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.
GUIDE and CRUISE use chi squared tests, and QUEST uses chi squared tests for unordered variables and analysis of variance (ANOVA) tests for ordered variables. CTree,14 an-other unbiased method, uses permutation tests. Pseu-docode for the GUIDE algorithm is given in Algo-rithm 2. The CRUISE, GUIDE, and QUEST trees are pruned the same way as CART.
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