scikit-learn
diff --git a/‎sklearn/metrics/_regression.py
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+14-8Lines changed: 14 additions & 8 deletions b/‎sklearn/metrics/_regression.py
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+14-8Lines changed: 14 additions & 8 deletions
diff --git a/‎sklearn/metrics/tests/test_regression.py
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-3Lines changed: 0 additions & 3 deletions b/‎sklearn/metrics/tests/test_regression.py
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-3Lines changed: 0 additions & 3 deletions
diff --git a/‎sklearn/tests/test_public_functions.py
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+1Lines changed: 1 addition & 0 deletions b/‎sklearn/tests/test_public_functions.py
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+1Lines changed: 1 addition & 0 deletions
@@ -1304,6 +1304,18 @@ def d2_tweedie_score(y_true, y_pred, *, sample_weight=None, power=0):
     return 1 - numerator / denominator
 
 
+@validate_params(
+    {
+        "y_true": ["array-like"],
+        "y_pred": ["array-like"],
+        "sample_weight": ["array-like", None],
+        "alpha": [Interval(Real, 0, 1, closed="both")],
+        "multioutput": [
+            StrOptions({"raw_values", "uniform_average"}),
+            "array-like",
+        ],
+    }
+)
 def d2_pinball_score(
     y_true, y_pred, *, sample_weight=None, alpha=0.5, multioutput="uniform_average"
 ):
@@ -1327,7 +1339,7 @@ def d2_pinball_score(
     y_pred : array-like of shape (n_samples,) or (n_samples, n_outputs)
         Estimated target values.
 
-    sample_weight : array-like of shape (n_samples,), optional
+    sample_weight : array-like of shape (n_samples,), default=None
         Sample weights.
 
     alpha : float, default=0.5
@@ -1434,15 +1446,9 @@ def d2_pinball_score(
         if multioutput == "raw_values":
             # return scores individually
             return output_scores
-        elif multioutput == "uniform_average":
+        else:  # multioutput == "uniform_average"
             # passing None as weights to np.average results in uniform mean
             avg_weights = None
-        else:
-            raise ValueError(
-                "multioutput is expected to be 'raw_values' "
-                "or 'uniform_average' but we got %r"
-                " instead." % multioutput
-            )
     else:
         avg_weights = multioutput
 
 
@@ -351,9 +351,6 @@ def test_regression_multioutput_array():
     with pytest.raises(ValueError, match=err_msg):
         mean_pinball_loss(y_true, y_pred, multioutput="variance_weighted")
 
-    with pytest.raises(ValueError, match=err_msg):
-        d2_pinball_score(y_true, y_pred, multioutput="variance_weighted")
-
     pbl = mean_pinball_loss(y_true, y_pred, multioutput="raw_values")
     mape = mean_absolute_percentage_error(y_true, y_pred, multioutput="raw_values")
     r = r2_score(y_true, y_pred, multioutput="raw_values")
 
@@ -117,6 +117,7 @@ def _check_function_param_validation(
     "sklearn.metrics.cluster.contingency_matrix",
     "sklearn.metrics.cohen_kappa_score",
     "sklearn.metrics.confusion_matrix",
+    "sklearn.metrics.d2_pinball_score",
     "sklearn.metrics.det_curve",
     "sklearn.metrics.hamming_loss",
     "sklearn.metrics.mean_absolute_error",