lr_auto_examples_preprocessing_plot_all_scaling.py`.

References
----------

.. [1] I.K. Yeo and R.A. Johnson, "A new family of power transformations to
       improve normality or symmetry." Biometrika, 87(4), pp.954-959,
       (2000).

.. [2] G.E.P. Box and D.R. Cox, "An Analysis of Transformations", Journal
       of the Royal Statistical Society B, 26, 211-252 (1964).

Examples
--------
>>> import numpy as np
>>> from sklearn.preprocessing import power_transform
>>> data = [[1, 2], [3, 2], [4, 5]]
>>> print(power_transform(data, method='box-cox'))
[[-1.332... -0.707...]
 [ 0.256... -0.707...]
 [ 1.076...  1.414...]]

.. warning:: Risk of data leak.
    Do not use :func:`~sklearn.preprocessing.power_transform` unless you
    know what you are doing. A common mistake is to apply it to the entire
    data *before* splitting into training and test sets. This will bias the
    model evaluation because information would have leaked from the test
    set to the training set.
    In general, we recommend using
    :class:`~sklearn.preprocessing.PowerTransformer` within a
    :ref:`Pipeline <pipeline>` in order to prevent most risks of data
    leaking, e.g.: `pipe = make_pipeline(PowerTransformer(),
    LogisticRegression())`.
r