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Abstract:We argue that, when establishing and benchmarking Machine Learning (ML) models, the research community should favour evaluation metrics that better capture the value delivered by their model in practical applications. For a specific class of use cases -- selective classification -- we show that not only can it be simple enough to do, but that it has import consequences and provides insights what to look for in a ``good'' ML model.
* Fabio Casati, Pierre-Andr\'e No\"el and Jie Yang (2021, December
14). On the Value of ML Models [Poster presentation]. Workshop on Human and
Machine Decisions, NeurIPS 2021, virtual.
https://sites.google.com/view/whmd2021* Poster presentation at Workshop on Human and Machine Decisions at
NeurIPS 2021 (WHMD 2021). https://sites.google.com/view/whmd2021