code: def classification_test():
      np.random.seed(0)
      # model_name = 'GradientBoostingClassifier'
      model_name = "LGBMClassifier"
  
      (
          df_titanic_train,
          df_titanic_test,
      ) = get_titanic_binary_classification_dataset()
      df_titanic_train["DELETE_THIS_FIELD"] = 1
  
      column_descriptions = {
          "survived": "output",
          "embarked": "categorical",
          "pclass": "categorical",
          "sex": "categorical",
          "this_does_not_exist": "ignore",
          "DELETE_THIS_FIELD": "ignore",
      }
  
      ml_predictor = Predictor(
          type_of_estimator="classifier",
          column_descriptions=column_descriptions,
      )
  
      ml_predictor.train(df_titanic_train, model_names=model_name)
  
      test_score = ml_predictor.score(
          df_titanic_test, df_titanic_test.survived
      )
  
      print("test_score")
      print(test_score)
  
      lower_bound = -0.16
      if model_name == "DeepLearningClassifier":
          lower_bound = -0.245
      if model_name == "LGBMClassifier":
          lower_bound = -0.225
  
      assert lower_bound < test_score < -0.135
  
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 4 for calling a global function with example usage: classification_test() without return types