Toggle Menu
Prev Up Next

scikit-learn 1.3.2
Other versions

Please cite us if you use the software.

# `sklearn.feature_selection`.RFECV

class sklearn.feature_selection.RFECV(estimator, *, step=1, min_features_to_select=1, cv=None, scoring=None, verbose=0, n_jobs=None, importance_getter='auto')

  Recursive feature elimination with cross-validation to select features.

  See glossary entry for cross-validation estimator.

  Read more in the User Guide.

  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`.

      New in version 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.

      - CV splitter,

      - An iterable yielding (train, test) splits as arrays of indices.

      For integer/None inputs, if `y` is binary or multiclass, `StratifiedKFold` is used. If the estimator is a classifier or if `y` is neither binary nor multiclass, `KFold` is used.

      Refer User Guide for the various cross-validation strategies that can be used here.

      Changed in version 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 `joblib.parallel_backend` context. `-1` means using all processors. See Glossary for more details.

      New in version 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 `TransformedTargetRegressor` or `named_steps.clf.feature_importances_` in case of `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.

      New in version 0.24.

  Attributes:
    `classes_`ndarray of shape (n_classes,)

      Classes labels 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_scorendarray of shape (n_subsets_of_features,)

        The cross-validation scores across (k)th fold.

      mean_test_scorendarray of shape (n_subsets_of_features,)

        Mean of scores over the folds.

      std_test_scorendarray of shape (n_subsets_of_features,)

        Standard deviation of scores over the folds.

      New in version 1.0.

    n_features_int

      The number of selected features with cross-validation.

    n_features_in_int

      Number of features seen during fit. Only defined if the underlying estimator exposes such an attribute when fit.

      New in version 0.24.

    feature_names_in_ndarray of shape (`n_features_in_`,)

      Names of features seen during fit. Defined only when `X` has feature names that are all strings.

      New in version 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])

  Methods

  `decision_function`(X) | Compute the decision function of `X`.
  `fit`(X, y[, groups]) | Fit the RFE model and automatically tune the number of selected features.
  `fit_transform`(X[, y]) | Fit to data, then transform it.
  `get_feature_names_out`([input_features]) | Mask feature names according to selected features.
  `get_metadata_routing`() | Get metadata routing of this object.
  `get_params`([deep]) | Get parameters for this estimator.
  `get_support`([indices]) | Get a mask, or integer index, of the features selected.
  `inverse_transform`(X) | Reverse the transformation operation.
  `predict`(X) | Reduce X to the selected features and predict using the estimator.
  `predict_log_proba`(X) | Predict class log-probabilities for X.
  `predict_proba`(X) | Predict class probabilities for X.
  `score`(X, y, **fit_params) | Reduce X to the selected features and return the score of the estimator.
  `set_fit_request`(*[, groups]) | Request metadata passed to the `fit` method.
  `set_output`(*[, transform]) | Set output container.
  `set_params`(**params) | Set the parameters of this estimator.
  `transform`(X) | Reduce X to the selected features.

  property classes_

    Classes labels available when `estimator` is a classifier.

    Returns:
      ndarray of shape (n_classes,)
  decision_function(X)

    Compute the decision function of `X`.

    Parameters:
      X{array-like or sparse matrix} of shape (n_samples, n_features)

        The input samples. Internally, it will be converted to `dtype=np.float32` and if a sparse matrix is provided to a sparse `csr_matrix`.

    Returns:
      score array, shape = [n_samples, n_classes] or [n_samples]

        The decision function of the input samples. The order of the classes corresponds to that in the attribute classes_. Regression and binary classification produce an array of shape [n_samples].

  fit(X, y, groups=None)

    Fit the RFE model and automatically tune the number of selected features.

    Parameters:
      X{array-like, sparse matrix} of shape (n_samples, n_features)

        Training vector, where `n_samples` is the number of samples and `n_features` is the total number of features.

      y array-like of shape (n_samples,)

        Target values (integers for classification, real numbers for regression).

      groups array-like of shape (n_samples,) or None, default=None

        Group labels for the samples used while splitting the dataset into train/test set. Only used in conjunction with a “Group” cv instance (e.g., `GroupKFold`).

        New in version 0.20.

    Returns:
      self object

        Fitted estimator.

  fit_transform(X, y=None, **fit_params)

    Fit to data, then transform it.

    Fits transformer to `X` and `y` with optional parameters `fit_params` and returns a transformed version of `X`.

    Parameters:
      X array-like of shape (n_samples, n_features)

        Input samples.

      y array-like of shape (n_samples,) or (n_samples, n_outputs), default=None

        Target values (None for unsupervised transformations).

      **fit_params dict

        Additional fit parameters.

    Returns:
      X_new ndarray array of shape (n_samples, n_features_new)

        Transformed array.

  get_feature_names_out(input_features=None)

    Mask feature names according to selected features.

    Parameters:
      input_features array-like of str or None, default=None

        Input features.

