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Sorry if this question has been asked before--I am having trouble searching this topic since I'm not sure of my wording.

Say you have a classification problem where there are more than two labels which are discrete but roughly correspond to an increase in some quality--call these labels A, B, and C. Also say in this problem it would be preferrable to over-estimate that quality, rather than to underestimate. Is there a type of metric that captures this skew and penalizes a predicted A on an actual B more than it penalizes a predicted C on an actual B? Or is this preference better handled in a different part of data science methodology?

How about treating the problem as an ordinal classification problem so that you assume there is an intrinsic order on your classes https://www.cs.waikato.ac.nz/~eibe/pubs/ordinal_tech_report.pdf

– Julio Jesus – 2020-12-02T19:55:27.083