code: def _generic_fit(fqdn, result, scorer, yP=None, *argl, **argd):
      
      out = None
      if len(argl) > 0:
          machine = argl[0]
          out = {}
          if hasattr(machine, "best_score_"):
              out["score"] = machine.best_score_
              
          #With fitting it is often useful to know how well the fitting set was
          #matched (by trying to predict a score on it). We can do this
          #automatically and show the result to the user.
          yL = _do_auto_predict(*argl[0:2])
          yscore = scorer(fqdn, yL, yP, *argl, **argd)
          if yscore is not None:
              out.update(yscore)
  
      return out
docs: Performs the generic fit tests that are common to both classifier and
      regressor; uses `scorer` to score the predicted values given by the machine
      when tested against its training set.
  
      Args:
      scorer (function): called on the result of `machine.predict(Xtrain,
        ytrain)`.