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Tree‐structured classifiers

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Abstract A tree‐structured classifier is a decision tree for predicting a class variable from one or more predictor variables. THAID was the first such algorithm. This article focuses on the classification and regression trees CART® C4.5, and GUIDE methods. The algorithms are briefly reviewed and their similarities and differences are compared on a real data set and by simulation. Copyright © 2010 John Wiley & Sons, Inc. This article is categorized under: Statistical Learning and Exploratory Methods of the Data Sciences > Classification and Regression Trees (CART)

RPART (left) and GUIDE (right) trees with nodes predicted as ‘band’ and ‘noband’ colored dark and light gray, respectively. At each intermediate node, an observation goes to the left branch if and only if the posted condition is satisfied. The sample size and error rate are printed on the left and bottom of each terminal node..

[ Normal View | Magnified View ]

C4.5 tree with nodes predicted as ‘band’ and ‘noband’ colored dark and light gray, respectively. The sample size and error rate are printed on the left and bottom of each terminal node..

[ Normal View | Magnified View ]

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Statistical Learning and Exploratory Methods of the Data Sciences > Classification and Regression Trees (CART)

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