class_docstr: Extended Isolation Forest
  
  Builds an Extended Isolation Forest model. Extended Isolation Forest generalizes its predecessor algorithm, 
  Isolation Forest. The original Isolation Forest algorithm suffers from bias due to tree branching. Extension of the 
  algorithm mitigates the bias by adjusting the branching, and the original algorithm becomes just a special case.
  Extended Isolation Forest's attribute "extension_level" allows leveraging the generalization. The minimum value is 0 and
  means the Isolation Forest's behavior. Maximum value is (numCols - 1) and stands for full extension. The rest of the 
  algorithm is analogical to the Isolation Forest algorithm. Each iteration builds a tree that partitions the sample 
  observations' space until it isolates observation. The length of the path from root to a leaf node of the resulting tree
  is used to calculate the anomaly score. Anomalies are easier to isolate, and their average
  tree path is expected to be shorter than paths of regular observations. Anomaly score is a number between 0 and 1. 
  A number closer to 0 is a normal point, and a number closer to 1 is a more anomalous point.
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docstr: :param model_id: Destination id for this model; auto-generated if not specified.
         Defaults to ``None``.
  :type model_id: Union[None, str, H2OEstimator], optional
  :param training_frame: Id of the training data frame.
         Defaults to ``None``.
  :type training_frame: Union[None, str, H2OFrame], optional
  :param ignored_columns: Names of columns to ignore for training.
         Defaults to ``None``.
  :type ignored_columns: List[str], optional
  :param ignore_const_cols: Ignore constant columns.
         Defaults to ``True``.
  :type ignore_const_cols: bool
  :param categorical_encoding: Encoding scheme for categorical features
         Defaults to ``"auto"``.
  :type categorical_encoding: Literal["auto", "enum", "one_hot_internal", "one_hot_explicit", "binary", "eigen", "label_encoder",
         "sort_by_response", "enum_limited"]
  :param score_each_iteration: Whether to score during each iteration of model training.
         Defaults to ``False``.
  :type score_each_iteration: bool
  :param score_tree_interval: Score the model after every so many trees. Disabled if set to 0.
         Defaults to ``0``.
  :type score_tree_interval: int
  :param ntrees: Number of Extended Isolation Forest trees.
         Defaults to ``100``.
  :type ntrees: int
  :param sample_size: Number of randomly sampled observations used to train each Extended Isolation Forest tree.
         Defaults to ``256``.
  :type sample_size: int
  :param extension_level: Maximum is N - 1 (N = numCols). Minimum is 0. Extended Isolation Forest with
         extension_Level = 0 behaves like Isolation Forest.
         Defaults to ``0``.
  :type extension_level: int
  :param seed: Seed for pseudo random number generator (if applicable)
         Defaults to ``-1``.
  :type seed: int
  :param disable_training_metrics: Disable calculating training metrics (expensive on large datasets)
         Defaults to ``True``.
  :type disable_training_metrics: bool
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code: def __init__(
      self,
      model_id=None,  # type: Optional[Union[None, str, H2OEstimator]]
      training_frame=None,  # type: Optional[Union[None, str, H2OFrame]]
      ignored_columns=None,  # type: Optional[List[str]]
      ignore_const_cols=True,  # type: bool
      categorical_encoding="auto",  # type: Literal["auto", "enum", "one_hot_internal", "one_hot_explicit", "binary", "eigen", "label_encoder", "sort_by_response", "enum_limited"]
      score_each_iteration=False,  # type: bool
      score_tree_interval=0,  # type: int
      ntrees=100,  # type: int
      sample_size=256,  # type: int
      extension_level=0,  # type: int
      seed=-1,  # type: int
      disable_training_metrics=True,  # type: bool
  ):
      """
      :param model_id: Destination id for this model; auto-generated if not specified.
             Defaults to ``None``.
      :type model_id: Union[None, str, H2OEstimator], optional
      :param training_frame: Id of the training data frame.
             Defaults to ``None``.
      :type training_frame: Union[None, str, H2OFrame], optional
      :param ignored_columns: Names of columns to ignore for training.
             Defaults to ``None``.
      :type ignored_columns: List[str], optional
      :param ignore_const_cols: Ignore constant columns.
             Defaults to ``True``.
      :type ignore_const_cols: bool
      :param categorical_encoding: Encoding scheme for categorical features
             Defaults to ``"auto"``.
      :type categorical_encoding: Literal["auto", "enum", "one_hot_internal", "one_hot_explicit", "binary", "eigen", "label_encoder",
             "sort_by_response", "enum_limited"]
      :param score_each_iteration: Whether to score during each iteration of model training.
             Defaults to ``False``.
      :type score_each_iteration: bool
      :param score_tree_interval: Score the model after every so many trees. Disabled if set to 0.
             Defaults to ``0``.
      :type score_tree_interval: int
      :param ntrees: Number of Extended Isolation Forest trees.
             Defaults to ``100``.
      :type ntrees: int
      :param sample_size: Number of randomly sampled observations used to train each Extended Isolation Forest tree.
             Defaults to ``256``.
      :type sample_size: int
      :param extension_level: Maximum is N - 1 (N = numCols). Minimum is 0. Extended Isolation Forest with
             extension_Level = 0 behaves like Isolation Forest.
             Defaults to ``0``.
      :type extension_level: int
      :param seed: Seed for pseudo random number generator (if applicable)
             Defaults to ``-1``.
      :type seed: int
      :param disable_training_metrics: Disable calculating training metrics (expensive on large datasets)
             Defaults to ``True``.
      :type disable_training_metrics: bool
      """
      super(H2OExtendedIsolationForestEstimator, self).__init__()
      self._parms = {}
      self._id = self._parms["model_id"] = model_id
      self.training_frame = training_frame
      self.ignored_columns = ignored_columns
      self.ignore_const_cols = ignore_const_cols
      self.categorical_encoding = categorical_encoding
      self.score_each_iteration = score_each_iteration
      self.score_tree_interval = score_tree_interval
      self.ntrees = ntrees
      self.sample_size = sample_size
      self.extension_level = extension_level
      self.seed = seed
      self.disable_training_metrics = disable_training_metrics
  
imports: from h2o.estimators.estimator_base import H2OEstimator
  from h2o.exceptions import H2OValueError
  from h2o.frame import H2OFrame
  from h2o.utils.typechecks import assert_is_type, Enum, numeric
label_desc: Use image node_id 1 to create a new H2OExtendedIsolationForestEstimator object from inherited base classes: H2OEstimator with example: obj = H2OExtendedIsolationForestEstimator(model_id, training_frame, ignored_columns, ignore_const_cols, categorical_encoding, score_each_iteration, score_tree_interval, ntrees, sample_size, extension_level, seed, disable_training_metrics)