diff --git a/src/safeds/data/tabular/transformation/_label_encoder.py b/src/safeds/data/tabular/transformation/_label_encoder.py
index e2004c15f..2dd4fe6db 100644
--- a/src/safeds/data/tabular/transformation/_label_encoder.py
+++ b/src/safeds/data/tabular/transformation/_label_encoder.py
@@ -1,8 +1,5 @@
 from __future__ import annotations
 
-import warnings
-from typing import Any
-
 from sklearn.preprocessing import OrdinalEncoder as sk_OrdinalEncoder
 
 from safeds.data.tabular.containers import Table
@@ -12,13 +9,6 @@
 )
 
 
-def warn(*_: Any, **__: Any) -> None:
-    pass
-
-
-warnings.warn = warn
-
-
 # noinspection PyProtectedMember
 class LabelEncoder(InvertibleTableTransformer):
     """The LabelEncoder encodes one or more given columns into labels."""
diff --git a/src/safeds/ml/classical/_util_sklearn.py b/src/safeds/ml/classical/_util_sklearn.py
index d5a987f48..145ee06f5 100644
--- a/src/safeds/ml/classical/_util_sklearn.py
+++ b/src/safeds/ml/classical/_util_sklearn.py
@@ -1,3 +1,4 @@
+import warnings
 from typing import Any
 
 from safeds.data.tabular.containers import Table, TaggedTable
@@ -84,7 +85,9 @@ def predict(model: Any, dataset: Table, feature_names: list[str] | None, target_
     result_set.columns = dataset.column_names
 
     try:
-        predicted_target_vector = model.predict(dataset_df.values)
+        with warnings.catch_warnings():
+            warnings.filterwarnings("ignore", message="X does not have valid feature names")
+            predicted_target_vector = model.predict(dataset_df.values)
         result_set[target_name] = predicted_target_vector
         return Table(result_set).tag_columns(target_name=target_name, feature_names=feature_names)
     except ValueError as exception: