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: For every categorical feature, only use this many most frequent categorical levels for model training. Only used
  for categorical_encoding == EnumLimited.
  
  Type: ``int``, defaults to ``10``.
docstr_tok: ["For","every","categorical","feature",",","only","use","this","many","most","frequent","categorical","levels","for","model","training",".","Only","used","for","categorical_encoding","==","EnumLimited",".","Type",":","``","int","``",",","defaults","to","``","10","``","."]
code: def max_categorical_levels(self):
      """
      For every categorical feature, only use this many most frequent categorical levels for model training. Only used
      for categorical_encoding == EnumLimited.
  
      Type: ``int``, defaults to ``10``.
      """
      return self._parms.get("max_categorical_levels")
  
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 34 for calling the H2ORuleFitEstimator obj's underlying member method code with example usage: obj.max_categorical_levels() and returns: self