class_docstr: DET curve visualization.
  
  It is recommend to use :func:`~sklearn.metrics.DetCurveDisplay.from_estimator`
  or :func:`~sklearn.metrics.DetCurveDisplay.from_predictions` to create a
  visualizer. All parameters are stored as attributes.
  
  Read more in the :ref:`User Guide <visualizations>`.
  
  .. versionadded:: 0.24
  
  Parameters
  ----------
  fpr : ndarray
      False positive rate.
  
  fnr : ndarray
      False negative rate.
  
  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 label of the positive class.
  
  Attributes
  ----------
  line_ : matplotlib Artist
      DET Curve.
  
  ax_ : matplotlib Axes
      Axes with DET Curve.
  
  figure_ : matplotlib Figure
      Figure containing the curve.
  
  See Also
  --------
  det_curve : Compute error rates for different probability thresholds.
  DetCurveDisplay.from_estimator : Plot DET curve given an estimator and
      some data.
  DetCurveDisplay.from_predictions : Plot DET curve given the true and
      predicted labels.
  
  Examples
  --------
  >>> import matplotlib.pyplot as plt
  >>> from sklearn.datasets import make_classification
  >>> from sklearn.metrics import det_curve, DetCurveDisplay
  >>> from sklearn.model_selection import train_test_split
  >>> from sklearn.svm import SVC
  >>> X, y = make_classification(n_samples=1000, random_state=0)
  >>> X_train, X_test, y_train, y_test = train_test_split(
  ...     X, y, test_size=0.4, random_state=0)
  >>> clf = SVC(random_state=0).fit(X_train, y_train)
  >>> y_pred = clf.decision_function(X_test)
  >>> fpr, fnr, _ = det_curve(y_test, y_pred)
  >>> display = DetCurveDisplay(
  ...     fpr=fpr, fnr=fnr, estimator_name="SVC"
  ... )
  >>> display.plot()
  <...>
  >>> plt.show()
code: def __init__(self, *, fpr, fnr, estimator_name=None, pos_label=None):
      self.fpr = fpr
      self.fnr = fnr
      self.estimator_name = estimator_name
      self.pos_label = pos_label
  
file_path: catboost/contrib/python/scikit-learn/py3/sklearn/metrics/_plot/det_curve.py
imports: import scipy
  from ...utils._plotting import _BinaryClassifierCurveDisplayMixin
  from .._ranking import det_curve
label_desc: Use image node_id 1 to create a new DetCurveDisplay object from inherited base classes: _BinaryClassifierCurveDisplayMixin with example: obj = DetCurveDisplay()