ensembler = utils.Ensemble(ensemble_predictors=self.ensemble_predictors, type_of_estimator=self.type_of_estimator, method=ensemble_method)
ml_predictor = Predictor(type_of_estimator=self.type_of_estimator, column_descriptions=self.column_descriptions, name=self.name)
print("Using machine learning to ensemble together a bunch of trained estimators!")
data_for_final_ensembling = data_for_final_ensembling.reset_index()
ml_predictor.train(raw_training_data=data_for_final_ensembling, ensembler=ensembler)
// predictions_on_ensemble_data = ensembler._get_all_predictions(data_for_final_ensembling)
// data_for_final_ensembling = pd.concat([data_for_final_ensembling, predictions_on_ensemble_data], axis=1)
self.trained_pipeline = ml_predictor
else:
// Create an instance of an Ensemble object that will get predictions from all the trained subpredictors