scikit-learn
diff --git a/‎doc/whats_new/v1.2.rst
Lines changed: 7 additions & 0 deletions b/‎doc/whats_new/v1.2.rst
Lines changed: 7 additions & 0 deletions
diff --git a/‎sklearn/preprocessing/_encoders.py
Lines changed: 17 additions & 11 deletions b/‎sklearn/preprocessing/_encoders.py
Lines changed: 17 additions & 11 deletions
diff --git a/‎sklearn/preprocessing/tests/test_encoders.py
Lines changed: 12 additions & 0 deletions b/‎sklearn/preprocessing/tests/test_encoders.py
Lines changed: 12 additions & 0 deletions
@@ -72,6 +72,13 @@ Changelog
   when the global configuration sets `transform_output="pandas"`.
   :pr:`25500` by :user:`Guillaume Lemaitre <glemaitre>`.
 
+:mod:`sklearn.preprocessing`
+............................
+
+- |Fix| :class:`preprocessing.OrdinalEncoder` now correctly supports
+  `encoded_missing_value` or `unknown_value` set to a categories' cardinality
+  when there is missing values in the training data. :pr:`25704` by `Thomas Fan`_.
+
 :mod:`sklearn.utils`
 ....................
 
 
@@ -1300,24 +1300,30 @@ def fit(self, X, y=None):
         # `_fit` will only raise an error when `self.handle_unknown="error"`
         self._fit(X, handle_unknown=self.handle_unknown, force_all_finite="allow-nan")
 
-        if self.handle_unknown == "use_encoded_value":
-            for feature_cats in self.categories_:
-                if 0 <= self.unknown_value < len(feature_cats):
-                    raise ValueError(
-                        "The used value for unknown_value "
-                        f"{self.unknown_value} is one of the "
-                        "values already used for encoding the "
-                        "seen categories."
-                    )
+        cardinalities = [len(categories) for categories in self.categories_]
 
         # stores the missing indices per category
         self._missing_indices = {}
         for cat_idx, categories_for_idx in enumerate(self.categories_):
             for i, cat in enumerate(categories_for_idx):
                 if is_scalar_nan(cat):
                     self._missing_indices[cat_idx] = i
+
+                    # missing values are not considered part of the cardinality
+                    # when considering unknown categories or encoded_missing_value
+                    cardinalities[cat_idx] -= 1
                     continue
 
+        if self.handle_unknown == "use_encoded_value":
+            for cardinality in cardinalities:
+                if 0 <= self.unknown_value < cardinality:
+                    raise ValueError(
+                        "The used value for unknown_value "
+                        f"{self.unknown_value} is one of the "
+                        "values already used for encoding the "
+                        "seen categories."
+                    )
+
         if self._missing_indices:
             if np.dtype(self.dtype).kind != "f" and is_scalar_nan(
                 self.encoded_missing_value
@@ -1336,9 +1342,9 @@ def fit(self, X, y=None):
                 # known category
                 invalid_features = [
                     cat_idx
-                    for cat_idx, categories_for_idx in enumerate(self.categories_)
+                    for cat_idx, cardinality in enumerate(cardinalities)
                     if cat_idx in self._missing_indices
-                    and 0 <= self.encoded_missing_value < len(categories_for_idx)
+                    and 0 <= self.encoded_missing_value < cardinality
                 ]
 
                 if invalid_features:
 
@@ -2003,3 +2003,15 @@ def test_predefined_categories_dtype():
     for n, cat in enumerate(enc.categories_):
         assert cat.dtype == object
         assert_array_equal(categories[n], cat)
+
+
+def test_ordinal_encoder_missing_unknown_encoding_max():
+    """Check missing value or unknown encoding can equal the cardinality."""
+    X = np.array([["dog"], ["cat"], [np.nan]], dtype=object)
+    X_trans = OrdinalEncoder(encoded_missing_value=2).fit_transform(X)
+    assert_allclose(X_trans, [[1], [0], [2]])
+
+    enc = OrdinalEncoder(handle_unknown="use_encoded_value", unknown_value=2).fit(X)
+    X_test = np.array([["snake"]])
+    X_trans = enc.transform(X_test)
+    assert_allclose(X_trans, [[2]])