# Should the output of regression models, like SVR, be normalized?

I have a regression problem which I solved using SVR. Accidentally, I normalized my output along with the inputs by removing the mean and dividing by standard deviation from each feature.

Surprisingly, the Rsquare score increased by 10%.

How can one explain the impact of output normalization for svm regression?

In regression problems it is customary to normalize the output too, because the scale of output and input features may differ. After getting the result of the SVR model, you have to add the mean to the result and multiply that by the standard deviation, if you have done that during normalizing.
How can one explain the impact of output normalization for svm regression?