Can a Random Forest be trained to appropriately predict count data?
How would this proceed? I have quite a extensive range of values so classification doesn't really make sense. If I would use regression would I simply truncate the results?
I'm quite lost here. Any ideas?
Best Answer
There is a R package called
mobForest
which can fit a real random forest for count data. It is based onmod()
(model-based recursive partitioning) in theparty
package. It performs Poisson regression if thefamily
argument is specified aspoisson()
. The package is no longer in the CRAN repository, but formerly available versions can be obtained from the archive.If you are not restricted to random forest / bagging, a boosting version is also available for count data. That is,
gbm
(generalized boosted regression models). It can also fit a Poisson model.