class_docstr: AdaBoost
  
  Builds an AdaBoost model
class_docstr_tok: ["AdaBoost","Builds","an","AdaBoost","model"]
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 weights_column: Column with observation weights. Giving some observation a weight of zero is equivalent
         to excluding it from the dataset; giving an observation a relative weight of 2 is equivalent to repeating
         that row twice. Negative weights are not allowed. Note: Weights are per-row observation weights and do
         not increase the size of the data frame. This is typically the number of times a row is repeated, but
         non-integer values are supported as well. During training, rows with higher weights matter more, due to
         the larger loss function pre-factor. If you set weight = 0 for a row, the returned prediction frame at
         that row is zero and this is incorrect. To get an accurate prediction, remove all rows with weight == 0.
         Defaults to ``None``.
  :type weights_column: str, optional
  :param nlearners: Number of AdaBoost weak learners.
         Defaults to ``50``.
  :type nlearners: int
  :param weak_learner: Choose a weak learner type. Defaults to AUTO, which means DRF.
         Defaults to ``"auto"``.
  :type weak_learner: Literal["auto", "drf", "glm", "gbm", "deep_learning"]
  :param learn_rate: Learning rate (from 0.0 to 1.0)
         Defaults to ``0.5``.
  :type learn_rate: float
  :param weak_learner_params: Customized parameters for the weak_learner algorithm.
         Defaults to ``None``.
  :type weak_learner_params: dict, optional
  :param seed: Seed for pseudo random number generator (if applicable)
         Defaults to ``-1``.
  :type seed: 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"]
      weights_column=None,  # type: Optional[str]
      nlearners=50,  # type: int
      weak_learner="auto",  # type: Literal["auto", "drf", "glm", "gbm", "deep_learning"]
      learn_rate=0.5,  # type: float
      weak_learner_params=None,  # type: Optional[dict]
      seed=-1,  # 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 weights_column: Column with observation weights. Giving some observation a weight of zero is equivalent
             to excluding it from the dataset; giving an observation a relative weight of 2 is equivalent to repeating
             that row twice. Negative weights are not allowed. Note: Weights are per-row observation weights and do
             not increase the size of the data frame. This is typically the number of times a row is repeated, but
             non-integer values are supported as well. During training, rows with higher weights matter more, due to
             the larger loss function pre-factor. If you set weight = 0 for a row, the returned prediction frame at
             that row is zero and this is incorrect. To get an accurate prediction, remove all rows with weight == 0.
             Defaults to ``None``.
      :type weights_column: str, optional
      :param nlearners: Number of AdaBoost weak learners.
             Defaults to ``50``.
      :type nlearners: int
      :param weak_learner: Choose a weak learner type. Defaults to AUTO, which means DRF.
             Defaults to ``"auto"``.
      :type weak_learner: Literal["auto", "drf", "glm", "gbm", "deep_learning"]
      :param learn_rate: Learning rate (from 0.0 to 1.0)
             Defaults to ``0.5``.
      :type learn_rate: float
      :param weak_learner_params: Customized parameters for the weak_learner algorithm.
             Defaults to ``None``.
      :type weak_learner_params: dict, optional
      :param seed: Seed for pseudo random number generator (if applicable)
             Defaults to ``-1``.
      :type seed: int
      """
      super(H2OAdaBoostEstimator, 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.weights_column = weights_column
      self.nlearners = nlearners
      self.weak_learner = weak_learner
      self.learn_rate = learn_rate
      self.weak_learner_params = weak_learner_params
      self.seed = seed
  
imports: import ast
  import json
  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
  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 H2OAdaBoostEstimator object from inherited base classes: H2OEstimator with example: obj = H2OAdaBoostEstimator(model_id, training_frame, ignored_columns, ignore_const_cols, categorical_encoding, weights_column, nlearners, weak_learner, learn_rate, weak_learner_params, seed)