not seem to convey the idea of any Scaling. A substantive description is needed. $\endgroup$ Jul 9, 2020 at 13:16

1 Answer 1


We scale data because certain algorithm will not work optimally esp. Gradient descent. You may check the internet for further detail.

Coming to the exact question -
I am assuming, you have done the analysis that the Oridinal feature will be used as a Continuous(Not Categorical) feature with its respective values. So, I will ignore this point

Models don't see the feature scale, it looks for the respective variance, interaction with other features, Correlation with the target etc.(in a very simple language) and scaling will not hamper these parameters.

So, you must scale if that is your only concern.
You may ignore it for some Models e.g. Decision Tree/RF will work fine even without scaling.


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