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Interpretation of feature importance values often relies on the relative
order of the features rather than on the value itself, referred to as ranking.
However, the order may be unstable due to the small sample sizes used in
calculating the importance values. We propose that post-hoc importance methods
produce a ranking and simultaneous confident intervals for the rankings. Based
on pairwise comparisons of the feature importance values, our method is
guaranteed to include the ``true'' (infinite sample) ranking with high
probability and allows for selecting top-k sets.
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