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7. Modeling with Decision Trees > Pruning the Tree

Pruning the Tree

One problem with training the tree using the methods described so far is that it can become overfitted—that is, it can become too specific to the training data. An overfitted tree may give an answer as being more certain than it really is by creating branches that decrease entropy slightly for the training set, but whose conditions are actually completely arbitrary.

Decision Trees in the Real World

Because decision trees are so easy to interpret, they are among the most widely used data-mining methods in business analysis, medical decision-making, and policy-making. Often, a decision tree is created automatically, and an expert uses it to understand the key factors and then refines it to better match her beliefs. This process allows machines to assist experts and to clearly show the reasoning process so that individuals can judge the quality of the prediction.


  

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