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Summary

  • 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.

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