class_docstr: RuleFit
  
  Builds a RuleFit on a parsed dataset, for regression or 
  classification. 
class_docstr_tok: ["RuleFit","Builds","a","RuleFit","on","a","parsed","dataset",",","for","regression","or","classification","."]
docstr: Distribution function
  
  Type: ``Literal["auto", "bernoulli", "multinomial", "gaussian", "poisson", "gamma", "tweedie", "laplace",
  "quantile", "huber"]``, defaults to ``"auto"``.
docstr_tok: ["Distribution","function","Type",":","``","Literal","[","``","auto","''",",","``","bernoulli","''",",","``","multinomial","''",",","``","gaussian","''",",","``","poisson","''",",","``","gamma","''",",","``","tweedie","''",",","``","laplace","''",",","''","quantile","''",",","``","huber","''","]","``",",","defaults","to","``","``","auto","''","``","."]
code: def distribution(self):
      """
      Distribution function
  
      Type: ``Literal["auto", "bernoulli", "multinomial", "gaussian", "poisson", "gamma", "tweedie", "laplace",
      "quantile", "huber"]``, defaults to ``"auto"``.
      """
      return self._parms.get("distribution")
  
imports: from h2o.utils.metaclass import deprecated_params, deprecated_property
  import h2o
  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 H2ORuleFitEstimator obj's underlying member method code with example usage: obj.distribution() and returns: self