Transcription of Classification and regression trees
{{id}} {{{paragraph}}}
Example: bankruptcy
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.
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 train, which takes three values (rear, front, or four-wheel drive). The X variables are listed in Table 2. Three are unordered (manuf, type ...
Loading..
Tags:
Information
Domain:
Source:
Link to this page:
Please notify us if you found a problem with this document: