Improving the precision of classification trees

Details Improving the precision of classification trees Improving the precision of classification trees

2010-11-02

arxiv.org/abs/1011.0608v1

Annals of Applied Statistics 2009, Vol. 3, No. 4, 1710-1737

Statistics

Applications

Published in at http://dx.doi.org/10.1214/09-AOAS260 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Ins

Scientific paper

10.1214/09-AOAS260

Besides serving as prediction models, classification trees are useful for finding important predictor variables and identifying interesting subgroups in the data. These functions can be compromised by weak split selection algorithms that have variable selection biases or that fail to search beyond local main effects at each node of the tree. The resulting models may include many irrelevant variables or select too few of the important ones. Either eventuality can lead to erroneous conclusions. Four techniques to improve the precision of the models are proposed and their effectiveness compared with that of other algorithms, including tree ensembles, on real and simulated data sets.

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