Confidence and prediction intervals for neural network ensembles
John G. Carney, Pádraig Cunningham, U. Bhagwan · 2003
We propose a technique that uses the bootstrap method to estimate confidence and prediction intervals for neural network (regression) ensembles. Our proposed technique can be applied to any ensemble technique that uses the bootstrap to generate the training sets for the ensemble, such as bagging and balancing. Confidence and prediction intervals are estimated that include a significantly improved estimate of underlying model uncertainty (i.e.) the uncertainty of our estimate of the "true" regression. Unlike existing techniques, this estimate of uncertainty will vary according to which ensemble technique is used if the effect of using a specific ensemble technique is to produce less model uncertainty than using another ensemble technique, then this will be reflected in the confidence and prediction intervals. Preliminary results illustrate how our technique can provide more accurate confidence and prediction intervals (intervals that better reflect the desired level of confidence (e.g.) 90%, 95%, etc.) for neural network ensembles than previous attempts.