Corpus ID: 54448334

An empirical study on hyperparameter tuning of decision trees

@article{Mantovani2018AnES,
  title={An empirical study on hyperparameter tuning of decision trees},
  author={R. Mantovani and Tom{\'a}s Horv{\'a}th and Ricardo Cerri and Sylvio Barbon Junior and J. Vanschoren and A. Carvalho},
  journal={ArXiv},
  year={2018},
  volume={abs/1812.02207}
}
Machine learning algorithms often contain many hyperparameters whose values affect the predictive performance of the induced models in intricate ways. Due to the high number of possibilities for these hyperparameter configurations, and their complex interactions, it is common to use optimization techniques to find settings that lead to high predictive accuracy. However, we lack insight into how to efficiently explore this vast space of configurations: which are the best optimization techniques… Expand
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