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Deep networks and decision forests (such as random forests and gradientboosted trees) are the leading machine learning methods for structured andtabular data, respectively. Many papers have empirically compared large numbersof classifiers on one or two different domains (e.g., on 100 different tabulardata settings). However, a careful conceptual and empirical comparison of thesetwo strategies using the most contemporary best practices has yet to beperformed. Conceptually, we illustrate that both can be profitably viewed as"partition and vote" schemes. Specifically, the representation space that theyboth learn is a partitioning of feature space into a union of convex polytopes.For inference, each decides on the basis of votes from the activated nodes.This formulation allows for a unified basic understanding of the relationshipbetween these methods. Empirically, we compare these two strategies on hundredsof tabular data settings, as well as several vision and auditory settings. Ourfocus is on datasets with at most 10,000 samples, which represent a largefraction of scientific and biomedical datasets. In general, we found forests toexcel at tabular and structured data (vision and audition) with small samplesizes, whereas deep nets performed better on structured data with larger samplesizes. This suggests that further gains in both scenarios may be realized viafurther combining aspects of forests and networks. We will continue revisingthis technical report in the coming months with updated results.

When are Deep Networks really better than Decision Forests at small sample sizes, and how?