class_docstr: Isotonic Regression
class_docstr_tok: ["Isotonic","Regression"]
docstr: 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.
  
  Type: ``Literal["auto", "random", "modulo", "stratified"]``, defaults to ``"auto"``.
docstr_tok: ["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",".","Type",":","``","Literal","[","``","auto","''",",","``","random","''",",","``","modulo","''",",","``","stratified","''","]","``",",","defaults","to","``","``","auto","''","``","."]
code: def fold_assignment(self):
      """
      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.
  
      Type: ``Literal["auto", "random", "modulo", "stratified"]``, defaults to ``"auto"``.
      """
      return self._parms.get("fold_assignment")
  
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 24 for calling the H2OIsotonicRegressionEstimator obj's underlying member method code with example usage: obj.fold_assignment() and returns: self