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资源说明：分类和回归树
Classification and regression trees are machine-learningmethods for constructing
predictionmodels from data. Themodels 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. Classification
trees are designed for dependent variables that take a finite number
of unordered values, with prediction error measured in terms of misclassification
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 introduction
to the subject by reviewing some widely available algorithms and
comparing their capabilities, strengths, and weakness in two examples. C 2011 John
Wiley & Sons, Inc. WIREs Data Mining Knowl Discov 2011 1 14–23 DOI: 10.1002/widm.8
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