The objectives of this section are:
to introduce you to the decision tree classifier
to explain the various parts of a decision tree
to present you with some advantages of using the decision tree classifier
to define Occam’s Razor and its application in decision tree construction
By the time you have completed this section you will be able to:
to recognize the various parts of a decision tree
to list some advantages of decision tree
to identify the best tree based on Occam’s Razor
There are many techniques used in classification, this section focuses on the Decision Tree classifier. A Decision Tree classifier looks like a tree with leaves and nodes. A decision tree uses information that we know about an item to help us arrive at a particular predefined conclusion. By asking a series of pointed questions a decision tree is constructed, and each directed edge below serves as a question and the nodes of the trees are the corresponding answers, the leaf nodes are the classes that each entry eventually fits into. To the right is a possible decision tree diagram for the Town Hall Information Data set provided in Section 1. As you can see each leaf node is either the class “No Show” or “Attended” and all the other nodes (internal and root nodes) serve as questions that help further split the data.