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# `sklearn.feature_selection`.RFE

class sklearn.feature_selection.RFE(estimator, *, n_features_to_select=None, step=1, verbose=0, importance_getter='auto')

  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 any specific attribute or callable. 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.

  Read more in the User Guide.

  Parameters:
    estimator `Estimator` instance

      A supervised learning estimator with a `fit` method that provides information about feature importance (e.g. `coef_`, `feature_importances_`).

    n_features_to_select int or float, default=None

      The number of features to select. If `None`, half of the features are selected. If integer, the parameter is the absolute number of features to select. If float between 0 and 1, it is the fraction of features to select.

      Changed in version 0.24: Added float values for fractions.

    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.

    verbose int, default=0

      Controls verbosity of output.

    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 (implemented with `attrgetter`). For example, give `regressor_.coef_` in case of `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.

      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.

    n_features_int

      The number of selected features.

    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_ndarray 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

  `RFECV`

    Recursive feature elimination with built-in cross-validated selection of the best number of features.

  `SelectFromModel`

    Feature selection based on thresholds of importance weights.

  `SequentialFeatureSelector`

    Sequential cross-validation based feature selection. Does not rely on importance weights.

  Notes

  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 5 most informative features in the Friedman #1 dataset.

      >>> from sklearn.datasets import make_friedman1
      >>> from sklearn.feature_selection import RFE
      >>> from sklearn.svm import SVR
      >>> X, y = make_friedman1(n_samples=50, n_features=10, random_state=0)
      >>> estimator = SVR(kernel="linear")
      >>> selector = RFE(estimator, n_features_to_select=5, step=1)
      >>> 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, **fit_params) | Fit the RFE model and then the underlying estimator on the 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_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, **fit_params)

    Fit the RFE model and then the underlying estimator on the selected features.

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

        The training input samples.

      y array-like of shape (n_samples,)

        The target values.

      **fit_params dict

        Additional parameters passed to the `fit` method of the underlying estimator.

    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_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.RFE`

Recursive feature elimination

Recursive feature elimination