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They are used to represent rules for recursively partitioning the data space into regions from which reliable predictions regarding classes can be made. These regions are usually delimited by axis-parallel or oblique hyperplanes. Axis-parallel hyperplanes are intuitively appealing and have been widely studied. However, there is still room for exploring different approaches. In this paper, a splitting rule that constructs axis-parallel hyperplanes by computing the Nash equilibrium of a game played at the node level is used to induct a Nash Equilibrium Decision Tree for binary classification. 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