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Ideally we would like to obtain a more complete understanding of variable importance for the set of models that predict almost equally well. This set of almost-equally-accurate predictive models is called the Rashomon set; it is the set of models with training loss below a threshold. A variable importance cloud (VIC) is precisely the joint set of variable importance values for all models in the Rashomon set. Specifically, we define a vector for a single predictive model, each element representing the dependence of the model on a feature. The VIC is the set of such vectors for all models in the Rashomon set.
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