code: def test_all_algos_classification(model_name=None):
      np.random.seed(0)
  
      (
          df_titanic_train,
          df_titanic_test,
      ) = utils.get_titanic_binary_classification_dataset()
  
      column_descriptions = {
          "survived": "output",
          "sex": "categorical",
          "embarked": "categorical",
          "pclass": "categorical",
      }
  
      ml_predictor = Predictor(
          type_of_estimator="classifier",
          column_descriptions=column_descriptions,
      )
  
      ml_predictor.train(
          df_titanic_train,
          model_names=[
              "LogisticRegression",
              "RandomForestClassifier",
              "RidgeClassifier",
              "GradientBoostingClassifier",
              "ExtraTreesClassifier",
              "AdaBoostClassifier",
              "SGDClassifier",
              "Perceptron",
              "PassiveAggressiveClassifier",
              "DeepLearningClassifier",
              "XGBClassifier",
              "LGBMClassifier",
              "LinearSVC",
          ],
      )
  
      test_score = ml_predictor.score(
          df_titanic_test, df_titanic_test.survived
      )
  
      print("test_score")
      print(test_score)
  
      # Linear models aren't super great on this dataset...
      assert -0.215 < test_score < -0.131
  
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 5 for calling a global function with example usage: test_all_algos_classification(model_name) without return types