Feature ranking with recursive feature elimination.
Given an external estimator that assigns weights to features (e.g., the
coefficients of a linear model), the goal of recursive feature elimination
(RFE) is to select features by recursively considering smaller and smaller
sets of features. First, the estimator is trained on the initial set of
features and the importance of each feature is obtained either through a
``coef_`` attribute or through a ``feature_importances_`` attribute.
Then, the least important features are pruned from current set of features.
That procedure is recursively repeated on the pruned set until the desired
number of features to select is eventually reached.