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()
docstr: Plot DET curve given an estimator and data.
  
  Read more in the :ref:`User Guide <visualizations>`.
  
  .. versionadded:: 1.0
  
  Parameters
  ----------
  estimator : estimator instance
      Fitted classifier or a fitted :class:`~sklearn.pipeline.Pipeline`
      in which the last estimator is a classifier.
  
  X : {array-like, sparse matrix} of shape (n_samples, n_features)
      Input values.
  
  y : array-like of shape (n_samples,)
      Target values.
  
  sample_weight : array-like of shape (n_samples,), default=None
      Sample weights.
  
  response_method : {'predict_proba', 'decision_function', 'auto'}                 default='auto'
      Specifies whether to use :term:`predict_proba` or
      :term:`decision_function` as the predicted target response. If set
      to 'auto', :term:`predict_proba` is tried first and if it does not
      exist :term:`decision_function` is tried next.
  
  pos_label : int, float, bool or str, default=None
      The label of the positive class. When `pos_label=None`, if `y_true`
      is in {-1, 1} or {0, 1}, `pos_label` is set to 1, otherwise an
      error will be raised.
  
  name : str, default=None
      Name of DET curve for labeling. If `None`, use the name of the
      estimator.
  
  ax : matplotlib axes, default=None
      Axes object to plot on. If `None`, a new figure and axes is
      created.
  
  **kwargs : dict
      Additional keywords arguments passed to matplotlib `plot` function.
  
  Returns
  -------
  display : :class:`~sklearn.metrics.DetCurveDisplay`
      Object that stores computed values.
  
  See Also
  --------
  det_curve : Compute error rates for different probability thresholds.
  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 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)
  >>> DetCurveDisplay.from_estimator(
  ...    clf, X_test, y_test)
  <...>
  >>> plt.show()
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code: def from_estimator(
      cls,
      estimator,
      X,
      y,
      *,
      sample_weight=None,
      response_method="auto",
      pos_label=None,
      name=None,
      ax=None,
      **kwargs,
  ):
      """Plot DET curve given an estimator and data.
  
          Read more in the :ref:`User Guide <visualizations>`.
  
          .. versionadded:: 1.0
  
          Parameters
          ----------
          estimator : estimator instance
              Fitted classifier or a fitted :class:`~sklearn.pipeline.Pipeline`
              in which the last estimator is a classifier.
  
          X : {array-like, sparse matrix} of shape (n_samples, n_features)
              Input values.
  
          y : array-like of shape (n_samples,)
              Target values.
  
          sample_weight : array-like of shape (n_samples,), default=None
              Sample weights.
  
          response_method : {'predict_proba', 'decision_function', 'auto'} \
                  default='auto'
              Specifies whether to use :term:`predict_proba` or
              :term:`decision_function` as the predicted target response. If set
              to 'auto', :term:`predict_proba` is tried first and if it does not
              exist :term:`decision_function` is tried next.
  
          pos_label : int, float, bool or str, default=None
              The label of the positive class. When `pos_label=None`, if `y_true`
              is in {-1, 1} or {0, 1}, `pos_label` is set to 1, otherwise an
              error will be raised.
  
          name : str, default=None
              Name of DET curve for labeling. If `None`, use the name of the
              estimator.
  
          ax : matplotlib axes, default=None
              Axes object to plot on. If `None`, a new figure and axes is
              created.
  
          **kwargs : dict
              Additional keywords arguments passed to matplotlib `plot` function.
  
          Returns
          -------
          display : :class:`~sklearn.metrics.DetCurveDisplay`
              Object that stores computed values.
  
          See Also
          --------
          det_curve : Compute error rates for different probability thresholds.
          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 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)
          >>> DetCurveDisplay.from_estimator(
          ...    clf, X_test, y_test)
          <...>
          >>> plt.show()
          """
      y_pred, pos_label, name = cls._validate_and_get_response_values(
          estimator,
          X,
          y,
          response_method=response_method,
          pos_label=pos_label,
          name=name,
      )
  
      return cls.from_predictions(
          y_true=y,
          y_pred=y_pred,
          sample_weight=sample_weight,
          name=name,
          ax=ax,
          pos_label=pos_label,
          **kwargs,
      )
  
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 2 for calling the DetCurveDisplay obj's underlying member method code with example usage: obj.from_estimator(cls, estimator, X, y) and returns: cls