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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docstr: Create a learning curve display from an estimator.
  
  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.
  
  Parameters
  ----------
  estimator : object type that implements the "fit" and "predict" methods
      An object of that type which is cloned for each validation.
  
  X : array-like of shape (n_samples, n_features)
      Training data, where `n_samples` is the number of samples and
      `n_features` is the number of features.
  
  y : array-like of shape (n_samples,) or (n_samples, n_outputs) or None
      Target relative to X for classification or regression;
      None for unsupervised learning.
  
  groups : array-like of shape (n_samples,), default=None
      Group labels for the samples used while splitting the dataset into
      train/test set. Only used in conjunction with a "Group" :term:`cv`
      instance (e.g., :class:`GroupKFold`).
  
  train_sizes : array-like of shape (n_ticks,),                 default=np.linspace(0.1, 1.0, 5)
      Relative or absolute numbers of training examples that will be used
      to generate the learning curve. If the dtype is float, it is
      regarded as a fraction of the maximum size of the training set
      (that is determined by the selected validation method), i.e. it has
      to be within (0, 1]. Otherwise it is interpreted as absolute sizes
      of the training sets. Note that for classification the number of
      samples usually have to be big enough to contain at least one
      sample from each class.
  
  cv : int, cross-validation generator or an iterable, default=None
      Determines the cross-validation splitting strategy.
      Possible inputs for cv are:
  
      - None, to use the default 5-fold cross validation,
      - int, to specify the number of folds in a `(Stratified)KFold`,
      - :term:`CV splitter`,
      - An iterable yielding (train, test) splits as arrays of indices.
  
      For int/None inputs, if the estimator is a classifier and `y` is
      either binary or multiclass,
      :class:`~sklearn.model_selection.StratifiedKFold` is used. In all
      other cases, :class:`~sklearn.model_selection.KFold` is used. These
      splitters are instantiated with `shuffle=False` so the splits will
      be the same across calls.
  
      Refer :ref:`User Guide <cross_validation>` for the various
      cross-validation strategies that can be used here.
  
  scoring : str or callable, default=None
      A string (see :ref:`scoring_parameter`) or
      a scorer callable object / function with signature
      `scorer(estimator, X, y)` (see :ref:`scoring`).
  
  exploit_incremental_learning : bool, default=False
      If the estimator supports incremental learning, this will be
      used to speed up fitting for different training set sizes.
  
  n_jobs : int, default=None
      Number of jobs to run in parallel. Training the estimator and
      computing the score are parallelized over the different training
      and test sets. `None` means 1 unless in a
      :obj:`joblib.parallel_backend` context. `-1` means using all
      processors. See :term:`Glossary <n_jobs>` for more details.
  
  pre_dispatch : int or str, default='all'
      Number of predispatched jobs for parallel execution (default is
      all). The option can reduce the allocated memory. The str can
      be an 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.
  
  fit_params : dict, default=None
      Parameters to pass to the fit method of the estimator.
  
  ax : matplotlib Axes, default=None
      Axes object to plot on. If `None`, a new figure and axes is
      created.
  
  negate_score : bool, default=False
      Whether or not to negate the scores obtained through
      :func:`~sklearn.model_selection.learning_curve`. This is
      particularly useful when using the error denoted by `neg_*` in
      `scikit-learn`.
  
  score_name : str, default=None
      The name of the score used to decorate the y-axis of the plot. 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.
  
  score_type : {"test", "train", "both"}, default="both"
      The type of score to plot. Can be one of `"test"`, `"train"`, or
      `"both"`.
  
  log_scale : bool, default="deprecated"
      Whether or not to use a logarithmic scale for the x-axis.
  
