code: def test_all_algos_regression():
      # 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)
  
      (
          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,
          model_names=[
              "LinearRegression",
              "RandomForestRegressor",
              "Ridge",
              "GradientBoostingRegressor",
              "AdaBoostRegressor",
              "SGDRegressor",
              "PassiveAggressiveRegressor",
              "Lasso",
              "LassoLars",
              "ElasticNet",
              "OrthogonalMatchingPursuit",
              "BayesianRidge",
              "ARDRegression",
              "MiniBatchKMeans",
              "DeepLearningRegressor",
              "LGBMRegressor",
              "XGBClassifier",
              "LinearSVR",
              "CatBoostRegressor",
          ],
      )
  
      test_score = ml_predictor.score(
          df_boston_test, df_boston_test.MEDV
      )
  
      print("test_score")
      print(test_score)
  
      assert -3.4 < test_score < -2.8
  
file_path: auto_ml/tests/advanced_tests/advanced_install_tests.py
imports: import datetime
  import os
  import random
  import sys
  import warnings
  from auto_ml import Predictor
  from auto_ml.utils_models import load_ml_model
  import dill
  import numpy
  from nose.tools import assert_equal, assert_not_equal, with_setup
  from sklearn.metrics import accuracy_score
  from sklearn.model_selection import train_test_split
  import utils_testing
label_desc: Use image node_id 6 for calling a global function with example usage: test_all_algos_regression() without return types