How should multiclass classifier performance be measured when one type of error is preferred over another?
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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?
classification accuracy metric
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rocinanterocinante
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$begingroup$
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?
classification accuracy metric
asked 1 hour ago
rocinanterocinante
1
New contributor
rocinante is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
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?
classification accuracy metric
asked 1 hour ago
rocinanterocinante
1
New contributor
rocinante is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
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?
classification accuracy metric
classification accuracy metric
asked 1 hour ago
rocinanterocinante
1
New contributor
rocinante is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 1 hour ago
rocinanterocinante
1
New contributor
rocinante is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 1 hour ago
rocinanterocinante
1
New contributor
rocinante is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 1 hour ago
rocinanterocinante
1
asked 1 hour ago
rocinanterocinante
1
1
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rocinante is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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New contributor
rocinante is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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rocinante is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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