It is sometimes stated that one does not care about the ''raw'' values of the variable importance outcome:


for each feature X_j using a random forest and rather should just compare PI(X_j) w.r.t their relative size or rank them.

Is there any intuitiv explanation for this? Or is this simply not correct?

I am conducting a ''variable importance study'' on some data set using the R party package by fitting a cforest and calculating the permutation importance (with missing data) using the function: varimp() as described by:

Alexander Hapfelmeier, Torsten Hothorn, Kurt Ulm, and Carolin Strobl. A new variable importance measure for random forests with missing data. Statistics and Computing, 24(1):21–34, 2014.

The obtained results are rather small i.e. in the range of $[0,0.0008]$, nevertheless comparing the relative sizes I can clearly distinguish between less and more important variables.

Thanks in advance


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Browse other questions tagged or ask your own question.