Conditional inference trees in party package R: how to predict the model and variance importance based on OOB data?

Question

I use cforest of the party package in R to calculate conditional inference trees. Similarly to Random Forest, I would like to retrieve variance explained and the variance importance based on the OOB data (I read that Random Forest returns variance explained and variable importance based on OOB data). To do so with cforest I used the following code:

model <- party::cforest(y ~ x1 + x2 + x3 + x4 , data=trainings_set ,  control=cforest_unbiased(ntree=1000, minsplit=25 , minbucket=8 , mtry=4))
model.pred <- predict(model, type="response" , OOB=TRUE)
R2=1 - sum((trainings_set$y-model.pred)^2)/sum((trainings_set$y-mean(trainings_set$y))^2)
varimp_model=party::varimp(model, conditional = TRUE, threshold = 0.2, OOB = TRUE)

I am interested in whether the command OOB=TRUE would lead to the model being predicted and variable importance being returned based on the OOB data of the trainings_set?

I posted this question before under a different title, posting it again (slightly redrafted), I hope someone might be able to provide an answer?

Answer 1

The OOB parameter in cforest function is for a logical defining out-of-bag predictions .

This is only TRUE when you pass a newdata parameter in cforest which is generally a test data frame. If the newdata parameter is there and you have set OOB=TRUE , then you will get out-of-bag predictions on this newdata .

I hope this clarifies your doubt.