Knowing the class distinguishing abilities of the features, to build better decision-making models
Explainability allows end-users to have a transparent and humane reckoning of an ML scheme's capability and utility. ML model's modus opernadi can be explained via the features which trained it. To this end, we found no work explaining the features' importance based on their class-dis...
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2024
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| Online Access: | https://aisel.aisnet.org/amcis2024/dsa/dsa/21/ https://depot.sorbonne.ae/handle/20.500.12458/1638 |
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