  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 of parametric models. We use data
depths and a fast resampling-based algorithm to implement a feature selection
procedure using e-values, providing consistency results. For a $p$-dimensional
feature space, this procedure requires fitting only the full model and
evaluating $p+1$ models, as opposed to the traditional requirement of fitting
and evaluating $2^p$ models. Through experiments across several model settings
and synthetic and real datasets, we establish that the e-values method as a
promising general alternative to existing model-specific methods of feature
selection.
