class_docstr: Precision Recall visualization.
  
  It is recommend to use
  :func:`~sklearn.metrics.PrecisionRecallDisplay.from_estimator` or
  :func:`~sklearn.metrics.PrecisionRecallDisplay.from_predictions` to create
  a :class:`~sklearn.metrics.PrecisionRecallDisplay`. All parameters are
  stored as attributes.
  
  Read more in the :ref:`User Guide <visualizations>`.
  
  Parameters
  ----------
  precision : ndarray
      Precision values.
  
  recall : ndarray
      Recall values.
  
  average_precision : float, default=None
      Average precision. If None, the average precision is not shown.
  
  estimator_name : str, default=None
      Name of estimator. If None, then the estimator name is not shown.
  
  pos_label : int, float, bool or str, default=None
      The class considered as the positive class. If None, the class will not
      be shown in the legend.
  
      .. versionadded:: 0.24
  
  prevalence_pos_label : float, default=None
      The prevalence of the positive label. It is used for plotting the
      chance level line. If None, the chance level line will not be plotted
      even if `plot_chance_level` is set to True when plotting.
  
      .. versionadded:: 1.3
  
  Attributes
  ----------
  line_ : matplotlib Artist
      Precision recall 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 precision recall curve.
  
  figure_ : matplotlib Figure
      Figure containing the curve.
  
  See Also
  --------
  precision_recall_curve : Compute precision-recall pairs for different
      probability thresholds.
  PrecisionRecallDisplay.from_estimator : Plot Precision Recall Curve given
      a binary classifier.
  PrecisionRecallDisplay.from_predictions : Plot Precision Recall Curve
      using predictions from a binary classifier.
  
  Notes
  -----
  The average precision (cf. :func:`~sklearn.metrics.average_precision_score`) in
  scikit-learn is computed without any interpolation. To be consistent with
  this metric, the precision-recall curve is plotted without any
  interpolation as well (step-wise style).
  
  You can change this style by passing the keyword argument
  `drawstyle="default"` in :meth:`plot`, :meth:`from_estimator`, or
  :meth:`from_predictions`. However, the curve will not be strictly
  consistent with the reported average precision.
  
  Examples
  --------
  >>> import matplotlib.pyplot as plt
  >>> from sklearn.datasets import make_classification
  >>> from sklearn.metrics import (precision_recall_curve,
  ...                              PrecisionRecallDisplay)
  >>> from sklearn.model_selection import train_test_split
  >>> from sklearn.svm import SVC
  >>> X, y = make_classification(random_state=0)
  >>> X_train, X_test, y_train, y_test = train_test_split(X, y,
  ...                                                     random_state=0)
  >>> clf = SVC(random_state=0)
  >>> clf.fit(X_train, y_train)
  SVC(random_state=0)
  >>> predictions = clf.predict(X_test)
  >>> precision, recall, _ = precision_recall_curve(y_test, predictions)
  >>> disp = PrecisionRecallDisplay(precision=precision, recall=recall)
  >>> disp.plot()
  <...>
  >>> plt.show()
code: def __init__(
      self,
      precision,
      recall,
      *,
      average_precision=None,
      estimator_name=None,
      pos_label=None,
      prevalence_pos_label=None,
  ):
      self.estimator_name = estimator_name
      self.precision = precision
      self.recall = recall
      self.average_precision = average_precision
      self.pos_label = pos_label
      self.prevalence_pos_label = prevalence_pos_label
  
file_path: scikit-learn/sklearn/metrics/_plot/precision_recall_curve.py
imports: from collections import Counter
  from ...utils._plotting import _BinaryClassifierCurveDisplayMixin
  from .._ranking import average_precision_score, precision_recall_curve
label_desc: Use image node_id 1 to create a new PrecisionRecallDisplay object from inherited base classes: _BinaryClassifierCurveDisplayMixin with example: obj = PrecisionRecallDisplay(precision, recall)