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# `sklearn.feature_selection`.VarianceThreshold

class sklearn.feature_selection.VarianceThreshold(threshold=0.0)

  Feature selector that removes all low-variance features.

  This feature selection algorithm looks only at the features (X), not the desired outputs (y), and can thus be used for unsupervised learning.

  Read more in the User Guide.

  Parameters:
    threshold float, default=0

      Features with a training-set variance lower than this threshold will be removed. The default is to keep all features with non-zero variance, i.e. remove the features that have the same value in all samples.

  Attributes:
    variances_array, shape (n_features,)

      Variances of individual features.

    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

  `SelectFromModel`

    Meta-transformer for selecting features based on importance weights.

  `SelectPercentile`

    Select features according to a percentile of the highest scores.

  `SequentialFeatureSelector`

    Transformer that performs Sequential Feature Selection.

  Notes

  Allows NaN in the input. Raises ValueError if no feature in X meets the variance threshold.

  Examples

  The following dataset has integer features, two of which are the same in every sample. These are removed with the default setting for threshold:

      >>> from sklearn.feature_selection import VarianceThreshold
      >>> X = [[0, 2, 0, 3], [0, 1, 4, 3], [0, 1, 1, 3]]
      >>> selector = VarianceThreshold()
      >>> selector.fit_transform(X)
      array([[2, 0],
             [1, 4],
             [1, 1]])

  Methods

  `fit`(X[, y]) | Learn empirical variances 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 empirical variances from X.

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

        Data from which to compute variances, where `n_samples` is the number of samples and `n_features` is the number of features.

      y any, default=None

        Ignored. This parameter exists only for compatibility with sklearn.pipeline.Pipeline.

    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.