class_docstr: Represents a single terminal node in an H2OTree with final prediction.
  
  :param id: Node's unique identifier (integer). Generated by H2O.
  :param prediction: The prediction value in the terminal node (numeric floating point).
  
  :examples:
  
  >>> from h2o.estimators import H2OGradientBoostingEstimator
  >>> from h2o.tree import H2OTree
  >>> from h2o.tree import H2OLeafNode
  >>> airlines = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/AirlinesTrain.csv")
  >>> gbm = H2OGradientBoostingEstimator(ntrees=1)
  >>> gbm.train(x=["Origin", "Dest"],
  ...           y="IsDepDelayed",
  ...           training_frame=airlines)
  # Retrieve the node ids      
  >>> tree = H2OTree(model = gbm, tree_number = 0 , tree_class = "NO")
  >>> tree.node_ids
  # Retrieve the predictions
  >>> tree.predictions
  # Set the id and prediction
  >>> H2OLeafNode(0, -0.23001842)
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docstr: Prediction value in the terminal node (numeric floating point).
  
  :examples:
  
  >>> from h2o.estimators import H2OGradientBoostingEstimator
  >>> from h2o.tree import H2OTree
  >>> from h2o.tree import H2OLeafNode
  >>> airlines = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/AirlinesTrain.csv")
  >>> gbm = H2OGradientBoostingEstimator(ntrees=1)
  >>> gbm.train(x=["Origin", "Dest"],
  ...           y="IsDepDelayed",
  ...           training_frame=airlines)
  # Retrieve the node ids      
  >>> tree = H2OTree(model = gbm, tree_number = 0 , tree_class = "NO")
  >>> tree.node_ids
  # Retrieve the predictions
  >>> tree.predictions
  # Set the id and prediction
  >>> leaf_node = H2OLeafNode(0, -0.23001842)
  # Retrieve the node prediction
  >>> leaf_node.prediction
code: def prediction(self):
      """Prediction value in the terminal node (numeric floating point).
  
      :examples:
  
      >>> from h2o.estimators import H2OGradientBoostingEstimator
      >>> from h2o.tree import H2OTree
      >>> from h2o.tree import H2OLeafNode
      >>> airlines = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/AirlinesTrain.csv")
      >>> gbm = H2OGradientBoostingEstimator(ntrees=1)
      >>> gbm.train(x=["Origin", "Dest"],
      ...           y="IsDepDelayed",
      ...           training_frame=airlines)
      # Retrieve the node ids
      >>> tree = H2OTree(model = gbm, tree_number = 0 , tree_class = "NO")
      >>> tree.node_ids
      # Retrieve the predictions
      >>> tree.predictions
      # Set the id and prediction
      >>> leaf_node = H2OLeafNode(0, -0.23001842)
      # Retrieve the node prediction
      >>> leaf_node.prediction
      """
      return self._prediction
  
imports: import math
  import h2o
label_desc: Use image node_id 3 for calling the H2OLeafNode obj's underlying member method code with example usage: obj.prediction() and returns: self