class_docstr: Naive Bayes
  
  The naive Bayes classifier assumes independence between predictor variables
  conditional on the response, and a Gaussian distribution of numeric predictors with
  mean and standard deviation computed from the training dataset. When building a naive
  Bayes classifier, every row in the training dataset that contains at least one NA will
  be skipped completely. If the test dataset has missing values, then those predictors
  are omitted in the probability calculation during prediction.
class_docstr_tok: ["Naive","Bayes","The","naive","Bayes","classifier","assumes","independence","between","predictor","variables","conditional","on","the","response",",","and","a","Gaussian","distribution","of","numeric","predictors","with","mean","and","standard","deviation","computed","from","the","training","dataset",".","When","building","a","naive","Bayes","classifier",",","every","row","in","the","training","dataset","that","contains","at","least","one","NA","will","be","skipped","completely",".","If","the","test","dataset","has","missing","values",",","then","those","predictors","are","omitted","in","the","probability","calculation","during","prediction","."]
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 nfolds: Number of folds for K-fold cross-validation (0 to disable or >= 2).
         Defaults to ``0``.
  :type nfolds: int
  :param seed: Seed for pseudo random number generator (only used for cross-validation and
         fold_assignment="Random" or "AUTO")
         Defaults to ``-1``.
  :type seed: int
  :param fold_assignment: Cross-validation fold assignment scheme, if fold_column is not specified. The
         'Stratified' option will stratify the folds based on the response variable, for classification problems.
         Defaults to ``"auto"``.
  :type fold_assignment: Literal["auto", "random", "modulo", "stratified"]
  :param fold_column: Column with cross-validation fold index assignment per observation.
         Defaults to ``None``.
  :type fold_column: str, optional
  :param keep_cross_validation_models: Whether to keep the cross-validation models.
         Defaults to ``True``.
  :type keep_cross_validation_models: bool
  :param keep_cross_validation_predictions: Whether to keep the predictions of the cross-validation models.
         Defaults to ``False``.
  :type keep_cross_validation_predictions: bool
  :param keep_cross_validation_fold_assignment: Whether to keep the cross-validation fold assignment.
         Defaults to ``False``.
  :type keep_cross_validation_fold_assignment: bool
  :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 score_each_iteration: Whether to score during each iteration of model training.
         Defaults to ``False``.
  :type score_each_iteration: bool
  :param balance_classes: Balance training data class counts via over/under-sampling (for imbalanced data).
         Defaults to ``False``.
  :type balance_classes: bool
  :param class_sampling_factors: Desired over/under-sampling ratios per class (in lexicographic order). If not
         specified, sampling factors will be automatically computed to obtain class balance during training.
         Requires balance_classes.
         Defaults to ``None``.
  :type class_sampling_factors: List[float], optional
  :param max_after_balance_size: Maximum relative size of the training data after balancing class counts (can be
         less than 1.0). Requires balance_classes.
         Defaults to ``5.0``.
  :type max_after_balance_size: float
  :param max_confusion_matrix_size: [Deprecated] Maximum size (# classes) for confusion matrices to be printed in
         the Logs
         Defaults to ``20``.
  :type max_confusion_matrix_size: int
  :param laplace: Laplace smoothing parameter
         Defaults to ``0.0``.
  :type laplace: float
  :param min_sdev: Min. standard deviation to use for observations with not enough data
         Defaults to ``0.001``.
  :type min_sdev: float
  :param eps_sdev: Cutoff below which standard deviation is replaced with min_sdev
         Defaults to ``0.0``.
  :type eps_sdev: float
  :param min_prob: Min. probability to use for observations with not enough data
         Defaults to ``0.001``.
  :type min_prob: float
  :param eps_prob: Cutoff below which probability is replaced with min_prob
         Defaults to ``0.0``.
  :type eps_prob: float
  :param compute_metrics: Compute metrics on training data
         Defaults to ``True``.
  :type compute_metrics: bool
  :param max_runtime_secs: Maximum allowed runtime in seconds for model training. Use 0 to disable.
         Defaults to ``0.0``.
  :type max_runtime_secs: float
  :param export_checkpoints_dir: Automatically export generated models to this directory.
         Defaults to ``None``.
  :type export_checkpoints_dir: str, optional
  :param gainslift_bins: Gains/Lift table number of bins. 0 means disabled.. Default value -1 means automatic
         binning.
         Defaults to ``-1``.
  :type gainslift_bins: int
  :param auc_type: Set default multinomial AUC type.
         Defaults to ``"auto"``.
  :type auc_type: Literal["auto", "none", "macro_ovr", "weighted_ovr", "macro_ovo", "weighted_ovo"]
code: def __init__(
      self,
      model_id=None,  # type: Optional[Union[None, str, H2OEstimator]]
      nfolds=0,  # type: int
      seed=-1,  # type: int
      fold_assignment="auto",  # type: Literal["auto", "random", "modulo", "stratified"]
      fold_column=None,  # type: Optional[str]
      keep_cross_validation_models=True,  # type: bool
      keep_cross_validation_predictions=False,  # type: bool
      keep_cross_validation_fold_assignment=False,  # type: bool
      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
      score_each_iteration=False,  # type: bool
      balance_classes=False,  # type: bool
      class_sampling_factors=None,  # type: Optional[List[float]]
      max_after_balance_size=5.0,  # type: float
      max_confusion_matrix_size=20,  # type: int
      laplace=0.0,  # type: float
      min_sdev=0.001,  # type: float
      eps_sdev=0.0,  # type: float
      min_prob=0.001,  # type: float
      eps_prob=0.0,  # type: float
      compute_metrics=True,  # type: bool
      max_runtime_secs=0.0,  # type: float
      export_checkpoints_dir=None,  # type: Optional[str]
      gainslift_bins=-1,  # type: int
      auc_type="auto",  # type: Literal["auto", "none", "macro_ovr", "weighted_ovr", "macro_ovo", "weighted_ovo"]
  ):
      """
      :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 nfolds: Number of folds for K-fold cross-validation (0 to disable or >= 2).
