We additionally gain insight into how the choice of ML model affects performance. At the same time, we demonstrate that ensembling is a powerful technique for improving prediction accuracy while simultaneously quantifying uncertainty. Traditionally, RF models are thought to work better on small datasets, while deep learning is reserved for large training sets. But while decision trees are adequate for capturing simple relationships, neural networks can in principle approximate any function to arbitrary accuracy . In our study, we demonstrate that deep learning can be effectively applied to small training datasets on the order of a few hundred training samples.