code: def test_regression_cv_3_and_r2(load_diabetes_dataset):
      #  test for regression using cv=3, and the r2 as metric.
      X, y = load_diabetes_dataset
  
      sel = SelectByShuffling(
          estimator=LinearRegression(),
          scoring="r2",
          cv=3,
          threshold=0.05,
          random_state=1,
      )
      sel.fit(X, y)
  
      # expected output
      Xtransformed = X[[1, 2, 3, 4, 5, 8]].copy()
  
      # test init params
      assert sel.cv == 3
      assert sel.scoring == "r2"
      assert sel.threshold == 0.05
      # fit params
      assert np.round(sel.initial_model_performance_, 3) == 0.489
      assert sel.features_to_drop_ == [0, 6, 7, 9]
      # test transform output
      pd.testing.assert_frame_equal(sel.transform(X), Xtransformed)
  
file_path: feature_engine/tests/test_selection/test_shuffle_features.py
imports: import numpy
  import pandas
  import pytest
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.linear_model import LinearRegression
  from sklearn.tree import DecisionTreeRegressor
  from feature_engine.selection import SelectByShuffling
label_desc: Use image node_id 2 for calling a global function with example usage: test_regression_cv_3_and_r2(load_diabetes_dataset) without return types