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Machine learning is a prominent topic in the field of AI.
Rule learning is a means to learn rules from instance data to classify unseen instances.
Decision tree learning can be used for concept learning, rule learning, or for learning of other discrete valued functions.
The ID3 family of algorithms infers decision trees by growing them from the root downward in a greedy manner.
ID3 searches a complete hypothesis space.
ID3’s inductive bias includes a preference for smaller trees; it grows trees only as large as needed.
A variety of extensions to basic ID3 have been developed; extensions include: methods for post-pruning trees, handling real-valued attributes, accommodating training examples with missing attribute values, or using alternative selection measures.