  In predictive maintenance, model performance is usually assessed by means of
precision, recall, and F1-score. However, employing the model with best
performance, e.g. highest F1-score, does not necessarily result in minimum
maintenance cost, but can instead lead to additional expenses. Thus, we propose
to perform model selection based on the economic costs associated with the
particular maintenance application. We show that cost-sensitive learning for
predictive maintenance can result in significant cost reduction and fault
tolerant policies, since it allows to incorporate various business constraints
and requirements.
