class_docstr: Calibration curve (also known as reliability diagram) visualization.
  
  It is recommended to use
  :func:`~sklearn.calibration.CalibrationDisplay.from_estimator` or
  :func:`~sklearn.calibration.CalibrationDisplay.from_predictions`
  to create a `CalibrationDisplay`. All parameters are stored as attributes.
  
  Read more about calibration in the :ref:`User Guide <calibration>` and
  more about the scikit-learn visualization API in :ref:`visualizations`.
  
  .. versionadded:: 1.0
  
  Parameters
  ----------
  prob_true : ndarray of shape (n_bins,)
      The proportion of samples whose class is the positive class (fraction
      of positives), in each bin.
  
  prob_pred : ndarray of shape (n_bins,)
      The mean predicted probability in each bin.
  
  y_prob : ndarray of shape (n_samples,)
      Probability estimates for the positive class, for each sample.
  
  estimator_name : str, default=None
      Name of estimator. If None, the estimator name is not shown.
  
  pos_label : int, float, bool or str, default=None
      The positive class when computing the calibration curve.
      By default, `estimators.classes_[1]` is considered as the
      positive class.
  
      .. versionadded:: 1.1
  
  Attributes
  ----------
  line_ : matplotlib Artist
      Calibration curve.
  
  ax_ : matplotlib Axes
      Axes with calibration curve.
  
  figure_ : matplotlib Figure
      Figure containing the curve.
  
  See Also
  --------
  calibration_curve : Compute true and predicted probabilities for a
      calibration curve.
  CalibrationDisplay.from_predictions : Plot calibration curve using true
      and predicted labels.
  CalibrationDisplay.from_estimator : Plot calibration curve using an
      estimator and data.
  
  Examples
  --------
  >>> from sklearn.datasets import make_classification
  >>> from sklearn.model_selection import train_test_split
  >>> from sklearn.linear_model import LogisticRegression
  >>> from sklearn.calibration import calibration_curve, CalibrationDisplay
  >>> X, y = make_classification(random_state=0)
  >>> X_train, X_test, y_train, y_test = train_test_split(
  ...     X, y, random_state=0)
  >>> clf = LogisticRegression(random_state=0)
  >>> clf.fit(X_train, y_train)
  LogisticRegression(random_state=0)
  >>> y_prob = clf.predict_proba(X_test)[:, 1]
  >>> prob_true, prob_pred = calibration_curve(y_test, y_prob, n_bins=10)
  >>> disp = CalibrationDisplay(prob_true, prob_pred, y_prob)
  >>> disp.plot()
  <...>
code: def __init__(
      self,
      prob_true,
      prob_pred,
      y_prob,
      *,
      estimator_name=None,
      pos_label=None,
  ):
      self.prob_true = prob_true
      self.prob_pred = prob_pred
      self.y_prob = y_prob
      self.estimator_name = estimator_name
      self.pos_label = pos_label
  
imports: import warnings
  from functools import partial
  from inspect import signature
  from math import log
  from numbers import Integral, Real
  import numpy
  from scipy.optimize import fmin_bfgs
  from scipy.special import expit, xlogy
  from sklearn.utils import Bunch
  from .base import BaseEstimator, ClassifierMixin, MetaEstimatorMixin, RegressorMixin, _fit_context, clone
  from .isotonic import IsotonicRegression
  from .model_selection import check_cv, cross_val_predict
  from .preprocessing import LabelEncoder, label_binarize
  from .svm import LinearSVC
  from .utils import _safe_indexing, column_or_1d, indexable
  from .utils._param_validation import HasMethods, Hidden, Interval, StrOptions, validate_params
  from .utils._plotting import _BinaryClassifierCurveDisplayMixin
  from .utils.metadata_routing import MetadataRouter, MethodMapping, _routing_enabled, process_routing
  from .utils.multiclass import check_classification_targets
  from .utils.parallel import Parallel, delayed
  from .utils.validation import _check_fit_params, _check_pos_label_consistency, _check_sample_weight, _num_samples, check_consistent_length, check_is_fitted
label_desc: Use image node_id 1 to create a new CalibrationDisplay object from inherited base classes: _BinaryClassifierCurveDisplayMixin with example: obj = CalibrationDisplay(prob_true, prob_pred, y_prob)