scikit-learn
diff --git a/‎sklearn/model_selection/_validation.py‎
Lines changed: 28 additions & 6 deletions b/‎sklearn/model_selection/_validation.py‎
Lines changed: 28 additions & 6 deletions
diff --git a/‎sklearn/model_selection/tests/test_validation.py‎
Lines changed: 0 additions & 9 deletions b/‎sklearn/model_selection/tests/test_validation.py‎
Lines changed: 0 additions & 9 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
@@ -329,7 +329,7 @@ def cross_validate(
 
     _warn_or_raise_about_fit_failures(results, error_score)
 
-    # For callabe scoring, the return type is only know after calling. If the
+    # For callable scoring, the return type is only know after calling. If the
     # return type is a dictionary, the error scores can now be inserted with
     # the correct key.
     if callable(scoring):
@@ -1432,6 +1432,26 @@ def _shuffle(y, groups, random_state):
     return _safe_indexing(y, indices)
 
 
+@validate_params(
+    {
+        "estimator": [HasMethods(["fit"])],
+        "X": ["array-like", "sparse matrix"],
+        "y": ["array-like", None],
+        "groups": ["array-like", None],
+        "train_sizes": ["array-like"],
+        "cv": ["cv_object"],
+        "scoring": [StrOptions(set(get_scorer_names())), callable, None],
+        "exploit_incremental_learning": ["boolean"],
+        "n_jobs": [Integral, None],
+        "pre_dispatch": [Integral, str],
+        "verbose": ["verbose"],
+        "shuffle": ["boolean"],
+        "random_state": ["random_state"],
+        "error_score": [StrOptions({"raise"}), Real],
+        "return_times": ["boolean"],
+        "fit_params": [dict, None],
+    }
+)
 def learning_curve(
     estimator,
     X,
@@ -1466,18 +1486,20 @@ def learning_curve(
 
     Parameters
     ----------
-    estimator : object type that implements the "fit" and "predict" methods
-        An object of that type which is cloned for each validation.
+    estimator : object type that implements the "fit" method
+        An object of that type which is cloned for each validation. It must
+        also implement "predict" unless `scoring` is a callable that doesn't
+        rely on "predict" to compute a score.
 
-    X : array-like of shape (n_samples, n_features)
+    X : {array-like, sparse matrix} of shape (n_samples, n_features)
         Training vector, where `n_samples` is the number of samples and
         `n_features` is the number of features.
 
-    y : array-like of shape (n_samples,) or (n_samples, n_outputs)
+    y : array-like of shape (n_samples,) or (n_samples, n_outputs) or None
         Target relative to X for classification or regression;
         None for unsupervised learning.
 
-    groups : array-like of  shape (n_samples,), default=None
+    groups : array-like of shape (n_samples,), default=None
         Group labels for the samples used while splitting the dataset into
         train/test set. Only used in conjunction with a "Group" :term:`cv`
         instance (e.g., :class:`GroupKFold`).
 
@@ -2094,7 +2094,6 @@ def test_fit_and_score_failing():
     failing_clf = FailingClassifier(FailingClassifier.FAILING_PARAMETER)
     # dummy X data
     X = np.arange(1, 10)
-    y = np.ones(9)
     fit_and_score_args = [failing_clf, X, None, dict(), None, None, 0, None, None]
     # passing error score to trigger the warning message
     fit_and_score_kwargs = {"error_score": "raise"}
@@ -2103,21 +2102,13 @@ def test_fit_and_score_failing():
         _fit_and_score(*fit_and_score_args, **fit_and_score_kwargs)
 
     # check that functions upstream pass error_score param to _fit_and_score
-    error_message = re.escape(
-        "error_score must be the string 'raise' or a numeric value. (Hint: if "
-        "using 'raise', please make sure that it has been spelled correctly.)"
-    )
-
     error_message_cross_validate = (
         "The 'error_score' parameter of cross_validate must be .*. Got .* instead."
     )
 
     with pytest.raises(ValueError, match=error_message_cross_validate):
         cross_val_score(failing_clf, X, cv=3, error_score="unvalid-string")
 
-    with pytest.raises(ValueError, match=error_message):
-        learning_curve(failing_clf, X, y, cv=3, error_score="unvalid-string")
-
     assert failing_clf.score() == 0.0  # FailingClassifier coverage
 
 
 
@@ -256,6 +256,7 @@ def _check_function_param_validation(
     "sklearn.metrics.v_measure_score",
     "sklearn.metrics.zero_one_loss",
     "sklearn.model_selection.cross_validate",
+    "sklearn.model_selection.learning_curve",
     "sklearn.model_selection.permutation_test_score",
     "sklearn.model_selection.train_test_split",
     "sklearn.model_selection.validation_curve",