code: def test_score_uncertainty():
      np.random.seed(0)
  
      (
          df_boston_train,
          df_boston_test,
      ) = utils.get_boston_regression_dataset()
  
      column_descriptions = {"MEDV": "output", "CHAS": "categorical"}
  
      df_boston_train, uncertainty_data = train_test_split(
          df_boston_train, test_size=0.5
      )
  
      ml_predictor = Predictor(
          type_of_estimator="regressor",
          column_descriptions=column_descriptions,
      )
  
      ml_predictor.train(
          df_boston_train,
          perform_feature_selection=True,
          train_uncertainty_model=True,
          uncertainty_data=uncertainty_data,
      )
  
      uncertainty_score = ml_predictor.score_uncertainty(
          df_boston_test, df_boston_test.MEDV
      )
  
      print("uncertainty_score")
      print(uncertainty_score)
  
      assert uncertainty_score > -0.2
  
file_path: auto_ml/tests/core_tests/uncertainty_tests.py
imports: import os
  import sys
  from auto_ml import Predictor
  import dill
  from nose.tools import assert_equal, assert_not_equal, with_setup
  import numpy
  import pandas
  from sklearn.model_selection import train_test_split
  import utils_testing
label_desc: Use image node_id 3 for calling a global function with example usage: test_score_uncertainty() without return types