docstr: Set whether or not the result should be multi-class (the sum of all probabilities is 1.0) or
   multi-label (each label has a probability between 0.0 to 1.0).
   Default is False i.e. multi-class
  
  Parameters
  ----------
  value : bool
      Whether or not the result should be multi-class (the sum of all probabilities is 1.0) or
      multi-label (each label has a probability between 0.0 to 1.0).
      Default is False i.e. multi-class
docstr_tok: ["Set","whether","or","not","the","result","should","be","multi-class","(","the","sum","of","all","probabilities","is","1.0",")","or","multi-label","(","each","label","has","a","probability","between","0.0","to","1.0",")",".","Default","is","False","i.e",".","multi-class","Parameters","--","--","--","--","--","value",":","bool","Whether","or","not","the","result","should","be","multi-class","(","the","sum","of","all","probabilities","is","1.0",")","or","multi-label","(","each","label","has","a","probability","between","0.0","to","1.0",")",".","Default","is","False","i.e",".","multi-class"]
code: def setMultilabel(self, value):
      """Set whether or not the result should be multi-class (the sum of all probabilities is 1.0) or
       multi-label (each label has a probability between 0.0 to 1.0).
       Default is False i.e. multi-class
  
      Parameters
      ----------
      value : bool
          Whether or not the result should be multi-class (the sum of all probabilities is 1.0) or
          multi-label (each label has a probability between 0.0 to 1.0).
          Default is False i.e. multi-class
      """
      return self._set(multilabel=value)
  
imports: from pyspark.ml.param import Param, Params, TypeConverters
label_desc: Use image node_id 3 for calling the HasClassifierActivationProperties obj's underlying member method code with example usage: obj.setMultilabel(value) and returns: self