scikit-learn
diff --git a/‎sklearn/metrics/_classification.py‎
Lines changed: 10 additions & 2 deletions b/‎sklearn/metrics/_classification.py‎
Lines changed: 10 additions & 2 deletions
diff --git a/‎sklearn/tests/test_public_functions.py‎
Lines changed: 1 addition & 0 deletions b/‎sklearn/tests/test_public_functions.py‎
Lines changed: 1 addition & 0 deletions
@@ -2864,6 +2864,14 @@ def log_loss(
     return _weighted_sum(loss, sample_weight, normalize)
 
 
+@validate_params(
+    {
+        "y_true": ["array-like"],
+        "pred_decision": ["array-like"],
+        "labels": ["array-like", None],
+        "sample_weight": ["array-like", None],
+    }
+)
 def hinge_loss(y_true, pred_decision, *, labels=None, sample_weight=None):
     """Average hinge loss (non-regularized).
 
@@ -2883,11 +2891,11 @@ def hinge_loss(y_true, pred_decision, *, labels=None, sample_weight=None):
 
     Parameters
     ----------
-    y_true : array of shape (n_samples,)
+    y_true : array-like of shape (n_samples,)
         True target, consisting of integers of two values. The positive label
         must be greater than the negative label.
 
-    pred_decision : array of shape (n_samples,) or (n_samples, n_classes)
+    pred_decision : array-like of shape (n_samples,) or (n_samples, n_classes)
         Predicted decisions, as output by decision_function (floats).
 
     labels : array-like, default=None
 
@@ -163,6 +163,7 @@ def _check_function_param_validation(
     "sklearn.metrics.fbeta_score",
     "sklearn.metrics.get_scorer",
     "sklearn.metrics.hamming_loss",
+    "sklearn.metrics.hinge_loss",
     "sklearn.metrics.jaccard_score",
     "sklearn.metrics.label_ranking_average_precision_score",
     "sklearn.metrics.label_ranking_loss",