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I have a data set with 24 predictor variables, all continuous, but with different scales and potential collinearity. I’m trying to decide whether to use randomForest or cforest in party with conditional importance permutation.

I recognize that I should probably use cforest if I want to overcome variable selection bias, but I find the ability to get partial dependence plots and percent variance explained from the randomForest package to be quite appealing.

I was wondering if anyone knew if it were possible to get partial dependence plots and percent variance explained from cforest?

Also, it appears that ctree uses a significance test to select variables; is this the same for cforest? And how might I get these significance values for each variable in cforest?

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ctree makes unbiased variable split selections. However, randomForest calculates two feature importance metrics. The mostly worthless one is based on split selections. The useful one is based on permutation tests. cforest is very slow and not worth it in my experience. I would just use randomForest and focus on permutation based importance measures. – Shea Parkes Sep 14 '12 at 19:52

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