code: def test_model_performance_single_corr_group(df_single):
      X, y = df_single
  
      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_2", "var_3", "var_4", "var_5"]].copy()
  
      # test init params
      assert transformer.method == "pearson"
      assert transformer.threshold == 0.8
      assert transformer.missing_values == "raise"
      assert transformer.selection_method == "model_performance"
      assert transformer.scoring == "roc_auc"
      assert transformer.cv == 3
  
      # test fit attrs
      assert transformer.correlated_feature_sets_ == [
          {"var_1", "var_2"}
      ]
      assert transformer.features_to_drop_ == ["var_1"]
      # 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 2 for calling a global function with example usage: test_model_performance_single_corr_group(df_single) without return types