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DOC fix ungrammatical sentence in doc of SelectFromModel #19240

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Feb 1, 2021
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DOC fix ungrammatical sentence in doc of SelectFromModel #19240

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e58faff
DOC: fix ungrammatical sentence in doc of SelectFromModel
EdwinWenink Jan 22, 2021
6ce7474
Refer to importance_getter in doc/modules/feature_selection.rst
EdwinWenink Jan 24, 2021
e47b2d5
Add reference to importance_getter in SelectFromModel docstring, cf. …
EdwinWenink Jan 24, 2021
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8 changes: 4 additions & 4 deletions doc/modules/feature_selection.rst
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Expand Up @@ -145,10 +145,10 @@ number of features.
Feature selection using SelectFromModel
=======================================

:class:`SelectFromModel` is a meta-transformer that can be used along with any
estimator that importance of each feature through a specific attribute (such as
``coef_``, ``feature_importances_``) or callable after fitting.
The features are considered unimportant and removed, if the corresponding
:class:`SelectFromModel` is a meta-transformer that can be used alongside any
estimator that assigns importance to each feature through a specific attribute (such as
``coef_``, ``feature_importances_``) or via an `importance_getter` callable after fitting.
The features are considered unimportant and removed if the corresponding
importance of the feature values are below the provided
``threshold`` parameter. Apart from specifying the threshold numerically,
there are built-in heuristics for finding a threshold using a string argument.
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3 changes: 2 additions & 1 deletion sklearn/feature_selection/_from_model.py
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Expand Up @@ -71,8 +71,9 @@ class SelectFromModel(MetaEstimatorMixin, SelectorMixin, BaseEstimator):
estimator : object
The base estimator from which the transformer is built.
This can be both a fitted (if ``prefit`` is set to True)
or a non-fitted estimator. The estimator must have either a
or a non-fitted estimator. The estimator should have a
``feature_importances_`` or ``coef_`` attribute after fitting.
Otherwise, the ``importance_getter`` parameter should be used.

threshold : string or float, default=None
The threshold value to use for feature selection. Features whose
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