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      •   Research Commons
      • University of Waikato Research
      • Computing and Mathematical Sciences
      • Computer Science Working Paper Series
      • 1997 Working Papers
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      •   Research Commons
      • University of Waikato Research
      • Computing and Mathematical Sciences
      • Computer Science Working Paper Series
      • 1997 Working Papers
      • View Item
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      Using model trees for classification

      Frank, Eibe; Wang, Yong; Inglis, Stuart J.; Holmes, Geoffrey; Witten, Ian H.
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      uow-cs-wp-1997-12.pdf
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      Frank, E., Wang, Y., Inglis, S., Holmes, G. & Witten, I.H. (1997). Using model trees for classification. (Working paper 97/12). Hamilton, New Zealand: University of Waikato, Department of Computer Science.
      Permanent Research Commons link: https://hdl.handle.net/10289/1075
      Abstract
      Model trees, which are a type of decision tree with linear regression functions at the leaves, form the basis of a recent successful technique for predicting continuous numeric values. They can be applied to classification problems by employing a standard method of transforming a classification problem into a problem of function approximation. Surprisingly, using this simple transformation the model tree inducer M5’, based on Quinlan’s M5, generates more accurate classifiers than the state-of-the-art decision tree learner C5.0, particularly when most of the attributes are numeric.
      Date
      1997-04
      Type
      Working Paper
      Series
      Computer Science Working Papers
      Report No.
      97/12
      Collections
      • 1997 Working Papers [31]
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