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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.

Speaker notes:

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