Constructing Prediction Intervals with Neural Networks: An Empirical Evaluation of Bootstrapping and Conformal Inference Methods. (arXiv:2210.05354v1 [stat.ML])

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 …

arxiv bootstrapping evaluation inference networks neural networks prediction