Corpus ID: 222208710

Prediction intervals for Deep Neural Networks

@article{Mancini2020PredictionIF,
  title={Prediction intervals for Deep Neural Networks},
  author={Tullio Mancini and Hector Calvo-Pardo and J. Olmo},
  journal={ArXiv},
  year={2020},
  volume={abs/2010.04044}
}
  • Tullio Mancini, Hector Calvo-Pardo, J. Olmo
  • Published 2020
  • Computer Science, Mathematics, Economics
  • ArXiv
  • The aim of this paper is to propose a suitable method for constructing prediction intervals for the output of neural network models. To do this, we adapt the extremely randomized trees method originally developed for random forests to construct ensembles of neural networks. The extra-randomness introduced in the ensemble reduces the variance of the predictions and yields gains in out-of-sample accuracy. An extensive Monte Carlo simulation exercise shows the good performance of this novel method… CONTINUE READING

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