Machine Learning

, Volume 32, Issue 1, pp 63–76

Using Model Trees for Classification

  • Eibe Frank
  • Yong Wang
  • Stuart Inglis
  • Geoffrey Holmes
  • Ian H. Witten
Article

DOI: 10.1023/A:1007421302149

Cite this article as:
Frank, E., Wang, Y., Inglis, S. et al. Machine Learning (1998) 32: 63. doi:10.1023/A:1007421302149

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.

Model treesclassification algorithmsM5C5.0decision trees
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Copyright information

© Kluwer Academic Publishers 1998

Authors and Affiliations

  • Eibe Frank
    • 1
  • Yong Wang
    • 1
  • Stuart Inglis
    • 1
  • Geoffrey Holmes
    • 1
  • Ian H. Witten
    • 1
  1. 1.Department of Computer ScienceUniversity of WaikatoHamiltonNew Zealand. E-mail