Constructing A Decision Tree Using Information Gain
A decision tree can be constructed top-down using the information gain in the following way:
- begin at the root node
- determine the attribute with the highest information gain which is not already used as an ancestor node
- add a child node for each possible value of that attribute
- attach all examples to the child node where the attribute values of the examples are identical to the attribute value attached to the node
- if all examples attached to the child node can be classified uniquely add that classification to that node and mark it as leaf node
- 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
Read more about this topic: Information Gain In Decision Trees
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