docs: def evaluate_model(recording, model_folder, verbose=False):
      """Evaluate model for a single recording."""
      from . import preprocess_dataset
      from . import features
  
      for target_folder in get_recognizer_folders(model_folder):
          # The source is later than the target. That means we need to
          # refresh the target
          if "preprocessed" in target_folder:
              logging.info("Start applying preprocessing methods...")
              t = target_folder
              _, _, preprocessing_queue = preprocess_dataset.get_parameters(t)
              handwriting = handwritten_data.HandwrittenData(recording)
              if verbose:
                  handwriting.show()
              handwriting.preprocessing(preprocessing_queue)
              if verbose:
                  logging.debug("After preprocessing: %s",
                                handwriting.get_sorted_pointlist())
                  handwriting.show()
          elif "feature-files" in target_folder:
              logging.info("Create feature file...")
              infofile_path = os.path.join(target_folder, "info.yml")
              with open(infofile_path, 'r') as ymlfile:
                  feature_description = yaml.load(ymlfile)
              feature_str_list = feature_description['features']
              feature_list = features.get_features(feature_str_list)
              feature_count = sum(map(lambda n: n.get_dimension(),
                                      feature_list))
              x = handwriting.feature_extraction(feature_list)
  
              # Create hdf5
              _, output_filename = tempfile.mkstemp(suffix='.hdf5', text=True)
              create_hdf5(output_filename, feature_count, [(x, 0)])
          elif "model" in target_folder:
              logfile, model_use = _evaluate_model_single_file(target_folder,
                                                               output_filename)
              return logfile
          else:
              logging.info("'%s' not found", target_folder)
      os.remove(output_filename)
      os.remove(model_use)