Usually regression models are evaluated using $R^2$. I understand this metric can be misleading too at times but as far as I understand the first parameter we look at is $R^2$.
There is another parameter which is often used and it is $MAPE$. Both are functions of errors between predicted and true value. I am just wondering if there are certain cases where one should be preferred above another?
$R^2$ can become negative if being used on test data (on which the model is not built). Is this a reason to not use $R^2$ in these cases?
Can anyone give a qualitative insight on where $MAPE$ should be used and where $R^2$ should be used.