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Feature Selection using e-values. (arXiv:2206.05391v2 [stat.ML] UPDATED)
stat.ML updates on arXiv.org arxiv.org
In the context of supervised parametric models, we introduce the concept of
e-values. An e-value is a scalar quantity that represents the proximity of the
sampling distribution of parameter estimates in a model trained on a subset of
features to that of the model trained on all features (i.e. the full model).
Under general conditions, a rank ordering of e-values separates models that
contain all essential features from those that do not.
The e-values are applicable to a wide range …