code: def test_cat(self):
      import numpy as np
  
      # Arrange
      np.random.seed(8)
      n, d = 1000, 100
      sf = tc.SFrame()
      for i in range(d):
          sf.add_column(tc.SArray(np.random.rand(n)), inplace=True)
          target = np.random.randint(2, size=n)
          sf["target"] = target
  
      sf["target"] = sf["target"].astype(str)
      sf["target"] = "cat-" + sf["target"]
      model = tc.random_forest_classifier.create(sf, "target")
  
      # Act
      evaluation = model.evaluate(sf)
  
      # Assert
      self.assertEqual(
          ["cat-0", "cat-1"],
          sorted(
              list(
                  evaluation["confusion_matrix"][
                      "target_label"
                  ].unique()
              )
          ),
      )
  
imports: from __future__ import print_function
  from __future__ import division
  from __future__ import absolute_import
  import unittest
  import turicreate
  import math
  import uuid
  import random
  import sys
  import copy
  from turicreate.toolkits import evaluation
  from turicreate.toolkits._main import ToolkitError
  import shutil
  import numpy
  from array import array
  import os
label_desc: Use image node_id 1 for calling the TestStringTarget obj's underlying member method code with example usage: obj.test_cat() without return types