docstr: Returns a confusion matrix based of H2O's default prediction threshold for a dataset.
  
  :param data: metric for which the confusion matrix will be calculated.
  
  :examples:
  
  >>> from h2o.estimators import H2OGeneralizedLinearEstimator
  >>> from h2o.grid.grid_search import H2OGridSearch
  >>> h2o_df = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/bigdata/laptop/glm_ordinal_logit/ordinal_multinomial_training_set.csv")
  >>> hyper_parameters = {'alpha': [0.01,0.5], 'lambda': [1e-5,1e-6]}
  >>> gs = H2OGridSearch(H2OGeneralizedLinearEstimator(family="ordinal"), hyper_parameters)
  >>> h2o_df['C11'] = h2o_df['C11'].asfactor()
  >>> gs.train(x=list(range(0,10)), y="C11", training_frame=h2o_df)
  >>> gs.confusion_matrix(h2o_df)
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code: def confusion_matrix(self, data):
      """
      Returns a confusion matrix based of H2O's default prediction threshold for a dataset.
  
      :param data: metric for which the confusion matrix will be calculated.
  
      :examples:
  
      >>> from h2o.estimators import H2OGeneralizedLinearEstimator
      >>> from h2o.grid.grid_search import H2OGridSearch
      >>> h2o_df = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/bigdata/laptop/glm_ordinal_logit/ordinal_multinomial_training_set.csv")
      >>> hyper_parameters = {'alpha': [0.01,0.5], 'lambda': [1e-5,1e-6]}
      >>> gs = H2OGridSearch(H2OGeneralizedLinearEstimator(family="ordinal"), hyper_parameters)
      >>> h2o_df['C11'] = h2o_df['C11'].asfactor()
      >>> gs.train(x=list(range(0,10)), y="C11", training_frame=h2o_df)
      >>> gs.confusion_matrix(h2o_df)
      """
      return {
          model.model_id: model.confusion_matrix(data)
          for model in self.models
      }
  
label_desc: Use image node_id 1 for calling the H2OOrdinalGridSearch obj's underlying member method code with example usage: obj.confusion_matrix(data) and returns: unknown