Machine Learning with Operational Costs
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...
Main Authors: | Tulabandhula, Theja, Rudin, Cynthia |
---|---|
Other Authors: | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science |
Format: | Article |
Language: | en_US |
Published: |
Association for Computing Machinery (ACM)
2013
|
Online Access: | http://hdl.handle.net/1721.1/81426 |
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