Scaling on Machine Learning Models
Scaling should only be applied on features since it has an effect on the training procedure, so when all the features (independent variables) are on the same(or similar) scales, generally training goes faster and it converges to a good solution especially if you're using a gradient-based training algorithm like SGD or Adam, but for the target (dependant) variable you don't need scaling since you would just add extra computation that won't affect the quality of the classifier (regressor) you'll get.
I am slightly confused about the scaling concept in Machine learning models.
In Classification, if the variables are having a different scale, I usually perform scaling on independent variable and label encoding on Target variable and inverse transform the predicted results to get the actual labels
In Regression, if my variables are in different, I know we have to scale the independent variables, should I also scale my Target variable?
Could someone please help me if my understanding is correct in the above scenario and should I scale my Target variable in Regression models?
Thanks in advance.