As a statistical model, the predicted output for each structure is a distribution of energies, which enables an assessment of the associated uncertainty for each of the test structures. The uncertainty in final energetic predictions, as well as ranking, is an important consideration in applying a statistical machine learning model in applications of CSP. The acceptable level of uncertainty for a particular application can be used in selecting training set sizes and the prediction uncertainties are important in the interpretation of results, for example the probability that a predicted, as-yet unobserved polymorph of a pharmaceutical molecule is lower in energy than known crystal structures. The uncertainties in relative energies can also be incorporated in probabilistic interpretations of structure-energy-property maps that have been developed for materials discovery using CSP.