scikit-learn
diff --git a/‎sklearn/datasets/_samples_generator.py
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+14-14Lines changed: 14 additions & 14 deletions b/‎sklearn/datasets/_samples_generator.py
Copy file name to clipboardExpand all lines: sklearn/datasets/_samples_generator.py
+14-14Lines changed: 14 additions & 14 deletions
diff --git a/‎sklearn/datasets/tests/test_samples_generator.py
Copy file name to clipboardExpand all lines: sklearn/datasets/tests/test_samples_generator.py
-12Lines changed: 0 additions & 12 deletions b/‎sklearn/datasets/tests/test_samples_generator.py
Copy file name to clipboardExpand all lines: sklearn/datasets/tests/test_samples_generator.py
-12Lines changed: 0 additions & 12 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
Copy file name to clipboardExpand all lines: sklearn/tests/test_public_functions.py
+1Lines changed: 1 addition & 0 deletions
@@ -309,6 +309,20 @@ def make_classification(
     return X, y
 
 
+@validate_params(
+    {
+        "n_samples": [Interval(Integral, 1, None, closed="left")],
+        "n_features": [Interval(Integral, 1, None, closed="left")],
+        "n_classes": [Interval(Integral, 1, None, closed="left")],
+        "n_labels": [Interval(Integral, 0, None, closed="left")],
+        "length": [Interval(Integral, 1, None, closed="left")],
+        "allow_unlabeled": ["boolean"],
+        "sparse": ["boolean"],
+        "return_indicator": [StrOptions({"dense", "sparse"}), "boolean"],
+        "return_distributions": ["boolean"],
+        "random_state": ["random_state"],
+    }
+)
 def make_multilabel_classification(
     n_samples=100,
     n_features=20,
@@ -398,18 +412,6 @@ def make_multilabel_classification(
         The probability of each feature being drawn given each class.
         Only returned if ``return_distributions=True``.
     """
-    if n_classes < 1:
-        raise ValueError(
-            "'n_classes' should be an integer greater than 0. Got {} instead.".format(
-                n_classes
-            )
-        )
-    if length < 1:
-        raise ValueError(
-            "'length' should be an integer greater than 0. Got {} instead.".format(
-                length
-            )
-        )
 
     generator = check_random_state(random_state)
     p_c = generator.uniform(size=n_classes)
@@ -469,8 +471,6 @@ def sample_example():
     if return_indicator in (True, "sparse", "dense"):
         lb = MultiLabelBinarizer(sparse_output=(return_indicator == "sparse"))
         Y = lb.fit([range(n_classes)]).transform(Y)
-    elif return_indicator is not False:
-        raise ValueError("return_indicator must be either 'sparse', 'dense' or False.")
     if return_distributions:
         return X, Y, p_c, p_w_c
     return X, Y
 
@@ -283,18 +283,6 @@ def test_make_multilabel_classification_return_indicator_sparse():
         assert sp.issparse(Y)
 
 
-@pytest.mark.parametrize(
-    "params, err_msg",
-    [
-        ({"n_classes": 0}, "'n_classes' should be an integer"),
-        ({"length": 0}, "'length' should be an integer"),
-    ],
-)
-def test_make_multilabel_classification_valid_arguments(params, err_msg):
-    with pytest.raises(ValueError, match=err_msg):
-        make_multilabel_classification(**params)
-
-
 def test_make_hastie_10_2():
     X, y = make_hastie_10_2(n_samples=100, random_state=0)
     assert X.shape == (100, 10), "X shape mismatch"
 
@@ -133,6 +133,7 @@ def _check_function_param_validation(
     "sklearn.datasets.make_classification",
     "sklearn.datasets.make_friedman1",
     "sklearn.datasets.make_low_rank_matrix",
+    "sklearn.datasets.make_multilabel_classification",
     "sklearn.datasets.make_regression",
     "sklearn.datasets.make_sparse_coded_signal",
     "sklearn.decomposition.sparse_encode",