code: def test_classifier_defaults():
      X, y = iris_data()
      clf1 = LogisticRegression(
          multi_class="ovr", solver="liblinear", random_state=1
      )
      clf2 = DecisionTreeClassifier(random_state=1)
  
      X_train, X_test, y_train, y_test = train_test_split(
          X, y, test_size=0.25, 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) == 0.97
      assert round(score2, 2) == 0.95
  
      t, p = paired_ttest_resampled(
          estimator1=clf1, estimator2=clf2, X=X, y=y, random_seed=1
      )
  
      if Version(sklearn_version) < Version("0.20"):
          assert round(t, 3) == -1.809, t
          assert round(p, 3) == 0.081, p
      else:
          assert round(t, 3) == -1.702, t
          assert round(p, 3) == 0.10, p
  
      # change maxdepth of decision tree classifier
  
      clf2 = DecisionTreeClassifier(max_depth=1, random_state=1)
  
      score3 = clf2.fit(X_train, y_train).score(X_test, y_test)
  
      assert round(score3, 2) == 0.63
  
      t, p = paired_ttest_resampled(
          estimator1=clf1, estimator2=clf2, X=X, y=y, random_seed=1
      )
  
      assert round(t, 3) == 39.214, t
      assert round(p, 3) == 0.000, p
  
file_path: mlxtend/mlxtend/evaluate/tests/test_paired_ttest_resampled.py
imports: import sys
  from packaging.version import Version
  from sklearn import __version__
  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_resampled
  from mlxtend.utils import assert_raises
label_desc: Use image node_id 2 for calling a global function with example usage: test_classifier_defaults() without return types