Goodness-of-Fit Measures for Induction Trees

  • Gilbert Ritschard
  • Djamel A. Zighed
Conference paper

DOI: 10.1007/978-3-540-39592-8_9

Part of the Lecture Notes in Computer Science book series (LNCS, volume 2871)
Cite this paper as:
Ritschard G., Zighed D.A. (2003) Goodness-of-Fit Measures for Induction Trees. In: Zhong N., Raś Z.W., Tsumoto S., Suzuki E. (eds) Foundations of Intelligent Systems. ISMIS 2003. Lecture Notes in Computer Science, vol 2871. Springer, Berlin, Heidelberg

Abstract

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 R2’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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

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

Personalised recommendations