diff --git a/sklearn/dummy.py b/sklearn/dummy.py
index 1db664826f5c9..63318b07ce580 100644
--- a/sklearn/dummy.py
+++ b/sklearn/dummy.py
@@ -227,7 +227,7 @@ def fit(self, X, y, sample_weight=None):
                         "The constant target value must be present in "
                         "the training data. You provided constant={}. "
                         "Possible values are: {}.".format(
-                            self.constant, list(self.classes_[k])
+                            self.constant, self.classes_[k].tolist()
                         )
                     )
                     raise ValueError(err_msg)
diff --git a/sklearn/impute/tests/test_impute.py b/sklearn/impute/tests/test_impute.py
index 936847e55e324..9d02eb7722cfc 100644
--- a/sklearn/impute/tests/test_impute.py
+++ b/sklearn/impute/tests/test_impute.py
@@ -259,7 +259,7 @@ def test_imputation_median_special_cases():
 @pytest.mark.parametrize("dtype", [None, object, str])
 def test_imputation_mean_median_error_invalid_type(strategy, dtype):
     X = np.array([["a", "b", 3], [4, "e", 6], ["g", "h", 9]], dtype=dtype)
-    msg = "non-numeric data:\ncould not convert string to float: '"
+    msg = "non-numeric data:\ncould not convert string to float:"
     with pytest.raises(ValueError, match=msg):
         imputer = SimpleImputer(strategy=strategy)
         imputer.fit_transform(X)
@@ -272,7 +272,7 @@ def test_imputation_mean_median_error_invalid_type_list_pandas(strategy, type):
     if type == "dataframe":
         pd = pytest.importorskip("pandas")
         X = pd.DataFrame(X)
-    msg = "non-numeric data:\ncould not convert string to float: '"
+    msg = "non-numeric data:\ncould not convert string to float:"
     with pytest.raises(ValueError, match=msg):
         imputer = SimpleImputer(strategy=strategy)
         imputer.fit_transform(X)
diff --git a/sklearn/preprocessing/_encoders.py b/sklearn/preprocessing/_encoders.py
index 4cdd864e92c81..8f441907d5471 100644
--- a/sklearn/preprocessing/_encoders.py
+++ b/sklearn/preprocessing/_encoders.py
@@ -774,8 +774,8 @@ def _map_drop_idx_to_infrequent(self, feature_idx, drop_idx):
         if infrequent_indices is not None and drop_idx in infrequent_indices:
             categories = self.categories_[feature_idx]
             raise ValueError(
-                f"Unable to drop category {categories[drop_idx]!r} from feature"
-                f" {feature_idx} because it is infrequent"
+                f"Unable to drop category {categories[drop_idx].item()!r} from"
+                f" feature {feature_idx} because it is infrequent"
             )
         return default_to_infrequent[drop_idx]
 
diff --git a/sklearn/utils/class_weight.py b/sklearn/utils/class_weight.py
index 0b59f63190c3b..19e7bcb7ba17a 100644
--- a/sklearn/utils/class_weight.py
+++ b/sklearn/utils/class_weight.py
@@ -74,8 +74,10 @@ def compute_class_weight(class_weight, *, classes, y):
 
         n_weighted_classes = len(classes) - len(unweighted_classes)
         if unweighted_classes and n_weighted_classes != len(class_weight):
+            unweighted_classes_user_friendly_str = np.array(unweighted_classes).tolist()
             raise ValueError(
-                f"The classes, {unweighted_classes}, are not in class_weight"
+                f"The classes, {unweighted_classes_user_friendly_str}, are not in"
+                " class_weight"
             )
 
     return weight
diff --git a/sklearn/utils/validation.py b/sklearn/utils/validation.py
index a37aef144f332..20e878256803b 100644
--- a/sklearn/utils/validation.py
+++ b/sklearn/utils/validation.py
@@ -2271,7 +2271,7 @@ def _check_pos_label_consistency(pos_label, y_true):
             or np.array_equal(classes, [1])
         )
     ):
-        classes_repr = ", ".join(repr(c) for c in classes)
+        classes_repr = ", ".join([repr(c) for c in classes.tolist()])
         raise ValueError(
             f"y_true takes value in {{{classes_repr}}} and pos_label is not "
             "specified: either make y_true take value in {0, 1} or "