Decision Tree Pruning: Biased or Optimal?

We evaluate the performance of weakestlink pruning of decision trees using crossvalidation. This technique maps tree pruning into a problem of tree selection: Find the best (i. e. the right-sized) tree, from a set of trees ranging in size from the unpruned tree to a null tree. For samples with at least 200 cases, extensive empirical evidence supports the following conclusions relative to tree selection: a fl lo-fold cross-validation is nearly unbiased; b not pruning a covering tree is highly biased; (c) lo-fold cross-validation is consistent with optimal tree selection for large sample sizes and (d) the accuracy of tree selection by lo-fold cross-validation is largely dependent on sample size, irrespective of the population distribution.

Authors

• Sholom M. Weiss Nitin Indurkhya

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