I am working on Model Selection problem, where there are two models which are predicting revenues for companies using 2 different formulas. So currently I have Actual revenue values and predicted revenue values. By using this data I have to do model selection. So first of all I have tried with various performance measures of Regression such as Explained Variance Score, Mean Absolute Error, Mean Squared Error , RMSE etc. these were giving weired values. So have removed outliers. It worked. But while checking distribution of errors I come to know that its not following Normal Distribution. So no use of applying above performance measures.
So now I am in confusion that how I should deal with this type of data. How to deal with Errors (Regression) which are not following Normal distribution? Is it okay if I will scale predicted and actual values to normal distribution? Please help… Thanks in advance