  We introduce an exact distributed algorithm to train Random Forest models as
well as other decision forest models without relying on approximating best
split search. We explain the proposed algorithm and compare it to related
approaches for various complexity measures (time, ram, disk, and network
complexity analysis). We report its running performances on artificial and
real-world datasets of up to 18 billions examples. This figure is several
orders of magnitude larger than datasets tackled in the existing literature.
Finally, we empirically show that Random Forest benefits from being trained on
more data, even in the case of already gigantic datasets. Given a dataset with
17.3B examples with 82 features (3 numerical, other categorical with high
arity), our implementation trains a tree in 22h.
