class_docstr: Isotonic Regression
class_docstr_tok: ["Isotonic","Regression"]
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 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 out_of_bounds: Method of handling values of X predictor that are outside of the bounds seen in training.
         Defaults to ``"na"``.
  :type out_of_bounds: Literal["na", "clip"]
  :param custom_metric_func: Reference to custom evaluation function, format: `language:keyName=funcName`
         Defaults to ``None``.
  :type custom_metric_func: str, optional
  :param nfolds: Number of folds for K-fold cross-validation (0 to disable or >= 2).
         Defaults to ``0``.
  :type nfolds: int
  :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 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
docstr_tok: [":","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","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","out_of_bounds",":","Method","of","handling","values","of","X","predictor","that","are","outside","of","the","bounds","seen","in","training",".","Defaults","to","``","``","na","''","``",".",":","type","out_of_bounds",":","Literal","[","``","na","''",",","``","clip","''","]",":","param","custom_metric_func",":","Reference","to","custom","evaluation","function",",","format",":","`","language",":","keyName=funcName","`","Defaults","to","``","None","``",".",":","type","custom_metric_func",":","str",",","optional",":","param","nfolds",":","Number","of","folds","for","K-fold","cross-validation","(","0","to","disable","or",">","=","2",")",".","Defaults","to","``","0","``",".",":","type","nfolds",":","int",":","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","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"]
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]]
      weights_column=None,  # type: Optional[str]
      out_of_bounds="na",  # type: Literal["na", "clip"]
      custom_metric_func=None,  # type: Optional[str]
      nfolds=0,  # type: int
      keep_cross_validation_models=True,  # type: bool
      keep_cross_validation_predictions=False,  # type: bool
      keep_cross_validation_fold_assignment=False,  # type: bool
      fold_assignment="auto",  # type: Literal["auto", "random", "modulo", "stratified"]
      fold_column=None,  # type: Optional[str]
  ):
      """
      :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 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 out_of_bounds: Method of handling values of X predictor that are outside of the bounds seen in training.
             Defaults to ``"na"``.
      :type out_of_bounds: Literal["na", "clip"]
      :param custom_metric_func: Reference to custom evaluation function, format: `language:keyName=funcName`
             Defaults to ``None``.
      :type custom_metric_func: str, optional
      :param nfolds: Number of folds for K-fold cross-validation (0 to disable or >= 2).
             Defaults to ``0``.
      :type nfolds: int
      :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 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
      """
      super(H2OIsotonicRegressionEstimator, 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.weights_column = weights_column
      self.out_of_bounds = out_of_bounds
      self.custom_metric_func = custom_metric_func
      self.nfolds = nfolds
      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.fold_assignment = fold_assignment
      self.fold_column = fold_column
  
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 H2OIsotonicRegressionEstimator object from inherited base classes: H2OEstimator with example: obj = H2OIsotonicRegressionEstimator(model_id, training_frame, validation_frame, response_column, ignored_columns, weights_column, out_of_bounds, custom_metric_func, nfolds, keep_cross_validation_models, keep_cross_validation_predictions, keep_cross_validation_fold_assignment, fold_assignment, fold_column)