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. C 2011 John Wiley & Sons, Inc.
whereas GUIDE can split on combinations of two variables at a time. If there are missing values, CART and CRUISE use alternate splits on other variables when needed, C4.5 sends each observation with a missing value in a split through every branch using Volume 1, January/February 2011 2011 John Wiley & …
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