code: def test_scoring():
      X, y = iris_data()
      clf1 = LogisticRegression(
          random_state=1, solver="liblinear", multi_class="ovr"
      )
      clf2 = DecisionTreeClassifier(random_state=1)
  
      X_train, X_test, y_train, y_test = train_test_split(
          X, y, test_size=0.5, random_state=123
      )
  
      score1 = clf1.fit(X_train, y_train).score(X_test, y_test)
      score2 = clf2.fit(X_train, y_train).score(X_test, y_test)
  
      assert round(score1, 2) == 0.96, round(score1, 2)
      assert round(score2, 2) == 0.91, round(score2, 2)
  
      t, p = paired_ttest_kfold_cv(
          estimator1=clf1,
          estimator2=clf2,
          X=X,
          y=y,
          scoring="accuracy",
          random_seed=1,
      )
  
      assert round(t, 3) == -1.861, t
      assert round(p, 3) == 0.096, p
  
      t, p = paired_ttest_kfold_cv(
          estimator1=clf1,
          estimator2=clf2,
          X=X,
          y=y,
          scoring="recall_micro",
          random_seed=1,
      )
  
      assert round(t, 3) == -1.861, t
      assert round(p, 3) == 0.096, p
  
imports: from sklearn.linear_model import Lasso, LogisticRegression, Ridge
  from sklearn.model_selection import train_test_split
  from sklearn.tree import DecisionTreeClassifier
  from mlxtend.data import boston_housing_data, iris_data
  from mlxtend.evaluate import paired_ttest_kfold_cv
label_desc: Use image node_id 2 for calling a global function with example usage: test_scoring() without return types