  A scientific phenomenon under study may often be manifested by data arising
from processes, i.e. sources, that may describe this phenomenon. In this contex
of multi-source data, we define the "out-of-source" error, that is the error
committed when a new observation of unknown source origin is allocated to one
of the sources using a rule that is trained on the known labeled data. We
present an unbiased estimator of this error, and discuss its variance. We
derive natural and easily verifiable assumptions under which the consistency of
our estimator is guaranteed for a broad class of loss functions and data
distributions. Finally, we evaluate our theoretical results via a simulation
study.
