  Virtual Diagnostic (VD) is a deep learning tool that can be used to predict a
diagnostic output. VDs are especially useful in systems where measuring the
output is invasive, limited, costly or runs the risk of damaging the output.
Given a prediction, it is necessary to relay how reliable that prediction is.
This is known as 'uncertainty quantification' of a prediction. In this paper,
we use ensemble methods and quantile regression neural networks to explore
different ways of creating and analyzing prediction's uncertainty on
experimental data from the Linac Coherent Light Source at SLAC. We aim to
accurately and confidently predict the current profile or longitudinal phase
space images of the electron beam. The ability to make informed decisions under
uncertainty is crucial for reliable deployment of deep learning tools on
safety-critical systems as particle accelerators.
