code: def evaluate(best_processed_path, model):
      
      x_test_char, x_test_type, y_test = prepare_feature(best_processed_path, option='test')
  
      y_predict = model.predict([x_test_char, x_test_type])
      y_predict = (y_predict.ravel() > 0.5).astype(int)
  
      f1score = f1_score(y_test, y_predict)
      precision = precision_score(y_test, y_predict)
      recall = recall_score(y_test, y_predict)
  
      return f1score, precision, recall
docs: Evaluate model on splitted 10 percent testing set