             Defaults to ``0``.
      :type nfolds: int
      :param seed: Seed for pseudo random number generator (only used for cross-validation and
             fold_assignment="Random" or "AUTO")
             Defaults to ``-1``.
      :type seed: int
      :param fold_assignment: Cross-validation fold assignment scheme, if fold_column is not specified. The
             'Stratified' option will stratify the folds based on the response variable, for classification problems.
             Defaults to ``"auto"``.
      :type fold_assignment: Literal["auto", "random", "modulo", "stratified"]
      :param fold_column: Column with cross-validation fold index assignment per observation.
             Defaults to ``None``.
      :type fold_column: str, optional
      :param keep_cross_validation_models: Whether to keep the cross-validation models.
             Defaults to ``True``.
      :type keep_cross_validation_models: bool
      :param keep_cross_validation_predictions: Whether to keep the predictions of the cross-validation models.
             Defaults to ``False``.
      :type keep_cross_validation_predictions: bool
      :param keep_cross_validation_fold_assignment: Whether to keep the cross-validation fold assignment.
             Defaults to ``False``.
      :type keep_cross_validation_fold_assignment: bool
      :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 score_each_iteration: Whether to score during each iteration of model training.
             Defaults to ``False``.
      :type score_each_iteration: bool
      :param balance_classes: Balance training data class counts via over/under-sampling (for imbalanced data).
             Defaults to ``False``.
      :type balance_classes: bool
      :param class_sampling_factors: Desired over/under-sampling ratios per class (in lexicographic order). If not
             specified, sampling factors will be automatically computed to obtain class balance during training.
             Requires balance_classes.
             Defaults to ``None``.
      :type class_sampling_factors: List[float], optional
      :param max_after_balance_size: Maximum relative size of the training data after balancing class counts (can be
             less than 1.0). Requires balance_classes.
             Defaults to ``5.0``.
      :type max_after_balance_size: float
      :param max_confusion_matrix_size: [Deprecated] Maximum size (# classes) for confusion matrices to be printed in
             the Logs
             Defaults to ``20``.
      :type max_confusion_matrix_size: int
      :param laplace: Laplace smoothing parameter
             Defaults to ``0.0``.
      :type laplace: float
      :param min_sdev: Min. standard deviation to use for observations with not enough data
             Defaults to ``0.001``.
      :type min_sdev: float
      :param eps_sdev: Cutoff below which standard deviation is replaced with min_sdev
             Defaults to ``0.0``.
      :type eps_sdev: float
      :param min_prob: Min. probability to use for observations with not enough data
             Defaults to ``0.001``.
      :type min_prob: float
      :param eps_prob: Cutoff below which probability is replaced with min_prob
             Defaults to ``0.0``.
      :type eps_prob: float
      :param compute_metrics: Compute metrics on training data
             Defaults to ``True``.
      :type compute_metrics: bool
      :param max_runtime_secs: Maximum allowed runtime in seconds for model training. Use 0 to disable.
             Defaults to ``0.0``.
      :type max_runtime_secs: float
      :param export_checkpoints_dir: Automatically export generated models to this directory.
             Defaults to ``None``.
      :type export_checkpoints_dir: str, optional
      :param gainslift_bins: Gains/Lift table number of bins. 0 means disabled.. Default value -1 means automatic
             binning.
             Defaults to ``-1``.
      :type gainslift_bins: int
      :param auc_type: Set default multinomial AUC type.
             Defaults to ``"auto"``.
      :type auc_type: Literal["auto", "none", "macro_ovr", "weighted_ovr", "macro_ovo", "weighted_ovo"]
      """
      super(H2ONaiveBayesEstimator, self).__init__()
      self._parms = {}
      self._id = self._parms["model_id"] = model_id
      self.nfolds = nfolds
      self.seed = seed
      self.fold_assignment = fold_assignment
      self.fold_column = fold_column
      self.keep_cross_validation_models = keep_cross_validation_models
      self.keep_cross_validation_predictions = (
          keep_cross_validation_predictions
      )
      self.keep_cross_validation_fold_assignment = (
          keep_cross_validation_fold_assignment
      )
      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.score_each_iteration = score_each_iteration
      self.balance_classes = balance_classes
      self.class_sampling_factors = class_sampling_factors
      self.max_after_balance_size = max_after_balance_size
      self.max_confusion_matrix_size = max_confusion_matrix_size
      self.laplace = laplace
      self.min_sdev = min_sdev
      self.eps_sdev = eps_sdev
      self.min_prob = min_prob
      self.eps_prob = eps_prob
      self.compute_metrics = compute_metrics
      self.max_runtime_secs = max_runtime_secs
      self.export_checkpoints_dir = export_checkpoints_dir
      self.gainslift_bins = gainslift_bins
      self.auc_type = auc_type
  
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 H2ONaiveBayesEstimator object from inherited base classes: H2OEstimator with example: obj = H2ONaiveBayesEstimator(model_id, nfolds, seed, fold_assignment, fold_column, keep_cross_validation_models, keep_cross_validation_predictions, keep_cross_validation_fold_assignment, training_frame, validation_frame, response_column, ignored_columns, ignore_const_cols, score_each_iteration, balance_classes, class_sampling_factors, max_after_balance_size, max_confusion_matrix_size, laplace, min_sdev, eps_sdev, min_prob, eps_prob, compute_metrics, max_runtime_secs, export_checkpoints_dir, gainslift_bins, auc_type)