scikit-learn
diff --git a/‎doc/whats_new/v1.1.rst
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diff --git a/‎sklearn/preprocessing/_encoders.py
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diff --git a/‎sklearn/preprocessing/tests/test_encoders.py
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@@ -36,6 +36,13 @@ Changelog
   a node if there are duplicates in the dataset.
   :pr:`23395` by :user:`Jérémie du Boisberranger <jeremiedbb>`.
 
+:mod:`sklearn.preprocessing`
+............................
+
+- |Fix| :meth:`preprocessing.OrdinalEncoder.inverse_transform` correctly handles
+  use cases where `unknown_value` or `encoded_missing_value` is `nan`. :pr:`24087`
+  by `Thomas Fan`_.
+
 .. _changes_1_1_1:
 
 Version 1.1.1
 
@@ -1371,19 +1371,23 @@ def inverse_transform(self, X):
         found_unknown = {}
 
         for i in range(n_features):
-            labels = X[:, i].astype("int64", copy=False)
+            labels = X[:, i]
 
             # replace values of X[:, i] that were nan with actual indices
             if i in self._missing_indices:
-                X_i_mask = _get_mask(X[:, i], self.encoded_missing_value)
+                X_i_mask = _get_mask(labels, self.encoded_missing_value)
                 labels[X_i_mask] = self._missing_indices[i]
 
             if self.handle_unknown == "use_encoded_value":
-                unknown_labels = labels == self.unknown_value
-                X_tr[:, i] = self.categories_[i][np.where(unknown_labels, 0, labels)]
+                unknown_labels = _get_mask(labels, self.unknown_value)
+
+                known_labels = ~unknown_labels
+                X_tr[known_labels, i] = self.categories_[i][
+                    labels[known_labels].astype("int64", copy=False)
+                ]
                 found_unknown[i] = unknown_labels
             else:
-                X_tr[:, i] = self.categories_[i][labels]
+                X_tr[:, i] = self.categories_[i][labels.astype("int64", copy=False)]
 
         # insert None values for unknown values
         if found_unknown:
 
@@ -1837,6 +1837,15 @@ def test_ordinal_encoder_unknown_missing_interaction():
     X_test_trans = oe.transform(X_test)
     assert_allclose(X_test_trans, [[np.nan], [-3]])
 
+    # Non-regression test for #24082
+    X_roundtrip = oe.inverse_transform(X_test_trans)
+
+    # np.nan is unknown so it maps to None
+    assert X_roundtrip[0][0] is None
+
+    # -3 is the encoded missing value so it maps back to nan
+    assert np.isnan(X_roundtrip[1][0])
+
 
 @pytest.mark.parametrize("with_pandas", [True, False])
 def test_ordinal_encoder_encoded_missing_value_error(with_pandas):
@@ -1862,3 +1871,55 @@ def test_ordinal_encoder_encoded_missing_value_error(with_pandas):
 
     with pytest.raises(ValueError, match=error_msg):
         oe.fit(X)
+
+
+@pytest.mark.parametrize(
+    "X_train, X_test_trans_expected, X_roundtrip_expected",
+    [
+        (
+            # missing value is not in training set
+            # inverse transform will considering encoded nan as unknown
+            np.array([["a"], ["1"]], dtype=object),
+            [[0], [np.nan], [np.nan]],
+            np.asarray([["1"], [None], [None]], dtype=object),
+        ),
+        (
+            # missing value in training set,
+            # inverse transform will considering encoded nan as missing
+            np.array([[np.nan], ["1"], ["a"]], dtype=object),
+            [[0], [np.nan], [np.nan]],
+            np.asarray([["1"], [np.nan], [np.nan]], dtype=object),
+        ),
+    ],
+)
+def test_ordinal_encoder_unknown_missing_interaction_both_nan(
+    X_train, X_test_trans_expected, X_roundtrip_expected
+):
+    """Check transform when unknown_value and encoded_missing_value is nan.
+
+    Non-regression test for #24082.
+    """
+    oe = OrdinalEncoder(
+        handle_unknown="use_encoded_value",
+        unknown_value=np.nan,
+        encoded_missing_value=np.nan,
+    ).fit(X_train)
+
+    X_test = np.array([["1"], [np.nan], ["b"]])
+    X_test_trans = oe.transform(X_test)
+
+    # both nan and unknown are encoded as nan
+    assert_allclose(X_test_trans, X_test_trans_expected)
+    X_roundtrip = oe.inverse_transform(X_test_trans)
+
+    n_samples = X_roundtrip_expected.shape[0]
+    for i in range(n_samples):
+        expected_val = X_roundtrip_expected[i, 0]
+        val = X_roundtrip[i, 0]
+
+        if expected_val is None:
+            assert val is None
+        elif is_scalar_nan(expected_val):
+            assert np.isnan(val)
+        else:
+            assert val == expected_val