scikit-learn
diff --git a/‎sklearn/metrics/_ranking.py
Copy file name to clipboardExpand all lines: sklearn/metrics/_ranking.py
+9-2Lines changed: 9 additions & 2 deletions b/‎sklearn/metrics/_ranking.py
Copy file name to clipboardExpand all lines: sklearn/metrics/_ranking.py
+9-2Lines changed: 9 additions & 2 deletions
diff --git a/‎sklearn/tests/test_public_functions.py
Copy file name to clipboardExpand all lines: sklearn/tests/test_public_functions.py
+1Lines changed: 1 addition & 0 deletions b/‎sklearn/tests/test_public_functions.py
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+1Lines changed: 1 addition & 0 deletions
@@ -1224,6 +1224,13 @@ def coverage_error(y_true, y_score, *, sample_weight=None):
     return np.average(coverage, weights=sample_weight)
 
 
+@validate_params(
+    {
+        "y_true": ["array-like", "sparse matrix"],
+        "y_score": ["array-like"],
+        "sample_weight": ["array-like", None],
+    }
+)
 def label_ranking_loss(y_true, y_score, *, sample_weight=None):
     """Compute Ranking loss measure.
 
@@ -1242,10 +1249,10 @@ def label_ranking_loss(y_true, y_score, *, sample_weight=None):
 
     Parameters
     ----------
-    y_true : {ndarray, sparse matrix} of shape (n_samples, n_labels)
+    y_true : {array-like, sparse matrix} of shape (n_samples, n_labels)
         True binary labels in binary indicator format.
 
-    y_score : ndarray of shape (n_samples, n_labels)
+    y_score : array-like of shape (n_samples, n_labels)
         Target scores, can either be probability estimates of the positive
         class, confidence values, or non-thresholded measure of decisions
         (as returned by "decision_function" on some classifiers).
 
@@ -127,6 +127,7 @@ def _check_function_param_validation(
     "sklearn.metrics.f1_score",
     "sklearn.metrics.hamming_loss",
     "sklearn.metrics.jaccard_score",
+    "sklearn.metrics.label_ranking_loss",
     "sklearn.metrics.log_loss",
     "sklearn.metrics.matthews_corrcoef",
     "sklearn.metrics.max_error",