Veghit
diff --git a/‎sklearn/datasets/_samples_generator.py
Lines changed: 19 additions & 10 deletions b/‎sklearn/datasets/_samples_generator.py
Lines changed: 19 additions & 10 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
@@ -855,6 +855,18 @@ def make_moons(n_samples=100, *, shuffle=True, noise=None, random_state=None):
     return X, y
 
 
+@validate_params(
+    {
+        "n_samples": [Interval(Integral, 1, None, closed="left"), "array-like"],
+        "n_features": [Interval(Integral, 1, None, closed="left")],
+        "centers": [Interval(Integral, 1, None, closed="left"), "array-like", None],
+        "cluster_std": [Interval(Real, 0, None, closed="left"), "array-like"],
+        "center_box": [tuple],
+        "shuffle": ["boolean"],
+        "random_state": ["random_state"],
+        "return_centers": ["boolean"],
+    }
+)
 def make_blobs(
     n_samples=100,
     n_features=2,
@@ -884,7 +896,7 @@ def make_blobs(
     n_features : int, default=2
         The number of features for each sample.
 
-    centers : int or ndarray of shape (n_centers, n_features), default=None
+    centers : int or array-like of shape (n_centers, n_features), default=None
         The number of centers to generate, or the fixed center locations.
         If n_samples is an int and centers is None, 3 centers are generated.
         If n_samples is array-like, centers must be
@@ -967,22 +979,19 @@ def make_blobs(
             centers = generator.uniform(
                 center_box[0], center_box[1], size=(n_centers, n_features)
             )
-        try:
-            assert len(centers) == n_centers
-        except TypeError as e:
+        if not isinstance(centers, Iterable):
             raise ValueError(
                 "Parameter `centers` must be array-like. Got {!r} instead".format(
                     centers
                 )
-            ) from e
-        except AssertionError as e:
+            )
+        if len(centers) != n_centers:
             raise ValueError(
                 "Length of `n_samples` not consistent with number of "
                 f"centers. Got n_samples = {n_samples} and centers = {centers}"
-            ) from e
-        else:
-            centers = check_array(centers)
-            n_features = centers.shape[1]
+            )
+        centers = check_array(centers)
+        n_features = centers.shape[1]
 
     # stds: if cluster_std is given as list, it must be consistent
     # with the n_centers
 
@@ -130,6 +130,7 @@ def _check_function_param_validation(
     "sklearn.datasets.load_svmlight_file",
     "sklearn.datasets.load_svmlight_files",
     "sklearn.datasets.make_biclusters",
+    "sklearn.datasets.make_blobs",
     "sklearn.datasets.make_checkerboard",
     "sklearn.datasets.make_circles",
     "sklearn.datasets.make_classification",