scikit-learn · jeremiedbb · Apr 4, 2023 · Apr 3, 2023 · Apr 3, 2023 · Apr 4, 2023
diff --git a/sklearn/metrics/pairwise.py b/sklearn/metrics/pairwise.py
@@ -1290,6 +1290,18 @@ def polynomial_kernel(X, Y=None, degree=3, gamma=None, coef0=1):
     return K
 
 
+@validate_params(
+    {
+        "X": ["array-like", "sparse matrix"],
+        "Y": ["array-like", "sparse matrix", None],
+        "gamma": [
+            Interval(Real, 0, None, closed="left"),
+            None,
+            Hidden(np.ndarray),
+        ],
+        "coef0": [Interval(Real, None, None, closed="neither")],
+    }
+)
 def sigmoid_kernel(X, Y=None, gamma=None, coef0=1):
     """Compute the sigmoid kernel between X and Y.
 
@@ -1299,10 +1311,10 @@ def sigmoid_kernel(X, Y=None, gamma=None, coef0=1):
 
     Parameters
     ----------
-    X : ndarray of shape (n_samples_X, n_features)
+    X : {array-like, sparse matrix} of shape (n_samples_X, n_features)
         A feature array.
 
-    Y : ndarray of shape (n_samples_Y, n_features), default=None
+    Y : {array-like, sparse matrix} of shape (n_samples_Y, n_features), default=None
         An optional second feature array. If `None`, uses `Y=X`.
 
     gamma : float, default=None

diff --git a/sklearn/tests/test_public_functions.py b/sklearn/tests/test_public_functions.py
@@ -217,6 +217,7 @@ def _check_function_param_validation(
     "sklearn.metrics.pairwise.paired_manhattan_distances",
     "sklearn.metrics.pairwise.polynomial_kernel",
     "sklearn.metrics.pairwise.rbf_kernel",
+    "sklearn.metrics.pairwise.sigmoid_kernel",
     "sklearn.metrics.precision_recall_curve",
     "sklearn.metrics.precision_recall_fscore_support",
     "sklearn.metrics.precision_score",