class_docstr: The dashboard class, wraps the dashboard component.
  
  :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 10000 rows and fewer than 1000 columns.
  :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
      ModelPerformance Dashboard. The default language is english ("en").
  :type locale: str
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docstr: Initialize the model performance dashboard.
docstr_tok: ["Initialize","the","model","performance","dashboard","."]
code: def __init__(
      self,
      model=None,
      dataset=None,
      true_y=None,
      classes=None,
      features=None,
      public_ip=None,
      port=None,
      locale=None,
  ):
      """Initialize the model performance dashboard."""
  
      self.input = ExplanationDashboardInput(
          None, model, dataset, true_y, classes, features
      )
  
      Dashboard.__init__(
          self,
          dashboard_type="ModelPerformance",
          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 ModelPerformanceDashboard object from inherited base classes: Dashboard with example: obj = ModelPerformanceDashboard(model, dataset, true_y, classes, features, public_ip, port, locale)