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Inductive Bias in Decision Tree Learning

  • Definition: Inductive bias is the set of assumptions that, together with the training data, deductively justifies the classifications assigned by the learner to future instances [1].
  • Central question: How does ID3 generalize from observed training examples to classify unseen instances? What is its inductive bias?
  • ID3 search strategy:
    • ID3 chooses the first acceptable tree it encounters.
    • ID3 selects in favour of shorter trees over longer ones.
    • ID3 selects trees that place the attributes with the highest information gain closest to the root.
  • Approximate inductive bias of ID3: Shorter trees are preferred over larger trees. Trees that place high information gain attributes close to the root are preferred over those that do not.

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