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Estimation of Generalization Error

  • There are many methods for estimating generalization errors.
  • Single-sample statistics
    • Statistical theory provides estimators for the generalization error in non-linear models with a "large" training set.
  • Split-sample or hold-out validation.
    • The most commonly used method: reserve some data as a "test set”, which must not be used during training.
    • The test set must represent the cases that the ANN should generalize to.
    • A re-run with the random test set provides an unbiased estimate of the generalization error.
    • The disadvantage of split-sample validation is that it reduces the amount of data available for both training and validation.

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