code: def run_classification_example(
      timeout: float = None, visualization=False, with_tuning=True
  ):
      problem = "classification"
      train_data_path = (
          f"{fedot_project_root()}/cases/data/scoring/scoring_train.csv"
      )
      test_data_path = (
          f"{fedot_project_root()}/cases/data/scoring/scoring_test.csv"
      )
  
      baseline_model = Fedot(problem=problem, timeout=timeout)
      baseline_model.fit(
          features=train_data_path,
          target="target",
          predefined_model="rf",
      )
  
      baseline_model.predict(features=test_data_path)
      print(baseline_model.get_metrics())
  
      auto_model = Fedot(
          problem=problem,
          timeout=timeout,
          n_jobs=-1,
          preset="best_quality",
          max_pipeline_fit_time=5,
          metric=["roc_auc", "precision"],
          with_tuning=with_tuning,
      )
      auto_model.fit(features=train_data_path, target="target")
      prediction = auto_model.predict_proba(features=test_data_path)
  
      print(
          auto_model.get_metrics(rounding_order=4)
      )  # we can control the rounding of metrics
      if visualization:
          auto_model.plot_prediction()
      return prediction
  
label_desc: Use image node_id 1 for calling a global function with example usage: run_classification_example(timeout, visualization, with_tuning) and returns: prediction