code: def lift_score(y_target, y_predicted, binary=True, positive_label=1):
      """Lift measures the degree to which the predictions of a
      classification model are better than randomly-generated predictions.
  
      The in terms of True Positives (TP), True Negatives (TN),
      False Positives (FP), and False Negatives (FN), the lift score is
      computed as:
      [ TP / (TP+FP) ] / [ (TP+FN) / (TP+TN+FP+FN) ]
  
  
      Parameters
      -----------
      y_target : array-like, shape=[n_samples]
          True class labels.
      y_predicted : array-like, shape=[n_samples]
          Predicted class labels.
      binary : bool (default: True)
          Maps a multi-class problem onto a
          binary, where
          the positive class is 1 and
          all other classes are 0.
      positive_label : int (default: 0)
          Class label of the positive class.
  
      Returns
      ----------
      score : float
          Lift score in the range [0, infinity]
  
      Examples
      -----------
      For usage examples, please see
      https://rasbt.github.io/mlxtend/user_guide/evaluate/lift_score/
      """
      if not isinstance(y_target, np.ndarray):
          targ_tmp = np.asarray(y_target)
      else:
          targ_tmp = y_target
      if not isinstance(y_predicted, np.ndarray):
          pred_tmp = np.asarray(y_predicted)
      else:
          pred_tmp = y_predicted
  
      pred_tmp = pred_tmp.T
      targ_tmp = targ_tmp.T
  
      if len(pred_tmp) != len(targ_tmp):
          raise AttributeError(
              "`y_target` and `y_predicted`"
              "don't have the same number of elements."
          )
      if binary:
          targ_tmp = np.where(targ_tmp != positive_label, 0, 1)
          pred_tmp = np.where(pred_tmp != positive_label, 0, 1)
  
      binary_check_targ_tmp = np.extract(targ_tmp > 1, targ_tmp)
      binary_check_pred_tmp = np.extract(pred_tmp > 1, pred_tmp)
  
      if len(binary_check_targ_tmp) or len(binary_check_pred_tmp):
          raise AttributeError(
              "`y_target` and `y_predicted`"
              " have different elements from 0 and 1."
          )
  
      return support(targ_tmp, pred_tmp) / (
          support(targ_tmp) * support(pred_tmp)
      )
  
label_desc: Use image node_id 1 for calling a global function with example usage: lift_score(y_target, y_predicted, binary, positive_label) and returns: unknown