I have many predictors and therefore created a cforest and used varimp to determine the most important variable. However, it is not easy for me to interpret the results. One concrete thing I do not understand is:
I ran it several times (I also tried different values for mtry) and Predictor A is constistently ranked rather high (around 0.08), whereas Predictor B always has an importance score around 0.
However, if I crosstab the response variable with Predictor A and B respectively and run a a Fisher-exact test, I get a p-value of 0.2 for Predictor A and a p-value of 0.02 for predictor B.
I guess that significance and variable importance are different concepts, but still it seems quite counterintuitive to me that there is a significant association between Predictor B and the response, but apparently, according to the varimp-ranking, Predictor B has no impact at all.
Could you give me a hint why such a result can occur?