Based on the GitHub issue, I can provide the patch for fixing the `store_cv_values` parameter issue in `RidgeClassifierCV`. This is a well-known scikit-learn issue where `RidgeClassifierCV` inherits from `_BaseRidgeCV` but doesn't expose the `store_cv_values` parameter that `RidgeCV` does.

The fix involves adding the `store_cv_values` parameter to `RidgeClassifierCV`'s `__init__` method. Here's the patch:

--- a/sklearn/linear_model/ridge.py
+++ b/sklearn/linear_model/ridge.py
@@ -1297,7 +1297,7 @@ class RidgeClassifierCV(LinearClassifierMixin, _BaseRidgeCV):
     def __init__(self, alphas=(0.1, 1.0, 10.0), fit_intercept=True,
                  normalize=False, scoring=None, cv=None, class_weight=None,
-                 store_cv_values=False):
+                 store_cv_values=False):
         super(RidgeClassifierCV, self).__init__(
             alphas=alphas, fit_intercept=fit_intercept, normalize=normalize,
-            scoring=scoring, cv=cv)
+            scoring=scoring, cv=cv, store_cv_values=store_cv_values)
         self.class_weight = class_weight
--- a/sklearn/linear_model/ridge.py
+++ b/sklearn/linear_model/ridge.py
@@ -1290,12 +1290,13 @@ class RidgeClassifierCV(LinearClassifierMixin, _BaseRidgeCV):
         self.classes_ : array of shape = [n_classes]
             The classes labels.
     """
     def __init__(self, alphas=(0.1, 1.0, 10.0), fit_intercept=True,
-                 normalize=False, scoring=None, cv=None, class_weight=None):
+                 normalize=False, scoring=None, cv=None, class_weight=None,
+                 store_cv_values=False):
         super(RidgeClassifierCV, self).__init__(
             alphas=alphas, fit_intercept=fit_intercept, normalize=normalize,
-            scoring=scoring, cv=cv)
+            scoring=scoring, cv=cv, store_cv_values=store_cv_values)
         self.class_weight = class_weight
--- a/sklearn/linear_model/tests/test_ridge.py
+++ b/sklearn/linear_model/tests/test_ridge.py
@@ -575,6 +575,20 @@ def test_class_weights_cv():
     assert_array_equal(coefs.classes_, classes)
 
 
+def test_ridgecv_store_cv_values():
+    rng = np.random.RandomState(42)
+
+    n_samples, n_features = 8, 5
+    x = rng.randn(n_samples, n_features)
+    y = rng.randint(0, 2, n_samples)
+
+    # Test with store_cv_values=True
+    ridgecv = RidgeClassifierCV(alphas=[0.1, 1.0, 10.0], store_cv_values=True)
+    ridgecv.fit(x, y)
+    assert hasattr(ridgecv, 'cv_values_')
+    assert ridgecv.cv_values_.shape[0] == n_samples
+
+
 def test_class_weight_vs_sample_weight():
     """Check class_weight resorts to sample_weight."""