code: def train(self, recall=0.95, index_predicates=True):  # pragma: no cover
          
          examples, y = flatten_training(self.training_pairs)
          self.classifier.fit(self.data_model.distances(examples), y)
  
          self.predicates = self.active_learner.learn_predicates(
              recall, index_predicates)
          self.blocker = blocking.Blocker(self.predicates)
          self.blocker.resetIndices()
docs: Keyword arguments:
  
          maximum_comparisons -- The maximum number of comparisons a
                                 blocking rule is allowed to make.
  
                                 Defaults to 1000000
  
          recall -- The proportion of true dupe pairs in our training
                    data that that we the learned blocks must cover. If
                    we lower the recall, there will be pairs of true
                    dupes that we will never directly compare.
  
                    recall should be a float between 0.0 and 1.0, the default
                    is 0.95
  
          index_predicates -- Should dedupe consider predicates that
                              rely upon indexing the data. Index predicates can
                              be slower and take susbstantial memory.
  
                              Defaults to True.