Toggle Menu
Prev Up Next

scikit-learn 1.3.2
Other versions

Please cite us if you use the software.

# `sklearn.preprocessing`.Binarizer

class sklearn.preprocessing.Binarizer(*, threshold=0.0, copy=True)

  Binarize data (set feature values to 0 or 1) according to a threshold.

  Values greater than the threshold map to 1, while values less than or equal to the threshold map to 0. With the default threshold of 0, only positive values map to 1.

  Binarization is a common operation on text count data where the analyst can decide to only consider the presence or absence of a feature rather than a quantified number of occurrences for instance.

  It can also be used as a pre-processing step for estimators that consider boolean random variables (e.g. modelled using the Bernoulli distribution in a Bayesian setting).

  Read more in the User Guide.

  Parameters:
    threshold float, default=0.0

      Feature values below or equal to this are replaced by 0, above it by 1. Threshold may not be less than 0 for operations on sparse matrices.

    copy bool, default=True

      Set to False to perform inplace binarization and avoid a copy (if the input is already a numpy array or a scipy.sparse CSR matrix).

  Attributes:
    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

  `binarize`

    Equivalent function without the estimator API.

  `KBinsDiscretizer`

    Bin continuous data into intervals.

  `OneHotEncoder`

    Encode categorical features as a one-hot numeric array.

  Notes

  If the input is a sparse matrix, only the non-zero values are subject to update by the `Binarizer` class.

  This estimator is stateless and does not need to be fitted. However, we recommend to call `fit_transform` instead of `transform`, as parameter validation is only performed in `fit`.

  Examples

      >>> from sklearn.preprocessing import Binarizer
      >>> X = [[ 1., -1.,  2.],
      ...      [ 2.,  0.,  0.],
      ...      [ 0.,  1., -1.]]
      >>> transformer = Binarizer().fit(X)  # fit does nothing.
      >>> transformer
      Binarizer()
      >>> transformer.transform(X)
      array([[1., 0., 1.],
             [1., 0., 0.],
             [0., 1., 0.]])

  Methods

  `fit`(X[, y]) | Only validates estimator's parameters.
  `fit_transform`(X[, y]) | Fit to data, then transform it.
  `get_feature_names_out`([input_features]) | Get output feature names for transformation.
  `get_metadata_routing`() | Get metadata routing of this object.
  `get_params`([deep]) | Get parameters for this estimator.
  `set_output`(*[, transform]) | Set output container.
  `set_params`(**params) | Set the parameters of this estimator.
  `set_transform_request`(*[, copy]) | Request metadata passed to the `transform` method.
  `transform`(X[, copy]) | Binarize each element of X.

  fit(X, y=None)

    Only validates estimator’s parameters.

    This method allows to: (i) validate the estimator’s parameters and (ii) be consistent with the scikit-learn transformer API.

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

        The data.

      y None

        Ignored.

    Returns:
      self object

        Fitted transformer.

  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)

    Get output feature names for transformation.

    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

        Same as input features.

  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.

  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.

  set_transform_request(*, copy: bool | None | str = '$UNCHANGED$') → Binarizer

    Request metadata passed to the `transform` 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 `transform` 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 `transform`.

    - `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:
      copy str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

        Metadata routing for `copy` parameter in `transform`.

    Returns:
      self object

        The updated object.

  transform(X, copy=None)

    Binarize each element of X.

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

        The data to binarize, element by element. scipy.sparse matrices should be in CSR format to avoid an un-necessary copy.

      copy bool

        Copy the input X or not.

    Returns:
      X_tr{ndarray, sparse matrix} of shape (n_samples, n_features)

        Transformed array.