core are parallelized over the permutations.
    ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
    ``-1`` means using all processors. See :term:`Glossary <n_jobs>`
    for more details.

random_state : int, RandomState instance or None, default=0
    Pass an int for reproducible output for permutation of
    ``y`` values among samples. See :term:`Glossary <random_state>`.

verbose : int, default=0
    The verbosity level.

scoring : str or callable, default=None
    A single str (see :ref:`scoring_parameter`) or a callable
    (see :ref:`scoring_callable`) to evaluate the predictions on the test set.

    If `None` the estimator's score method is used.

fit_params : dict, default=None
    Parameters to pass to the fit method of the estimator.

    .. deprecated:: 1.6
        This parameter is deprecated and will be removed in version 1.6. Use
        ``params`` instead.

params : dict, default=None
    Parameters to pass to the `fit` method of the estimator, the scorer
    and the cv splitter.

    - If `enable_metadata_routing=False` (default): Parameters directly passed to
      the `fit` method of the estimator.

    - If `enable_metadata_routing=True`: Parameters safely routed to the `fit`
      method of the estimator, `cv` object and `scorer`. See :ref:`Metadata Routing
      User Guide <metadata_routing>` for more details.

    .. versionadded:: 1.6

Returns
-------
score : float
    The true score without permuting targets.

permutation_scores : array of shape (n_permutations,)
    The scores obtained for each permutations.

pvalue : float
    The p-value, which approximates the probability that the score would
    be obtained by chance. This is calculated as:

    `(C + 1) / (n_permutations + 1)`

    Where C is the number of permutations whose score >= the true score.

    The best possible p-value is 1/(n_permutations + 1), the worst is 1.0.

Notes
-----
This function implements Test 1 in:

Ojala and Garriga. `Permutation Tests for Studying Classifier Performance
<http://www.jmlr.org/papers/volume11/ojala10a/ojala10a.pdf>`_. The
Journal of Machine Learning Research (2010) vol. 11

Examples
--------
>>> from sklearn.datasets import make_classification
>>> from sklearn.linear_model import LogisticRegression
>>> from sklearn.model_selection import permutation_test_score
>>> X, y = make_classification(random_state=0)
>>> estimator = LogisticRegression()
>>> score, permutation_scores, pvalue = permutation_test_score(
...     estimator, X, y, random_state=0
... )
>>> print(f"Original Score: {score:.3f}")
Original Score: 0.810
>>> print(
...     f"Permutation Scores: {permutation_scores.mean():.3f} +/- "
...     f"{permutation_scores.std():.3f}"
... )
Permutation Scores: 0.505 +/- 0.057
>>> print(f"P-value: {pvalue:.3f}")
P-value: 0.010
ú