Machine Learning

, Volume 32, Issue 1, pp 63–76

Using Model Trees for Classification

Authors

  • Eibe Frank
    • Department of Computer ScienceUniversity of Waikato
  • Yong Wang
    • Department of Computer ScienceUniversity of Waikato
  • Stuart Inglis
    • Department of Computer ScienceUniversity of Waikato
  • Geoffrey Holmes
    • Department of Computer ScienceUniversity of Waikato
  • Ian H. Witten
    • Department of Computer ScienceUniversity of Waikato
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 trees classification algorithms M5 C5.0 decision trees

Copyright information

© Kluwer Academic Publishers 1998