Decision Tree Grafting From the All Tests But One Partition

  title={Decision Tree Grafting From the All Tests But One Partition},
  author={Geoffrey I. Webb},
Decision tree grafting adds nodes to an existing decision tree with the objective of reducing prediction error. A new grafting algorithm is presented that considers one set of training data only for each leaf of the initial decision tree, the set of cases that fail at most one test on the path to the leaf. This new technique is demonstrated to retain the error reduction power of the original grafting algorithm while dramatically reducing compute time and the complexity of the inferred tree… CONTINUE READING
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