      .. deprecated:: 1.3
         `log_scale` is deprecated in 1.3 and will be removed in 1.5.
         Use `display.ax_.xscale` and `display.ax_.yscale` instead.
  
  std_display_style : {"errorbar", "fill_between"} or None, default="fill_between"
      The style used to display the score standard deviation around the
      mean score. If `None`, no representation of the standard deviation
      is displayed.
  
  line_kw : dict, default=None
      Additional keyword arguments passed to the `plt.plot` used to draw
      the mean score.
  
  fill_between_kw : dict, default=None
      Additional keyword arguments passed to the `plt.fill_between` used
      to draw the score standard deviation.
  
  errorbar_kw : dict, default=None
      Additional keyword arguments passed to the `plt.errorbar` used to
      draw mean score and standard deviation score.
  
  Returns
  -------
  display : :class:`~sklearn.model_selection.LearningCurveDisplay`
      Object that stores computed values.
  
  Examples
  --------
  >>> import matplotlib.pyplot as plt
  >>> from sklearn.datasets import load_iris
  >>> from sklearn.model_selection import LearningCurveDisplay
  >>> from sklearn.tree import DecisionTreeClassifier
  >>> X, y = load_iris(return_X_y=True)
  >>> tree = DecisionTreeClassifier(random_state=0)
  >>> LearningCurveDisplay.from_estimator(tree, X, y)
  <...>
  >>> plt.show()
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code: def from_estimator(
      cls,
      estimator,
      X,
      y,
      *,
      groups=None,
      train_sizes=np.linspace(0.1, 1.0, 5),
      cv=None,
      scoring=None,
      exploit_incremental_learning=False,
      n_jobs=None,
      pre_dispatch="all",
      verbose=0,
      shuffle=False,
      random_state=None,
      error_score=np.nan,
      fit_params=None,
      ax=None,
      negate_score=False,
      score_name=None,
      score_type="both",
      log_scale="deprecated",
      std_display_style="fill_between",
      line_kw=None,
      fill_between_kw=None,
      errorbar_kw=None,
  ):
      """Create a learning curve display from an estimator.
  
          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.
  
          Parameters
          ----------
          estimator : object type that implements the "fit" and "predict" methods
              An object of that type which is cloned for each validation.
  
          X : array-like of shape (n_samples, n_features)
              Training data, where `n_samples` is the number of samples and
              `n_features` is the number of features.
  
          y : array-like of shape (n_samples,) or (n_samples, n_outputs) or None
              Target relative to X for classification or regression;
              None for unsupervised learning.
  
          groups : array-like of shape (n_samples,), default=None
              Group labels for the samples used while splitting the dataset into
              train/test set. Only used in conjunction with a "Group" :term:`cv`
              instance (e.g., :class:`GroupKFold`).
  
          train_sizes : array-like of shape (n_ticks,), \
                  default=np.linspace(0.1, 1.0, 5)
              Relative or absolute numbers of training examples that will be used
              to generate the learning curve. If the dtype is float, it is
              regarded as a fraction of the maximum size of the training set
              (that is determined by the selected validation method), i.e. it has
              to be within (0, 1]. Otherwise it is interpreted as absolute sizes
              of the training sets. Note that for classification the number of
              samples usually have to be big enough to contain at least one
              sample from each class.
  
          cv : int, cross-validation generator or an iterable, default=None
              Determines the cross-validation splitting strategy.
              Possible inputs for cv are:
  
              - None, to use the default 5-fold cross validation,
              - int, to specify the number of folds in a `(Stratified)KFold`,
              - :term:`CV splitter`,
              - An iterable yielding (train, test) splits as arrays of indices.
  
              For int/None inputs, if the estimator is a classifier and `y` is
              either binary or multiclass,
              :class:`~sklearn.model_selection.StratifiedKFold` is used. In all
              other cases, :class:`~sklearn.model_selection.KFold` is used. These
              splitters are instantiated with `shuffle=False` so the splits will
              be the same across calls.
  
