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