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  • Most algorithms apply a top-down, greedy search through the space of possible trees.
    • e.g., ID3 [5] or its successor C4.5 [6]
  • ID3
    • Learns trees by constructing them top down.
    • Initial question: “Which attribute should be tested at the root of the tree?” ->each attribute is evaluated using a statistical test to see how well it classifies.
    • A descendant of the root node is created for each possible value of this attribute.
    • Entire process is repeated using the training examples associated with each descendant node to select the best attribute to test at that point in the tree.

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