class_docstr: Represents a single node with either numerical or categorical split in an H2OTree with all its attributes.
  
  :param id: Node's unique identifier (integer). Generated by H2O.
  :param threshold: Split threshold, typically when the split column is numerical.
  :param left_child: Integer identifier of the left child node, if there is any. Otherwise None.
  :param right_child: Integer identifier of the right child node, if there is any. Otherwise None.
  :param split_feature: The name of the column this node splits on.
  :param na_direction: The direction of NA values. ``LEFT`` means NA values go to the left child node; ``RIGHT`` means NA values go to the right child node. A value of None means occurence of NA for the given split column is not possible on this node due to an earlier split on the very same feature.
  :param left_levels: Categorical levels on the edge from this node to the left child node. None for non-categorical splits.
  :param right_levels: Categorical levels on the edge from this node to the right child node. None for non-categorical splits.
  
  :examples:
  
  >>> from h2o.tree import H2OTree
  >>> from h2o.tree import H2OSplitNode
  >>> from h2o.estimators import H2OGradientBoostingEstimator
  >>> 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)
  >>> tree = H2OTree(model = gbm, tree_number = 0 , tree_class = "NO")
  >>> threshold = 'nan'
  >>> left_child = 9
  >>> right_child = 10
  >>> split_feature = "DepTime"
  >>> na_direction = "Left"
  >>> left_levels = ['FAT', 'LAS', 'PSP']
  >>> right_levels = ['BWI', 'CLT', 'IND', 'MCO',
  ...                 'PHL', 'PHX', 'RIC', 'TPA']
  >>> H2OSplitNode(5, threshold, left_child,
  ...              right_child, split_feature,
  ...              na_direction, left_levels,
  ...              right_levels)
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docstr: Split threshold for each node. Not only numerical features have numerical split. For splits on categorical features,
  the numerical split threshold represents an index in the categorical feature's domain.
  
  :examples:
  
  >>> from h2o.tree import H2OTree
  >>> from h2o.tree import H2OSplitNode
  >>> from h2o.estimators import H2OGradientBoostingEstimator
  >>> 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)
  >>> tree = H2OTree(model = gbm, tree_number = 0 , tree_class = "NO")
  >>> threshold = 'nan'
  >>> left_child = 9
  >>> right_child = 10
  >>> split_feature = "DepTime"
  >>> na_direction = "Left"
  >>> left_levels = ['FAT', 'LAS', 'PSP']
  >>> right_levels = ['BWI', 'CLT', 'IND', 'MCO',
  ...                 'PHL', 'PHX', 'RIC', 'TPA']
  >>> split_node = H2OSplitNode(5, threshold, left_child,
  ...                           right_child, split_feature,
  ...                           na_direction, left_levels,
  ...                           right_levels)
  >>> split_node.threshold
code: def threshold(self):
      """
      Split threshold for each node. Not only numerical features have numerical split. For splits on categorical features,
      the numerical split threshold represents an index in the categorical feature's domain.
  
      :examples:
  
      >>> from h2o.tree import H2OTree
      >>> from h2o.tree import H2OSplitNode
      >>> from h2o.estimators import H2OGradientBoostingEstimator
      >>> 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)
      >>> tree = H2OTree(model = gbm, tree_number = 0 , tree_class = "NO")
      >>> threshold = 'nan'
      >>> left_child = 9
      >>> right_child = 10
      >>> split_feature = "DepTime"
      >>> na_direction = "Left"
      >>> left_levels = ['FAT', 'LAS', 'PSP']
      >>> right_levels = ['BWI', 'CLT', 'IND', 'MCO',
      ...                 'PHL', 'PHX', 'RIC', 'TPA']
      >>> split_node = H2OSplitNode(5, threshold, left_child,
      ...                           right_child, split_feature,
      ...                           na_direction, left_levels,
      ...                           right_levels)
      >>> split_node.threshold
      """
      return self._threshold
  
imports: import math
  import h2o
label_desc: Use image node_id 3 for calling the H2OSplitNode obj's underlying member method code with example usage: obj.threshold() and returns: self