An Exact Probability Metric for Decision Tree Splitting and Stopping

@article{Martin1997AnEP,
  title={An Exact Probability Metric for Decision Tree Splitting and Stopping},
  author={J. Kent Martin},
  journal={Machine Learning},
  year={1997},
  volume={28},
  pages={257-291}
}
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… CONTINUE READING

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