class_docstr: ROC Curve visualization.
  
  It is recommend to use
  :func:`~sklearn.metrics.RocCurveDisplay.from_estimator` or
  :func:`~sklearn.metrics.RocCurveDisplay.from_predictions` to create
  a :class:`~sklearn.metrics.RocCurveDisplay`. All parameters are
  stored as attributes.
  
  Read more in the :ref:`User Guide <visualizations>`.
  
  Parameters
  ----------
  fpr : ndarray
      False positive rate.
  
  tpr : ndarray
      True positive rate.
  
  roc_auc : float, default=None
      Area under ROC curve. If None, the roc_auc score is not shown.
  
  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 class considered as the positive class when computing the roc auc
      metrics. By default, `estimators.classes_[1]` is considered
      as the positive class.
  
      .. versionadded:: 0.24
  
  Attributes
  ----------
  line_ : matplotlib Artist
      ROC Curve.
  
  chance_level_ : matplotlib Artist or None
      The chance level line. It is `None` if the chance level is not plotted.
  
      .. versionadded:: 1.3
  
  ax_ : matplotlib Axes
      Axes with ROC Curve.
  
  figure_ : matplotlib Figure
      Figure containing the curve.
  
  See Also
  --------
  roc_curve : Compute Receiver operating characteristic (ROC) curve.
  RocCurveDisplay.from_estimator : Plot Receiver Operating Characteristic
      (ROC) curve given an estimator and some data.
  RocCurveDisplay.from_predictions : Plot Receiver Operating Characteristic
      (ROC) curve given the true and predicted values.
  roc_auc_score : Compute the area under the ROC curve.
  
  Examples
  --------
  >>> import matplotlib.pyplot as plt
  >>> import numpy as np
  >>> from sklearn import metrics
  >>> y = np.array([0, 0, 1, 1])
  >>> pred = np.array([0.1, 0.4, 0.35, 0.8])
  >>> fpr, tpr, thresholds = metrics.roc_curve(y, pred)
  >>> roc_auc = metrics.auc(fpr, tpr)
  >>> display = metrics.RocCurveDisplay(fpr=fpr, tpr=tpr, roc_auc=roc_auc,
  ...                                   estimator_name='example estimator')
  >>> display.plot()
  <...>
  >>> plt.show()
code: def __init__(
      self,
      *,
      fpr,
      tpr,
      roc_auc=None,
      estimator_name=None,
      pos_label=None,
  ):
      self.estimator_name = estimator_name
      self.fpr = fpr
      self.tpr = tpr
      self.roc_auc = roc_auc
      self.pos_label = pos_label
  
file_path: scikit-learn/sklearn/metrics/_plot/roc_curve.py
imports: from ...utils._plotting import _BinaryClassifierCurveDisplayMixin
  from .._ranking import auc, roc_curve
label_desc: Use image node_id 1 to create a new RocCurveDisplay object from inherited base classes: _BinaryClassifierCurveDisplayMixin with example: obj = RocCurveDisplay()