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I have a complex dataset, number of features is much bigger than number of samples. The question is - which features are important for classification into 2 groups.

I think that (after some engeneering of features taking into account possible interactions) ctree is a good instrument for doing this. However I need to present results in a paper.

Do I need to cross-validate ctree in order to be able to present some "significance", e.g. "feature X appears 10 times out of 12 as a root split - may be it is important"? I would go with random forest feature importance (and shuffle the labels to find p-values), but as far as I know RF is parametric and ctree is non-parametric which is preferable...

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    $\begingroup$ To my knowledge random forest is non-parametric, why would you assume otherwise? $\endgroup$ Commented Feb 1, 2019 at 10:40
  • $\begingroup$ but are not the decision trees that random forest build based on parametric assumptions? I am sure that regression trees yes, each split is performed according to distribution of residuals. I also know that - theoretically - random forest can be built based on any type of trees, ctree also, but I do not know where it was implemented... $\endgroup$ Commented Feb 1, 2019 at 11:25
  • $\begingroup$ What assumptions? $\endgroup$ Commented Feb 1, 2019 at 13:32
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    $\begingroup$ Perhaps you should step back and look at the bigger picture: parametric vs non-parametric statistics: projecteuclid.org/download/pdf_1/euclid.ss/1009213726 $\endgroup$ Commented Feb 2, 2019 at 3:29
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    $\begingroup$ stats.stackexchange.com/questions/147587/… $\endgroup$ Commented Feb 2, 2019 at 3:30

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