Class PowerTransformer

sklearn/preprocessing/_data.py:3257–3652

Apply a power transform featurewise to make data more Gaussian-like. Power transforms are a family of parametric, monotonic transformations that are applied to make data more Gaussian-like. This is useful for modeling issues related to heteroscedasticity (non-constant variance), or

class PowerTransformer(OneToOneFeatureMixin, TransformerMixin, BaseEstimator):
    """Apply a power transform featurewise to make data more Gaussian-like.

    Power transforms are a family of parametric, monotonic transformations
    that are applied to make data more Gaussian-like. This is useful for
    modeling issues related to heteroscedasticity (non-constant variance),
    or other situations where normality is desired.

    Currently, PowerTransformer supports the Box-Cox transform and the
    Yeo-Johnson transform. The optimal parameter for stabilizing variance and
    minimizing skewness is estimated through maximum likelihood.

    Box-Cox requires input data to be strictly positive, while Yeo-Johnson
    supports both positive or negative data.

    By default, zero-mean, unit-variance normalization is applied to the
    transformed data.

    For an example visualization, refer to :ref:`Compare PowerTransformer with
    other scalers <plot_all_scaling_power_transformer_section>`. To see the
    effect of Box-Cox and Yeo-Johnson transformations on different
    distributions, see:
    :ref:`sphx_glr_auto_examples_preprocessing_plot_map_data_to_normal.py`.

    Read more in the :ref:`User Guide <preprocessing_transformer>`.

    .. versionadded:: 0.20

    Parameters
    ----------
    method : {'yeo-johnson', 'box-cox'}, default='yeo-johnson'
        The power transform method. Available methods are:

        - 'yeo-johnson' [1]_, works with positive and negative values
        - 'box-cox' [2]_, only works with strictly positive values

    standardize : bool, default=True
        Set to True to apply zero-mean, unit-variance normalization to the
        transformed output.

    copy : bool, default=True
        Set to False to perform inplace computation during transformation.

    Attributes
    ----------
    lambdas_ : ndarray of float of shape (n_features,)
        The parameters of the power transformation for the selected features.

    n_features_in_ : int
        Number of features seen during :term:`fit`.

        .. versionadded:: 0.24

    feature_names_in_ : ndarray of shape (`n_features_in_`,)
        Names of features seen during :term:`fit`. Defined only when `X`
        has feature names that are all strings.

        .. versionadded:: 1.0