diff --git a/tests/classification/test_accuracy.py b/tests/classification/test_accuracy.py
index 80d15aff4b7..b07fb742db4 100644
--- a/tests/classification/test_accuracy.py
+++ b/tests/classification/test_accuracy.py
@@ -345,7 +345,7 @@ def test_class_not_present(metric_class, metric_fn, ignore_index, expected):
     assert torch.allclose(expected, result_cl, equal_nan=True)
 
 
-@pytest.mark.parametrize('average', ['micro', 'macro', 'weighted'])
+@pytest.mark.parametrize("average", ["micro", "macro", "weighted"])
 def test_same_input(average):
     preds = _input_miss_class.preds
     target = _input_miss_class.target
diff --git a/tests/classification/test_f_beta.py b/tests/classification/test_f_beta.py
index 0fffda47c31..6c9e7d38d48 100644
--- a/tests/classification/test_f_beta.py
+++ b/tests/classification/test_f_beta.py
@@ -427,10 +427,10 @@ def test_top_k(
     assert torch.isclose(metric_fn(preds, target, top_k=k, average=average, num_classes=3), result)
 
 
-@pytest.mark.parametrize('average', ['micro', 'macro', 'weighted'])
+@pytest.mark.parametrize("average", ["micro", "macro", "weighted"])
 @pytest.mark.parametrize(
-    'metric_class, metric_functional, sk_fn',
-    [(partial(FBeta, beta=2.0), partial(fbeta, beta=2.0), partial(fbeta_score, beta=2.0)), (F1, f1, f1_score)]
+    "metric_class, metric_functional, sk_fn",
+    [(partial(FBeta, beta=2.0), partial(fbeta, beta=2.0), partial(fbeta_score, beta=2.0)), (F1, f1, f1_score)],
 )
 def test_same_input(metric_class, metric_functional, sk_fn, average):
     preds = _input_miss_class.preds
diff --git a/tests/classification/test_precision_recall.py b/tests/classification/test_precision_recall.py
index 935eb13f36f..c65a63d84c3 100644
--- a/tests/classification/test_precision_recall.py
+++ b/tests/classification/test_precision_recall.py
@@ -440,9 +440,9 @@ def test_class_not_present(metric_class, metric_fn, ignore_index, expected):
     assert torch.allclose(expected, result_cl, equal_nan=True)
 
 
-@pytest.mark.parametrize('average', ['micro', 'macro', 'weighted'])
+@pytest.mark.parametrize("average", ["micro", "macro", "weighted"])
 @pytest.mark.parametrize(
-    'metric_class, metric_functional, sk_fn', [(Precision, precision, precision_score), (Recall, recall, recall_score)]
+    "metric_class, metric_functional, sk_fn", [(Precision, precision, precision_score), (Recall, recall, recall_score)]
 )
 def test_same_input(metric_class, metric_functional, sk_fn, average):
     preds = _input_miss_class.preds
diff --git a/torchmetrics/functional/classification/f_beta.py b/torchmetrics/functional/classification/f_beta.py
index 66bfee63910..9d6ddba3f5b 100644
--- a/torchmetrics/functional/classification/f_beta.py
+++ b/torchmetrics/functional/classification/f_beta.py
@@ -51,9 +51,9 @@ def _fbeta_compute(
         precision = precision[~cond]
         recall = recall[~cond]
 
-    num = (1 + beta**2) * precision * recall
-    denom = beta**2 * precision + recall
-    denom[denom == 0.] = 1.0  # avoid division by 0
+    num = (1 + beta ** 2) * precision * recall
+    denom = beta ** 2 * precision + recall
+    denom[denom == 0.0] = 1.0  # avoid division by 0
 
     # if classes matter and a given class is not present in both the preds and the target,
     # computing the score for this class is meaningless, thus they should be ignored