Choosing Between Two Learning Algorithms Based on Calibrated Tests

@inproceedings{Bouckaert2003ChoosingBT,
  title={Choosing Between Two Learning Algorithms Based on Calibrated Tests},
  author={Remco R. Bouckaert},
  booktitle={ICML},
  year={2003}
}
Designing a hypothesis test to determine the best of two machine learning algorithms with only a small data set available is not a simple task. Many popular tests suffer from low power (5x2 cv [2]), or high Type I error (Weka’s 10x10 cross validation [11]). Furthermore, many tests show a low level of replicability, so that tests performed by different scientists with the same pair of algorithms, the same data sets and the same hypothesis test still may present different results. We show that… CONTINUE READING
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Choosing between two learning algorithms based on calibrated tests

  • R. R. Bouckaert
  • Working paper, Computer Science Department…
  • 2003
Highly Influential
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Machine Learning

  • T. Mitchell
  • McGraw Hil
  • 1997
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