  Random Forests are one of the most popular classifiers in machine learning.
The larger they are, the more precise is the outcome of their predictions.
However, this comes at a cost: their running time for classification grows
linearly with the number of trees, i.e. the size of the forest. In this paper,
we propose a method to aggregate large Random Forests into a single,
semantically equivalent decision diagram. Our experiments on various popular
datasets show speed-ups of several orders of magnitude, while, at the same
time, also significantly reducing the size of the required data structure.
