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Basically my decision tree can’t classify a value using the normal algorithm.
I get to a node, and there are two options (say, sunny and windy), but at this node my value is different (for example, rainy).
Are there any methods to deal with this, e.g. change the tree or just estimate based on other data?
I was thinking of assigning the most common value at that node but this is just a guess.
Have you considered fuzzy logic for the rich/poor continuum? As for things that can’t be expressed as a continuum, I can’t think of a way it can be done. Rainy weather, for example, is so fundamentally different from sunny and windy weather in how we experience and react to it, I’m not sure how you expect a computer (or whatever it is you’re writing your decision tree for) to figure out what to do. (Aside from simply having an “I don’t know what to do” output state, but I’m assuming you wanted something more meaningful than that.)