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 visualization.
  
  Parameters
  ----------
  ax : matplotlib axes, default=None
      Axes object to plot on. If `None`, a new figure and axes is
      created.
  
  name : str, default=None
      Name of DET curve for labeling. If `None`, use `estimator_name` if
      it is not `None`, otherwise no labeling is shown.
  
  **kwargs : dict
      Additional keywords arguments passed to matplotlib `plot` function.
  
  Returns
  -------
  display : :class:`~sklearn.metrics.DetCurveDisplay`
      Object that stores computed values.
docstr_tok: ["Plot","visualization",".","Parameters","--","--","--","--","--","ax",":","matplotlib","axes",",","default=None","Axes","object","to","plot","on",".","If","`","None","`",",","a","new","figure","and","axes","is","created",".","name",":","str",",","default=None","Name","of","DET","curve","for","labeling",".","If","`","None","`",",","use","`","estimator_name","`","if","it","is","not","`","None","`",",","otherwise","no","labeling","is","shown",".","*","*","kwargs",":","dict","Additional","keywords","arguments","passed","to","matplotlib","`","plot","`","function",".","Returns","--","--","--","-","display",":",":","class",":","`","~sklearn.metrics.DetCurveDisplay","`","Object","that","stores","computed","values","."]
code: def plot(self, ax=None, *, name=None, **kwargs):
      """Plot visualization.
  
      Parameters
      ----------
      ax : matplotlib axes, default=None
          Axes object to plot on. If `None`, a new figure and axes is
          created.
  
      name : str, default=None
          Name of DET curve for labeling. If `None`, use `estimator_name` if
          it is not `None`, otherwise no labeling is shown.
  
      **kwargs : dict
          Additional keywords arguments passed to matplotlib `plot` function.
  
      Returns
      -------
      display : :class:`~sklearn.metrics.DetCurveDisplay`
          Object that stores computed values.
      """
      self.ax_, self.figure_, name = self._validate_plot_params(
          ax=ax, name=name
      )
  
      line_kwargs = {} if name is None else {"label": name}
      line_kwargs.update(**kwargs)
  
      (self.line_,) = self.ax_.plot(
          sp.stats.norm.ppf(self.fpr),
          sp.stats.norm.ppf(self.fnr),
          **line_kwargs,
      )
      info_pos_label = (
          f" (Positive label: {self.pos_label})"
          if self.pos_label is not None
          else ""
      )
  
      xlabel = "False Positive Rate" + info_pos_label
      ylabel = "False Negative Rate" + info_pos_label
      self.ax_.set(xlabel=xlabel, ylabel=ylabel)
  
      if "label" in line_kwargs:
          self.ax_.legend(loc="lower right")
  
      ticks = [0.001, 0.01, 0.05, 0.20, 0.5, 0.80, 0.95, 0.99, 0.999]
      tick_locations = sp.stats.norm.ppf(ticks)
      tick_labels = [
          "{:.0%}".format(s)
          if (100 * s).is_integer()
          else "{:.1%}".format(s)
          for s in ticks
      ]
      self.ax_.set_xticks(tick_locations)
      self.ax_.set_xticklabels(tick_labels)
      self.ax_.set_xlim(-3, 3)
      self.ax_.set_yticks(tick_locations)
      self.ax_.set_yticklabels(tick_labels)
      self.ax_.set_ylim(-3, 3)
  
      return self
  
file_path: scikit-learn/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 4 for calling the DetCurveDisplay obj's underlying member method code with example usage: obj.plot(ax) and returns: self