  Safe operation of machine learning models requires architectures that
explicitly delimit their operational ranges. We evaluate the ability of anomaly
detection algorithms to provide indicators correlated with degraded model
performance. By placing acceptance thresholds over such indicators, hard
boundaries are formed that define the model's coverage. As a use case, we
consider the extraction of exoplanetary spectra from transit light curves,
specifically within the context of ESA's upcoming Ariel mission. Isolation
Forests are shown to effectively identify contexts where prediction models are
likely to fail. Coverage/error trade-offs are evaluated under conditions of
data and concept drift. The best performance is seen when Isolation Forests
model projections of the prediction model's explainability SHAP values.
