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Decision trees (also referred to as classification and regression
trees) are the traditional building blocks of data mining and one of
the classic machine learning algorithms. Since their development in
the 1980's they have been the most widely deployed machine learning
based data mining model builder. The attraction lies in the simplicity
of the resulting model, where a decision tree (at least one that is
not too large) is quite easy to view, to understand, and, indeed, to
explain to management. Decision trees do not always deliver the best
performance and represent a trade off between performance and
simplicity of explanation. The decision tree structure can represent
both classification and regression models.
In this chapter we discuss the decision tree structure as a knowledge
representation language (Section 12.1). A
heuristic search algorithm is presented for finding a good decision
tree in Section 12.2. The measures used are
discussed in Section 12.3.
Section 12.4 then illustrates the building of a
decision tree in Rattle and directly through R. The options for
building a decision tree are covered in Section 12.5.
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