code: def test_model_performance_2_correlated_groups(df_test):
      X, y = df_test
  
      transformer = SmartCorrelatedSelection(
          variables=None,
          method="pearson",
          threshold=0.8,
          missing_values="raise",
          selection_method="model_performance",
          estimator=RandomForestClassifier(
              n_estimators=10, random_state=1
          ),
          scoring="roc_auc",
          cv=3,
      )
  
      Xt = transformer.fit_transform(X, y)
  
      # expected result
      df = X[
          [
              "var_0",
              "var_1",
              "var_2",
              "var_3",
              "var_5",
              "var_7",
              "var_10",
              "var_11",
          ]
      ].copy()
  
      # test fit attrs
      assert transformer.correlated_feature_sets_ == [
          {"var_0", "var_8"},
          {"var_4", "var_6", "var_7", "var_9"},
      ]
      assert transformer.features_to_drop_ == [
          "var_4",
          "var_6",
          "var_8",
          "var_9",
      ]
      # test transform output
      pd.testing.assert_frame_equal(Xt, df)
  
file_path: feature_engine/tests/test_selection/test_smart_correlation_selection.py
imports: import pandas
  import pytest
  from sklearn.datasets import make_classification
  from sklearn.ensemble import RandomForestClassifier
  from feature_engine.selection import SmartCorrelatedSelection
label_desc: Use image node_id 3 for calling a global function with example usage: test_model_performance_2_correlated_groups(df_test) without return types