Machine Learning

, Volume 28, Issue 2, pp 257–291

An Exact Probability Metric for Decision Tree Splitting and Stopping

  • J. Kent Martin

DOI: 10.1023/A:1007367629006

Cite this article as:
Martin, J.K. Machine Learning (1997) 28: 257. doi:10.1023/A:1007367629006


ID3's information gain heuristic is well-known to be biased towards multi-valued attributes. This bias is only partially compensated for by C4.5's gain ratio. Several alternatives have been proposed and are examined here (distance, orthogonality, a Beta function, and two chi-squared tests). All of these metrics are biased towards splits with smaller branches, where low-entropy splits are likely to occur by chance. Both classical and Bayesian statistics lead to the multiple hypergeometric distribution as the exact posterior probability of the null hypothesis that the class distribution is independent of the split. Both gain and the chi-squared tests arise in asymptotic approximations to the hypergeometric, with similar criteria for their admissibility. Previous failures of pre-pruning are traced in large part to coupling these biased approximations with one another or with arbitrary thresholds; problems which are overcome by the hypergeometric. The choice of split-selection metric typically has little effect on accuracy, but can profoundly affect complexity and the effectiveness and efficiency of pruning. Empirical results show that hypergeometric pre-pruning should be done in most cases, as trees pruned in this way are simpler and more efficient, and typically no less accurate than unpruned or post-pruned trees.

pre-pruningpost-pruninghypergeometricFisher's Exact Test
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Copyright information

© Kluwer Academic Publishers 1997

Authors and Affiliations

  • J. Kent Martin
    • 1
  1. 1.Department of Information and Computer ScienceUniversity of California, IrvineIrvine