code: def test_perform_feature_scaling_true_regression(model_name=None):
      np.random.seed(0)
  
      (
          df_boston_train,
          df_boston_test,
      ) = utils.get_boston_regression_dataset()
  
      column_descriptions = {"MEDV": "output", "CHAS": "categorical"}
  
      ml_predictor = Predictor(
          type_of_estimator="regressor",
          column_descriptions=column_descriptions,
      )
  
      ml_predictor.train(df_boston_train, perform_feature_scaling=True)
  
      test_score = ml_predictor.score(
          df_boston_test, df_boston_test.MEDV
      )
  
      print("test_score")
      print(test_score)
  
      assert -3.0 < test_score < -2.7
  
file_path: auto_ml/tests/core_tests/api_coverage_tests_regressors.py
imports: import datetime
  import os
  import random
  import sys
  from auto_ml import Predictor
  from auto_ml.utils_models import load_ml_model
  import dill
  from nose.tools import assert_equal, assert_not_equal, with_setup
  import numpy
  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_perform_feature_scaling_true_regression(model_name) without return types