class_docstr: PSVM
class_docstr_tok: ["PSVM"]
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 validation_frame: Id of the validation data frame.
         Defaults to ``None``.
  :type validation_frame: Union[None, str, H2OFrame], optional
  :param response_column: Response variable column.
         Defaults to ``None``.
  :type response_column: str, 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 hyper_param: Penalty parameter C of the error term
         Defaults to ``1.0``.
  :type hyper_param: float
  :param kernel_type: Type of used kernel
         Defaults to ``"gaussian"``.
  :type kernel_type: Literal["gaussian"]
  :param gamma: Coefficient of the kernel (currently RBF gamma for gaussian kernel, -1 means 1/#features)
         Defaults to ``-1.0``.
  :type gamma: float
  :param rank_ratio: Desired rank of the ICF matrix expressed as an ration of number of input rows (-1 means use
         sqrt(#rows)).
         Defaults to ``-1.0``.
  :type rank_ratio: float
  :param positive_weight: Weight of positive (+1) class of observations
         Defaults to ``1.0``.
  :type positive_weight: float
  :param negative_weight: Weight of positive (-1) class of observations
         Defaults to ``1.0``.
  :type negative_weight: float
  :param disable_training_metrics: Disable calculating training metrics (expensive on large datasets)
         Defaults to ``True``.
  :type disable_training_metrics: bool
  :param sv_threshold: Threshold for accepting a candidate observation into the set of support vectors
         Defaults to ``0.0001``.
  :type sv_threshold: float
  :param fact_threshold: Convergence threshold of the Incomplete Cholesky Factorization (ICF)
         Defaults to ``1e-05``.
  :type fact_threshold: float
  :param feasible_threshold: Convergence threshold for primal-dual residuals in the IPM iteration
         Defaults to ``0.001``.
  :type feasible_threshold: float
  :param surrogate_gap_threshold: Feasibility criterion of the surrogate duality gap (eta)
         Defaults to ``0.001``.
  :type surrogate_gap_threshold: float
  :param mu_factor: Increasing factor mu
         Defaults to ``10.0``.
  :type mu_factor: float
  :param max_iterations: Maximum number of iteration of the algorithm
         Defaults to ``200``.
  :type max_iterations: int
  :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]]
      validation_frame=None,  # type: Optional[Union[None, str, H2OFrame]]
      response_column=None,  # type: Optional[str]
      ignored_columns=None,  # type: Optional[List[str]]
      ignore_const_cols=True,  # type: bool
      hyper_param=1.0,  # type: float
      kernel_type="gaussian",  # type: Literal["gaussian"]
      gamma=-1.0,  # type: float
      rank_ratio=-1.0,  # type: float
      positive_weight=1.0,  # type: float
      negative_weight=1.0,  # type: float
      disable_training_metrics=True,  # type: bool
      sv_threshold=0.0001,  # type: float
      fact_threshold=1e-05,  # type: float
      feasible_threshold=0.001,  # type: float
      surrogate_gap_threshold=0.001,  # type: float
      mu_factor=10.0,  # type: float
      max_iterations=200,  # type: int
      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 validation_frame: Id of the validation data frame.
             Defaults to ``None``.
      :type validation_frame: Union[None, str, H2OFrame], optional
      :param response_column: Response variable column.
             Defaults to ``None``.
      :type response_column: str, 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 hyper_param: Penalty parameter C of the error term
             Defaults to ``1.0``.
      :type hyper_param: float
      :param kernel_type: Type of used kernel
             Defaults to ``"gaussian"``.
      :type kernel_type: Literal["gaussian"]
      :param gamma: Coefficient of the kernel (currently RBF gamma for gaussian kernel, -1 means 1/#features)
             Defaults to ``-1.0``.
      :type gamma: float
      :param rank_ratio: Desired rank of the ICF matrix expressed as an ration of number of input rows (-1 means use
             sqrt(#rows)).
             Defaults to ``-1.0``.
      :type rank_ratio: float
      :param positive_weight: Weight of positive (+1) class of observations
             Defaults to ``1.0``.
      :type positive_weight: float
      :param negative_weight: Weight of positive (-1) class of observations
             Defaults to ``1.0``.
      :type negative_weight: float
      :param disable_training_metrics: Disable calculating training metrics (expensive on large datasets)
             Defaults to ``True``.
      :type disable_training_metrics: bool
      :param sv_threshold: Threshold for accepting a candidate observation into the set of support vectors
             Defaults to ``0.0001``.
      :type sv_threshold: float
      :param fact_threshold: Convergence threshold of the Incomplete Cholesky Factorization (ICF)
             Defaults to ``1e-05``.
      :type fact_threshold: float
      :param feasible_threshold: Convergence threshold for primal-dual residuals in the IPM iteration
             Defaults to ``0.001``.
      :type feasible_threshold: float
      :param surrogate_gap_threshold: Feasibility criterion of the surrogate duality gap (eta)
             Defaults to ``0.001``.
      :type surrogate_gap_threshold: float
      :param mu_factor: Increasing factor mu
             Defaults to ``10.0``.
      :type mu_factor: float
      :param max_iterations: Maximum number of iteration of the algorithm
             Defaults to ``200``.
      :type max_iterations: int
      :param seed: Seed for pseudo random number generator (if applicable)
             Defaults to ``-1``.
      :type seed: int
      """
      super(H2OSupportVectorMachineEstimator, self).__init__()
      self._parms = {}
      self._id = self._parms["model_id"] = model_id
      self.training_frame = training_frame
      self.validation_frame = validation_frame
      self.response_column = response_column
      self.ignored_columns = ignored_columns
      self.ignore_const_cols = ignore_const_cols
      self.hyper_param = hyper_param
      self.kernel_type = kernel_type
      self.gamma = gamma
      self.rank_ratio = rank_ratio
      self.positive_weight = positive_weight
      self.negative_weight = negative_weight
      self.disable_training_metrics = disable_training_metrics
      self.sv_threshold = sv_threshold
      self.fact_threshold = fact_threshold
      self.feasible_threshold = feasible_threshold
      self.surrogate_gap_threshold = surrogate_gap_threshold
      self.mu_factor = mu_factor
      self.max_iterations = max_iterations
      self.seed = seed
  
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 H2OSupportVectorMachineEstimator object from inherited base classes: H2OEstimator with example: obj = H2OSupportVectorMachineEstimator(model_id, training_frame, validation_frame, response_column, ignored_columns, ignore_const_cols, hyper_param, kernel_type, gamma, rank_ratio, positive_weight, negative_weight, disable_training_metrics, sv_threshold, fact_threshold, feasible_threshold, surrogate_gap_threshold, mu_factor, max_iterations, seed)