class_docstr: Jointly trains 2 surrogate decision trees.
  Refines diff (or sim) regions by sample generation.
code: def accuracy(self, x, y_true, tree):
      from sklearn.metrics import (
          f1_score,
          accuracy_score,
          precision_score,
          recall_score,
      )
  
      y_pred = self.predict(x, tree)
      acc = accuracy_score(y_true, y_pred)
      return np.around(acc, 4) * 100
  
imports: import numpy
  import pandas
  from aix360.algorithms.imd.rule import Rule
label_desc: Use image node_id 12 for calling the JointSurrogateTree obj's underlying member method code with example usage: obj.accuracy(x, y_true, tree) and returns: unknown