              Refer :ref:`User Guide <cross_validation>` for the various
              cross-validation strategies that can be used here.
  
          scoring : str or callable, default=None
              A string (see :ref:`scoring_parameter`) or
              a scorer callable object / function with signature
              `scorer(estimator, X, y)` (see :ref:`scoring`).
  
          exploit_incremental_learning : bool, default=False
              If the estimator supports incremental learning, this will be
              used to speed up fitting for different training set sizes.
  
          n_jobs : int, default=None
              Number of jobs to run in parallel. Training the estimator and
              computing the score are parallelized over the different training
              and test sets. `None` means 1 unless in a
              :obj:`joblib.parallel_backend` context. `-1` means using all
              processors. See :term:`Glossary <n_jobs>` for more details.
  
          pre_dispatch : int or str, default='all'
              Number of predispatched jobs for parallel execution (default is
              all). The option can reduce the allocated memory. The str can
              be an 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.
  
          fit_params : dict, default=None
              Parameters to pass to the fit method of the estimator.
  
          ax : matplotlib Axes, default=None
              Axes object to plot on. If `None`, a new figure and axes is
              created.
  
          negate_score : bool, default=False
              Whether or not to negate the scores obtained through
              :func:`~sklearn.model_selection.learning_curve`. This is
              particularly useful when using the error denoted by `neg_*` in
              `scikit-learn`.
  
          score_name : str, default=None
              The name of the score used to decorate the y-axis of the plot. 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.
  
          score_type : {"test", "train", "both"}, default="both"
              The type of score to plot. Can be one of `"test"`, `"train"`, or
              `"both"`.
  
          log_scale : bool, default="deprecated"
              Whether or not to use a logarithmic scale for the x-axis.
  
              .. deprecated:: 1.3
                 `log_scale` is deprecated in 1.3 and will be removed in 1.5.
                 Use `display.ax_.xscale` and `display.ax_.yscale` instead.
  
          std_display_style : {"errorbar", "fill_between"} or None, default="fill_between"
              The style used to display the score standard deviation around the
              mean score. If `None`, no representation of the standard deviation
              is displayed.
  
          line_kw : dict, default=None
              Additional keyword arguments passed to the `plt.plot` used to draw
              the mean score.
  
          fill_between_kw : dict, default=None
              Additional keyword arguments passed to the `plt.fill_between` used
              to draw the score standard deviation.
  
          errorbar_kw : dict, default=None
              Additional keyword arguments passed to the `plt.errorbar` used to
              draw mean score and standard deviation score.
  
          Returns
          -------
          display : :class:`~sklearn.model_selection.LearningCurveDisplay`
              Object that stores computed values.
  
          Examples
          --------
          >>> import matplotlib.pyplot as plt
          >>> from sklearn.datasets import load_iris
          >>> from sklearn.model_selection import LearningCurveDisplay
          >>> from sklearn.tree import DecisionTreeClassifier
          >>> X, y = load_iris(return_X_y=True)
          >>> tree = DecisionTreeClassifier(random_state=0)
          >>> LearningCurveDisplay.from_estimator(tree, X, y)
          <...>
          >>> plt.show()
          """
      check_matplotlib_support(f"{cls.__name__}.from_estimator")
  
      score_name = _validate_score_name(
          score_name, scoring, negate_score
      )
  
      train_sizes, train_scores, test_scores = learning_curve(
          estimator,
          X,
          y,
          groups=groups,
          train_sizes=train_sizes,
          cv=cv,
          scoring=scoring,
          exploit_incremental_learning=exploit_incremental_learning,
          n_jobs=n_jobs,
          pre_dispatch=pre_dispatch,
          verbose=verbose,
          shuffle=shuffle,
          random_state=random_state,
          error_score=error_score,
          return_times=False,
          fit_params=fit_params,
      )
  
      viz = cls(
          train_sizes=train_sizes,
          train_scores=train_scores,
          test_scores=test_scores,
          score_name=score_name,
      )
      return viz.plot(
          ax=ax,
          negate_score=negate_score,
          score_type=score_type,
          log_scale=log_scale,
          std_display_style=std_display_style,
          line_kw=line_kw,
          fill_between_kw=fill_between_kw,
          errorbar_kw=errorbar_kw,
      )
  
file_path: catboost/contrib/python/scikit-learn/py3/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 3 for calling the LearningCurveDisplay obj's underlying member method code with example usage: obj.from_estimator(cls, estimator, X, y) and returns: viz