# Count data model validation

I'm using different models to model count data, the purpose of modelling is prediction. Values vary from 0 to 7. I try to use cross-validation method to assess out-of-sample predictive perfomance, but what error measure should I use? Is RMSE enough? What other methods of models comparing and assessment can I use?

You can also define your own error function, if that is not suitable for your needs. Say, if small errors of 1-2 are not important when the baseline is large, you could define some sort of relative error (say $|y_i - \hat y_i|/y_i$).
• It's impossible to answer this question in general. The error measure you use should represent the cost of that error that you are trying to minimize. If you only care about hits/misses you could use something akin to classification error: $\sum_i^n I(y_i \neq \hat y_i)/n$ where all errors, large or small, count the same. What is the underlying problem you are solving? – ilir Apr 30 '14 at 8:30