        - If `input_features` is `None`, then `feature_names_in_` is used as feature names in. If `feature_names_in_` is not defined, then the following input feature names are generated: `["x0", "x1", ..., "x(n_features_in_ - 1)"]`.

        - If `input_features` is an array-like, then `input_features` must match `feature_names_in_` if `feature_names_in_` is defined.

    Returns:
      feature_names_out ndarray of str objects

        Transformed feature names.

  get_metadata_routing()

    Get metadata routing of this object.

    Please check User Guide on how the routing mechanism works.

    Returns:
      routing MetadataRequest

        A `MetadataRequest` encapsulating routing information.

  get_params(deep=True)

    Get parameters for this estimator.

    Parameters:
      deep bool, default=True

        If True, will return the parameters for this estimator and contained subobjects that are estimators.

    Returns:
      params dict

        Parameter names mapped to their values.

  get_support(indices=False)

    Get a mask, or integer index, of the features selected.

    Parameters:
      indices bool, default=False

        If True, the return value will be an array of integers, rather than a boolean mask.

    Returns:
      support array

        An index that selects the retained features from a feature vector. If `indices` is False, this is a boolean array of shape [# input features], in which an element is True iff its corresponding feature is selected for retention. If `indices` is True, this is an integer array of shape [# output features] whose values are indices into the input feature vector.

  inverse_transform(X)

    Reverse the transformation operation.

    Parameters:
      X array of shape [n_samples, n_selected_features]

        The input samples.

    Returns:
      X_r array of shape [n_samples, n_original_features]

        `X` with columns of zeros inserted where features would have been removed by `transform`.

  predict(X)

    Reduce X to the selected features and predict using the estimator.

    Parameters:
      X array of shape [n_samples, n_features]

        The input samples.

    Returns:
      y array of shape [n_samples]

        The predicted target values.

  predict_log_proba(X)

    Predict class log-probabilities for X.

    Parameters:
      X array of shape [n_samples, n_features]

        The input samples.

    Returns:
      p array of shape (n_samples, n_classes)

        The class log-probabilities of the input samples. The order of the classes corresponds to that in the attribute classes_.

  predict_proba(X)

    Predict class probabilities for X.

    Parameters:
      X{array-like or sparse matrix} of shape (n_samples, n_features)

        The input samples. Internally, it will be converted to `dtype=np.float32` and if a sparse matrix is provided to a sparse `csr_matrix`.

    Returns:
      p array of shape (n_samples, n_classes)

        The class probabilities of the input samples. The order of the classes corresponds to that in the attribute classes_.

  score(X, y, **fit_params)

    Reduce X to the selected features and return the score of the estimator.

    Parameters:
      X array of shape [n_samples, n_features]

        The input samples.

      y array of shape [n_samples]

        The target values.

      **fit_params dict

        Parameters to pass to the `score` method of the underlying estimator.

        New in version 1.0.

    Returns:
      score float

        Score of the underlying base estimator computed with the selected features returned by `rfe.transform(X)` and `y`.

  set_fit_request(*, groups: bool | None | str = '$UNCHANGED$') → RFECV

    Request metadata passed to the `fit` method.

    Note that this method is only relevant if `enable_metadata_routing=True` (see `sklearn.set_config`). Please see User Guide on how the routing mechanism works.

    The options for each parameter are:

    - `True`: metadata is requested, and passed to `fit` if provided. The request is ignored if metadata is not provided.

    - `False`: metadata is not requested and the meta-estimator will not pass it to `fit`.

    - `None`: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

    - `str`: metadata should be passed to the meta-estimator with this given alias instead of the original name.

    The default (`sklearn.utils.metadata_routing.UNCHANGED`) retains the existing request. This allows you to change the request for some parameters and not others.

    New in version 1.3.

    Note

    This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a `Pipeline`. Otherwise it has no effect.

    Parameters:
      groups str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

        Metadata routing for `groups` parameter in `fit`.

    Returns:
      self object

        The updated object.

  set_output(*, transform=None)

    Set output container.

    See Introducing the set_output API for an example on how to use the API.

    Parameters:
      transform{“default”, “pandas”}, default=None

        Configure output of `transform` and `fit_transform`.

        - `"default"`: Default output format of a transformer

        - `"pandas"`: DataFrame output

        - `None`: Transform configuration is unchanged

    Returns:
      self estimator instance

        Estimator instance.

  set_params(**params)

    Set the parameters of this estimator.

    The method works on simple estimators as well as on nested objects (such as `Pipeline`). The latter have parameters of the form `<component>__<parameter>` so that it’s possible to update each component of a nested object.

    Parameters:
      **params dict

        Estimator parameters.

    Returns:
      self estimator instance

        Estimator instance.

  transform(X)

    Reduce X to the selected features.

    Parameters:
      X array of shape [n_samples, n_features]

        The input samples.

    Returns:
      X_r array of shape [n_samples, n_selected_features]

        The input samples with only the selected features.

## Examples using `sklearn.feature_selection.RFECV`

Recursive feature elimination with cross-validation

Recursive feature elimination with cross-validation