Chapter 10. Classification Trees

In Chapter 9, Linear Regression we discussed regression. In the previous chapter, we were interested in classification using k-NN and Naïve Bayes. In this chapter, we will continue the topic of classification and discuss it in the context of decision trees. Decision trees notably allow class predictions (group membership) of previously unseen observations (testing datasets or prediction datasets) using statistical criteria applied on the seen data (training set).

Here, we will briefly examine the statistical criteria of six algorithms:

  • ID3
  • C4.5
  • C5.0
  • Classification and regression trees (CART)
  • Random forest
  • Conditional inference trees

We will also examine how to use decision trees in R, notably, how to measure the reliability ...

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