# using rmse and mape for model evaluation

ok, so I have a memory-based model to predict the values of variable X and I've also measured the real values of X. I have calculated both the RMSE and MAPE for the difference Xreal-Xpredicted. I know that there is absolutely no point in saying a model is good or bad if the RMSE value is less than a particular value. I was wondering though if there is a rule of thumb providing insights on the subject. For example my data range from 0 to 4 and I have an RMSE of 1,1. Is there any empirical rule suggesting this is a good/bad estimation?

The ratio of the RMSE to the standard deviation of the data is directly related to $R^2$, so for (extensive) discussion about this point, please review questions on r-squared such as stats.stackexchange.com/questions/13314/… and stats.stackexchange.com/questions/14585. – whuber Apr 23 '12 at 18:38
What is the nature of your outcome, $X$? Is it a continuous, ordinal, polytomous, or even binary variable? My concern is whether classification accuracy could be an issue. It's always useful to, at least, describe what metric you're trying to predict. In general, there is no global measure of predictive accuracy. The most adequate substitute is a prespecified minimally sufficient, expected, and superior level of predictive accuracy based on input from an expert in the field. This is a different story if you have a competing predictive model. – AdamO Apr 23 '12 at 20:01