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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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