How should a decision tree handle an attribute that can be anything?
I think your description of anything matches the idea of missing values. Basically by stating that a value could take any value you say that you do not know the value. In standard Breiman description of CART or random forests there is a way to handle missing values. If you use Python stack, however, the implementations does not allow missing values. If that is the case one surrogate would be a different new value for categorical data. For numerical continuous variables I don’t know something better than imputing with average, but it is not quite the same thing.