  Mondrian Forests are a powerful data stream classification method, but their
large memory footprint makes them ill-suited for low-resource platforms such as
connected objects. We explored using reduced-precision floating-point
representations to lower memory consumption and evaluated its effect on
classification performance. We applied the Mondrian Forest implementation
provided by OrpailleCC, a C++ collection of data stream algorithms, to two
canonical datasets in human activity recognition: Recofit and Banos \emph{et
al}. Results show that the precision of floating-point values used by tree
nodes can be reduced from 64 bits to 8 bits with no significant difference in
F1 score. In some cases, reduced precision was shown to improve classification
performance, presumably due to its regularization effect. We conclude that
numerical precision is a relevant hyperparameter in the Mondrian Forest, and
that commonly-used double precision values may not be necessary for optimal
performance. Future work will evaluate the generalizability of these findings
to other data stream classifiers.
