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: Names of columns to ignore for training.
  
  Type: ``List[str]``.
docstr_tok: ["Names","of","columns","to","ignore","for","training",".","Type",":","``","List","[","str","]","``","."]
code: def ignored_columns(self):
      """
      Names of columns to ignore for training.
  
      Type: ``List[str]``.
      """
      return self._parms.get("ignored_columns")
  
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 4 for calling the H2OExtendedIsolationForestEstimator obj's underlying member method code with example usage: obj.ignored_columns() and returns: self