class_docstr: The dashboard class, wraps the dashboard component.
  
  :param explanation: An object that represents an explanation.
  :type explanation: ExplanationMixin
  :param model: An object that represents a model.
      It is assumed that for the classification case
      flit has a method of predict_proba()
      returning the prediction probabilities for each
      class and for the regression case a method of predict()
      returning the prediction value.
  :type model: object
  :param dataset: A matrix of feature vector examples
      (# examples x # features),
      the same samples used to build the explanation.
      Overwrites any existing dataset on the explanation object.
      Must have fewer than 100000 rows and fewer than 1000 columns.
      Note dashboard may become slow or crash for more than 10000 rows.
  :type dataset: numpy.ndarray or list[][]
  :param true_y: The true labels for the provided dataset.
      Overwrites any existing dataset on the explanation object.
  :type true_y: numpy.ndarray or list[]
  :param classes: The class names.
  :type classes: numpy.ndarray or list[]
  :param features: Feature names.
  :type features: numpy.ndarray or list[]
  :param public_ip: Optional. If running on a remote vm,
      the external public ip address of the VM.
  :type public_ip: str
  :param port: The port to use on locally hosted service.
  :type port: int
  :param locale: The language in which user wants to load and access the
      Explanation Dashboard. The default language is english ("en").
  :type locale: str
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docstr: Initialize the ExplanationDashboard.
docstr_tok: ["Initialize","the","ExplanationDashboard","."]
code: def __init__(
      self,
      explanation,
      model=None,
      dataset=None,
      true_y=None,
      classes=None,
      features=None,
      public_ip=None,
      port=None,
      locale=None,
  ):
      """Initialize the ExplanationDashboard."""
      self.input = ExplanationDashboardInput(
          explanation, model, dataset, true_y, classes, features
      )
  
      super(ExplanationDashboard, self).__init__(
          dashboard_type="Interpret",
          model_data=self.input.dashboard_input,
          public_ip=public_ip,
          port=port,
          locale=locale,
      )
  
      def predict():
          data = request.get_json(force=True)
          return jsonify(self.input.on_predict(data))
  
      self.add_url_rule(predict, "/predict", methods=["POST"])
  
imports: from flask import jsonify, request
  from .dashboard import Dashboard
  from .explanation_dashboard_input import ExplanationDashboardInput
label_desc: Use image node_id 1 to create a new ExplanationDashboard object from inherited base classes: Dashboard with example: obj = ExplanationDashboard(explanation, model, dataset, true_y, classes, features, public_ip, port, locale)