diff --git a/sklearn/datasets/_twenty_newsgroups.py b/sklearn/datasets/_twenty_newsgroups.py
index 7fe17cbcb0a7a..24046367c69c6 100644
--- a/sklearn/datasets/_twenty_newsgroups.py
+++ b/sklearn/datasets/_twenty_newsgroups.py
@@ -479,7 +479,7 @@ def fetch_20newsgroups_vectorized(
         vectorizer = CountVectorizer(dtype=np.int16)
         X_train = vectorizer.fit_transform(data_train.data).tocsr()
         X_test = vectorizer.transform(data_test.data).tocsr()
-        feature_names = vectorizer.get_feature_names()
+        feature_names = vectorizer.get_feature_names_out()
 
         joblib.dump((X_train, X_test, feature_names), target_file, compress=9)
 
diff --git a/sklearn/utils/tests/test_validation.py b/sklearn/utils/tests/test_validation.py
index 2cbbaac35a31b..167118fb4ff8f 100644
--- a/sklearn/utils/tests/test_validation.py
+++ b/sklearn/utils/tests/test_validation.py
@@ -1414,8 +1414,8 @@ def test_check_pandas_sparse_invalid(ntype1, ntype2):
     pd = pytest.importorskip("pandas", minversion="0.25.0")
     df = pd.DataFrame(
         {
-            "col1": pd.arrays.SparseArray([0, 1, 0], dtype=ntype1),
-            "col2": pd.arrays.SparseArray([1, 0, 1], dtype=ntype2),
+            "col1": pd.arrays.SparseArray([0, 1, 0], dtype=ntype1, fill_value=0),
+            "col2": pd.arrays.SparseArray([1, 0, 1], dtype=ntype2, fill_value=0),
         }
     )
 
@@ -1456,8 +1456,8 @@ def test_check_pandas_sparse_valid(ntype1, ntype2, expected_subtype):
     pd = pytest.importorskip("pandas", minversion="0.25.0")
     df = pd.DataFrame(
         {
-            "col1": pd.arrays.SparseArray([0, 1, 0], dtype=ntype1),
-            "col2": pd.arrays.SparseArray([1, 0, 1], dtype=ntype2),
+            "col1": pd.arrays.SparseArray([0, 1, 0], dtype=ntype1, fill_value=0),
+            "col2": pd.arrays.SparseArray([1, 0, 1], dtype=ntype2, fill_value=0),
         }
     )
     arr = check_array(df, accept_sparse=["csr", "csc"])