code: def test_include_bad_y_vals_train_classification():
      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,
      )
  
      df_titanic_train.iloc[1]["survived"] = None
      df_titanic_train.iloc[8]["survived"] = None
      df_titanic_train.iloc[26]["survived"] = None
  
      ml_predictor.train(df_titanic_train)
  
      test_score = ml_predictor.score(
          df_titanic_test.to_dict("records"), df_titanic_test.survived
      )
  
      print("test_score")
      print(test_score)
  
      assert -0.17 < test_score < -0.135
  
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
  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 11 for calling a global function with example usage: test_include_bad_y_vals_train_classification() without return types