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?

**Answer**

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.

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