Goodness-of-Fit Measures for Induction Trees

  • Gilbert Ritschard
  • Djamel A. Zighed
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2871)


This paper is concerned with the goodness-of-fit of induced decision trees. Namely, we explore the possibility to measure the goodness-of-fit as it is classically done in statistical modeling. We show how Chi-square statistics and especially the Log-likelihood Ratio statistic that is abundantly used in the modeling of cross tables, can be adapted for induction trees. The Log-likelihood Ratio is well suited for testing the significance of the difference between two nested trees. In addition, we derive from it pseudo R 2’s. We propose also adapted forms of the Akaike (AIC) and Bayesian (BIC) information criteria that prove useful in selecting the best compromise model between fit and complexity.


Terminal Node American Statistical Association Induction Tree Asymptotic Variance Tree Building 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Gilbert Ritschard
    • 1
  • Djamel A. Zighed
    • 2
  1. 1.Dept of EconometricsUniversity of GenevaGeneva 4Switzerland
  2. 2.Laboratoire ERICUniversity of Lyon 2Bron CedexFrance

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