diff --git a/sklearn/metrics/_classification.py b/sklearn/metrics/_classification.py
index 0602ec77aa500..8d6979020dbf2 100644
--- a/sklearn/metrics/_classification.py
+++ b/sklearn/metrics/_classification.py
@@ -1451,6 +1451,25 @@ def _check_set_wise_labels(y_true, y_pred, average, labels, pos_label):
     return labels
 
 
+@validate_params(
+    {
+        "y_true": ["array-like", "sparse matrix"],
+        "y_pred": ["array-like", "sparse matrix"],
+        "beta": [Interval(Real, 0.0, None, closed="both")],
+        "labels": ["array-like", None],
+        "pos_label": [Real, str, "boolean", None],
+        "average": [
+            StrOptions({"micro", "macro", "samples", "weighted", "binary"}),
+            None,
+        ],
+        "warn_for": [list, tuple, set],
+        "sample_weight": ["array-like", None],
+        "zero_division": [
+            Options(Real, {0, 1}),
+            StrOptions({"warn"}),
+        ],
+    }
+)
 def precision_recall_fscore_support(
     y_true,
     y_pred,
@@ -1539,7 +1558,7 @@ def precision_recall_fscore_support(
             meaningful for multilabel classification where this differs from
             :func:`accuracy_score`).
 
-    warn_for : tuple or set, for internal use
+    warn_for : list, tuple or set, for internal use
         This determines which warnings will be made in the case that this
         function is being used to return only one of its metrics.
 
@@ -1616,8 +1635,6 @@ def precision_recall_fscore_support(
      array([2, 2, 2]))
     """
     _check_zero_division(zero_division)
-    if beta < 0:
-        raise ValueError("beta should be >=0 in the F-beta score")
     labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)
 
     # Calculate tp_sum, pred_sum, true_sum ###
diff --git a/sklearn/metrics/tests/test_classification.py b/sklearn/metrics/tests/test_classification.py
index 222f445555200..8281ec9877d21 100644
--- a/sklearn/metrics/tests/test_classification.py
+++ b/sklearn/metrics/tests/test_classification.py
@@ -386,23 +386,6 @@ def test_average_precision_score_tied_values():
     assert average_precision_score(y_true, y_score) != 1.0
 
 
-@ignore_warnings
-def test_precision_recall_fscore_support_errors():
-    y_true, y_pred, _ = make_prediction(binary=True)
-
-    # Bad beta
-    with pytest.raises(ValueError):
-        precision_recall_fscore_support(y_true, y_pred, beta=-0.1)
-
-    # Bad pos_label
-    with pytest.raises(ValueError):
-        precision_recall_fscore_support(y_true, y_pred, pos_label=2, average="binary")
-
-    # Bad average option
-    with pytest.raises(ValueError):
-        precision_recall_fscore_support([0, 1, 2], [1, 2, 0], average="mega")
-
-
 def test_precision_recall_f_unused_pos_label():
     # Check warning that pos_label unused when set to non-default value
     # but average != 'binary'; even if data is binary.
diff --git a/sklearn/tests/test_public_functions.py b/sklearn/tests/test_public_functions.py
index 9b2b56cdb3eb8..afe44e3bf2723 100644
--- a/sklearn/tests/test_public_functions.py
+++ b/sklearn/tests/test_public_functions.py
@@ -130,6 +130,7 @@ def _check_function_param_validation(
     "sklearn.metrics.multilabel_confusion_matrix",
     "sklearn.metrics.mutual_info_score",
     "sklearn.metrics.pairwise.additive_chi2_kernel",
+    "sklearn.metrics.precision_recall_fscore_support",
     "sklearn.metrics.r2_score",
     "sklearn.metrics.roc_curve",
     "sklearn.metrics.zero_one_loss",