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The construction of classification trees is nearly always top-down, locally optimal, and data-driven. Such recursive designs are often globally inefficient, for instance, in terms of the mean depth necessary to reach a given classification rate. We consider statistical models for which exact global optimization is feasible, and thereby demonstrate that recursive and global procedures may result in very different tree graphs and overall performance