  Artificial neural networks (ANNs) are popular tools for accomplishing many
machine learning tasks, including predicting continuous outcomes. However, the
general lack of confidence measures provided with ANN predictions limit their
applicability. Supplementing point predictions with prediction intervals (PIs)
is common for other learning algorithms, but the complex structure and training
of ANNs renders constructing PIs difficult. This work provides the network
design choices and inferential methods for creating better performing PIs with
ANNs. A two-step experiment is executed across 11 data sets, including an
imaged-based data set. Two distribution-free methods for constructing PIs,
bootstrapping and conformal inference, are considered. The results of the first
experimental step reveal that the choices inherent to building an ANN affect PI
performance. Guidance is provided for optimizing PI performance with respect to
each network feature and PI method. In the second step, 20 algorithms for
constructing PIs, each using the principles of bootstrapping or conformal
inference, are implemented to determine which provides the best performance
while maintaining reasonable computational burden. In general, this trade-off
is optimized when implementing the cross-conformal method, which maintained
interval coverage and efficiency with decreased computational burden.
