expression like '2*n_jobs'.

verbose : int, default=0
    Controls the verbosity: the higher, the more messages.

shuffle : bool, default=False
    Whether to shuffle training data before taking prefixes of it
    based on``train_sizes``.

random_state : int, RandomState instance or None, default=None
    Used when ``shuffle`` is True. Pass an int for reproducible
    output across multiple function calls.
    See :term:`Glossary <random_state>`.

error_score : 'raise' or numeric, default=np.nan
    Value to assign to the score if an error occurs in estimator fitting.
    If set to 'raise', the error is raised.
    If a numeric value is given, FitFailedWarning is raised.

    .. versionadded:: 0.20

return_times : bool, default=False
    Whether to return the fit and score times.

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 1.8. Use
        ``params`` instead.

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

    - 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. See :ref:`Metadata Routing User Guide
      <metadata_routing>` for more details.

    .. versionadded:: 1.6

Returns
-------
train_sizes_abs : array of shape (n_unique_ticks,)
    Numbers of training examples that has been used to generate the
    learning curve. Note that the number of ticks might be less
    than n_ticks because duplicate entries will be removed.

train_scores : array of shape (n_ticks, n_cv_folds)
    Scores on training sets.

test_scores : array of shape (n_ticks, n_cv_folds)
    Scores on test set.

fit_times : array of shape (n_ticks, n_cv_folds)
    Times spent for fitting in seconds. Only present if ``return_times``
    is True.

score_times : array of shape (n_ticks, n_cv_folds)
    Times spent for scoring in seconds. Only present if ``return_times``
    is True.

Examples
--------
>>> from sklearn.datasets import make_classification
>>> from sklearn.tree import DecisionTreeClassifier
>>> from sklearn.model_selection import learning_curve
>>> X, y = make_classification(n_samples=100, n_features=10, random_state=42)
>>> tree = DecisionTreeClassifier(max_depth=4, random_state=42)
>>> train_size_abs, train_scores, test_scores = learning_curve(
...     tree, X, y, train_sizes=[0.3, 0.6, 0.9]
... )
>>> for train_size, cv_train_scores, cv_test_scores in zip(
...     train_size_abs, train_scores, test_scores
... ):
...     print(f"{train_size} samples were used to train the model")
...     print(f"The average train accuracy is {cv_train_scores.mean():.2f}")
...     print(f"The average test accuracy is {cv_test_scores.mean():.2f}")
24 samples were used to train the model
The average train accuracy is 1.00
The average test accuracy is 0.85
48 samples were used to train the model
The average train accuracy is 1.00
The average test accuracy is 0.90
72 samples were used to train the model
The average train accuracy is 1.00
The average test accuracy is 0.93
Ú