ML Anthology

Using Model Trees for Classification

Eibe Frank, Yong Wang, Stuart Inglis, Geoffrey Holmes, Ian H. Witten
MLJ 1998 pp. 63-76
doi:10.1023/A:1007421302149 /mlj/1998/frank1998mlj-using/

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.

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Cite

Text

Frank et al. "Using Model Trees for Classification." Machine Learning, 1998. doi:10.1023/A:1007421302149

Markdown

[Frank et al. "Using Model Trees for Classification." Machine Learning, 1998.](https://mlanthology.org/mlj/1998/frank1998mlj-using/) doi:10.1023/A:1007421302149

BibTeX

@article{frank1998mlj-using,
  title     = {{Using Model Trees for Classification}},
  author    = {Frank, Eibe and Wang, Yong and Inglis, Stuart and Holmes, Geoffrey and Witten, Ian H.},
  journal   = {Machine Learning},
  year      = {1998},
  pages     = {63-76},
  doi       = {10.1023/A:1007421302149},
  volume    = {32},
  url       = {https://mlanthology.org/mlj/1998/frank1998mlj-using/}
}