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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.

What is a good attribute to split on first? Explain why.
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).
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.