Function power_transform

sklearn/preprocessing/_data.py:3659–3759

Parametric, monotonic transformation 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

(X, method="yeo-johnson", *, standardize=True, copy=True)

    prefer_skip_nested_validation=False,
)
def power_transform(X, method="yeo-johnson", *, standardize=True, copy=True):
    """Parametric, monotonic transformation 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, power_transform 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.

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

    Parameters
    ----------
    X : array-like of shape (n_samples, n_features)
        The data to be transformed using a power transformation.

    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

        .. versionchanged:: 0.23
            The default value of the `method` parameter changed from
            'box-cox' to 'yeo-johnson' in 0.23.

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

    copy : bool, default=True
        If False, try to avoid a copy and transform in place.
        This is not guaranteed to always work in place; e.g. if the data is
        a numpy array with an int dtype, a copy will be returned even with
        copy=False.

    Returns
    -------
    X_trans : ndarray of shape (n_samples, n_features)
        The transformed data.

    See Also
    --------
    PowerTransformer : Equivalent transformation with the
        Transformer API (e.g. as part of a preprocessing
        :class:`~sklearn.pipeline.Pipeline`).

    quantile_transform : Maps data to a standard normal distribution with
        the parameter `output_distribution='normal'`.