Up Next
Go up to Top
Go forward to 2 Decision Tree Evaluation

1 Decision-tree Learning

Consider the data on 4 Boolean attributes a, b, c, and d, where d is the target classification.
a b c d
e1 true true false false
e2 false true false true
e3 false true true true
e4 false false true false
e5 true false false false
In this question we will consider decision-tree learning based on this data.
  1. What is a good attribute to split on first? Explain why.
  2. Draw a decision tree that the top-down myopic decision tree learning algorithm could build. For each node (including the leaves) show which examples are used to determine the classification at that node. (The root note of the tree will be labelled with the list of all of the examples).
  3. Explain how the learning bias inherent in learning decision-trees can be used to classify unseen instances. Give an instance that is not in the training data, show how the above tree classifies that instance. Justify why this is an appropriate classification.
  • Solution to part (a)
  • Solution to part(b)
  • Solution to part (c)

  • Computational Intelligence online material, ©David Poole, Alan Mackworth and Randy Goebel, 1998

    Up Next