# Machine learning with operational costs

@article{Tulabandhula2013MachineLW, title={Machine learning with operational costs}, author={Theja Tulabandhula and Cynthia Rudin}, journal={ArXiv}, year={2013}, volume={abs/1112.0698} }

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…

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