## Machine Learning with Operational Costs

*Theja Tulabandhula, Cynthia Rudin*; 14(Jul):1989−2028, 2013.

### Abstract

This work proposes a way to align statistical modeling with
decision making. We provide a method that propagates the
uncertainty in predictive modeling to the uncertainty in
operational cost, where operational cost is the amount spent by
the practitioner in solving the problem. The method allows us to
explore the range of operational costs associated with the set
of reasonable statistical models, so as to provide a useful way
for practitioners to understand uncertainty. To do this, the
operational cost is cast as a regularization term in a learning
algorithm's objective function, allowing either an optimistic or
pessimistic view of possible costs, depending on the
regularization parameter. From another perspective, if we have
prior knowledge about the operational cost, for instance that it
should be low, this knowledge can help to restrict the
hypothesis space, and can help with generalization. We provide a
theoretical generalization bound for this scenario. We also show
that learning with operational costs is related to robust
optimization.

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