code: def test_multi_class_without_probability(self):
      # Generate some random data.
      # This unit test should not rely on scikit learn for test data.
      x, y = [], []
      for _ in range(50):
          x.append(
              [
                  random.gauss(200, 30),
                  random.gauss(-100, 22),
                  random.gauss(100, 42),
              ]
          )
          y.append(random.choice([1, 2, 10, 12]))
      y[0], y[1], y[2], y[3] = 1, 2, 10, 12
      column_names = ["x1", "x2", "x3"]
      prob = svmutil.svm_problem(y, x)
  
      df = pd.DataFrame(x, columns=column_names)
  
      for param1 in self.non_kernel_parameters:
          for param2 in self.kernel_parameters:
              param_str = " ".join([self.base_param, param1, param2])
              param = svmutil.svm_parameter(param_str)
  
              model = svm_train(prob, param)
  
              # Get predictions with probabilities as dictionaries
              (df["prediction"], _, _) = svm_predict(y, x, model, " -q")
  
              spec = libsvm.convert(model, column_names, "target")
  
              metrics = evaluate_classifier(spec, df, verbose=False)
              self.assertEqual(metrics["num_errors"], 0)
  
imports: import random
  import tempfile
  import unittest
  import pandas
  import pytest
  from coremltools._deps import _HAS_LIBSVM, _HAS_SKLEARN, _SKLEARN_VERSION, MSG_LIBSVM_NOT_FOUND, MSG_SKLEARN_NOT_FOUND
  from coremltools.models.utils import _is_macos, _macos_version, evaluate_classifier, evaluate_classifier_with_probabilities
label_desc: Use image node_id 5 for calling the NuSVCLibSVMTest obj's underlying member method code with example usage: obj.test_multi_class_without_probability() without return types