code: def regression_test():
      # a random seed of 42 has ExtraTreesRegressor getting the best CV score, and that model doesn't generalize as well as GradientBoostingRegressor.
      np.random.seed(0)
      model_name = "LGBMRegressor"
  
      df_boston_train, df_boston_test = get_boston_regression_dataset()
      many_dfs = []
      for i in range(100):
          many_dfs.append(df_boston_train)
      df_boston_train = pd.concat(many_dfs)
  
      column_descriptions = {"MEDV": "output", "CHAS": "categorical"}
  
      ml_predictor = Predictor(
          type_of_estimator="regressor",
          column_descriptions=column_descriptions,
      )
  
      ml_predictor.train(
          df_boston_train,
          model_names=[model_name],
          perform_feature_scaling=False,
      )
  
      test_score = ml_predictor.score(
          df_boston_test, df_boston_test.MEDV
      )
  
      print("test_score")
      print(test_score)
  
      lower_bound = -3.2
      if model_name == "DeepLearningRegressor":
          lower_bound = -7.8
      if model_name == "LGBMRegressor":
          lower_bound = -4.95
      if model_name == "XGBRegressor":
          lower_bound = -3.4
  
      assert lower_bound < test_score < -2.8
  
file_path: auto_ml/tests/core_tests/quick_test.py
imports: import datetime
  import os
  import random
  import sys
  from auto_ml import Predictor
  from auto_ml.utils_models import load_ml_model
  from nose.tools import assert_equal, assert_not_equal, with_setup
  from sklearn.metrics import accuracy_score
  import dill
  import numpy
  import pandas
  from sklearn.datasets import load_boston
  from sklearn.model_selection import train_test_split
label_desc: Use image node_id 2 for calling a global function with example usage: regression_test() without return types