Confident feature ranking

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 sets for the ranking. Based on pair-wise comparisons of the feature importance values, our method is guaranteed to include the ``true'' (infinite sample) ranking with high probability and allows for a selection of top-k sets.