A comparison of some error estimates for neural network models

Authors:
Robert Tibshirani
Affiliations:
Department of Preventive Medicine and Biostatistics and Department of Statistics, University of Toronto, Toronto, Ontario, Canada
Venue:
Neural Computation
Year:
1996

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Abstract

We discuss a number of methods for estimating the standard error of predicted values from a multilayer perceptron. These methods include the delta method based on the Hessian, bootstrap estimators, and the “sandwich” estimator. The methods are described and compared in a number of examples. We find that the bootstrap methods perform best, partly because they capture variability due to the choice of starting weights.