Experiments on real datasets find that balancing methods increase predictive multiplicity in Rashomon sets of models, measured via ambiguity, discrepancy, and a new obscurity metric.
& Averbuch-Elor, H
2 Pith papers cite this work. Polarity classification is still indexing.
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Compares counterfactual generation methods with balancing strategies on bank failure data, finding NICF with cost-sensitive learning produces the highest quality explanations on validity, proximity, and sparsity.
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An Experimental Study on the Rashomon Effect of Balancing Methods in Imbalanced Classification
Experiments on real datasets find that balancing methods increase predictive multiplicity in Rashomon sets of models, measured via ambiguity, discrepancy, and a new obscurity metric.
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Explainable bank failure prediction models: Counterfactual explanations to reduce the failure risk
Compares counterfactual generation methods with balancing strategies on bank failure data, finding NICF with cost-sensitive learning produces the highest quality explanations on validity, proximity, and sparsity.