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.glm import H2OGeneralizedLinearEstimator
  >>> from h2o.grid.grid_search import H2OGridSearch
  >>> iris = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris.csv")
  >>> hyper_parameters = {'alpha': [0.01,0.5], 'lambda': [1e-5,1e-6]}
  >>> gs = H2OGridSearch(H2OGeneralizedLinearEstimator(family = "multinomial"),
  ...                                                  hyper_parameters)
  >>> gs.train(x=[0,1,2,3], y=4, training_frame=iris)
  >>> gs.confusion_matrix(iris)
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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.glm import H2OGeneralizedLinearEstimator
      >>> from h2o.grid.grid_search import H2OGridSearch
      >>> iris = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris.csv")
      >>> hyper_parameters = {'alpha': [0.01,0.5], 'lambda': [1e-5,1e-6]}
      >>> gs = H2OGridSearch(H2OGeneralizedLinearEstimator(family = "multinomial"),
      ...                                                  hyper_parameters)
      >>> gs.train(x=[0,1,2,3], y=4, training_frame=iris)
      >>> gs.confusion_matrix(iris)
      """
      return {
          model.model_id: model.confusion_matrix(data)
          for model in self.models
      }
  
label_desc: Use image node_id 1 for calling the H2OMultinomialGridSearch obj's underlying member method code with example usage: obj.confusion_matrix(data) and returns: unknown