Much of the functionality now in
sklearn.feature_selection.univariate_selection._BaseFilter [1] applies more
generally to all feature selection: extracting a column subset of some X
given some mask (transform), reversing that operation (inverse_transform)
and reporting the mask itself (get_support). It should (without any
controversy, I expect) be pulled out into a mixin as I do in [2].
There I name it FeatureSelectionMixin, but there is already
a sklearn.feature_selection.selector_mixin.SelectorMixin for general
estimators which also assign features importances (or they can be inferred
from coefs_). This naming is potentially confusing, so I was wondering if
someone could come up with a better name that makes it clear that the
former is more general (SelectorMixin will be a sub-class of
FeatureSelectionMixin).
Any ideas?
Thanks,
- Joel
[1]
https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/feature_selection/univariate_selection.py#L250
[2]
https://github.com/jnothman/scikit-learn/commit/9b8fb21bedc4da3eed5583c3e7e5f2d2b4b548f8
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