Transcription of Classification and regression trees
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Classification and regression trees
Overview Classification and regression trees Wei-Yin Loh Classification and regression trees are machine-learning methods for constructing prediction models from data. The models are obtained by recursively partitioning the data space and fitting a simple prediction model within each partition. As a result, the partitioning can be represented graphically as a decision tree. Clas- sification trees are designed for dependent variables that take a finite number of unordered values, with prediction error measured in terms of misclassifica- tion cost.
duce ensemble models using bagging16 and random forest17 techniques. Table 1 summarizes the features of the algorithms. To see how the algorithms perform in a real ap-plication, we apply them to a data set on new cars for the 1993 model year.18 There are 93 cars and 25 variables. We let the Y variable be the type of drive
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