Counterfactual explanations for LVQ classifiers can be computed by solving closed-form linear, quadratic, or non-convex QCQP programs derived from the nearest-prototype rule, yielding faster and closer counterfactuals than generic optimizers.
Khandani, Adlar J
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Efficient computation of counterfactual explanations of LVQ models
Counterfactual explanations for LVQ classifiers can be computed by solving closed-form linear, quadratic, or non-convex QCQP programs derived from the nearest-prototype rule, yielding faster and closer counterfactuals than generic optimizers.