Efficient algorithms for decision tree cross-validation
<|reference_start|>Efficient algorithms for decision tree cross-validation: Cross-validation is a useful and generally applicable technique often employed in machine learning, including decision tree induction. An important disadvantage of straightforward implementation of the technique is its computational overhead. In this paper we show that, for decision trees, the computational overhead of cross-validation can be reduced significantly by integrating the cross-validation with the normal decision tree induction process. We discuss how existing decision tree algorithms can be adapted to this aim, and provide an analysis of the speedups these adaptations may yield. The analysis is supported by experimental results.<|reference_end|>
@article{blockeel2001efficient,
    title={Efficient algorithms for decision tree cross-validation},
    author={Hendrik Blockeel and Jan Struyf},
    journal={H. Blockeel and J. Struyf. Efficient algorithms for decision tree
  cross-validation. Proceedings of the Eighteenth International Conference on
  Machine Learning (C. Brodley and A. Danyluk, eds.), Morgan Kaufmann, 2001,
  pp. 11-18},
    year={2001},
    archivePrefix={arXiv},
    eprint={cs/0110036},
    primaryClass={cs.LG}
}