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# `sklearn.feature_selection`.SequentialFeatureSelector

class sklearn.feature_selection.SequentialFeatureSelector(estimator, *, n_features_to_select='auto', tol=None, direction='forward', scoring=None, cv=5, n_jobs=None)

  Transformer that performs Sequential Feature Selection.

  This Sequential Feature Selector adds (forward selection) or removes (backward selection) features to form a feature subset in a greedy fashion. At each stage, this estimator chooses the best feature to add or remove based on the cross-validation score of an estimator. In the case of unsupervised learning, this Sequential Feature Selector looks only at the features (X), not the desired outputs (y).

  Read more in the User Guide.

  New in version 0.24.

  Parameters:
    estimator estimator instance

      An unfitted estimator.

    n_features_to_select“auto”, int or float, default=”auto”

      If `"auto"`, the behaviour depends on the `tol` parameter:

      - if `tol` is not `None`, then features are selected while the score change does not exceed `tol`.

      - otherwise, 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.

      New in version 1.1: The option `"auto"` was added in version 1.1.

      Changed in version 1.3: The default changed from `"warn"` to `"auto"` in 1.3.

    tol float, default=None

      If the score is not incremented by at least `tol` between two consecutive feature additions or removals, stop adding or removing.

      `tol` can be negative when removing features using `direction="backward"`. It can be useful to reduce the number of features at the cost of a small decrease in the score.

      `tol` is enabled only when `n_features_to_select` is `"auto"`.

      New in version 1.1.

    direction{‘forward’, ‘backward’}, default=’forward’

      Whether to perform forward selection or backward selection.

    scoring str or callable, default=None

      A single str (see The scoring parameter: defining model evaluation rules) or a callable (see Defining your scoring strategy from metric functions) to evaluate the predictions on the test set.

      NOTE that when using a custom scorer, it should return a single value.

      If None, the estimator’s score method is used.

    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 in a `(Stratified)KFold`,

      - CV splitter,

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

      For integer/None inputs, if the estimator is a classifier and `y` is either binary or multiclass, `StratifiedKFold` is used. In all other cases, `KFold` is used. These splitters are instantiated with `shuffle=False` so the splits will be the same across calls.

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

    n_jobs int, default=None

      Number of jobs to run in parallel. When evaluating a new feature to add or remove, the cross-validation procedure is parallel over the folds. `None` means 1 unless in a `joblib.parallel_backend` context. `-1` means using all processors. See Glossary for more details.

  Attributes:
    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.

    n_features_to_select_int

      The number of features that were selected.

    support_ndarray of shape (n_features,), dtype=bool

      The mask of selected features.

  See also

  `GenericUnivariateSelect`

    Univariate feature selector with configurable strategy.

  `RFE`

    Recursive feature elimination based on importance weights.

  `RFECV`

    Recursive feature elimination based on importance weights, with automatic selection of the number of features.

  `SelectFromModel`

    Feature selection based on thresholds of importance weights.

  Examples

      >>> from sklearn.feature_selection import SequentialFeatureSelector
      >>> from sklearn.neighbors import KNeighborsClassifier
      >>> from sklearn.datasets import load_iris
      >>> X, y = load_iris(return_X_y=True)
      >>> knn = KNeighborsClassifier(n_neighbors=3)
      >>> sfs = SequentialFeatureSelector(knn, n_features_to_select=3)
      >>> sfs.fit(X, y)
      SequentialFeatureSelector(estimator=KNeighborsClassifier(n_neighbors=3),
                                n_features_to_select=3)
      >>> sfs.get_support()
      array([ True, False,  True,  True])
      >>> sfs.transform(X).shape
      (150, 3)

  Methods

  `fit`(X[, y]) | Learn the features to select from X.
  `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.
  `set_output`(*[, transform]) | Set output container.
  `set_params`(**params) | Set the parameters of this estimator.
  `transform`(X) | Reduce X to the selected features.

  fit(X, y=None)

    Learn the features to select from X.

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

        Training vectors, where `n_samples` is the number of samples and `n_features` is the number of predictors.

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

        Target values. This parameter may be ignored for unsupervised learning.

    Returns:
      self object

        Returns the instance itself.

  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`.

  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.SequentialFeatureSelector`

Release Highlights for scikit-learn 0.24

Release Highlights for scikit-learn 0.24

Model-based and sequential feature selection

Model-based and sequential feature selection