
Contents
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TREE-STRUCTURED REPRESENTATIONS IN CLASSIFICATION TASKS TREE-STRUCTURED REPRESENTATIONS IN CLASSIFICATION TASKS
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Algorithm TREE-CLASS: Algorithm TREE-CLASS:
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NATURAL FREQUENCY TREES NATURAL FREQUENCY TREES
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FROM NATURAL FREQUENCY TREES TO CLASSIFICATION AND DECISION TREES FROM NATURAL FREQUENCY TREES TO CLASSIFICATION AND DECISION TREES
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Radical Pruning Radical Pruning
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FAST-AND-FRUGAL TREES FAST-AND-FRUGAL TREES
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Definition Definition
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Theorem Theorem
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CONSTRUCTING FAST-AND-FRUGAL DECISION TREES CONSTRUCTING FAST-AND-FRUGAL DECISION TREES
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ORDERING OF CUES ORDERING OF CUES
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THE SHAPE OF TREES THE SHAPE OF TREES
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PERFORMANCE OF FAST-AND-FRUGAL DECISION TREES PERFORMANCE OF FAST-AND-FRUGAL DECISION TREES
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Definition Definition
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COMPARING FAST-AND-FRUGAL DECISION TREES TO TRADITIONAL MODELS COMPARING FAST-AND-FRUGAL DECISION TREES TO TRADITIONAL MODELS
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CONCLUSION CONCLUSION
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NOTES NOTES
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23 On the Psychology of the Recognition Heuristic: Retrieval Primacy as a Key Determinant of Its Use
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6 Naive and yet Enlightened: From Natural Frequencies to Fast and Frugal Decision Trees
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Published:April 2011
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Abstract
The trees for classification and for decision that are introduced in this chapter are naïve, fast, and frugal. Why they are “naïve,” that is, why they ignore conditional dependencies between cues, is extensively illustrated. Why they are “frugal,” in the sense that they tend to use much fewer cues than those provided by the environment, is also explained in detail. The trees' “fastness” appears as a consequence of their frugality. The main property of these trees is that they implement one-reason classification/decision in analogy with heuristics for one-reason decision making. The lexicographic nature of these trees is shown with the help of a characterization theorem. As will also become clear, fast-and-frugal trees generalize take-the-best.
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