Designing off-sample performance metrics
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Modern machine learning systems are traditionally designed and tested with the overall goal of achieving the best possible performance on average. In this work, we consider an approach to building learning systems which treats the question of "how should we quantify good off-sample performance?" as a key design decision. We describe this proposal using a simple and general formulation, place the current dominant paradigm within the proper historical context, and then survey the literature for more recent developments that depart from tradition and can be viewed as special cases of our proposed methodology.
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