code: def test_no_explain_linear(self):
      a = AutoML(
          results_path=self.automl_dir,
          total_time_limit=1,
          algorithms=["Linear"],
          train_ensemble=False,
          validation_strategy={
              "validation_type": "kfold",
              "k_folds": 2,
              "shuffle": True,
              "stratify": True,
          },
          explain_level=0,
          start_random_models=1,
      )
  
      X, y = datasets.make_regression(
          n_samples=100,
          n_features=5,
          n_informative=4,
          shuffle=False,
          random_state=0,
      )
      X = pd.DataFrame(X, columns=[f"f_{i}" for i in range(X.shape[1])])
  
      a.fit(X, y)
  
      result_files = os.listdir(
          os.path.join(self.automl_dir, "1_Linear")
      )
  
      # There should be no files with:
      # - permutation importance
      # - shap importance
      # - shap dependence
      # - shap decisions
  
      # Check permutation importance
      produced = False
      for f in result_files:
          if "importance.csv" in f and "shap" not in f:
              produced = True
              break
      self.assertFalse(produced)
      # Check shap importance
      produced = False
      for f in result_files:
          if "importance.csv" in f and "shap" in f:
              produced = True
              break
      self.assertFalse(produced)
      # Check shap dependence
      produced = False
      for f in result_files:
          if "dependence.png" in f:
              produced = True
              break
      self.assertFalse(produced)
      # Check shap decisions
      produced = False
      for f in result_files:
          if "decisions.png" in f:
              produced = True
              break
      self.assertFalse(produced)
      # Check coefficients
      produced = False
      for f in result_files:
          if "coefs.csv" in f:
              produced = True
              break
      self.assertFalse(produced)
  
file_path: mljar-supervised/tests/tests_automl/test_explain_levels.py
imports: import os
  import unittest
  import tempfile
  import json
  import numpy
  import pandas
  import shutil
  from sklearn import datasets
  from supervised import AutoML
  from supervised.algorithms.random_forest import additional
  from supervised.algorithms.xgboost import additional
label_desc: Use image node_id 4 for calling the AutoMLExplainLevelsTest obj's underlying member method code with example usage: obj.test_no_explain_linear() without return types