0
$\begingroup$

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?

enter image description here

$\endgroup$
2
  • $\begingroup$ scaled float feature. Would you elaborate what could be understood from it? $\endgroup$ Jul 9 '20 at 13:12
  • $\begingroup$ Left column does not seem to convey the idea of any Scaling. A substantive description is needed. $\endgroup$ Jul 9 '20 at 13:16
0
$\begingroup$

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.

$\endgroup$

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.