code: def test_lime_explainer_good_regressor(self):
      np.random.seed(1)
      rf = RandomForestClassifier(n_estimators=500)
      rf.fit(self.train, self.labels_train)
      i = np.random.randint(0, self.test.shape[0])
  
      explainer = LimeTabularExplainer(
          self.train,
          mode="classification",
          feature_names=self.feature_names,
          class_names=self.target_names,
          discretize_continuous=True,
      )
  
      exp = explainer.explain_instance(
          self.test[i],
          rf.predict_proba,
          num_features=2,
          model_regressor=LinearRegression(),
      )
  
      self.assertIsNotNone(exp)
      keys = [x[0] for x in exp.as_list()]
      self.assertEqual(
          1,
          sum([1 if "petal width" in x else 0 for x in keys]),
          "Petal Width is a major feature",
      )
      self.assertEqual(
          1,
          sum([1 if "petal length" in x else 0 for x in keys]),
          "Petal Length is a major feature",
      )
  
imports: import unittest
  import numpy
  import collections
  import sklearn
  import sklearn.datasets
  import sklearn.ensemble
  import sklearn.linear_model
  from numpy.testing import assert_array_equal
  from sklearn.datasets import load_iris, make_classification, make_multilabel_classification
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.linear_model import LinearRegression
  from lime.discretize import QuartileDiscretizer, DecileDiscretizer, EntropyDiscretizer
  from lime.lime_tabular import LimeTabularExplainer
label_desc: Use image node_id 2 for calling the TestLimeTabular obj's underlying member method code with example usage: obj.test_lime_explainer_good_regressor() without return types