I seem to have noticed that random forests don’t perform as well when the data is high dimensional and sparse (eg text classification) – is anyone aware of any tricks to make them perform better in that situation?
(I know that eg a linear SVM often performs well w/ high dimensional sparse datasets, but sometimes I don’t want to worry about scaling the variables properly, or sometimes I have a mixed sparse-dense dataset, etc.)
Thanks!