class_docstr: Single Decision Tree
class_docstr_tok: ["Single","Decision","Tree"]
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 response_column: Response variable column.
         Defaults to ``None``.
  :type response_column: str, optional
  :param max_depth: Max depth of tree.
         Defaults to ``20``.
  :type max_depth: int
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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"]
      response_column=None,  # type: Optional[str]
      max_depth=20,  # type: int
  ):
      """
      :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 response_column: Response variable column.
             Defaults to ``None``.
      :type response_column: str, optional
      :param max_depth: Max depth of tree.
             Defaults to ``20``.
      :type max_depth: int
      """
      super(H2OSingleDecisionTreeEstimator, 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.response_column = response_column
      self.max_depth = max_depth
  
file_path: h2o-3/h2o-py/h2o/estimators/single_decision_tree.py
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  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 H2OSingleDecisionTreeEstimator object from inherited base classes: H2OEstimator with example: obj = H2OSingleDecisionTreeEstimator(model_id, training_frame, ignored_columns, ignore_const_cols, categorical_encoding, response_column, max_depth)