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 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.
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© 2003 Springer-Verlag Berlin Heidelberg
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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. https://doi.org/10.1007/978-3-540-39592-8_9
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DOI: https://doi.org/10.1007/978-3-540-39592-8_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-20256-1
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