class_docstr: Learning Curve visualization.
  
  It is recommended to use
  :meth:`~sklearn.model_selection.LearningCurveDisplay.from_estimator` to
  create a :class:`~sklearn.model_selection.LearningCurveDisplay` instance.
  All parameters are stored as attributes.
  
  Read more in the :ref:`User Guide <visualizations>` for general information
  about the visualization API and
  :ref:`detailed documentation <learning_curve>` regarding the learning
  curve visualization.
  
  .. versionadded:: 1.2
  
  Parameters
  ----------
  train_sizes : ndarray of shape (n_unique_ticks,)
      Numbers of training examples that has been used to generate the
      learning curve.
  
  train_scores : ndarray of shape (n_ticks, n_cv_folds)
      Scores on training sets.
  
  test_scores : ndarray of shape (n_ticks, n_cv_folds)
      Scores on test set.
  
  score_name : str, default=None
      The name of the score used in `learning_curve`. It will override the name
      inferred from the `scoring` parameter. If `score` is `None`, we use `"Score"` if
      `negate_score` is `False` and `"Negative score"` otherwise. If `scoring` is a
      string or a callable, we infer the name. We replace `_` by spaces and capitalize
      the first letter. We remove `neg_` and replace it by `"Negative"` if
      `negate_score` is `False` or just remove it otherwise.
  
  Attributes
  ----------
  ax_ : matplotlib Axes
      Axes with the learning curve.
  
  figure_ : matplotlib Figure
      Figure containing the learning curve.
  
  errorbar_ : list of matplotlib Artist or None
      When the `std_display_style` is `"errorbar"`, this is a list of
      `matplotlib.container.ErrorbarContainer` objects. If another style is
      used, `errorbar_` is `None`.
  
  lines_ : list of matplotlib Artist or None
      When the `std_display_style` is `"fill_between"`, this is a list of
      `matplotlib.lines.Line2D` objects corresponding to the mean train and
      test scores. If another style is used, `line_` is `None`.
  
  fill_between_ : list of matplotlib Artist or None
      When the `std_display_style` is `"fill_between"`, this is a list of
      `matplotlib.collections.PolyCollection` objects. If another style is
      used, `fill_between_` is `None`.
  
  See Also
  --------
  sklearn.model_selection.learning_curve : Compute the learning curve.
  
  Examples
  --------
  >>> import matplotlib.pyplot as plt
  >>> from sklearn.datasets import load_iris
  >>> from sklearn.model_selection import LearningCurveDisplay, learning_curve
  >>> from sklearn.tree import DecisionTreeClassifier
  >>> X, y = load_iris(return_X_y=True)
  >>> tree = DecisionTreeClassifier(random_state=0)
  >>> train_sizes, train_scores, test_scores = learning_curve(
  ...     tree, X, y)
  >>> display = LearningCurveDisplay(train_sizes=train_sizes,
  ...     train_scores=train_scores, test_scores=test_scores, score_name="Score")
  >>> display.plot()
  <...>
  >>> plt.show()
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code: def __init__(
      self, *, train_sizes, train_scores, test_scores, score_name=None
  ):
      self.train_sizes = train_sizes
      self.train_scores = train_scores
      self.test_scores = test_scores
      self.score_name = score_name
  
file_path: scikit-learn/sklearn/model_selection/_plot.py
imports: import warnings
  import numpy
  from ..utils import check_matplotlib_support
  from ..utils._plotting import _interval_max_min_ratio, _validate_score_name
  from ._validation import learning_curve, validation_curve
label_desc: Use image node_id 1 to create a new LearningCurveDisplay object from inherited base classes: _BaseCurveDisplay with example: obj = LearningCurveDisplay()