class_docstr: Validation Curve visualization.
  
  It is recommended to use
  :meth:`~sklearn.model_selection.ValidationCurveDisplay.from_estimator` to
  create a :class:`~sklearn.model_selection.ValidationCurveDisplay` 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
  <validation_curve>` regarding the validation curve visualization.
  
  .. versionadded:: 1.3
  
  Parameters
  ----------
  param_name : str
      Name of the parameter that has been varied.
  
  param_range : array-like of shape (n_ticks,)
      The values of the parameter that have been evaluated.
  
  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 `validation_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 validation curve.
  
  figure_ : matplotlib Figure
      Figure containing the validation 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.validation_curve : Compute the validation curve.
  
  Examples
  --------
  >>> import numpy as np
  >>> import matplotlib.pyplot as plt
  >>> from sklearn.datasets import make_classification
  >>> from sklearn.model_selection import ValidationCurveDisplay, validation_curve
  >>> from sklearn.linear_model import LogisticRegression
  >>> X, y = make_classification(n_samples=1_000, random_state=0)
  >>> logistic_regression = LogisticRegression()
  >>> param_name, param_range = "C", np.logspace(-8, 3, 10)
  >>> train_scores, test_scores = validation_curve(
  ...     logistic_regression, X, y, param_name=param_name, param_range=param_range
  ... )
  >>> display = ValidationCurveDisplay(
  ...     param_name=param_name, param_range=param_range,
  ...     train_scores=train_scores, test_scores=test_scores, score_name="Score"
  ... )
  >>> display.plot()
  <...>
  >>> plt.show()
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docstr: Plot visualization.
  
  Parameters
  ----------
  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.validation_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"`.
  
  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 standard deviation representation 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.ValidationCurveDisplay`
      Object that stores computed values.
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code: def plot(
      self,
      ax=None,
      *,
      negate_score=False,
      score_name=None,
      score_type="both",
      std_display_style="fill_between",
      line_kw=None,
      fill_between_kw=None,
      errorbar_kw=None,
  ):
      """Plot visualization.
  
      Parameters
      ----------
      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.validation_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"`.
  
      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 standard deviation representation 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.ValidationCurveDisplay`
          Object that stores computed values.
      """
      self._plot_curve(
          self.param_range,
          ax=ax,
          negate_score=negate_score,
          score_name=score_name,
          score_type=score_type,
          log_scale="deprecated",
          std_display_style=std_display_style,
          line_kw=line_kw,
          fill_between_kw=fill_between_kw,
          errorbar_kw=errorbar_kw,
      )
      self.ax_.set_xlabel(f"{self.param_name}")
      return self
  
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 2 for calling the ValidationCurveDisplay obj's underlying member method code with example usage: obj.plot(ax) and returns: self