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# `sklearn.feature_selection`.SelectKBest

class sklearn.feature_selection.SelectKBest(score_func=<function f_classif>, *, k=10)

  Select features according to the k highest scores.

  Read more in the User Guide.

  Parameters:
    score_func callable, default=f_classif

      Function taking two arrays X and y, and returning a pair of arrays (scores, pvalues) or a single array with scores. Default is f_classif (see below “See Also”). The default function only works with classification tasks.

      New in version 0.18.

    k int or “all”, default=10

      Number of top features to select. The “all” option bypasses selection, for use in a parameter search.

  Attributes:
    scores_array-like of shape (n_features,)

      Scores of features.

    pvalues_array-like of shape (n_features,)

      p-values of feature scores, None if `score_func` returned only scores.

    n_features_in_int

      Number of features seen during 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.

  See also

  `f_classif`

    ANOVA F-value between label/feature for classification tasks.

  `mutual_info_classif`

    Mutual information for a discrete target.

  `chi2`

    Chi-squared stats of non-negative features for classification tasks.

  `f_regression`

    F-value between label/feature for regression tasks.

  `mutual_info_regression`

    Mutual information for a continuous target.

  `SelectPercentile`

    Select features based on percentile of the highest scores.

  `SelectFpr`

    Select features based on a false positive rate test.

  `SelectFdr`

    Select features based on an estimated false discovery rate.

  `SelectFwe`

    Select features based on family-wise error rate.

  `GenericUnivariateSelect`

    Univariate feature selector with configurable mode.

  Notes

  Ties between features with equal scores will be broken in an unspecified way.

  Examples

      >>> from sklearn.datasets import load_digits
      >>> from sklearn.feature_selection import SelectKBest, chi2
      >>> X, y = load_digits(return_X_y=True)
      >>> X.shape
      (1797, 64)
      >>> X_new = SelectKBest(chi2, k=20).fit_transform(X, y)
      >>> X_new.shape
      (1797, 20)

  Methods

  `fit`(X, y) | Run score function on (X, y) and get the appropriate 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.
  `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)

    Run score function on (X, y) and get the appropriate features.

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

        The training input samples.

      y array-like of shape (n_samples,)

        The target values (class labels in classification, real numbers in regression).

    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.SelectKBest`

Release Highlights for scikit-learn 1.1

Release Highlights for scikit-learn 1.1

Pipeline ANOVA SVM

Pipeline ANOVA SVM

Univariate Feature Selection

Univariate Feature Selection

Concatenating multiple feature extraction methods

Concatenating multiple feature extraction methods

Selecting dimensionality reduction with Pipeline and GridSearchCV

Selecting dimensionality reduction with Pipeline and GridSearchCV