scikit-learn
diff --git a/‎sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py
Lines changed: 9 additions & 6 deletions b/‎sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py
Lines changed: 9 additions & 6 deletions
diff --git a/‎sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py
Lines changed: 17 additions & 5 deletions b/‎sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py
Lines changed: 17 additions & 5 deletions
@@ -270,18 +270,21 @@ def _check_categories(self, X):
                 if missing.any():
                     categories = categories[~missing]
 
+                if hasattr(self, "feature_names_in_"):
+                    feature_name = f"'{self.feature_names_in_[f_idx]}'"
+                else:
+                    feature_name = f"at index {f_idx}"
+
                 if categories.size > self.max_bins:
                     raise ValueError(
-                        f"Categorical feature at index {f_idx} is "
-                        "expected to have a "
-                        f"cardinality <= {self.max_bins}"
+                        f"Categorical feature {feature_name} is expected to "
+                        f"have a cardinality <= {self.max_bins}"
                     )
 
                 if (categories >= self.max_bins).any():
                     raise ValueError(
-                        f"Categorical feature at index {f_idx} is "
-                        "expected to be encoded with "
-                        f"values < {self.max_bins}"
+                        f"Categorical feature {feature_name} is expected to "
+                        f"be encoded with values < {self.max_bins}"
                     )
             else:
                 categories = None
 
@@ -1141,20 +1141,32 @@ def test_categorical_spec_no_categories(Est, categorical_features, as_array):
 @pytest.mark.parametrize(
     "Est", (HistGradientBoostingClassifier, HistGradientBoostingRegressor)
 )
-def test_categorical_bad_encoding_errors(Est):
+@pytest.mark.parametrize(
+    "use_pandas, feature_name", [(False, "at index 0"), (True, "'f0'")]
+)
+def test_categorical_bad_encoding_errors(Est, use_pandas, feature_name):
     # Test errors when categories are encoded incorrectly
 
     gb = Est(categorical_features=[True], max_bins=2)
 
-    X = np.array([[0, 1, 2]]).T
+    if use_pandas:
+        pd = pytest.importorskip("pandas")
+        X = pd.DataFrame({"f0": [0, 1, 2]})
+    else:
+        X = np.array([[0, 1, 2]]).T
     y = np.arange(3)
-    msg = "Categorical feature at index 0 is expected to have a cardinality <= 2"
+    msg = f"Categorical feature {feature_name} is expected to have a cardinality <= 2"
     with pytest.raises(ValueError, match=msg):
         gb.fit(X, y)
 
-    X = np.array([[0, 2]]).T
+    if use_pandas:
+        X = pd.DataFrame({"f0": [0, 2]})
+    else:
+        X = np.array([[0, 2]]).T
     y = np.arange(2)
-    msg = "Categorical feature at index 0 is expected to be encoded with values < 2"
+    msg = (
+        f"Categorical feature {feature_name} is expected to be encoded with values < 2"
+    )
     with pytest.raises(ValueError, match=msg):
         gb.fit(X, y)