Information Gain in Decision Trees - Constructing A Decision Tree Using Information Gain

Constructing A Decision Tree Using Information Gain

A decision tree can be constructed top-down using the information gain in the following way:

  1. begin at the root node
  2. determine the attribute with the highest information gain which is not already used as an ancestor node
  3. add a child node for each possible value of that attribute
  4. attach all examples to the child node where the attribute values of the examples are identical to the attribute value attached to the node
  5. if all examples attached to the child node can be classified uniquely add that classification to that node and mark it as leaf node
  6. go back to step two if there are unused attributes left, otherwise add the classification of most of the examples attached to the child node

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