Is it a good practice to evaluate model performance by comparing the metrics of rescaled (inverse transformed) predictions and true target values?
The error as a percentage of the true value in each case is probably the same - it just seems bigger because the standardised values are a lot smaller than the unstandardised ones (standardised values will be mostly between -1 and 1 and the unstandardised values will be whatever they are in your dataset). Try measuring the fit of your linear model using Pearson’s correlation coefficient, checking how your predicted values correlate to the true values. This should be the same for both the scaled and unscaled cases and will give you a better idea of whether your model is good. You could also plot your predicted values against the true values in a scatter plot so that you can see roughly what’s going on.