I am looking for a way to quantify the performance of multi-class model labelers, and thus compare them. I want to account for the fact that some classes are ‘closer’ than others (for example a car is ‘closer’ to a ‘truck’ than a ‘flower’ is. So, if a labeler classifies a car as a truck that is better than classifying the car as a flower. I am considering using a Jaccard similarity score. Will this do what I want?
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There is no commonly established metric do that. You'll have to write custom code based on manually indicating rank ordered preferences of misclassifications.
Brian Spiering
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