# How to validate classification model with ordinal information

I have a Naive Bayes model that predicts 3 classes. As you increase each class it means that the condition is more severe. 0 means no condition, 1 is concern and 2 is that they have the condition. I have a model built but I am not sure how to correctly validate the accuracy.

Since 2 is further away from 0 it is less bad if a model classifies a 2 as a 1 instead of a zero. This means that my raw accuracy score is not completely correct.

Any help would be appreciated!

You could compare the inverse of the distance. Say we use the absolute value, meaning $$distance(x,y) = | x-y|$$.
The inverse of the distance from $$0$$ to $$2$$, e.g. $$\frac{1}{|2-0|} = 0.5$$, but from $$1$$ to $$2$$ is $$1$$. You just need to be careful when computing the ditance of a class to itself, setting it always to $$0$$, for example.