class_docstr: Recursive feature elimination with cross-validation to select features.
  
  The number of features selected is tuned automatically by fitting an :class:`RFE`
  selector on the different cross-validation splits (provided by the `cv` parameter).
  The performance of the :class:`RFE` selector are evaluated using `scorer` for
  different number of selected features and aggregated together. Finally, the scores
  are averaged across folds and the number of features selected is set to the number
  of features that maximize the cross-validation score.
  See glossary entry for :term:`cross-validation estimator`.
  
  Read more in the :ref:`User Guide <rfe>`.
  
  Parameters
  ----------
  estimator : ``Estimator`` instance
      A supervised learning estimator with a ``fit`` method that provides
      information about feature importance either through a ``coef_``
      attribute or through a ``feature_importances_`` attribute.
  
  step : int or float, default=1
      If greater than or equal to 1, then ``step`` corresponds to the
      (integer) number of features to remove at each iteration.
      If within (0.0, 1.0), then ``step`` corresponds to the percentage
      (rounded down) of features to remove at each iteration.
      Note that the last iteration may remove fewer than ``step`` features in
      order to reach ``min_features_to_select``.
  
  min_features_to_select : int, default=1
      The minimum number of features to be selected. This number of features
      will always be scored, even if the difference between the original
      feature count and ``min_features_to_select`` isn't divisible by
      ``step``.
  
      .. versionadded:: 0.20
  
  cv : int, cross-validation generator or an iterable, default=None
      Determines the cross-validation splitting strategy.
      Possible inputs for cv are:
  
      - None, to use the default 5-fold cross-validation,
      - integer, to specify the number of folds.
      - :term:`CV splitter`,
      - An iterable yielding (train, test) splits as arrays of indices.
  
      For integer/None inputs, if ``y`` is binary or multiclass,
      :class:`~sklearn.model_selection.StratifiedKFold` is used. If the
      estimator is a classifier or if ``y`` is neither binary nor multiclass,
      :class:`~sklearn.model_selection.KFold` is used.
  
      Refer :ref:`User Guide <cross_validation>` for the various
      cross-validation strategies that can be used here.
  
      .. versionchanged:: 0.22
          ``cv`` default value of None changed from 3-fold to 5-fold.
  
  scoring : str, callable or None, default=None
      A string (see model evaluation documentation) or
      a scorer callable object / function with signature
      ``scorer(estimator, X, y)``.
  
  verbose : int, default=0
      Controls verbosity of output.
  
  n_jobs : int or None, default=None
      Number of cores to run in parallel while fitting across folds.
      ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
      ``-1`` means using all processors. See :term:`Glossary <n_jobs>`
      for more details.
  
      .. versionadded:: 0.18
  
  importance_getter : str or callable, default='auto'
      If 'auto', uses the feature importance either through a `coef_`
      or `feature_importances_` attributes of estimator.
  
      Also accepts a string that specifies an attribute name/path
      for extracting feature importance.
      For example, give `regressor_.coef_` in case of
      :class:`~sklearn.compose.TransformedTargetRegressor`  or
      `named_steps.clf.feature_importances_` in case of
      :class:`~sklearn.pipeline.Pipeline` with its last step named `clf`.
  
      If `callable`, overrides the default feature importance getter.
      The callable is passed with the fitted estimator and it should
      return importance for each feature.
  
      .. versionadded:: 0.24
  
  Attributes
  ----------
  classes_ : ndarray of shape (n_classes,)
      The classes labels. Only available when `estimator` is a classifier.
  
  estimator_ : ``Estimator`` instance
      The fitted estimator used to select features.
  
  cv_results_ : dict of ndarrays
      A dict with keys:
  
      split(k)_test_score : ndarray of shape (n_subsets_of_features,)
          The cross-validation scores across (k)th fold.
  
      mean_test_score : ndarray of shape (n_subsets_of_features,)
          Mean of scores over the folds.
  
      std_test_score : ndarray of shape (n_subsets_of_features,)
          Standard deviation of scores over the folds.
  
      .. versionadded:: 1.0
  
  n_features_ : int
      The number of selected features with cross-validation.
  
  n_features_in_ : int
      Number of features seen during :term:`fit`. Only defined if the
      underlying estimator exposes such an attribute when fit.
  
      .. versionadded:: 0.24
  
  feature_names_in_ : ndarray of shape (`n_features_in_`,)
      Names of features seen during :term:`fit`. Defined only when `X`
      has feature names that are all strings.
  
      .. versionadded:: 1.0
  
  ranking_ : narray of shape (n_features,)
      The feature ranking, such that `ranking_[i]`
      corresponds to the ranking
      position of the i-th feature.
      Selected (i.e., estimated best)
      features are assigned rank 1.
  
  support_ : ndarray of shape (n_features,)
      The mask of selected features.
  
  See Also
  --------
  RFE : Recursive feature elimination.
  
  Notes
  -----
  The size of all values in ``cv_results_`` is equal to
  ``ceil((n_features - min_features_to_select) / step) + 1``,
  where step is the number of features removed at each iteration.
  
  Allows NaN/Inf in the input if the underlying estimator does as well.
  
  References
  ----------
  
  .. [1] Guyon, I., Weston, J., Barnhill, S., & Vapnik, V., "Gene selection
         for cancer classification using support vector machines",
         Mach. Learn., 46(1-3), 389--422, 2002.
  
  Examples
  --------
  The following example shows how to retrieve the a-priori not known 5
  informative features in the Friedman #1 dataset.
  
  >>> from sklearn.datasets import make_friedman1
  >>> from sklearn.feature_selection import RFECV
  >>> from sklearn.svm import SVR
  >>> X, y = make_friedman1(n_samples=50, n_features=10, random_state=0)
  >>> estimator = SVR(kernel="linear")
  >>> selector = RFECV(estimator, step=1, cv=5)
  >>> selector = selector.fit(X, y)
  >>> selector.support_
  array([ True,  True,  True,  True,  True, False, False, False, False,
         False])
  >>> selector.ranking_
  array([1, 1, 1, 1, 1, 6, 4, 3, 2, 5])
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code: def __init__(
      self,
      estimator,
      *,
      step=1,
      min_features_to_select=1,
      cv=None,
      scoring=None,
      verbose=0,
      n_jobs=None,
      importance_getter="auto",
  ):
      self.estimator = estimator
      self.step = step
      self.importance_getter = importance_getter
      self.cv = cv
      self.scoring = scoring
      self.verbose = verbose
      self.n_jobs = n_jobs
      self.min_features_to_select = min_features_to_select
  
imports: from numbers import Integral
  import numpy
  from joblib import effective_n_jobs
  from ..base import BaseEstimator, MetaEstimatorMixin, _fit_context, clone, is_classifier
  from ..metrics import check_scoring
  from ..model_selection import check_cv
  from ..model_selection._validation import _score
  from ..utils._param_validation import HasMethods, Interval, RealNotInt
  from ..utils.metadata_routing import _raise_for_unsupported_routing, _RoutingNotSupportedMixin
  from ..utils.metaestimators import _safe_split, available_if
  from ..utils.parallel import Parallel, delayed
  from ..utils.validation import check_is_fitted
  from ._base import SelectorMixin, _get_feature_importances
label_desc: Use image node_id 1 to create a new RFECV object from inherited base classes: RFE with example: obj = RFECV(estimator)