Learning through Decision Trees

Once the several records of a process variable have been generalized into a pattern, as indicated in the previous paragraph, we need a mechanism to induce relationships among features of the generalized descriptions. In this section we will discuss the virtues of inductive learning through decision trees. 

Once the distinguishing features for each input variable have been extracted through the generalization of descriptions of the available records, these features become the inputs of the inductive learning procedure through decision trees. 

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