In regression problems, you can use various different metrics to check how well your model is doing:

 * Mean Absolute Deviation (MAD): In $[0, \infty)$, the smaller the better
 * Root Mean Squared Error (RMSE): In $[0, \infty)$, the smaller the better
 * Median Absolute Error (MAE): In $[0, \infty)$, the smaller the better
 * Mean Squared Log Error (MSLE): In $[0, \infty)$, the smaller the better
 * R², coefficient of determination: In $(-\infty, 1]$, the bigger the better

Are there any strong reasons not to use one or the other?