code: def test_regression():
      X, y = boston_housing_data()
      X = X[:, [1, 2, 6, 8, 12]]
      lr = LinearRegression()
      efs_r = EFS(
          lr,
          min_features=3,
          max_features=4,
          scoring="neg_mean_squared_error",
          cv=10,
          print_progress=False,
      )
      efs_r = efs_r.fit(X, y)
      assert efs_r.best_idx_ == (0, 2, 4)
      assert round(efs_r.best_score_, 4) == -40.8777
  
imports: import numpy
  import pandas
  from numpy.testing import assert_almost_equal
  from packaging.version import Version
  from sklearn import __version__
  from sklearn.datasets import load_iris
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.linear_model import LinearRegression
  from sklearn.model_selection import GroupKFold
  from sklearn.neighbors import KNeighborsClassifier
  from mlxtend.classifier import SoftmaxRegression
  from mlxtend.data import boston_housing_data
  from mlxtend.feature_selection import ExhaustiveFeatureSelector
  from mlxtend.utils import assert_raises
label_desc: Use image node_id 9 for calling a global function with example usage: test_regression() without return types