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I have a classification problem where my dependent variable has 3 possible values. They are ordered variables.

I have been using trees and random forests in R to tackle this problem, but have to convert the problem into a binary one, so i'm predicting if the dependent variable is or isn't 1, then is or isn't 2, then is or isn't 3 in 3 different models.

Can I build a random forest or decision tree with an ordinal variable as the dependent variable?

Are there any better ways I should be approaching this problem?

Thanks

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I know that randomForest in R, at least, will handle 3 level categorical variables just fine. You could also do 1 vs other and 2 vs 3; you'd only need 2 models. But I'm not sure if this makes a difference.

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With the OneR package (which basically builds a one level tree with the best predictor) you can have any number of levels in all input variables and in the output variable:

https://cran.r-project.org/web/packages/OneR/vignettes/OneR.html

(Full disclosure: I am the author of this package)

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    $\begingroup$ This works really well. I wished something with the same interface would exist for multi-level trees. $\endgroup$
    – graup
    Mar 26, 2018 at 14:50
  • $\begingroup$ @graup: Thank you for the great feedback - I really appreciate that! $\endgroup$
    – vonjd
    Mar 26, 2018 at 15:38

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