Predicting count data with random forest?

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?


There is a R package called mobForest which can fit a real random forest for count data. It is based on mod() (model-based recursive partitioning) in the party package. It performs Poisson regression if the family argument is specified as poisson(). 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.

Source : Link , Question Author : JEquihua , Answer Author : Randel

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