class_docstr: Args
  ----
  treatment : array, shape = (num_samples, )
      An array of treatment group indicator values.
  
  control_name : string
      The name of the control condition as a string. Must be contained in the treatment array.
  
  treatment_names : list, length = cate.shape[1]
      A list of treatment group names. NB: The order of the items in the
      list must correspond to the order in which the conditional average
      treatment effect estimates are in cate_array.
  
  y_proba : array, shape = (num_samples, )
      The predicted probability of conversion using the Y ~ X model across
      the total sample.
  
  cate : array, shape = (num_samples, len(set(treatment)))
      Conditional average treatment effect estimations from any model.
  
  value : array, shape = (num_samples, )
      Value of converting each unit.
  
  conversion_cost : shape = (num_samples, len(set(treatment)))
      The cost of a treatment that is triggered if a unit converts after having been in the treatment, such as a promotion code.
  
  impression_cost : shape = (num_samples, len(set(treatment)))
     The cost of a treatment that is the same for each unit whether or not they convert, such as a cost associated with a promotion channel.
  
  
  Notes
  -----
  Because we get the conditional average treatment effects from
  cate-learners relative to the control condition, we subtract the
  cate for the unit in their actual treatment group from y_proba for that
  unit, in order to recover the control outcome. We then add the cates
  to the control outcome to obtain y_proba under each condition. These
  outcomes are counterfactual because just one of them is actually
  observed.
docstr: Predict the best treatment group based on the highest counterfactual
  value for a treatment.
docstr_tok: ["Predict","the","best","treatment","group","based","on","the","highest","counterfactual","value","for","a","treatment","."]
code: def predict_best(self):
      """
      Predict the best treatment group based on the highest counterfactual
      value for a treatment.
      """
      self._get_counterfactuals()
      self._get_counterfactual_values()
      return self.best_treatment
  
imports: import numpy
label_desc: Use image node_id 2 for calling the CounterfactualValueEstimator obj's underlying member method code with example usage: obj.predict_best() and returns: self