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. regression trees are for dependent variables that take continuous or ordered discrete values, with prediction error typically measured by the squared difference between the observed and predicted values. This article gives an in- troduction to the subject by reviewing some widely available algorithms and comparing their capabilities, strengths, and weakness in two examples.
FIGURE 1| Partitions (left) and decision tree structure (right) for a classification tree model with three classes labeled 1, 2, and 3. At each intermediate node, a case goes to the left child node if and only if the condition is satisfied. The predicted class is given beneath each leaf node. unordered variable with m distinct unordered values
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