I am composing a regression tree to determine corporate dividends with dividends as the determinant and 13 predictor variables. To check the accuracy of the tree I am using RMSE values computed using a random sample set. The problem is my RMSE values are huge since the range of dividends (the determinant) is so large.
I was wondering if I should scale the determinant or maybe all of the input data? If I scale the data I get a smaller RMSE but the tree is difficult to read since it has scaled values. Thanks.