class_docstr: Encapsulates results from an estimator on a testing set to provide a
  simplified API from other modules. All parameters are optional, just
  fill the ones you need for your analysis.
  
  Parameters
  ----------
  estimator : sklearn estimator
      Must have a ``feature_importances_`` attribute.
  y_true : array-like
      Target predicted classes (estimator predictions).
  y_pred : array-like
      Correct target values (ground truth).
  y_score : array-like
      Target scores (estimator predictions).
  feature_names : array-like
      Feature names.
  target_names : list
      List containing the names of the target classes
  estimator_name : str
      Identifier for the model. This can be later used to identify the
      estimator when generating reports.
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docstr: ROC plot
docstr_tok: ["ROC","plot"]
code: def roc(self):
      """ROC plot"""
      return plot.roc(self.y_true, self.y_score, ax=_gen_ax())
  
imports: import matplotlib.pyplot
  from sklearn_evaluation.plot.util import requires_properties
  from sklearn_evaluation.report.serialize import EvaluatorHTMLSerializer
  from sklearn_evaluation.report.report import Report
  from .util import estimator_type, class_name
  from .None import plot
  from .telemetry import SKLearnEvaluationLogger
label_desc: Use image node_id 13 for calling the ClassifierEvaluator obj's underlying member method code with example usage: obj.roc() and returns: plot