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Decision forests, including Random Forests and Gradient Boosting Trees, haverecently demonstrated state-of-the-art performance in a variety of machinelearning settings. Decision forests are typically ensembles of axis-aligneddecision trees; that is, trees that split only along feature dimensions. Incontrast, many recent extensions to decision forests are based on axis-obliquesplits. Unfortunately, these extensions forfeit one or more of the favorableproperties of decision forests based on axis-aligned splits, such as robustnessto many noise dimensions, interpretability, or computational efficiency. Weintroduce yet another decision forest, called "Sparse Projection ObliqueRandomer Forests" (SPORF). SPORF uses very sparse random projections, i.e.,linear combinations of a small subset of features. SPORF significantly improvesaccuracy over existing state-of-the-art algorithms on a standard benchmarksuite for classification with >100 problems of varying dimension, sample size,and number of classes. To illustrate how SPORF addresses the limitations ofboth axis-aligned and existing oblique decision forest methods, we conductextensive simulated experiments. SPORF typically yields improved performanceover existing decision forests, while mitigating computational efficiency andscalability and maintaining interpretability. SPORF can easily be incorporatedinto other ensemble methods such as boosting to obtain potentially similargains.

Sparse Projection Oblique Randomer Forests