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## Predictive Learning and Decision Trees

In this chapter, we provide an overview of predictive learning and decision trees. Before introducing formal notation, consider a very simple data set represented by the following data matrix:

 Table 2.1: Asimple data set. Each row represents a data “point” and each column corresponds to an “attribute.” Sometimes, attribute values could be unknown or missing (denoted by a ‘?’ below). TI PE Response 1.0 M2 good 2.0 M1 bad … … … 4.5 M5 ?

Each row in the matrix represents an “observation” or data point. Each column corresponds to an attribute of the observations: TI, PE, and Response, in this example. TI is a numeric attribute, PE is an ordinal attribute, and Response is a categorical ...

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